examples/Entry and Exit Comparison.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Outline\n",
"\n",
"The purpose of this notebook is to calculate zone entry and exit-related data for tracked games, and generate TOI and shot-related data for the same games.\n",
"\n",
"Here's what we'll do:\n",
"- Download NZ data (not included)\n",
"- Aggregate NZ data \n",
"- Augment with PBP/TOI data as needed\n",
"- Calculate metrics at team, individual, line, and pair levels, for and against. For example:\n",
" - Entries per 60\n",
" - Failed entries per 60\n",
" - Controlled entries per 60\n",
" - Failed entries per 60\n",
" - Controlled entry%\n",
" - Failed entry%\n",
" - Shots per entry\n",
" - Controlled exits per 60"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from os import listdir, chdir, getcwd\n",
"import pandas as pd\n",
"from pylab import *\n",
"from tqdm import tqdm # progress bar\n",
"%matplotlib inline\n",
"current_wd = getcwd()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Aggregate NZ data\n",
"\n",
"They're in a bunch of separate .xlsx files. We just aggregate them together.\n",
"\n",
"Each excel file has three sheets we want:\n",
"\n",
"- Shot Data\n",
"- Raw Entries\n",
"- Zone Exits Raw Data\n",
"\n",
"First, let's copy everything to csv, which will make for faster file read-in later on."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Want to combine all files\n",
"folders = ['/Users/muneebalam/Downloads/Game Reports 1718/', \n",
" #'/Users/muneebalam/Downloads/Passing Game Archive 1718/',\n",
" '/Users/muneebalam/Downloads/Game Reports 1617/']\n",
"sheets = {'shots': 'Shot Data', 'entries': 'Raw Entries', 'exits': 'Zone Exits Raw Data'}\n",
"\n",
"copy = False\n",
"\n",
"if copy:\n",
" for folder in folders:\n",
" chdir(folder)\n",
" files = listdir()\n",
" files = [f for f in files if f[-5:] == '.xlsx']\n",
" for file in tqdm(files, desc='Converting to csv'):\n",
" xl = pd.ExcelFile(file)\n",
" sheetnames = xl.sheet_names\n",
" for s in sheetnames:\n",
" df = xl.parse(s)\n",
" fout = '{0:s}_{1:s}.csv'.format(file[:-5], s)\n",
" df.to_csv(fout, index=False)\n",
" print('Done with', folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's check--do the sheets we're interested in (listed in cell above) contain the same columns?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading files: 100%|██████████| 220/220 [00:01<00:00, 147.72it/s]\n",
"Reading files: 6%|▌ | 18/301 [00:00<00:01, 171.62it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done with shots /Users/muneebalam/Downloads/Game Reports 1718/\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading files: 100%|██████████| 301/301 [00:01<00:00, 184.26it/s]\n",
"Reading files: 8%|▊ | 17/220 [00:00<00:01, 167.05it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done with shots /Users/muneebalam/Downloads/Game Reports 1617/\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading files: 100%|██████████| 220/220 [00:01<00:00, 192.22it/s]\n",
"Reading files: 7%|▋ | 20/301 [00:00<00:01, 197.02it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done with entries /Users/muneebalam/Downloads/Game Reports 1718/\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading files: 100%|██████████| 301/301 [00:01<00:00, 200.99it/s]\n",
"Reading files: 13%|█▎ | 29/220 [00:00<00:00, 279.97it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done with entries /Users/muneebalam/Downloads/Game Reports 1617/\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading files: 100%|██████████| 220/220 [00:00<00:00, 272.17it/s]\n",
"Reading files: 9%|▉ | 27/301 [00:00<00:01, 267.12it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"exits Zone Exits Raw Data 20330 Dallas at Colorado_Zone Exits Raw Data.csv No columns to parse from file ('No columns to parse from file',)\n",
"Done with exits /Users/muneebalam/Downloads/Game Reports 1718/\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading files: 100%|██████████| 301/301 [00:01<00:00, 251.42it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done with exits /Users/muneebalam/Downloads/Game Reports 1617/\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"colnames = {}\n",
"for skey, sval in sheets.items():\n",
" colnames[sval] = {}\n",
" for folder in folders:\n",
" chdir(folder)\n",
" files = listdir()\n",
" files = [f for f in files if f[f.rfind('_')+1:-4] == sval]\n",
" for file in tqdm(files, desc='Reading files'):\n",
" try:\n",
" cnames = pd.read_csv(file).columns\n",
" except Exception as e:\n",
" print(skey, sval, file, e, e.args)\n",
" continue\n",
" \n",
" cnames = tuple(sorted(cnames))\n",
" if cnames not in colnames[sval]:\n",
" colnames[sval][cnames] = set()\n",
" colnames[sval][cnames].add('{0:s}-{1:s}'.format(folder[-5:-1], file[:5])) # Season and game number\n",
" print('Done with', skey, folder)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shot Data\n",
"A1 517\n",
"A2 517\n",
"A3 517\n",
"Away Goalies 517\n",
"Away: 517\n",
"Date 517\n",
"Game ID 517\n",
"Goalie 517\n",
"Home Goalies 517\n",
"Home: 517\n",
"SOG? 517\n",
"Strength 517\n",
"Team 517\n",
"Time 517\n",
"PBP Time 516\n",
"Period 516\n",
"Unnamed: 28 513\n",
"Unnamed: 29 478\n",
"Screen 462\n",
"A1 Zone 297\n",
"A2 Zone 297\n",
"A3 Zone 297\n",
"G? 297\n",
"Oddman 297\n",
"RB/2C 297\n",
"RB/2C SOG? 297\n",
"SC? 297\n",
"Shot Type? 297\n",
"RB/2C G? 296\n",
"Home Score State 295\n",
"Shooter 295\n",
"Player 221\n",
"Chance? 220\n",
"Goal? 220\n",
"Goal?.1 220\n",
"Oddman? 220\n",
"P1 Z 220\n",
"P2 Z 220\n",
"P3 Z 220\n",
"Rebound? 220\n",
"SOG 220\n",
"State 220\n",
"Type 220\n",
"\n",
"Raw Entries\n",
"Controlled? 516\n",
"Defended by 516\n",
"Dump recovered? 516\n",
"Entry by 516\n",
"Entry type 516\n",
"Fail 516\n",
"Fen total 516\n",
"Game 516\n",
"Goal total 516\n",
"Goalie touch? 516\n",
"Location 516\n",
"Middle driver 516\n",
"Opp 516\n",
"Opp strength 516\n",
"Team strength 516\n",
"Time 516\n",
"Period 515\n",
"\n",
"Zone Exits Raw Data\n",
"Attempt 515\n",
"Direction 515\n",
"Entry? 515\n",
"Pass Target 515\n",
"Period 515\n",
"Pressured? 515\n",
"Result 515\n",
"Time 515\n",
"\n"
]
}
],
"source": [
"def intersect(*sets):\n",
" if len(sets) == 0:\n",
" return set()\n",
" if len(sets) == 1:\n",
" return sets[0]\n",
" if len(sets) == 2:\n",
" return set(sets[0]) & set(sets[1])\n",
" return set(sets[0]) & intersect(*tuple(sets[1:]))\n",
" \n",
"for sval in colnames:\n",
" # Figure out column name frequency\n",
" colcount = {}\n",
" for clist in colnames[sval].keys():\n",
" for c in clist:\n",
" if c not in colcount:\n",
" colcount[c] = 0\n",
" colcount[c] += len(colnames[sval][clist])\n",
" colcount = [(k, v) for k, v in colcount.items()]\n",
" colcount = sorted(colcount, key = lambda x: x[1], reverse=True)\n",
" print(sval)\n",
" for k, v in colcount:\n",
" # Only ones with more than 200, for brevity's sake\n",
" if v >= 200:\n",
" print(k, v)\n",
" print('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"They look like they're in decent shape--there's a lot of overlap. I'll keep items with at least 515 occurrences."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Shot Data': ['A1', 'A2', 'A3', 'Away Goalies', 'Away: ', 'Date', 'Game ID', 'Goalie', 'Home Goalies', 'Home:', 'PBP Time', 'Period', 'SOG?', 'Strength', 'Team', 'Time'], 'Raw Entries': ['Controlled?', 'Defended by', 'Dump recovered?', 'Entry by', 'Entry type', 'Fail', 'Fen total', 'Game', 'Goal total', 'Goalie touch?', 'Location', 'Middle driver', 'Opp', 'Opp strength', 'Period', 'Team strength', 'Time'], 'Zone Exits Raw Data': ['Attempt', 'Direction', 'Entry?', 'Pass Target', 'Period', 'Pressured?', 'Result', 'Time']}\n"
]
}
],
"source": [
"cols_to_keep = {}\n",
"for sval in colnames:\n",
" # Figure out column name frequency\n",
" colcount = {}\n",
" for clist in colnames[sval].keys():\n",
" for c in clist:\n",
" if c not in colcount:\n",
" colcount[c] = 0\n",
" colcount[c] += len(colnames[sval][clist])\n",
" cols_to_keep[sval] = [k for k, v in colcount.items() if v >= 515]\n",
"print(cols_to_keep)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So now, we'll combine."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (7,9) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done aggregating and reading files\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (13,16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"dfs = {k: [] for k in sheets.keys()}\n",
"\n",
"generate = False\n",
"\n",
"for skey, sval in sheets.items():\n",
" fout = skey + ' combined.csv'\n",
" \n",
" if generate:\n",
" for folder in folders:\n",
" chdir(folder)\n",
" files = listdir()\n",
" files = [f for f in files if f[f.rfind('_')+1:-4] == sval]\n",
" for file in tqdm(files, desc='Reading files'):\n",
" try:\n",
" df = pd.read_csv(file)\n",
"\n",
" # Exclude columns I don't want\n",
" cols = set(df.columns) - set(cols_to_keep[sval])\n",
" df = df.drop(cols, axis=1, errors='ignore')\n",
" df = df.assign(Season=2000 + int(folder[-5:-3]), Game=int(file[:5]))\n",
" dfs[skey].append(df)\n",
" except Exception as e:\n",
" print(skey, sval, file, e, e.args)\n",
" continue\n",
"\n",
" print('Done with', skey, folder)\n",
" dfs[skey] = pd.concat(dfs[skey])\n",
" dfs[skey].to_csv(fout, index=False)\n",
" dfs[skey] = pd.read_csv(fout)\n",
"chdir(current_wd)\n",
"print('Done aggregating and reading files')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Here's what the dataframes look like:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A1</th>\n",
" <th>A2</th>\n",
" <th>A3</th>\n",
" <th>Away Goalies</th>\n",
" <th>Away:</th>\n",
" <th>Date</th>\n",
" <th>Game</th>\n",
" <th>Game ID</th>\n",
" <th>Goalie</th>\n",
" <th>Home Goalies</th>\n",
" <th>Home:</th>\n",
" <th>PBP Time</th>\n",
" <th>Period</th>\n",
" <th>SOG?</th>\n",
" <th>Season</th>\n",
" <th>Strength</th>\n",
" <th>Team</th>\n",
" <th>Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>16NYI</td>\n",
" <td>13NYI</td>\n",
" <td>NYI</td>\n",
" <td>35.0</td>\n",
" <td>PHI</td>\n",
" <td>2017-01-15</td>\n",
" <td>20279</td>\n",
" <td>20279</td>\n",
" <td>33.0</td>\n",
" <td>31</td>\n",
" <td>WSH</td>\n",
" <td>00:53:00</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>5v5</td>\n",
" <td>NYI</td>\n",
" <td>19:23:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NYI</td>\n",
" <td>NYI</td>\n",
" <td>NYI</td>\n",
" <td>30.0</td>\n",
" <td>PHI</td>\n",
" <td>2017-01-15</td>\n",
" <td>20279</td>\n",
" <td>20279</td>\n",
" <td>33.0</td>\n",
" <td>70</td>\n",
" <td>WSH</td>\n",
" <td>02:10:00</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>5v5</td>\n",
" <td>NYI</td>\n",
" <td>19:16:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>CAR</td>\n",
" <td>CAR</td>\n",
" <td>CAR</td>\n",
" <td>NaN</td>\n",
" <td>PHI</td>\n",
" <td>2017-01-15</td>\n",
" <td>20279</td>\n",
" <td>20279</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>WSH</td>\n",
" <td>02:15:00</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>5v5</td>\n",
" <td>CAR</td>\n",
" <td>18:31:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>66NYI</td>\n",
" <td>72NYI</td>\n",
" <td>NYI</td>\n",
" <td>NaN</td>\n",
" <td>PHI</td>\n",
" <td>2017-01-15</td>\n",
" <td>20279</td>\n",
" <td>20279</td>\n",
" <td>33.0</td>\n",
" <td>NaN</td>\n",
" <td>WSH</td>\n",
" <td>02:17:00</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>5v5</td>\n",
" <td>NYI</td>\n",
" <td>18:21:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>25NYI</td>\n",
" <td>66NYI</td>\n",
" <td>NYI</td>\n",
" <td>NaN</td>\n",
" <td>PHI</td>\n",
" <td>2017-01-15</td>\n",
" <td>20279</td>\n",
" <td>20279</td>\n",
" <td>33.0</td>\n",
" <td>NaN</td>\n",
" <td>WSH</td>\n",
" <td>02:18:00</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>5v5</td>\n",
" <td>NYI</td>\n",
" <td>17:51:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A1 A2 A3 Away Goalies Away: Date Game Game ID Goalie \\\n",
"0 16NYI 13NYI NYI 35.0 PHI 2017-01-15 20279 20279 33.0 \n",
"1 NYI NYI NYI 30.0 PHI 2017-01-15 20279 20279 33.0 \n",
"2 CAR CAR CAR NaN PHI 2017-01-15 20279 20279 1.0 \n",
"3 66NYI 72NYI NYI NaN PHI 2017-01-15 20279 20279 33.0 \n",
"4 25NYI 66NYI NYI NaN PHI 2017-01-15 20279 20279 33.0 \n",
"\n",
" Home Goalies Home: PBP Time Period SOG? Season Strength Team Time \n",
"0 31 WSH 00:53:00 1.0 NaN 2017 5v5 NYI 19:23:00 \n",
"1 70 WSH 02:10:00 1.0 NaN 2017 5v5 NYI 19:16:00 \n",
"2 NaN WSH 02:15:00 1.0 NaN 2017 5v5 CAR 18:31:00 \n",
"3 NaN WSH 02:17:00 1.0 NaN 2017 5v5 NYI 18:21:00 \n",
"4 NaN WSH 02:18:00 1.0 NaN 2017 5v5 NYI 17:51:00 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs['shots'].head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Controlled?</th>\n",
" <th>Defended by</th>\n",
" <th>Dump recovered?</th>\n",
" <th>Entry by</th>\n",
" <th>Entry type</th>\n",
" <th>Fail</th>\n",
" <th>Fen total</th>\n",
" <th>Game</th>\n",
" <th>Goal total</th>\n",
" <th>Goalie touch?</th>\n",
" <th>Location</th>\n",
" <th>Middle driver</th>\n",
" <th>Opp</th>\n",
" <th>Opp strength</th>\n",
" <th>Period</th>\n",
" <th>Season</th>\n",
" <th>Team strength</th>\n",
" <th>Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>55WPG</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>55WPG</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>20193</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>Home</td>\n",
" <td>N</td>\n",
" <td>DAL</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>2017</td>\n",
" <td>5</td>\n",
" <td>19:45:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>44WPG</td>\n",
" <td>N</td>\n",
" <td>3DAL</td>\n",
" <td>F</td>\n",
" <td>3DAL</td>\n",
" <td>0.0</td>\n",
" <td>20193</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>Home</td>\n",
" <td>NaN</td>\n",
" <td>DAL</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>2017</td>\n",
" <td>5</td>\n",
" <td>19:33:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>13WPG</td>\n",
" <td>D</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>20193</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>Home</td>\n",
" <td>NaN</td>\n",
" <td>DAL</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>2017</td>\n",
" <td>5</td>\n",
" <td>19:12:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>12DAL</td>\n",
" <td>D</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>20193</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>Home</td>\n",
" <td>NaN</td>\n",
" <td>DAL</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>2017</td>\n",
" <td>5</td>\n",
" <td>19:02:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>16WPG</td>\n",
" <td>9WPG</td>\n",
" <td>D</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>20193</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>Home</td>\n",
" <td>NaN</td>\n",
" <td>DAL</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>2017</td>\n",
" <td>5</td>\n",
" <td>18:56:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Controlled? Defended by Dump recovered? Entry by Entry type Fail \\\n",
"0 55WPG N N 55WPG C NaN \n",
"1 NaN 44WPG N 3DAL F 3DAL \n",
"2 NaN N N 13WPG D NaN \n",
"3 NaN N N 12DAL D NaN \n",
"4 NaN N 16WPG 9WPG D NaN \n",
"\n",
" Fen total Game Goal total Goalie touch? Location Middle driver Opp \\\n",
"0 0.0 20193 0.0 R Home N DAL \n",
"1 0.0 20193 0.0 R Home NaN DAL \n",
"2 0.0 20193 0.0 R Home NaN DAL \n",
"3 0.0 20193 0.0 R Home NaN DAL \n",
"4 1.0 20193 0.0 R Home NaN DAL \n",
"\n",
" Opp strength Period Season Team strength Time \n",
"0 5.0 1.0 2017 5 19:45:00 \n",
"1 5.0 1.0 2017 5 19:33:00 \n",
"2 5.0 1.0 2017 5 19:12:00 \n",
"3 5.0 1.0 2017 5 19:02:00 \n",
"4 5.0 1.0 2017 5 18:56:00 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs['entries'].head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Period</th>\n",
" <th>Time</th>\n",
" <th>Attempt</th>\n",
" <th>Result</th>\n",
" <th>Pressured?</th>\n",
" <th>Pass Target</th>\n",
" <th>Entry?</th>\n",
" <th>Direction</th>\n",
" <th>Game</th>\n",
" <th>Season</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>19:19:00</td>\n",
" <td>25EDM</td>\n",
" <td>P</td>\n",
" <td>N</td>\n",
" <td>93EDM</td>\n",
" <td>Y</td>\n",
" <td>L</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>18:33:00</td>\n",
" <td>63NJ</td>\n",
" <td>M</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>R</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>18:25:00</td>\n",
" <td>14NJ</td>\n",
" <td>M</td>\n",
" <td>N</td>\n",
" <td>11NJ</td>\n",
" <td>N</td>\n",
" <td>L</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>18:05:00</td>\n",
" <td>25NJ</td>\n",
" <td>P</td>\n",
" <td>N</td>\n",
" <td>11NJ</td>\n",
" <td>Y</td>\n",
" <td>L</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>18:00:00</td>\n",
" <td>55EDM</td>\n",
" <td>C</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>Y</td>\n",
" <td>C</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Period Time Attempt Result Pressured? Pass Target Entry? Direction \\\n",
"0 1 19:19:00 25EDM P N 93EDM Y L \n",
"1 1 18:33:00 63NJ M N N N R \n",
"2 1 18:25:00 14NJ M N 11NJ N L \n",
"3 1 18:05:00 25NJ P N 11NJ Y L \n",
"4 1 18:00:00 55EDM C N N Y C \n",
"\n",
" Game Season \n",
"0 20198 2017 \n",
"1 20198 2017 \n",
"2 20198 2017 \n",
"3 20198 2017 \n",
"4 20198 2017 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs['exits'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Augment with PBP/TOI\n",
"\n",
"Let's look at how complete the shot data is. I'll take a 10% sample of Caps games and compare shot counts in the tracked data with the PBP.\n",
"\n",
"First, the games selected:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Selected</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Season</th>\n",
" <th>Game</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">2016</th>\n",
" <th>20022</th>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20071</th>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20860</th>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2017</th>\n",
" <th>20047</th>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20555</th>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Selected\n",
"Season Game \n",
"2016 20022 True\n",
" 20071 True\n",
" 20860 True\n",
"2017 20047 True\n",
" 20555 True"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(8) # Obv, for shots, pick Ovechkin\n",
"wsh_games = dfs['shots'].query('Team == \"WSH\"')[['Season', 'Game']].drop_duplicates().sort_values(['Season', 'Game'])\n",
"wsh_games.loc[:, 'RandomNum'] = np.random.randint(low=0, high=100, size=len(wsh_games))\n",
"wsh_games.loc[:, 'Selected'] = wsh_games.RandomNum.apply(lambda x: x <= 10)\n",
"wsh_games = wsh_games.query('Selected == True').drop('RandomNum', axis=1)\n",
"wsh_games.set_index(['Season', 'Game']) # Just for viewing purposes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the shot counts from the tracked data:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Team</th>\n",
" <th>Opp</th>\n",
" <th>WSH</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Season</th>\n",
" <th>Game</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">2016</th>\n",
" <th>20022</th>\n",
" <td>37</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20071</th>\n",
" <td>39</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20860</th>\n",
" <td>47</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2017</th>\n",
" <th>20047</th>\n",
" <td>43</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20555</th>\n",
" <td>35</td>\n",
" <td>47</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Team Opp WSH\n",
"Season Game \n",
"2016 20022 37 39\n",
" 20071 39 45\n",
" 20860 47 39\n",
"2017 20047 43 46\n",
" 20555 35 47"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wsh_shots = dfs['shots'].merge(wsh_games[['Season', 'Game']], how='inner', on=['Season', 'Game']) \\\n",
" .query('Strength == \"5v5\"')\n",
"wsh_shots.loc[:, 'Team'] = wsh_shots.Team.apply(lambda x: x if x == 'WSH' else 'Opp')\n",
"wsh_shots = wsh_shots[['Season', 'Game', 'Team']].assign(Count=1) \\\n",
" .groupby(['Season', 'Game', 'Team'], as_index=False) \\\n",
" .count()\n",
"wsh_shots = wsh_shots.pivot_table(index=['Season', 'Game'], columns='Team', values='Count')\n",
"wsh_shots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's pull shot counts for those games from our scraped data."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Team</th>\n",
" <th>Opp</th>\n",
" <th>WSH</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Season</th>\n",
" <th>Game</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">2016</th>\n",
" <th>20022</th>\n",
" <td>40</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20071</th>\n",
" <td>40</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20860</th>\n",
" <td>43</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2017</th>\n",
" <th>20047</th>\n",
" <td>43</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20555</th>\n",
" <td>42</td>\n",
" <td>52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Team Opp WSH\n",
"Season Game \n",
"2016 20022 40 42\n",
" 20071 40 43\n",
" 20860 43 31\n",
"2017 20047 43 44\n",
" 20555 42 52"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scrapenhl2.scrape import teams\n",
"df1 = teams.get_team_pbp(2016, 'WSH').assign(Season=2016)\n",
"df2 = teams.get_team_pbp(2017, 'WSH').assign(Season=2017)\n",
"df3 = pd.concat([df1, df2]).merge(wsh_games[['Season', 'Game']], how='inner', on=['Season', 'Game'])\n",
"\n",
"# Go to 5v5 only\n",
"from scrapenhl2.manipulate import manipulate as manip\n",
"df3 = manip.filter_for_five_on_five(manip.filter_for_corsi(df3))\n",
"\n",
"counts = df3[['Season', 'Game', 'Team']]\n",
"counts.loc[:, 'Team'] = counts.Team.apply(lambda x: 'WSH' if x == 15 else 'Opp')\n",
"\n",
"counts = counts.assign(Count=1) \\\n",
" .groupby(['Season', 'Game', 'Team'], as_index=False) \\\n",
" .count()\n",
"counts = counts.pivot_table(index=['Season', 'Game'], columns='Team', values='Count')\n",
"counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The counts are pretty close, so we'll use the tracked data.\n",
"\n",
"We still need TOI, though. I'll pull a second-by-second log into a dataframe."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not read VGK IOError: Failed to open file: /Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/data/teams/toi/2016/VGK.feather ('IOError: Failed to open file: /Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/data/teams/toi/2016/VGK.feather',)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Season</th>\n",
" <th>Game</th>\n",
" <th>Time</th>\n",
" <th>WSH1</th>\n",
" <th>WSH2</th>\n",
" <th>WSH3</th>\n",
" <th>WSH4</th>\n",
" <th>WSH5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>20007</td>\n",
" <td>1</td>\n",
" <td>Matt Niskanen</td>\n",
" <td>Karl Alzner</td>\n",
" <td>Evgeny Kuznetsov</td>\n",
" <td>Alex Ovechkin</td>\n",
" <td>T.J. Oshie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016</td>\n",
" <td>20007</td>\n",
" <td>2</td>\n",
" <td>Matt Niskanen</td>\n",
" <td>Karl Alzner</td>\n",
" <td>Evgeny Kuznetsov</td>\n",
" <td>Alex Ovechkin</td>\n",
" <td>T.J. Oshie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016</td>\n",
" <td>20007</td>\n",
" <td>3</td>\n",
" <td>Matt Niskanen</td>\n",
" <td>Karl Alzner</td>\n",
" <td>Evgeny Kuznetsov</td>\n",
" <td>Alex Ovechkin</td>\n",
" <td>T.J. Oshie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016</td>\n",
" <td>20007</td>\n",
" <td>4</td>\n",
" <td>Matt Niskanen</td>\n",
" <td>Karl Alzner</td>\n",
" <td>Evgeny Kuznetsov</td>\n",
" <td>Alex Ovechkin</td>\n",
" <td>T.J. Oshie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016</td>\n",
" <td>20007</td>\n",
" <td>5</td>\n",
" <td>Matt Niskanen</td>\n",
" <td>Karl Alzner</td>\n",
" <td>Evgeny Kuznetsov</td>\n",
" <td>Alex Ovechkin</td>\n",
" <td>T.J. Oshie</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Season Game Time WSH1 WSH2 WSH3 \\\n",
"0 2016 20007 1 Matt Niskanen Karl Alzner Evgeny Kuznetsov \n",
"1 2016 20007 2 Matt Niskanen Karl Alzner Evgeny Kuznetsov \n",
"2 2016 20007 3 Matt Niskanen Karl Alzner Evgeny Kuznetsov \n",
"3 2016 20007 4 Matt Niskanen Karl Alzner Evgeny Kuznetsov \n",
"4 2016 20007 5 Matt Niskanen Karl Alzner Evgeny Kuznetsov \n",
"\n",
" WSH4 WSH5 \n",
"0 Alex Ovechkin T.J. Oshie \n",
"1 Alex Ovechkin T.J. Oshie \n",
"2 Alex Ovechkin T.J. Oshie \n",
"3 Alex Ovechkin T.J. Oshie \n",
"4 Alex Ovechkin T.J. Oshie "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scrapenhl2.scrape import players, team_info\n",
"\n",
"dfs['toi'] = {}\n",
"\n",
"team_convert = {'LA': 'LAK', 'NJ': 'NJD', 'TB': 'TBL', 'SJ': 'SJS', \n",
" 'L.A': 'LAK', 'N.J': 'NJD', 'T.B': 'TBL', 'S.J': 'SJS'}\n",
"for team in dfs['shots'].Team.unique():\n",
" if team in team_convert:\n",
" team = team_convert[team]\n",
" if not isinstance(team, str) or len(team) != 3:\n",
" #print('Skipping', team)\n",
" continue\n",
" \n",
" toi = []\n",
" for season in range(2016, 2018):\n",
" try:\n",
" toi.append(teams.get_team_toi(season, team).assign(Season=season))\n",
" except Exception as e:\n",
" print('Could not read', team, e, e.args)\n",
" \n",
" toi = pd.concat(toi)\n",
" \n",
" # Filter for appropriate games using an inner join, and filter for 5v5\n",
" toi = toi.merge(dfs['shots'][['Season', 'Game']].drop_duplicates(), \n",
" how='inner', on=['Season', 'Game']) \n",
" toi = manip.filter_for_five_on_five(toi)\n",
" \n",
" # Get only certain columns\n",
" toi = toi[['Season', 'Game', 'Time', 'Team1', 'Team2', 'Team3', 'Team4', 'Team5']]\n",
" renaming = {'Team{0:d}'.format(i): '{0:s}{1:d}'.format(team, i) for i in range(1, 6)}\n",
" toi = toi.rename(columns=renaming)\n",
" \n",
" # Convert to player names\n",
" for col in toi.columns[3:]:\n",
" toi.loc[:, col] = toi[col].apply(lambda x: players.player_as_str(x))\n",
" dfs['toi'][team] = toi\n",
"\n",
"dfs['toi']['WSH'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One final item: let's filter the entries, exits, and shots dataframes to 5v5 using this TOI data."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:4: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n",
" after removing the cwd from sys.path.\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:337: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[key] = _infer_fill_value(value)\n"
]
},
{
"data": {
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" A1 A2 A3 Away Goalies Away: Date Game Game ID Goalie \\\n",
"0 16NYI 13NYI NYI 35.0 PHI 2017-01-15 20279 20279 33.0 \n",
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"\n",
" Home Goalies ... VGK1 VGK2 VGK3 VGK4 VGK5 VAN1 VAN2 VAN3 VAN4 VAN5 \n",
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"4 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"[5 rows x 175 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fives = {}\n",
"\n",
"from scrapenhl2.manipulate import add_onice_players as onice\n",
"dfs['shots'].loc[:, 'Time2'] = dfs['shots'].Time.str.extract(r'(\\d{1,2}:\\d{1,2}):\\d{1,2}$')\n",
"dfs['shots'] = onice.add_times_to_file(dfs['shots'].dropna(subset=['Time']),\n",
" periodcol='Period', timecol='Time2', time_format='remaining')\n",
"fives['shots'] = dfs['shots']\n",
"for team, toi in dfs['toi'].items():\n",
" toi = toi.rename(columns={'Time': '_Secs'})\n",
" fives['shots'] = fives['shots'].merge(toi, how='left', on=['Season', 'Game', '_Secs'])\n",
"\n",
"fives['shots'].head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n",
" \"\"\"Entry point for launching an IPython kernel.\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:337: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[key] = _infer_fill_value(value)\n"
]
},
{
"data": {
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},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs['entries'].loc[:, 'Time2'] = dfs['entries'].Time.str.extract(r'(\\d{1,2}:\\d{1,2}):\\d{1,2}$')\n",
"dfs['entries'] = onice.add_times_to_file(dfs['entries'].dropna(subset=['Time']),\n",
" periodcol='Period', timecol='Time2', time_format='remaining')\n",
"\n",
"fives['entries'] = dfs['entries']\n",
"for team, toi in dfs['toi'].items():\n",
" toi = toi.rename(columns={'Time': '_Secs'})\n",
" fives['entries'] = fives['entries'].merge(toi, how='left', on=['Season', 'Game', '_Secs'])\n",
"\n",
"fives['entries'].head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n",
" \"\"\"Entry point for launching an IPython kernel.\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:337: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[key] = _infer_fill_value(value)\n"
]
},
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" <td>N</td>\n",
" <td>R</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>18:25:00</td>\n",
" <td>14NJ</td>\n",
" <td>M</td>\n",
" <td>N</td>\n",
" <td>11NJ</td>\n",
" <td>N</td>\n",
" <td>L</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>18:05:00</td>\n",
" <td>25NJ</td>\n",
" <td>P</td>\n",
" <td>N</td>\n",
" <td>11NJ</td>\n",
" <td>Y</td>\n",
" <td>L</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>18:00:00</td>\n",
" <td>55EDM</td>\n",
" <td>C</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>Y</td>\n",
" <td>C</td>\n",
" <td>20198</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 167 columns</p>\n",
"</div>"
],
"text/plain": [
" Period Time Attempt Result Pressured? Pass Target Entry? Direction \\\n",
"0 1 19:19:00 25EDM P N 93EDM Y L \n",
"1 1 18:33:00 63NJ M N N N R \n",
"2 1 18:25:00 14NJ M N 11NJ N L \n",
"3 1 18:05:00 25NJ P N 11NJ Y L \n",
"4 1 18:00:00 55EDM C N N Y C \n",
"\n",
" Game Season ... VGK1 VGK2 VGK3 VGK4 VGK5 VAN1 VAN2 VAN3 VAN4 VAN5 \n",
"0 20198 2017 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"1 20198 2017 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"2 20198 2017 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"3 20198 2017 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4 20198 2017 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"[5 rows x 167 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs['exits'].loc[:, 'Time2'] = dfs['exits'].Time.str.extract(r'(\\d{1,2}:\\d{1,2}):\\d{1,2}$')\n",
"dfs['exits'] = onice.add_times_to_file(dfs['exits'].dropna(subset=['Time']),\n",
" periodcol='Period', timecol='Time2', time_format='remaining')\n",
"\n",
"fives['exits'] = dfs['exits']\n",
"for team, toi in dfs['toi'].items():\n",
" toi = toi.rename(columns={'Time': '_Secs'})\n",
" fives['exits'] = fives['exits'].merge(toi, how='left', on=['Season', 'Game', '_Secs'])\n",
"\n",
"fives['exits'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at games included by team."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NYI 46\n",
"CAR 28\n",
"BOS 28\n",
"LAK 25\n",
"MIN 32\n",
"MTL 36\n",
"NYR 52\n",
"TOR 35\n",
"PIT 37\n",
"EDM 34\n",
"ANA 29\n",
"CBJ 43\n",
"FLA 28\n",
"NJD 30\n",
"STL 25\n",
"TBL 39\n",
"COL 29\n",
"SJS 27\n",
"ARI 21\n",
"PHI 99\n",
"DET 27\n",
"NSH 29\n",
"WPG 28\n",
"CHI 23\n",
"WSH 34\n",
"CGY 39\n",
"OTT 27\n",
"BUF 25\n",
"DAL 28\n",
"VGK 12\n",
"VAN 21\n",
"Total 1016\n"
]
}
],
"source": [
"teamcolnames = [x for x in fives['shots'].columns if x == x.upper() and len(x) == 4 and x[-1] == '1']\n",
"gps = []\n",
"for teamcol in teamcolnames:\n",
" gp = len(dfs['toi'][teamcol[:-1]][['Season', 'Game']].drop_duplicates())\n",
" print(teamcol[:3], gp)\n",
" gps.append(gp)\n",
"print('Total', sum(gps))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's reduce this to just Caps games now."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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" <th>A2</th>\n",
" <th>A3</th>\n",
" <th>Away Goalies</th>\n",
" <th>Away:</th>\n",
" <th>Date</th>\n",
" <th>Game</th>\n",
" <th>Game ID</th>\n",
" <th>Goalie</th>\n",
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" <th>...</th>\n",
" <th>VGK1</th>\n",
" <th>VGK2</th>\n",
" <th>VGK3</th>\n",
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" <th>4143</th>\n",
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" <th>4144</th>\n",
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" <td>31.0</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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"text/plain": [
" A1 A2 A3 Away Goalies Away: Date Game Game ID \\\n",
"4143 51TOR TOR TOR 35.0 PHI 2017-01-15 20084 20084 \n",
"4144 TOR TOR TOR 30.0 PHI 2017-01-15 20084 20084 \n",
"4145 34TOR TOR TOR NaN PHI 2017-01-15 20084 20084 \n",
"4146 19WSH 77WSH 74WSH NaN PHI 2017-01-15 20084 20084 \n",
"4147 12TOR 43TOR TOR NaN PHI 2017-01-15 20084 20084 \n",
"\n",
" Goalie Home Goalies ... VGK1 VGK2 VGK3 VGK4 VGK5 VAN1 VAN2 VAN3 VAN4 \\\n",
"4143 70.0 31 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4144 70.0 70 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4145 70.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4146 31.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4147 70.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" VAN5 \n",
"4143 NaN \n",
"4144 NaN \n",
"4145 NaN \n",
"4146 NaN \n",
"4147 NaN \n",
"\n",
"[5 rows x 175 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wsh = {key: fives[key].dropna(subset=['WSH1']) for key in ['shots', 'entries', 'exits']}\n",
"wsh['shots'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's convert numbers to names. I don't have a lookup table handy, so I'll do it by hand."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dmitry Orlov 1532\n",
"John Carlson 1450\n",
"Matt Niskanen 1344\n",
"Alex Ovechkin 1294\n",
"Brooks Orpik 1293\n",
"Evgeny Kuznetsov 1214\n",
"Nicklas Backstrom 1181\n",
"T.J. Oshie 1084\n",
"Lars Eller 1023\n",
"Karl Alzner 957\n",
"Brett Connolly 865\n",
"Jay Beagle 839\n",
"Andre Burakovsky 798\n",
"Tom Wilson 775\n",
"Marcus Johansson 685\n",
"Justin Williams 656\n",
"Nate Schmidt 585\n",
"Jakub Vrana 522\n",
"Daniel Winnik 430\n",
"Christian Djoos 376\n",
"Madison Bowey 323\n",
"Devante Smith-Pelly 307\n",
"Alex Chiasson 257\n",
"Zach Sanford 217\n",
"Chandler Stephenson 184\n",
"Taylor Chorney 174\n",
"Aaron Ness 152\n",
"Kevin Shattenkirk 106\n",
"Nathan Walker 59\n",
"Tyler Graovac 38\n",
"Name: Name, dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wsh['shots'][['WSH1', 'WSH2', 'WSH3', 'WSH4', 'WSH5']] \\\n",
" .melt(var_name='P', value_name='Name') \\\n",
" ['Name'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n"
]
},
{
"data": {
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" <td>PHI</td>\n",
" <td>2017-01-15</td>\n",
" <td>20084</td>\n",
" <td>20084</td>\n",
" <td>31.0</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>PHI</td>\n",
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" <td>20084</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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],
"text/plain": [
" A1 A2 A3 Away Goalies Away: Date Game Game ID \\\n",
"4143 51TOR TOR TOR 35.0 PHI 2017-01-15 20084 20084 \n",
"4144 TOR TOR TOR 30.0 PHI 2017-01-15 20084 20084 \n",
"4145 34TOR TOR TOR NaN PHI 2017-01-15 20084 20084 \n",
"4146 19WSH 77WSH 74WSH NaN PHI 2017-01-15 20084 20084 \n",
"4147 12TOR 43TOR TOR NaN PHI 2017-01-15 20084 20084 \n",
"\n",
" Goalie Home Goalies ... VGK1 VGK2 VGK3 VGK4 VGK5 VAN1 VAN2 VAN3 VAN4 \\\n",
"4143 70.0 31 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4144 70.0 70 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4145 70.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4146 31.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4147 70.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" VAN5 \n",
"4143 NaN \n",
"4144 NaN \n",
"4145 NaN \n",
"4146 NaN \n",
"4147 NaN \n",
"\n",
"[5 rows x 175 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wsh_players = {'Alex Ovechkin': 8, 'Nicklas Backstrom': 19, 'Andre Burakovsky': 65,\n",
" 'T.J. Oshie': 77, 'John Carlson': 74, 'Evgeny Kuznetsov': 92,\n",
" 'Dmitry Orlov': 9, 'Christian Djoos': 29, 'Devante Smith-Pelly': 25,\n",
" 'Jay Beagle': 83, 'Brooks Orpik': 44, 'Chandler Stephenson': 18,\n",
" 'Jakub Vrana': 13, 'Tom Wilson': 43, 'Lars Eller': 20,\n",
" 'Alex Chiasson': 39, 'Brett Connolly': 10, 'Madison Bowey': 22, 'Kevin Shattenkirk': 22,\n",
" 'Tyler Graovac': 91, '': 36, 'Matt Niskanen': 2, \n",
" 'Aaron Ness': 55, 'Nathan Walker': 79, 'Taylor Chorney': 4,\n",
" 'Zach Sanford': 12, 'Karl Alzner': 27, 'Marcus Johansson': 90, \n",
" 'Zachary Sanford': 12,\n",
" 'Justin Williams': 14, 'Daniel Winnik': 26, 'Nate Schmidt': 88}\n",
"\n",
"for col in ['WSH1', 'WSH2', 'WSH3', 'WSH4', 'WSH5']:\n",
" for key in ['shots', 'entries', 'exits']:\n",
" wsh[key].loc[:, col] = wsh[key][col].apply(lambda x: str(wsh_players[x]) + 'WSH' if x in wsh_players else x)\n",
"wsh['shots'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Entries per 60"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Season</th>\n",
" <th>Entry type</th>\n",
" <th>Team</th>\n",
" <th>Count</th>\n",
" <th>TOI</th>\n",
" <th>OppCount</th>\n",
" <th>Per60</th>\n",
" <th>OppPer60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>ANA</td>\n",
" <td>607</td>\n",
" <td>47929.0</td>\n",
" <td>542</td>\n",
" <td>45.592439</td>\n",
" <td>40.710217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>ARI</td>\n",
" <td>483</td>\n",
" <td>37469.0</td>\n",
" <td>574</td>\n",
" <td>46.406363</td>\n",
" <td>55.149590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>BOS</td>\n",
" <td>665</td>\n",
" <td>48305.0</td>\n",
" <td>589</td>\n",
" <td>49.560087</td>\n",
" <td>43.896077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>BUF</td>\n",
" <td>560</td>\n",
" <td>44498.0</td>\n",
" <td>609</td>\n",
" <td>45.305407</td>\n",
" <td>49.269630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>CAR</td>\n",
" <td>830</td>\n",
" <td>58917.0</td>\n",
" <td>742</td>\n",
" <td>50.715413</td>\n",
" <td>45.338357</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Season Entry type Team Count TOI OppCount Per60 OppPer60\n",
"0 2016 C ANA 607 47929.0 542 45.592439 40.710217\n",
"1 2016 C ARI 483 37469.0 574 46.406363 55.149590\n",
"2 2016 C BOS 665 48305.0 589 49.560087 43.896077\n",
"3 2016 C BUF 560 44498.0 609 45.305407 49.269630\n",
"4 2016 C CAR 830 58917.0 742 50.715413 45.338357"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Team comparison\n",
"\n",
"# Drop extra team cols\n",
"allteamcols = [x for x in fives['entries'].columns if not x.upper() == x]\n",
"allteams = fives['entries'][allteamcols]\n",
"\n",
"# Remove fails and faceoffs\n",
"allteams = allteams[pd.isnull(allteams.Fail)]\n",
"allteams = allteams[allteams['Entry type'] != 'FAC']\n",
"\n",
"# Counts by game and team\n",
"allteams = allteams[['Season', 'Game', 'Entry by', 'Entry type']]\n",
"# Extract ending text part of entry. \n",
"import re\n",
"def extract_team(string):\n",
" result = re.search('\\d*(\\w{2,3})$', str(string))\n",
" if result:\n",
" return result.group(1)\n",
" return string\n",
"allteams.loc[:, 'Team'] = allteams['Entry by'].apply(lambda x: extract_team(x))\n",
"\n",
"# Get rid of typo teams -- about 100 in 77000\n",
"valid_teams = set(allteams.Team.value_counts().index[:31])\n",
"allteams = allteams[allteams.Team.apply(lambda x: x in valid_teams)]\n",
"\n",
"allteams = allteams.drop('Entry by', axis=1) \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Game', 'Team', 'Entry type'], as_index=False) \\\n",
" .count()\n",
" \n",
"# Add opp numbers\n",
"gametotals = allteams.drop('Team', axis=1) \\\n",
" .groupby(['Season', 'Game', 'Entry type'], as_index=False) \\\n",
" .sum() \\\n",
" .rename(columns={'Count': 'OppCount'})\n",
"\n",
"fives['toi'] = pd.concat([dfs['toi'][team] \\\n",
" [['Season', 'Game']] \\\n",
" .assign(TOI=1) \\\n",
" .groupby(['Season', 'Game'], as_index=False) \\\n",
" .count() for team in dfs['toi']]).drop_duplicates()\n",
"\n",
"# Sum by season and calculate per 60\n",
"allteams = allteams.merge(fives['toi'], how='left', on=['Season', 'Game'])\n",
"allteams = allteams.merge(gametotals, how='inner', on=['Season', 'Game', 'Entry type']) \\\n",
" .drop('Game', axis=1) \\\n",
" .groupby(['Season', 'Entry type', 'Team'], as_index=False) \\\n",
" .sum()\n",
"allteams.loc[:, 'OppCount'] = allteams['OppCount'] - allteams['Count']\n",
"\n",
"allteams.loc[:, 'Per60'] = allteams['Count'] / (allteams.TOI / 3600)\n",
"allteams.loc[:, 'OppPer60'] = allteams['OppCount'] / (allteams.TOI / 3600)\n",
"\n",
"allteams.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11c175f98>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Vq4FQy8PVV19NTk4Offv2Zdq0aQA899xznHbaaYwdO5Zjjz0Way033XQTgwcPZsiQIbz6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\ncMm5xogJx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"text/plain": [
"<matplotlib.figure.Figure at 0x11a3b8128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f = figure(figsize=[8, 8])\n",
"\n",
"tmp = allteams[allteams['Entry type'] != 'X'] \\\n",
" .drop(['Count', 'OppCount', 'TOI'], axis=1) \\\n",
" .pivot_table(index=['Entry type', 'Team'], columns='Season', values='Per60') \\\n",
" .reset_index()\n",
" \n",
"for etype in tmp['Entry type'].unique():\n",
" tmp2 = tmp[tmp['Entry type'] == etype]\n",
" scatter(tmp2.loc[:, 2016].values, tmp2.loc[:, 2017].values, label=etype, s=200, alpha=0.5)\n",
"\n",
"for s, etype, t, r1, r2 in tmp.itertuples():\n",
" annotate(t, xy=(r1, r2), ha='center', va='center') \n",
"\n",
"from scrapenhl2.plot import visualization_helper as vhelper\n",
"vhelper.add_good_bad_fast_slow(bottomleft='Bad', topleft='Improved', topright='Good', bottomright='Declined')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])\n",
"\n",
"title('Team 5v5 entry rate, 2016 vs 2017')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"legend(loc=2, bbox_to_anchor=(1, 1))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11c113358>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ngIP5v7RsjDEczPeQnlNAtMvOjLHJOje+iiiRmIRXnfDNNlBKHbNOCVHMGt+X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"text/plain": [
"<matplotlib.figure.Figure at 0x11c71deb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f = figure(figsize=[8, 8])\n",
"\n",
"tmp = allteams[allteams['Entry type'] != 'X'] \\\n",
" .drop(['Count', 'OppCount', 'TOI'], axis=1) \\\n",
" .pivot_table(index=['Season', 'Team'], columns='Entry type', values='Per60') \\\n",
" .reset_index()\n",
" \n",
"tmp.loc[:, 'Ctrl%'] = tmp.C / (tmp.C + tmp.D)\n",
"tmp = tmp.drop(['C', 'D'], axis=1)\n",
"tmp = tmp.pivot_table(index='Team', columns='Season', values='Ctrl%').reset_index()\n",
" \n",
"scatter(tmp.loc[:, 2016].values, tmp.loc[:, 2017].values, s=200, alpha=0.5)\n",
"\n",
"for i, t, r1, r2 in tmp.itertuples():\n",
" annotate(t, xy=(r1, r2), ha='center', va='center') \n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Bad', topleft='Improved', topright='Good', bottomright='Declined')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])\n",
"\n",
"title('Team 5v5 controlled entry rate, 2016 vs 2017')\n",
"xlabel('2016')\n",
"ylabel('2017')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11eb71cc0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qKtA0zdQ0zeohSjhb/4yf2pYzWOxBQ+IXTjQkQ7Q9/NS2nLP1z8kVpZ1W5tjr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KAAAg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H9rlc+aMxPP3Kyhg3jpy7KNHMAF8pld7nSDRIa43X66WmpqbOQx+ps+uST4qFeTweCRxShM/n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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bf73518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = subplots(1, 2, sharex=True, sharey=True, figsize=[12, 6])\n",
"\n",
"for i, season in enumerate(allteams.Season.unique()):\n",
" tmp = allteams[(allteams.Season == season) & (allteams['Entry type'] != 'X')]\n",
" for etype in tmp['Entry type'].unique():\n",
" tmp2 = tmp[tmp['Entry type'] == etype]\n",
" axes[i].scatter(tmp2.Per60.values, tmp2.OppPer60.values, s=250, alpha=0.3, label=etype)\n",
" axes[i].set_title('Entries for and against, {0:d}'.format(season))\n",
" axes[i].set_xlabel('Entries per 60')\n",
" if i == 0:\n",
" axes[i].set_ylabel('Entries against per 60')\n",
" for _, t, r1, r2 in tmp[['Team', 'Per60', 'OppPer60']].itertuples():\n",
" axes[i].annotate(t, xy=(r1, r2), ha='center', va='center')\n",
" vhelper.add_good_bad_fast_slow(bottomleft='NZ Jam', topleft='Bad', topright='Racetrack', bottomright='Good')\n",
" vhelper.add_cfpct_ref_lines_to_plot(ax=axes[i])\n",
" \n",
"legend(loc=2, bbox_to_anchor=(1, 1))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
" warnings.warn(\"No labelled objects found. \"\n"
]
},
{
"data": {
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Mm45hGLRq1Sqmr9GqVauwAb0itjwezyF9foWojcPhwG63N3r/RG2YB6SkpACE9cuVxrlI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uhKoO1lQ1pbumgB27Z8yZLnnwFg65c11G78FA4akeYIsy/XFqpQKtc5fzNay7z3ih9zbtmGPSYc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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11eb96208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tmp = allteams[allteams['Entry type'] != 'X'] \\\n",
" .drop(['Count', 'OppCount', 'TOI'], axis=1) \\\n",
" .melt(id_vars=['Season', 'Entry type', 'Team']) \\\n",
" .pivot_table(index=['Season', 'Team', 'variable'], columns='Entry type', values='value') \\\n",
" .reset_index()\n",
"\n",
"tmp.loc[:, 'CE%'] = tmp.C / (tmp.C + tmp.D)\n",
"tmp = tmp.drop(['C', 'D'], axis=1) \\\n",
" .pivot_table(index=['Season', 'Team'], columns='variable', values='CE%') \\\n",
" .rename(columns={'Per60': 'TeamCE%', 'OppPer60': 'OppCE%'}) \\\n",
" .reset_index()\n",
" \n",
"fig, axes = subplots(1, 2, sharex=True, sharey=True, figsize=[12, 6])\n",
"\n",
"for i, season in enumerate(tmp.Season.unique()):\n",
" tmp2 = tmp[(tmp.Season == season)]\n",
" axes[i].scatter(tmp2['TeamCE%'].values, tmp2['OppCE%'].values, s=250, alpha=0.3)\n",
" \n",
" axes[i].set_title('Controlled entries for and against, {0:d}'.format(season))\n",
" axes[i].set_xlabel('Team CE%')\n",
" if i == 0:\n",
" axes[i].set_ylabel('Opp CE%')\n",
" for _, t, r1, r2 in tmp2[['Team', 'TeamCE%', 'OppCE%']].itertuples():\n",
" axes[i].annotate(t, xy=(r1, r2), ha='center', va='center')\n",
" \n",
" vhelper.add_good_bad_fast_slow(bottomleft='NZ Jam', topleft='Bad', topright='Racetrack', bottomright='Good')\n",
" vhelper.add_cfpct_ref_lines_to_plot(ax=axes[i])\n",
" \n",
"legend(loc=2, bbox_to_anchor=(1, 1))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Controlled?</th>\n",
" <th>Defended by</th>\n",
" <th>Dump recovered?</th>\n",
" <th>Entry by</th>\n",
" <th>Entry type</th>\n",
" <th>Fail</th>\n",
" <th>Fen total</th>\n",
" <th>Game</th>\n",
" <th>Goal total</th>\n",
" <th>Goalie touch?</th>\n",
" <th>...</th>\n",
" <th>Opp strength</th>\n",
" <th>Period</th>\n",
" <th>Season</th>\n",
" <th>Team strength</th>\n",
" <th>Time</th>\n",
" <th>Time2</th>\n",
" <th>_Secs</th>\n",
" <th>Team</th>\n",
" <th>variable</th>\n",
" <th>Player</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2850</th>\n",
" <td>19WSH</td>\n",
" <td>3PIT</td>\n",
" <td>N</td>\n",
" <td>19WSH</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>20007</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.0</td>\n",
" <td>2016</td>\n",
" <td>5</td>\n",
" <td>19:28:00</td>\n",
" <td>19:28</td>\n",
" <td>33</td>\n",
" <td>WSH</td>\n",
" <td>WSH1</td>\n",
" <td>2WSH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7131</th>\n",
" <td>19WSH</td>\n",
" <td>3PIT</td>\n",
" <td>N</td>\n",
" <td>19WSH</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>20007</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.0</td>\n",
" <td>2016</td>\n",
" <td>5</td>\n",
" <td>19:28:00</td>\n",
" <td>19:28</td>\n",
" <td>33</td>\n",
" <td>WSH</td>\n",
" <td>WSH2</td>\n",
" <td>27WSH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11412</th>\n",
" <td>19WSH</td>\n",
" <td>3PIT</td>\n",
" <td>N</td>\n",
" <td>19WSH</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>20007</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.0</td>\n",
" <td>2016</td>\n",
" <td>5</td>\n",
" <td>19:28:00</td>\n",
" <td>19:28</td>\n",
" <td>33</td>\n",
" <td>WSH</td>\n",
" <td>WSH3</td>\n",
" <td>65WSH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15693</th>\n",
" <td>19WSH</td>\n",
" <td>3PIT</td>\n",
" <td>N</td>\n",
" <td>19WSH</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>20007</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.0</td>\n",
" <td>2016</td>\n",
" <td>5</td>\n",
" <td>19:28:00</td>\n",
" <td>19:28</td>\n",
" <td>33</td>\n",
" <td>WSH</td>\n",
" <td>WSH4</td>\n",
" <td>19WSH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19974</th>\n",
" <td>19WSH</td>\n",
" <td>3PIT</td>\n",
" <td>N</td>\n",
" <td>19WSH</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>20007</td>\n",
" <td>0.0</td>\n",
" <td>R</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.0</td>\n",
" <td>2016</td>\n",
" <td>5</td>\n",
" <td>19:28:00</td>\n",
" <td>19:28</td>\n",
" <td>33</td>\n",
" <td>WSH</td>\n",
" <td>WSH5</td>\n",
" <td>90WSH</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" Controlled? Defended by Dump recovered? Entry by Entry type Fail \\\n",
"2850 19WSH 3PIT N 19WSH C NaN \n",
"7131 19WSH 3PIT N 19WSH C NaN \n",
"11412 19WSH 3PIT N 19WSH C NaN \n",
"15693 19WSH 3PIT N 19WSH C NaN \n",
"19974 19WSH 3PIT N 19WSH C NaN \n",
"\n",
" Fen total Game Goal total Goalie touch? ... Opp strength Period \\\n",
"2850 0.0 20007 0.0 R ... 5 1.0 \n",
"7131 0.0 20007 0.0 R ... 5 1.0 \n",
"11412 0.0 20007 0.0 R ... 5 1.0 \n",
"15693 0.0 20007 0.0 R ... 5 1.0 \n",
"19974 0.0 20007 0.0 R ... 5 1.0 \n",
"\n",
" Season Team strength Time Time2 _Secs Team variable Player \n",
"2850 2016 5 19:28:00 19:28 33 WSH WSH1 2WSH \n",
"7131 2016 5 19:28:00 19:28 33 WSH WSH2 27WSH \n",
"11412 2016 5 19:28:00 19:28 33 WSH WSH3 65WSH \n",
"15693 2016 5 19:28:00 19:28 33 WSH WSH4 19WSH \n",
"19974 2016 5 19:28:00 19:28 33 WSH WSH5 90WSH \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"entries = wsh['entries']\n",
"\n",
"# Drop extra team cols\n",
"colnames = [x for x in entries.columns if not x.upper() == x or x[:3] == 'WSH']\n",
"entries = entries[colnames]\n",
"\n",
"# Remove fails\n",
"entries = entries[pd.isnull(entries.Fail)]\n",
"\n",
"# Flag entries as WSH or Opp\n",
"entries.loc[:, 'Team'] = entries['Entry by'].apply(lambda x: 'WSH' if str(x)[-3:] == 'WSH' else 'Opp')\n",
"\n",
"# Remove faceoffs\n",
"# entries2 = entries\n",
"entries2 = entries[entries['Entry type'] != 'FAC']\n",
"\n",
"# Melt to long\n",
"idvars = [x for x in entries2.columns if x not in ['WSH1', 'WSH2', 'WSH3', 'WSH4', 'WSH5']]\n",
"entries2 = entries2.melt(id_vars=idvars, value_name='Player').sort_values(['Season', 'Game', '_Secs', 'variable'])\n",
"\n",
"entries2.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Season</th>\n",
" <th>Entry type</th>\n",
" <th>Team</th>\n",
" <th>Count</th>\n",
" <th>TOI</th>\n",
" <th>Per60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>Opp</td>\n",
" <td>601</td>\n",
" <td>60976</td>\n",
" <td>35.482813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>WSH</td>\n",
" <td>686</td>\n",
" <td>60976</td>\n",
" <td>40.501181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>Opp</td>\n",
" <td>609</td>\n",
" <td>60976</td>\n",
" <td>35.955130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>WSH</td>\n",
" <td>611</td>\n",
" <td>60976</td>\n",
" <td>36.073209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>Opp</td>\n",
" <td>36</td>\n",
" <td>60976</td>\n",
" <td>2.125426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>WSH</td>\n",
" <td>48</td>\n",
" <td>60976</td>\n",
" <td>2.833902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>Opp</td>\n",
" <td>361</td>\n",
" <td>37848</td>\n",
" <td>34.337349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>WSH</td>\n",
" <td>429</td>\n",
" <td>37848</td>\n",
" <td>40.805327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>Opp</td>\n",
" <td>385</td>\n",
" <td>37848</td>\n",
" <td>36.620165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>WSH</td>\n",
" <td>376</td>\n",
" <td>37848</td>\n",
" <td>35.764109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>Opp</td>\n",
" <td>44</td>\n",
" <td>37848</td>\n",
" <td>4.185162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>WSH</td>\n",
" <td>41</td>\n",
" <td>37848</td>\n",
" <td>3.899810</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Season Entry type Team Count TOI Per60\n",
"0 2016 C Opp 601 60976 35.482813\n",
"1 2016 C WSH 686 60976 40.501181\n",
"2 2016 D Opp 609 60976 35.955130\n",
"3 2016 D WSH 611 60976 36.073209\n",
"4 2016 X Opp 36 60976 2.125426\n",
"5 2016 X WSH 48 60976 2.833902\n",
"6 2017 C Opp 361 37848 34.337349\n",
"7 2017 C WSH 429 37848 40.805327\n",
"8 2017 D Opp 385 37848 36.620165\n",
"9 2017 D WSH 376 37848 35.764109\n",
"10 2017 X Opp 44 37848 4.185162\n",
"11 2017 X WSH 41 37848 3.899810"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Season level\n",
"\n",
"# Count by entry type\n",
"entries60 = entries2[['Season', 'Entry type', 'Team', 'Game', 'Period', 'Time']] \\\n",
" .drop_duplicates() \\\n",
" [['Season', 'Entry type', 'Team']] \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Entry type', 'Team'], as_index=False) \\\n",
" .count()\n",
"\n",
"# Add TOI\n",
"toi = dfs['toi']['WSH'] \\\n",
" [['Season']].assign(TOI=1) \\\n",
" .groupby('Season', as_index=False).count()\n",
"\n",
"entries60 = entries60.merge(toi, how='left', on='Season')\n",
"entries60.loc[:, 'Per60'] = entries60.Count / (entries60.TOI / 3600)\n",
"\n",
"entries60"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1159e0320>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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psaTFzc3N3W6omZlVn66cCX0MWBoRrY/fbZI0AiC9NrU1UUTMiYjGiGisq6vrWWvNzKyq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\nIWRmZrlxCJmZWW4cQmZmlpv/D+KplU0nsdF4AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c15e7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tmp = entries60.assign(Height=entries60.Per60).sort_values(['Season', 'Team', 'Entry type'])\n",
"tmp.loc[:, 'Left'] = tmp.Team.apply(lambda x: 0 if x == 'WSH' else 1) + tmp.Season.apply(lambda x: 0 if x == 2016 else 0.4)\n",
"tmp.loc[:, 'Bottom'] = tmp.groupby(['Season', 'Team']).Height.cumsum() - tmp.Height\n",
"for etype in tmp['Entry type'].unique():\n",
" tmp2 = tmp[tmp['Entry type'] == etype]\n",
" bar(left=tmp2.Left.values, height=tmp2.Height.values, bottom=tmp2.Bottom.values, label=etype, width=0.3)\n",
"\n",
"xlabs = tmp.drop_duplicates(subset=['Season', 'Team', 'Left'])\n",
"xticks([x for x in xlabs.Left], ['{0:d} {1:s}'.format(s, t) for s, t in zip(tmp2.Season, tmp2.Team)])\n",
"legend(loc=2, bbox_to_anchor=(1, 1))\n",
"title('Zone entries per 60, Caps vs opponents')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Season</th>\n",
" <th>Entry type</th>\n",
" <th>Player</th>\n",
" <th>Opp</th>\n",
" <th>WSH</th>\n",
" <th>TOI</th>\n",
" <th>WSH60</th>\n",
" <th>Opp60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>10WSH</td>\n",
" <td>91.0</td>\n",
" <td>133.0</td>\n",
" <td>10314</td>\n",
" <td>46.422339</td>\n",
" <td>31.762653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>10WSH</td>\n",
" <td>95.0</td>\n",
" <td>97.0</td>\n",
" <td>10314</td>\n",
" <td>33.856894</td>\n",
" <td>33.158813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>10WSH</td>\n",
" <td>10.0</td>\n",
" <td>4.0</td>\n",
" <td>10314</td>\n",
" <td>1.396161</td>\n",
" <td>3.490401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>10WSH</td>\n",
" <td>72.0</td>\n",
" <td>71.0</td>\n",
" <td>6894</td>\n",
" <td>37.075718</td>\n",
" <td>37.597911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>10WSH</td>\n",
" <td>58.0</td>\n",
" <td>81.0</td>\n",
" <td>6894</td>\n",
" <td>42.297650</td>\n",
" <td>30.287206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>10WSH</td>\n",
" <td>13.0</td>\n",
" <td>9.0</td>\n",
" <td>6894</td>\n",
" <td>4.699739</td>\n",
" <td>6.788512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>12WSH</td>\n",
" <td>59.0</td>\n",
" <td>67.0</td>\n",
" <td>5321</td>\n",
" <td>45.329825</td>\n",
" <td>39.917309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>12WSH</td>\n",
" <td>52.0</td>\n",
" <td>58.0</td>\n",
" <td>5321</td>\n",
" <td>39.240744</td>\n",
" <td>35.181357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>12WSH</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>5321</td>\n",
" <td>2.029694</td>\n",
" <td>1.353129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>13WSH</td>\n",
" <td>13.0</td>\n",
" <td>14.0</td>\n",
" <td>1433</td>\n",
" <td>35.170970</td>\n",
" <td>32.658758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>13WSH</td>\n",
" <td>12.0</td>\n",
" <td>11.0</td>\n",
" <td>1433</td>\n",
" <td>27.634334</td>\n",
" <td>30.146546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>13WSH</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>1433</td>\n",
" <td>5.024424</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>13WSH</td>\n",
" <td>101.0</td>\n",
" <td>146.0</td>\n",
" <td>10468</td>\n",
" <td>50.210164</td>\n",
" <td>34.734429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>13WSH</td>\n",
" <td>117.0</td>\n",
" <td>78.0</td>\n",
" <td>10468</td>\n",
" <td>26.824608</td>\n",
" <td>40.236912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>13WSH</td>\n",
" <td>13.0</td>\n",
" <td>2.0</td>\n",
" <td>10468</td>\n",
" <td>0.687810</td>\n",
" <td>4.470768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>14WSH</td>\n",
" <td>147.0</td>\n",
" <td>161.0</td>\n",
" <td>14716</td>\n",
" <td>39.385703</td>\n",
" <td>35.960859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>14WSH</td>\n",
" <td>143.0</td>\n",
" <td>158.0</td>\n",
" <td>14716</td>\n",
" <td>38.651808</td>\n",
" <td>34.982332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>14WSH</td>\n",
" <td>5.0</td>\n",
" <td>11.0</td>\n",
" <td>14716</td>\n",
" <td>2.690949</td>\n",
" <td>1.223158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>18WSH</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>466</td>\n",
" <td>30.901288</td>\n",
" <td>7.725322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>18WSH</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>466</td>\n",
" <td>46.351931</td>\n",
" <td>54.077253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>18WSH</td>\n",
" <td>39.0</td>\n",
" <td>39.0</td>\n",
" <td>4723</td>\n",
" <td>29.726869</td>\n",
" <td>29.726869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>18WSH</td>\n",
" <td>55.0</td>\n",
" <td>60.0</td>\n",
" <td>4723</td>\n",
" <td>45.733644</td>\n",
" <td>41.922507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>18WSH</td>\n",
" <td>8.0</td>\n",
" <td>4.0</td>\n",
" <td>4723</td>\n",
" <td>3.048910</td>\n",
" <td>6.097819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>19WSH</td>\n",
" <td>181.0</td>\n",
" <td>201.0</td>\n",
" <td>17399</td>\n",
" <td>41.588597</td>\n",
" <td>37.450428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>19WSH</td>\n",
" <td>171.0</td>\n",
" <td>177.0</td>\n",
" <td>17399</td>\n",
" <td>36.622794</td>\n",
" <td>35.381344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>19WSH</td>\n",
" <td>9.0</td>\n",
" <td>12.0</td>\n",
" <td>17399</td>\n",
" <td>2.482901</td>\n",
" <td>1.862176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>19WSH</td>\n",
" <td>114.0</td>\n",
" <td>143.0</td>\n",
" <td>11360</td>\n",
" <td>45.316901</td>\n",
" <td>36.126761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>19WSH</td>\n",
" <td>110.0</td>\n",
" <td>109.0</td>\n",
" <td>11360</td>\n",
" <td>34.542254</td>\n",
" <td>34.859155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>19WSH</td>\n",
" <td>9.0</td>\n",
" <td>13.0</td>\n",
" <td>11360</td>\n",
" <td>4.119718</td>\n",
" <td>2.852113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>20WSH</td>\n",
" <td>136.0</td>\n",
" <td>181.0</td>\n",
" <td>15087</td>\n",
" <td>43.189501</td>\n",
" <td>32.451780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>83WSH</td>\n",
" <td>66.0</td>\n",
" <td>62.0</td>\n",
" <td>7677</td>\n",
" <td>29.073857</td>\n",
" <td>30.949590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>83WSH</td>\n",
" <td>84.0</td>\n",
" <td>88.0</td>\n",
" <td>7677</td>\n",
" <td>41.266120</td>\n",
" <td>39.390387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>83WSH</td>\n",
" <td>9.0</td>\n",
" <td>13.0</td>\n",
" <td>7677</td>\n",
" <td>6.096131</td>\n",
" <td>4.220399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>88WSH</td>\n",
" <td>129.0</td>\n",
" <td>189.0</td>\n",
" <td>14731</td>\n",
" <td>46.188310</td>\n",
" <td>31.525355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>88WSH</td>\n",
" <td>155.0</td>\n",
" <td>138.0</td>\n",
" <td>14731</td>\n",
" <td>33.724798</td>\n",
" <td>37.879302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>88WSH</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>14731</td>\n",
" <td>1.221913</td>\n",
" <td>1.955061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>8WSH</td>\n",
" <td>201.0</td>\n",
" <td>224.0</td>\n",
" <td>18229</td>\n",
" <td>44.237204</td>\n",
" <td>39.694991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>8WSH</td>\n",
" <td>187.0</td>\n",
" <td>163.0</td>\n",
" <td>18229</td>\n",
" <td>32.190466</td>\n",
" <td>36.930166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>8WSH</td>\n",
" <td>10.0</td>\n",
" <td>19.0</td>\n",
" <td>18229</td>\n",
" <td>3.752263</td>\n",
" <td>1.974875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>8WSH</td>\n",
" <td>126.0</td>\n",
" <td>148.0</td>\n",
" <td>11351</td>\n",
" <td>46.938596</td>\n",
" <td>39.961237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>8WSH</td>\n",
" <td>127.0</td>\n",
" <td>109.0</td>\n",
" <td>11351</td>\n",
" <td>34.569641</td>\n",
" <td>40.278390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>8WSH</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>11351</td>\n",
" <td>4.122985</td>\n",
" <td>3.488679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>90WSH</td>\n",
" <td>178.0</td>\n",
" <td>181.0</td>\n",
" <td>16914</td>\n",
" <td>38.524299</td>\n",
" <td>37.885775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>90WSH</td>\n",
" <td>177.0</td>\n",
" <td>170.0</td>\n",
" <td>16914</td>\n",
" <td>36.183044</td>\n",
" <td>37.672934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>90WSH</td>\n",
" <td>6.0</td>\n",
" <td>17.0</td>\n",
" <td>16914</td>\n",
" <td>3.618304</td>\n",
" <td>1.277049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>91WSH</td>\n",
" <td>11.0</td>\n",
" <td>7.0</td>\n",
" <td>1441</td>\n",
" <td>17.487856</td>\n",
" <td>27.480916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>91WSH</td>\n",
" <td>14.0</td>\n",
" <td>14.0</td>\n",
" <td>1441</td>\n",
" <td>34.975711</td>\n",
" <td>34.975711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>91WSH</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>1441</td>\n",
" <td>7.494795</td>\n",
" <td>2.498265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>92WSH</td>\n",
" <td>187.0</td>\n",
" <td>203.0</td>\n",
" <td>17422</td>\n",
" <td>41.946964</td>\n",
" <td>38.640799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>92WSH</td>\n",
" <td>182.0</td>\n",
" <td>158.0</td>\n",
" <td>17422</td>\n",
" <td>32.648376</td>\n",
" <td>37.607623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>92WSH</td>\n",
" <td>6.0</td>\n",
" <td>19.0</td>\n",
" <td>17422</td>\n",
" <td>3.926070</td>\n",
" <td>1.239812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>92WSH</td>\n",
" <td>113.0</td>\n",
" <td>149.0</td>\n",
" <td>11133</td>\n",
" <td>48.181083</td>\n",
" <td>36.540016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>92WSH</td>\n",
" <td>124.0</td>\n",
" <td>89.0</td>\n",
" <td>11133</td>\n",
" <td>28.779305</td>\n",
" <td>40.097009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>92WSH</td>\n",
" <td>13.0</td>\n",
" <td>8.0</td>\n",
" <td>11133</td>\n",
" <td>2.586904</td>\n",
" <td>4.203719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>9WSH</td>\n",
" <td>236.0</td>\n",
" <td>261.0</td>\n",
" <td>22548</td>\n",
" <td>41.671102</td>\n",
" <td>37.679617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>9WSH</td>\n",
" <td>222.0</td>\n",
" <td>223.0</td>\n",
" <td>22548</td>\n",
" <td>35.604045</td>\n",
" <td>35.444385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>9WSH</td>\n",
" <td>13.0</td>\n",
" <td>20.0</td>\n",
" <td>22548</td>\n",
" <td>3.193188</td>\n",
" <td>2.075572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>9WSH</td>\n",
" <td>127.0</td>\n",
" <td>193.0</td>\n",
" <td>15255</td>\n",
" <td>45.545723</td>\n",
" <td>29.970501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>9WSH</td>\n",
" <td>160.0</td>\n",
" <td>163.0</td>\n",
" <td>15255</td>\n",
" <td>38.466077</td>\n",
" <td>37.758112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>9WSH</td>\n",
" <td>16.0</td>\n",
" <td>11.0</td>\n",
" <td>15255</td>\n",
" <td>2.595870</td>\n",
" <td>3.775811</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>137 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Season Entry type Player Opp WSH TOI WSH60 Opp60\n",
"0 2016 C 10WSH 91.0 133.0 10314 46.422339 31.762653\n",
"23 2016 D 10WSH 95.0 97.0 10314 33.856894 33.158813\n",
"46 2016 X 10WSH 10.0 4.0 10314 1.396161 3.490401\n",
"68 2017 C 10WSH 72.0 71.0 6894 37.075718 37.597911\n",
"91 2017 D 10WSH 58.0 81.0 6894 42.297650 30.287206\n",
"114 2017 X 10WSH 13.0 9.0 6894 4.699739 6.788512\n",
"1 2016 C 12WSH 59.0 67.0 5321 45.329825 39.917309\n",
"24 2016 D 12WSH 52.0 58.0 5321 39.240744 35.181357\n",
"47 2016 X 12WSH 2.0 3.0 5321 2.029694 1.353129\n",
"2 2016 C 13WSH 13.0 14.0 1433 35.170970 32.658758\n",
"25 2016 D 13WSH 12.0 11.0 1433 27.634334 30.146546\n",
"48 2016 X 13WSH NaN 2.0 1433 5.024424 NaN\n",
"69 2017 C 13WSH 101.0 146.0 10468 50.210164 34.734429\n",
"92 2017 D 13WSH 117.0 78.0 10468 26.824608 40.236912\n",
"115 2017 X 13WSH 13.0 2.0 10468 0.687810 4.470768\n",
"3 2016 C 14WSH 147.0 161.0 14716 39.385703 35.960859\n",
"26 2016 D 14WSH 143.0 158.0 14716 38.651808 34.982332\n",
"49 2016 X 14WSH 5.0 11.0 14716 2.690949 1.223158\n",
"4 2016 C 18WSH 1.0 4.0 466 30.901288 7.725322\n",
"27 2016 D 18WSH 7.0 6.0 466 46.351931 54.077253\n",
"70 2017 C 18WSH 39.0 39.0 4723 29.726869 29.726869\n",
"93 2017 D 18WSH 55.0 60.0 4723 45.733644 41.922507\n",
"116 2017 X 18WSH 8.0 4.0 4723 3.048910 6.097819\n",
"5 2016 C 19WSH 181.0 201.0 17399 41.588597 37.450428\n",
"28 2016 D 19WSH 171.0 177.0 17399 36.622794 35.381344\n",
"50 2016 X 19WSH 9.0 12.0 17399 2.482901 1.862176\n",
"71 2017 C 19WSH 114.0 143.0 11360 45.316901 36.126761\n",
"94 2017 D 19WSH 110.0 109.0 11360 34.542254 34.859155\n",
"117 2017 X 19WSH 9.0 13.0 11360 4.119718 2.852113\n",
"6 2016 C 20WSH 136.0 181.0 15087 43.189501 32.451780\n",
".. ... ... ... ... ... ... ... ...\n",
"86 2017 C 83WSH 66.0 62.0 7677 29.073857 30.949590\n",
"109 2017 D 83WSH 84.0 88.0 7677 41.266120 39.390387\n",
"132 2017 X 83WSH 9.0 13.0 7677 6.096131 4.220399\n",
"18 2016 C 88WSH 129.0 189.0 14731 46.188310 31.525355\n",
"41 2016 D 88WSH 155.0 138.0 14731 33.724798 37.879302\n",
"63 2016 X 88WSH 8.0 5.0 14731 1.221913 1.955061\n",
"19 2016 C 8WSH 201.0 224.0 18229 44.237204 39.694991\n",
"42 2016 D 8WSH 187.0 163.0 18229 32.190466 36.930166\n",
"64 2016 X 8WSH 10.0 19.0 18229 3.752263 1.974875\n",
"87 2017 C 8WSH 126.0 148.0 11351 46.938596 39.961237\n",
"110 2017 D 8WSH 127.0 109.0 11351 34.569641 40.278390\n",
"133 2017 X 8WSH 11.0 13.0 11351 4.122985 3.488679\n",
"20 2016 C 90WSH 178.0 181.0 16914 38.524299 37.885775\n",
"43 2016 D 90WSH 177.0 170.0 16914 36.183044 37.672934\n",
"65 2016 X 90WSH 6.0 17.0 16914 3.618304 1.277049\n",
"88 2017 C 91WSH 11.0 7.0 1441 17.487856 27.480916\n",
"111 2017 D 91WSH 14.0 14.0 1441 34.975711 34.975711\n",
"134 2017 X 91WSH 1.0 3.0 1441 7.494795 2.498265\n",
"21 2016 C 92WSH 187.0 203.0 17422 41.946964 38.640799\n",
"44 2016 D 92WSH 182.0 158.0 17422 32.648376 37.607623\n",
"66 2016 X 92WSH 6.0 19.0 17422 3.926070 1.239812\n",
"89 2017 C 92WSH 113.0 149.0 11133 48.181083 36.540016\n",
"112 2017 D 92WSH 124.0 89.0 11133 28.779305 40.097009\n",
"135 2017 X 92WSH 13.0 8.0 11133 2.586904 4.203719\n",
"22 2016 C 9WSH 236.0 261.0 22548 41.671102 37.679617\n",
"45 2016 D 9WSH 222.0 223.0 22548 35.604045 35.444385\n",
"67 2016 X 9WSH 13.0 20.0 22548 3.193188 2.075572\n",
"90 2017 C 9WSH 127.0 193.0 15255 45.545723 29.970501\n",
"113 2017 D 9WSH 160.0 163.0 15255 38.466077 37.758112\n",
"136 2017 X 9WSH 16.0 11.0 15255 2.595870 3.775811\n",
"\n",
"[137 rows x 8 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Individual level, on-ice\n",
"\n",
"# Count by entry type\n",
"entries60 = entries2[['Season', 'Entry type', 'Team', 'Game', 'Period', 'Time', 'Player']] \\\n",
" [['Season', 'Entry type', 'Team', 'Player']] \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Entry type', 'Team', 'Player'], as_index=False) \\\n",
" .count() \\\n",
" .pivot_table(index=['Season', 'Entry type', 'Player'], columns='Team', values='Count') \\\n",
" .reset_index()\n",
"\n",
"# Add TOI\n",
"toi = dfs['toi']['WSH'] \\\n",
" [['Season', 'WSH1', 'WSH2', 'WSH3', 'WSH4', 'WSH5']] \\\n",
" .melt(id_vars='Season', value_name='Player') \\\n",
" .drop('variable', axis=1) \\\n",
" .assign(TOI=1) \\\n",
" .groupby(['Season', 'Player'], as_index=False).count()\n",
"toi.loc[:, 'Player'] = toi['Player'].apply(lambda x: str(wsh_players[x]) + 'WSH' if x in wsh_players else x)\n",
"\n",
"entries60 = entries60.merge(toi, how='left', on=['Season', 'Player']) \\\n",
" .sort_values(['Player', 'Season', 'Entry type'])\n",
"entries60.loc[:, 'WSH60'] = entries60.WSH / (entries60.TOI / 3600)\n",
"entries60.loc[:, 'Opp60'] = entries60.Opp / (entries60.TOI / 3600)\n",
"\n",
"entries60"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/reshape/pivot.py:135: FutureWarning: 'Player' is both a column name and an index level.\n",
"Defaulting to column but this will raise an ambiguity error in a future version\n",
" grouped = data.groupby(keys)\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/reshape/pivot.py:135: FutureWarning: 'Season' is both a column name and an index level.\n",
"Defaulting to column but this will raise an ambiguity error in a future version\n",
" grouped = data.groupby(keys)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
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RRRdht9sBmDp1Krt27WLo0KEYY8jMzGTu3LlMmjSJRYsWMXDgQLp168aoUaMa9VyVigUt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\nXCVfywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bde7da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"title('Entry%, 2016 vs 2017')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"\n",
"tmp = entries60[['Season', 'Player', 'WSH', 'Opp']] \\\n",
" .groupby(['Season', 'Player', 'WSH', 'Opp'], as_index=False) \\\n",
" .sum()\n",
"\n",
"tmp.loc[:, 'Entry%'] = tmp.WSH / (tmp.WSH + tmp.Opp)\n",
"tmp = tmp.drop(['WSH', 'Opp'], axis=1)\n",
"tmp = tmp.pivot_table(index='Player', columns='Season', values='Entry%')\n",
"\n",
"scatter(tmp.loc[:, 2016].values, tmp.loc[:, 2017].values, s=200, alpha=0.5)\n",
"for p, e1, e2 in tmp.itertuples():\n",
" annotate(p[:-3], xy=(e1, e2), ha='center', va='center')\n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Bad', topleft='Improved', topright='Good', bottomright='Declined')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
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xx/Pv0KnXaSz4vyeIiUtgyKV/Ir5xIojwxX9e4lhGGuP+9m8ADh7N4/HLTiUm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\nNntfawbwFHCBiKTgeRp5ynu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YHgOGYLV6GoA6nNyPrPRUILwW2aqtoDEOeM/v+7tEZJOI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\nN27oFWOQa6KksWPHeofhXMIqKiqODyn9vzIzDh8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"text/plain": [
"<matplotlib.figure.Figure at 0x11ead2940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"title('Entries per 60, 2016 vs 2017')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"\n",
"tmp = entries60[['Season', 'Player', 'WSH60']] \\\n",
" .groupby(['Season', 'Player'], as_index=False) \\\n",
" .sum()\n",
"\n",
"tmp = tmp.pivot_table(index='Player', columns='Season', values='WSH60')\n",
"\n",
"scatter(tmp.loc[:, 2016].values, tmp.loc[:, 2017].values, s=200, alpha=0.5)\n",
"for p, e1, e2 in tmp.itertuples():\n",
" annotate(p[:-3], xy=(e1, e2), ha='center', va='center')\n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Bad', topleft='Improved', topright='Good', bottomright='Declined')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
"text/plain": [
"(59.225931217350521, 86.247630499935255)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Or810AKp26NDkygt2JJeWwC4+Pl4T2NUxfTpVQ3g6gNJk79ga+v+ab74i88jO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\nIwLprY9gxNi/cbASTT3BhXimLN7C9px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rgR8j4gQVr/c0+dzdoAe4SlIPyQ8+Y3RHvevl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\ns4ybgpmZZdwUzMws46ZgZmaZfwB5urTccdJOwwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118d2fb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"title('Entries against per 60, 2016 vs 2017')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"\n",
"tmp = entries60[['Season', 'Player', 'Opp60']] \\\n",
" .groupby(['Season', 'Player'], as_index=False) \\\n",
" .sum()\n",
"\n",
"tmp = tmp.pivot_table(index='Player', columns='Season', values='Opp60')\n",
"\n",
"scatter(tmp.loc[:, 2016].values, tmp.loc[:, 2017].values, s=200, alpha=0.5)\n",
"for p, e1, e2 in tmp.itertuples():\n",
" annotate(p[:-3], xy=(e1, e2), ha='center', va='center')\n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Good', topleft='Declined', topright='Bad', bottomright='Improved')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])\n",
"\n",
"ylim(bottom=xlim()[0])\n",
"xlim(right=ylim()[1])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sums = entries60[['Season', 'Player', 'WSH', 'Opp']] \\\n",
" .groupby(['Season', 'Player'], as_index=False) \\\n",
" .sum()\n",
" \n",
"cep = entries60[entries60['Entry type'] == 'C'] \\\n",
" [['Season', 'Player', 'WSH', 'Opp']] \\\n",
" .merge(sums, how='inner', on=['Season', 'Player'], suffixes=['', '_Tot']) \n",
" \n",
"cep.loc[:, 'CE%'] = cep.WSH / (cep.WSH + cep.WSH_Tot)\n",
"cep.loc[:, 'Opp CE%'] = cep.Opp / (cep.Opp + cep.Opp_Tot)\n",
"\n",
"ce = cep[['Season', 'Player', 'CE%']] \\\n",
" .pivot_table(index='Player', columns='Season', values='CE%') \n",
"oppce = cep[['Season', 'Player', 'Opp CE%']] \\\n",
" .pivot_table(index='Player', columns='Season', values='Opp CE%') "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
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8AkUp5a1AjQT8PapcAexRSh0IYl81Gk058XZ/f9Hgmzy+v7794A3qxTUA4LfF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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118d3c438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"title('On-ice controlled entry%, 2016 vs 2017')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"\n",
"scatter(ce.loc[:, 2016].values, ce.loc[:, 2017].values, s=200, alpha=0.5)\n",
"for p, e1, e2 in ce.itertuples():\n",
" annotate(p[:-3], xy=(e1, e2), ha='center', va='center')\n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Bad', topleft='Improved', topright='Good', bottomright='Declined')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:678: RuntimeWarning: invalid value encountered in subtract\n",
" m = (y2 - y1) / (x2 - x1)\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
"text/plain": [
"(0.2, 0.43302679618489265)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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k1YkI+Xw+gsFg2fGhig9YK/3apiZQ0nUAj3+xtkJ43+NPKXdcUOQjyeMkKzO1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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c5d59e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"title('Opp on-ice controlled entry%, 2016 vs 2017')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"\n",
"scatter(oppce.loc[:, 2016].values, oppce.loc[:, 2017].values, s=200, alpha=0.5)\n",
"for p, e1, e2 in oppce.itertuples():\n",
" annotate(p[:-3], xy=(e1, e2), ha='center', va='center')\n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Good', topleft='Declined', topright='Bad', bottomright='Improved')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])\n",
"xlim(left=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Entry type</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>X</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Entry by</th>\n",
" <th>Season</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">10WSH</th>\n",
" <th>2016</th>\n",
" <td>46.422339</td>\n",
" <td>41.671102</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>5.024424</td>\n",
" <td>45.316901</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">13WSH</th>\n",
" <th>2016</th>\n",
" <td>45.329825</td>\n",
" <td>33.856894</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>2.690949</td>\n",
" <td>36.129352</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14WSH</th>\n",
" <th>2016</th>\n",
" <td>35.170970</td>\n",
" <td>39.240744</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">18WSH</th>\n",
" <th>2016</th>\n",
" <td>NaN</td>\n",
" <td>27.634334</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>2.482901</td>\n",
" <td>36.100533</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">19WSH</th>\n",
" <th>2016</th>\n",
" <td>39.385703</td>\n",
" <td>38.651808</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>1.908928</td>\n",
" <td>32.501567</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">20WSH</th>\n",
" <th>2016</th>\n",
" <td>30.901288</td>\n",
" <td>46.351931</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>2.690583</td>\n",
" <td>43.442623</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">22WSH</th>\n",
" <th>2016</th>\n",
" <td>41.588597</td>\n",
" <td>36.622794</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>3.063308</td>\n",
" <td>46.379706</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">25WSH</th>\n",
" <th>2016</th>\n",
" <td>43.189501</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>3.620343</td>\n",
" <td>32.065217</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26WSH</th>\n",
" <th>2016</th>\n",
" <td>36.322870</td>\n",
" <td>35.315172</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27WSH</th>\n",
" <th>2016</th>\n",
" <td>34.921715</td>\n",
" <td>47.085202</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">29WSH</th>\n",
" <th>2016</th>\n",
" <td>NaN</td>\n",
" <td>46.562287</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>4.443429</td>\n",
" <td>39.339144</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2WSH</th>\n",
" <th>2016</th>\n",
" <td>39.478977</td>\n",
" <td>38.444593</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>4.062223</td>\n",
" <td>36.016623</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39WSH</th>\n",
" <th>2017</th>\n",
" <td>2.478814</td>\n",
" <td>31.587886</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">43WSH</th>\n",
" <th>2016</th>\n",
" <td>40.978286</td>\n",
" <td>36.864000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>30.949106</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">44WSH</th>\n",
" <th>2016</th>\n",
" <td>34.238736</td>\n",
" <td>44.104135</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>2.728191</td>\n",
" <td>41.505765</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">4WSH</th>\n",
" <th>2016</th>\n",
" <td>NaN</td>\n",
" <td>34.322034</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>41.637232</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55WSH</th>\n",
" <th>2017</th>\n",
" <td>2.558506</td>\n",
" <td>49.250301</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">65WSH</th>\n",
" <th>2016</th>\n",
" <td>41.377119</td>\n",
" <td>36.501901</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>2.150281</td>\n",
" <td>33.962264</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">74WSH</th>\n",
" <th>2016</th>\n",
" <td>36.501901</td>\n",
" <td>37.285281</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>4.244616</td>\n",
" <td>29.073857</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">77WSH</th>\n",
" <th>2016</th>\n",
" <td>44.863590</td>\n",
" <td>37.281080</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>1.221913</td>\n",
" <td>46.938596</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79WSH</th>\n",
" <th>2017</th>\n",
" <td>3.752263</td>\n",
" <td>17.487856</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82WSH</th>\n",
" <th>2016</th>\n",
" <td>38.377583</td>\n",
" <td>30.749014</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">83WSH</th>\n",
" <th>2016</th>\n",
" <td>43.005615</td>\n",
" <td>42.163182</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>3.618304</td>\n",
" <td>48.181083</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88WSH</th>\n",
" <th>2016</th>\n",
" <td>35.654771</td>\n",
" <td>33.724798</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">8WSH</th>\n",
" <th>2016</th>\n",
" <td>46.188310</td>\n",
" <td>32.190466</td>\n",
" <td>1.396161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>3.926070</td>\n",
" <td>45.545723</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90WSH</th>\n",
" <th>2016</th>\n",
" <td>44.237204</td>\n",
" <td>36.183044</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91WSH</th>\n",
" <th>2017</th>\n",
" <td>3.193188</td>\n",
" <td>42.297650</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">92WSH</th>\n",
" <th>2016</th>\n",
" <td>38.524299</td>\n",
" <td>32.648376</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>37.075718</td>\n",
" <td>26.824608</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93WSH</th>\n",
" <th>2017</th>\n",
" <td>50.210164</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">9WSH</th>\n",
" <th>2016</th>\n",
" <td>41.946964</td>\n",
" <td>35.604045</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>29.726869</td>\n",
" <td>45.733644</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">WSH</th>\n",
" <th>2016</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.029694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>34.542254</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Entry type C D X\n",
"Entry by Season \n",
"10WSH 2016 46.422339 41.671102 NaN\n",
" 2017 5.024424 45.316901 NaN\n",
"13WSH 2016 45.329825 33.856894 NaN\n",
" 2017 2.690949 36.129352 NaN\n",
"14WSH 2016 35.170970 39.240744 NaN\n",
"18WSH 2016 NaN 27.634334 NaN\n",
" 2017 2.482901 36.100533 NaN\n",
"19WSH 2016 39.385703 38.651808 NaN\n",
" 2017 1.908928 32.501567 NaN\n",
"20WSH 2016 30.901288 46.351931 NaN\n",
" 2017 2.690583 43.442623 NaN\n",
"22WSH 2016 41.588597 36.622794 NaN\n",
" 2017 3.063308 46.379706 NaN\n",
"25WSH 2016 43.189501 NaN NaN\n",
" 2017 3.620343 32.065217 NaN\n",
"26WSH 2016 36.322870 35.315172 NaN\n",
"27WSH 2016 34.921715 47.085202 NaN\n",
"29WSH 2016 NaN 46.562287 NaN\n",
" 2017 4.443429 39.339144 NaN\n",
"2WSH 2016 39.478977 38.444593 NaN\n",
" 2017 4.062223 36.016623 NaN\n",
"39WSH 2017 2.478814 31.587886 NaN\n",
"43WSH 2016 40.978286 36.864000 NaN\n",
" 2017 NaN 30.949106 NaN\n",
"44WSH 2016 34.238736 44.104135 NaN\n",
" 2017 2.728191 41.505765 NaN\n",
"4WSH 2016 NaN 34.322034 NaN\n",
" 2017 NaN 41.637232 NaN\n",
"55WSH 2017 2.558506 49.250301 NaN\n",
"65WSH 2016 41.377119 36.501901 NaN\n",
" 2017 2.150281 33.962264 NaN\n",
"74WSH 2016 36.501901 37.285281 NaN\n",
" 2017 4.244616 29.073857 NaN\n",
"77WSH 2016 44.863590 37.281080 NaN\n",
" 2017 1.221913 46.938596 NaN\n",
"79WSH 2017 3.752263 17.487856 NaN\n",
"82WSH 2016 38.377583 30.749014 NaN\n",
"83WSH 2016 43.005615 42.163182 NaN\n",
" 2017 3.618304 48.181083 NaN\n",
"88WSH 2016 35.654771 33.724798 NaN\n",
"8WSH 2016 46.188310 32.190466 1.396161\n",
" 2017 3.926070 45.545723 NaN\n",
"90WSH 2016 44.237204 36.183044 NaN\n",
"91WSH 2017 3.193188 42.297650 NaN\n",
"92WSH 2016 38.524299 32.648376 NaN\n",
" 2017 37.075718 26.824608 NaN\n",
"93WSH 2017 50.210164 NaN NaN\n",
"9WSH 2016 41.946964 35.604045 NaN\n",
" 2017 29.726869 45.733644 NaN\n",
"WSH 2016 NaN NaN 2.029694\n",
" 2017 NaN NaN 34.542254"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Individual level, individual\n",
"\n",
"# Count by entry type\n",
"ientries60 = entries2[pd.notnull(entries2['Entry by'])]\n",
"ientries60 = ientries60[ientries60['Entry by'].str.contains('WSH')] \\\n",
" [['Season', 'Entry type', 'Team', 'Game', 'Period', 'Time', 'Entry by', 'Fen total']] \\\n",
" .drop_duplicates() \\\n",
" [['Season', 'Entry type', 'Team', 'Entry by', 'Fen total']] \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Entry type', 'Team', 'Entry by'], as_index=False) \\\n",
" .count() \\\n",
" .pivot_table(index=['Season', 'Entry type', 'Entry by'], columns='Team', values='Count') \\\n",
" .reset_index()\n",
"\n",
"# Add TOI\n",
"toi = dfs['toi']['WSH'] \\\n",
" [['Season', 'WSH1', 'WSH2', 'WSH3', 'WSH4', 'WSH5']] \\\n",
" .melt(id_vars='Season', value_name='Entry by') \\\n",
" .drop('variable', axis=1) \\\n",
" .assign(TOI=1) \\\n",
" .groupby(['Season', 'Entry by'], as_index=False).count()\n",
"toi.loc[:, 'Entry by'] = toi['Entry by'].apply(lambda x: str(wsh_players[x]) + 'WSH' if x in wsh_players else x)\n",
"\n",
"ientries60 = ientries60.merge(toi, how='left', on=['Season', 'Entry by']) \\\n",
" .sort_values(['Entry by', 'Season', 'Entry type'])\n",
"ientries60.loc[:, 'WSH60'] = entries60.WSH / (entries60.TOI / 3600)\n",
"\n",
"ientries60.drop(['TOI', 'WSH'], axis=1).pivot_table(index=['Entry by', 'Season'], columns='Entry type', values='WSH60')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:679: RuntimeWarning: invalid value encountered in subtract\n",
" b = y1 - m*x1\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
"image/png": 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7DbQqI58Iq5neQ0YSERVTrS0/+yC9B48EoO/w0Wxf+yVQO5guYFeorKysZXA2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\nYSrZjyJgUDOHs5z2uqa6gn2gPX5gFwe++4aMHZu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"text/plain": [
"<matplotlib.figure.Figure at 0x11a3b8860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tmp = entries60[['Season', 'Entry type', 'Player', 'WSH']] \\\n",
" .merge(ientries60[['Season', 'Entry type', 'Entry by', 'WSH']] \\\n",
" .rename(columns={'Entry by': 'Player', 'WSH': 'iWSH'}), \n",
" how='left', on=['Season', 'Entry type', 'Player']) \\\n",
" .drop('Entry type', axis=1) \\\n",
" .groupby(['Season', 'Player'], as_index=False) \\\n",
" .sum()\n",
" \n",
"tmp.loc[:, 'iE%'] = tmp.iWSH / tmp.WSH\n",
"tmp = tmp.drop({'WSH', 'iWSH'}, axis=1) \\\n",
" .pivot_table(index='Player', columns='Season', values='iE%') \\\n",
" .fillna(0)\n",
" \n",
"title('Individual entry share, 2016 vs 2017')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"\n",
"scatter(tmp.loc[:, 2016].values, tmp.loc[:, 2017].values, s=200, alpha=0.5)\n",
"for p, e1, e2 in tmp.itertuples():\n",
" annotate(p[:-3], xy=(e1, e2), ha='center', va='center')\n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Bad', topleft='Improved', topright='Good', bottomright='Declined')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shots per entry\n",
"\n",
"Luckily, Corey has a Fenwick-post-entry column. We'll just use that."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Team</th>\n",
" <th>Season</th>\n",
" <th>Entry type</th>\n",
" <th>Opp</th>\n",
" <th>WSH</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>0.623960</td>\n",
" <td>0.555394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>0.700831</td>\n",
" <td>0.561772</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>0.224959</td>\n",
" <td>0.261866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>0.311688</td>\n",
" <td>0.252660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>0.777778</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>0.568182</td>\n",
" <td>0.439024</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Team Season Entry type Opp WSH\n",
"0 2016 C 0.623960 0.555394\n",
"3 2017 C 0.700831 0.561772\n",
"1 2016 D 0.224959 0.261866\n",
"4 2017 D 0.311688 0.252660\n",
"2 2016 X 0.777778 0.500000\n",
"5 2017 X 0.568182 0.439024"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Season level\n",
"\n",
"# Count by entry type\n",
"spe = entries2[['Season', 'Entry type', 'Team', 'Game', 'Period', 'Time', 'Fen total']] \\\n",
" .drop_duplicates() \\\n",
" [['Season', 'Entry type', 'Team', 'Fen total']] \\\n",
" .groupby(['Season', 'Entry type', 'Team'], as_index=False) \\\n",
" .mean() \\\n",
" .pivot_table(index=['Season', 'Entry type'], columns='Team', values='Fen total') \\\n",
" .reset_index() \\\n",
" .sort_values(['Entry type', 'Season'])\n",
"\n",
"spe"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Team</th>\n",
" <th>Season</th>\n",
" <th>Entry type</th>\n",
" <th>Player</th>\n",
" <th>Opp</th>\n",
" <th>WSH</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>10WSH</td>\n",
" <td>0.626374</td>\n",
" <td>0.541353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>10WSH</td>\n",
" <td>0.263158</td>\n",
" <td>0.226804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>10WSH</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>10WSH</td>\n",
" <td>0.875000</td>\n",
" <td>0.478873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>10WSH</td>\n",
" <td>0.293103</td>\n",
" <td>0.234568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>10WSH</td>\n",
" <td>0.538462</td>\n",
" <td>0.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>12WSH</td>\n",
" <td>0.644068</td>\n",
" <td>0.597015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>12WSH</td>\n",
" <td>0.288462</td>\n",
" <td>0.310345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>12WSH</td>\n",
" <td>1.000000</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>13WSH</td>\n",
" <td>0.692308</td>\n",
" <td>0.428571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>13WSH</td>\n",
" <td>0.166667</td>\n",
" <td>0.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>13WSH</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>13WSH</td>\n",
" <td>0.693069</td>\n",
" <td>0.541096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>13WSH</td>\n",
" <td>0.358974</td>\n",
" <td>0.205128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>13WSH</td>\n",
" <td>0.461538</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>14WSH</td>\n",
" <td>0.632653</td>\n",
" <td>0.596273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>14WSH</td>\n",
" <td>0.237762</td>\n",
" <td>0.272152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>14WSH</td>\n",
" <td>0.200000</td>\n",
" <td>0.363636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>18WSH</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>18WSH</td>\n",
" <td>0.285714</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>18WSH</td>\n",
" <td>0.538462</td>\n",
" <td>0.717949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>18WSH</td>\n",
" <td>0.345455</td>\n",
" <td>0.183333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>18WSH</td>\n",
" <td>0.625000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>19WSH</td>\n",
" <td>0.674033</td>\n",
" <td>0.542289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>19WSH</td>\n",
" <td>0.198830</td>\n",
" <td>0.242938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>19WSH</td>\n",
" <td>0.888889</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>19WSH</td>\n",
" <td>0.596491</td>\n",
" <td>0.601399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>19WSH</td>\n",
" <td>0.290909</td>\n",
" <td>0.311927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>19WSH</td>\n",
" <td>0.333333</td>\n",
" <td>0.076923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>20WSH</td>\n",
" <td>0.625000</td>\n",
" <td>0.546961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>83WSH</td>\n",
" <td>0.757576</td>\n",
" <td>0.516129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>83WSH</td>\n",
" <td>0.333333</td>\n",
" <td>0.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>83WSH</td>\n",
" <td>0.777778</td>\n",
" <td>0.615385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>88WSH</td>\n",
" <td>0.589147</td>\n",
" <td>0.507937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>88WSH</td>\n",
" <td>0.309677</td>\n",
" <td>0.210145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>88WSH</td>\n",
" <td>0.500000</td>\n",
" <td>0.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>8WSH</td>\n",
" <td>0.651741</td>\n",
" <td>0.549107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>8WSH</td>\n",
" <td>0.181818</td>\n",
" <td>0.269939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>8WSH</td>\n",
" <td>0.800000</td>\n",
" <td>0.736842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>8WSH</td>\n",
" <td>0.650794</td>\n",
" <td>0.513514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>8WSH</td>\n",
" <td>0.322835</td>\n",
" <td>0.275229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>8WSH</td>\n",
" <td>0.545455</td>\n",
" <td>0.384615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>90WSH</td>\n",
" <td>0.640449</td>\n",
" <td>0.563536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>90WSH</td>\n",
" <td>0.203390</td>\n",
" <td>0.241176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>90WSH</td>\n",
" <td>0.666667</td>\n",
" <td>0.470588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>91WSH</td>\n",
" <td>0.909091</td>\n",
" <td>0.285714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>91WSH</td>\n",
" <td>0.285714</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>91WSH</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>92WSH</td>\n",
" <td>0.657754</td>\n",
" <td>0.591133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>92WSH</td>\n",
" <td>0.219780</td>\n",
" <td>0.234177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>92WSH</td>\n",
" <td>0.666667</td>\n",
" <td>0.526316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>92WSH</td>\n",
" <td>0.716814</td>\n",
" <td>0.483221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>92WSH</td>\n",
" <td>0.387097</td>\n",
" <td>0.269663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>92WSH</td>\n",
" <td>0.461538</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>9WSH</td>\n",
" <td>0.639831</td>\n",
" <td>0.528736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>9WSH</td>\n",
" <td>0.193694</td>\n",
" <td>0.278027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>2016</td>\n",
" <td>X</td>\n",
" <td>9WSH</td>\n",
" <td>0.923077</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>9WSH</td>\n",
" <td>0.724409</td>\n",
" <td>0.575130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>9WSH</td>\n",
" <td>0.256250</td>\n",
" <td>0.257669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>2017</td>\n",
" <td>X</td>\n",
" <td>9WSH</td>\n",
" <td>0.687500</td>\n",
" <td>0.272727</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>137 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
"Team Season Entry type Player Opp WSH\n",
"0 2016 C 10WSH 0.626374 0.541353\n",
"23 2016 D 10WSH 0.263158 0.226804\n",
"46 2016 X 10WSH 1.000000 0.000000\n",
"68 2017 C 10WSH 0.875000 0.478873\n",
"91 2017 D 10WSH 0.293103 0.234568\n",
"114 2017 X 10WSH 0.538462 0.555556\n",
"1 2016 C 12WSH 0.644068 0.597015\n",
"24 2016 D 12WSH 0.288462 0.310345\n",
"47 2016 X 12WSH 1.000000 0.333333\n",
"2 2016 C 13WSH 0.692308 0.428571\n",
"25 2016 D 13WSH 0.166667 0.181818\n",
"48 2016 X 13WSH NaN 0.000000\n",
"69 2017 C 13WSH 0.693069 0.541096\n",
"92 2017 D 13WSH 0.358974 0.205128\n",
"115 2017 X 13WSH 0.461538 1.000000\n",
"3 2016 C 14WSH 0.632653 0.596273\n",
"26 2016 D 14WSH 0.237762 0.272152\n",
"49 2016 X 14WSH 0.200000 0.363636\n",
"4 2016 C 18WSH 0.000000 0.500000\n",
"27 2016 D 18WSH 0.285714 0.500000\n",
"70 2017 C 18WSH 0.538462 0.717949\n",
"93 2017 D 18WSH 0.345455 0.183333\n",
"116 2017 X 18WSH 0.625000 0.500000\n",
"5 2016 C 19WSH 0.674033 0.542289\n",
"28 2016 D 19WSH 0.198830 0.242938\n",
"50 2016 X 19WSH 0.888889 0.666667\n",
"71 2017 C 19WSH 0.596491 0.601399\n",
"94 2017 D 19WSH 0.290909 0.311927\n",
"117 2017 X 19WSH 0.333333 0.076923\n",
"6 2016 C 20WSH 0.625000 0.546961\n",
".. ... ... ... ... ...\n",
"86 2017 C 83WSH 0.757576 0.516129\n",
"109 2017 D 83WSH 0.333333 0.181818\n",
"132 2017 X 83WSH 0.777778 0.615385\n",
"18 2016 C 88WSH 0.589147 0.507937\n",
"41 2016 D 88WSH 0.309677 0.210145\n",
"63 2016 X 88WSH 0.500000 0.200000\n",
"19 2016 C 8WSH 0.651741 0.549107\n",
"42 2016 D 8WSH 0.181818 0.269939\n",
"64 2016 X 8WSH 0.800000 0.736842\n",
"87 2017 C 8WSH 0.650794 0.513514\n",
"110 2017 D 8WSH 0.322835 0.275229\n",
"133 2017 X 8WSH 0.545455 0.384615\n",
"20 2016 C 90WSH 0.640449 0.563536\n",
"43 2016 D 90WSH 0.203390 0.241176\n",
"65 2016 X 90WSH 0.666667 0.470588\n",
"88 2017 C 91WSH 0.909091 0.285714\n",
"111 2017 D 91WSH 0.285714 0.000000\n",
"134 2017 X 91WSH 0.000000 1.000000\n",
"21 2016 C 92WSH 0.657754 0.591133\n",
"44 2016 D 92WSH 0.219780 0.234177\n",
"66 2016 X 92WSH 0.666667 0.526316\n",
"89 2017 C 92WSH 0.716814 0.483221\n",
"112 2017 D 92WSH 0.387097 0.269663\n",
"135 2017 X 92WSH 0.461538 0.500000\n",
"22 2016 C 9WSH 0.639831 0.528736\n",
"45 2016 D 9WSH 0.193694 0.278027\n",
"67 2016 X 9WSH 0.923077 0.500000\n",
"90 2017 C 9WSH 0.724409 0.575130\n",
"113 2017 D 9WSH 0.256250 0.257669\n",
"136 2017 X 9WSH 0.687500 0.272727\n",
"\n",
"[137 rows x 5 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Individual level, on-ice\n",
"\n",
"# Count by entry type\n",
"spe = entries2[['Season', 'Entry type', 'Team', 'Game', 'Period', 'Time', 'Player', 'Fen total']] \\\n",
" [['Season', 'Entry type', 'Team', 'Player', 'Fen total']] \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Entry type', 'Team', 'Player'], as_index=False) \\\n",
" .mean() \\\n",
" .pivot_table(index=['Season', 'Entry type', 'Player'], columns='Team', values='Fen total') \\\n",
" .reset_index() \\\n",
" .sort_values(['Player', 'Season', 'Entry type'])\n",
"\n",
"spe"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:679: RuntimeWarning: invalid value encountered in subtract\n",
" b = y1 - m*x1\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11a432cc0>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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yb/yCCy5wT5kypeY0y1q06DsyKeVnUsq+UspeUspngtvmSSnnBf98XEo5edasWVdHRETk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"text/plain": [
"<matplotlib.figure.Figure at 0x11ef5c630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tmp = spe[spe['Entry type'] != 'X'] \\\n",
" .drop('Opp', axis=1) \\\n",
" .pivot_table(index=['Player', 'Entry type'], columns='Season', values='WSH') \\\n",
" .fillna(0) \\\n",
" .reset_index()\n",
" \n",
"for etype in tmp['Entry type'].unique():\n",
" tmp2 = tmp[tmp['Entry type'] == etype]\n",
" scatter(tmp2.loc[:, 2016].values, tmp2.loc[:, 2017].values, label=etype, s=200, alpha=0.5)\n",
"\n",
"for s, p, etype, e1, e2 in tmp.itertuples():\n",
" annotate(p[:-3], xy=(e1, e2), ha='center', va='center') \n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Bad', topleft='Improved', topright='Good', bottomright='Declined')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])\n",
"\n",
"title('On-ice shots per entry')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"legend(loc=2, bbox_to_anchor=(1, 1))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:578: RuntimeWarning: divide by zero encountered in true_divide\n",
" return cf/cfpct - cf\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:679: RuntimeWarning: invalid value encountered in subtract\n",
" b = y1 - m*x1\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:614: RuntimeWarning: divide by zero encountered in true_divide\n",
" bottomx = bottomy / slopes\n",
"/Users/muneebalam/PycharmProjects/scrapenhl2/scrapenhl2/plot/visualization_helper.py:615: RuntimeWarning: divide by zero encountered in true_divide\n",
" topx = topy / slopes\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11f13bdd8>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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LZ1GYd7BBObkVipqgfXIk6X3OpcO0v47JmHHW2RcAflOiT5PodaLeM2ao4KzA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c58b9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tmp = spe[spe['Entry type'] != 'X'] \\\n",
" .drop('WSH', axis=1) \\\n",
" .pivot_table(index=['Player', 'Entry type'], columns='Season', values='Opp') \\\n",
" .fillna(0) \\\n",
" .reset_index()\n",
" \n",
"for etype in tmp['Entry type'].unique():\n",
" tmp2 = tmp[tmp['Entry type'] == etype]\n",
" scatter(tmp2.loc[:, 2016].values, tmp2.loc[:, 2017].values, label=etype, s=200, alpha=0.5)\n",
"\n",
"for s, p, etype, e1, e2 in tmp.itertuples():\n",
" annotate(p[:-3], xy=(e1, e2), ha='center', va='center') \n",
" \n",
"vhelper.add_good_bad_fast_slow(bottomleft='Good', topleft='Declined', topright='Bad', bottomright='Improved')\n",
"vhelper.add_cfpct_ref_lines_to_plot(ax=gca(), refs=[50])\n",
"\n",
"title('On-ice shots against per entry')\n",
"xlabel('2016')\n",
"ylabel('2017')\n",
"legend(loc=2, bbox_to_anchor=(1, 1))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Entry type</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>X</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Entry by</th>\n",
" <th>Season</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">10WSH</th>\n",
" <th>2016</th>\n",
" <td>0.600000</td>\n",
" <td>0.161290</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.500000</td>\n",
" <td>0.304348</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">13WSH</th>\n",
" <th>2016</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.615385</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14WSH</th>\n",
" <th>2016</th>\n",
" <td>0.575758</td>\n",
" <td>0.292683</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">18WSH</th>\n",
" <th>2016</th>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>1.000000</td>\n",
" <td>0.333333</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">19WSH</th>\n",
" <th>2016</th>\n",
" <td>0.592593</td>\n",
" <td>0.325581</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.547619</td>\n",
" <td>0.214286</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">20WSH</th>\n",
" <th>2016</th>\n",
" <td>0.537037</td>\n",
" <td>0.454545</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.575758</td>\n",
" <td>0.200000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">22WSH</th>\n",
" <th>2016</th>\n",
" <td>0.250000</td>\n",
" <td>0.250000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>1.000000</td>\n",
" <td>0.111111</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">25WSH</th>\n",
" <th>2016</th>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.500000</td>\n",
" <td>0.307692</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26WSH</th>\n",
" <th>2016</th>\n",
" <td>0.533333</td>\n",
" <td>0.282609</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27WSH</th>\n",
" <th>2016</th>\n",
" <td>0.500000</td>\n",
" <td>0.235294</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">29WSH</th>\n",
" <th>2016</th>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.833333</td>\n",
" <td>0.272727</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2WSH</th>\n",
" <th>2016</th>\n",
" <td>0.733333</td>\n",
" <td>0.428571</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.833333</td>\n",
" <td>0.333333</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39WSH</th>\n",
" <th>2017</th>\n",
" <td>0.400000</td>\n",
" <td>0.130435</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">43WSH</th>\n",
" <th>2016</th>\n",
" <td>0.343750</td>\n",
" <td>0.323529</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.363636</td>\n",
" <td>0.187500</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">44WSH</th>\n",
" <th>2016</th>\n",
" <td>1.250000</td>\n",
" <td>0.178571</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>1.000000</td>\n",
" <td>0.266667</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">4WSH</th>\n",
" <th>2016</th>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>0.444444</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55WSH</th>\n",
" <th>2017</th>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">65WSH</th>\n",
" <th>2016</th>\n",
" <td>0.446809</td>\n",
" <td>0.190476</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.703704</td>\n",
" <td>0.181818</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">74WSH</th>\n",
" <th>2016</th>\n",
" <td>0.266667</td>\n",
" <td>0.294118</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.384615</td>\n",
" <td>0.333333</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">77WSH</th>\n",
" <th>2016</th>\n",
" <td>0.629630</td>\n",
" <td>0.258065</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.466667</td>\n",
" <td>0.545455</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79WSH</th>\n",
" <th>2017</th>\n",
" <td>0.666667</td>\n",
" <td>0.600000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82WSH</th>\n",
" <th>2016</th>\n",
" <td>0.545455</td>\n",
" <td>0.083333</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">83WSH</th>\n",
" <th>2016</th>\n",
" <td>0.521739</td>\n",
" <td>0.250000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.473684</td>\n",
" <td>0.083333</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88WSH</th>\n",
" <th>2016</th>\n",
" <td>0.566667</td>\n",
" <td>0.269231</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">8WSH</th>\n",
" <th>2016</th>\n",
" <td>0.606061</td>\n",
" <td>0.214286</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.500000</td>\n",
" <td>0.285714</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90WSH</th>\n",
" <th>2016</th>\n",
" <td>0.576271</td>\n",
" <td>0.194444</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91WSH</th>\n",
" <th>2017</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">92WSH</th>\n",
" <th>2016</th>\n",
" <td>0.541176</td>\n",
" <td>0.228571</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.539683</td>\n",
" <td>0.400000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93WSH</th>\n",
" <th>2017</th>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">9WSH</th>\n",
" <th>2016</th>\n",
" <td>0.531250</td>\n",
" <td>0.219512</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.625000</td>\n",
" <td>0.161290</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">WSH</th>\n",
" <th>2016</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.510638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.439024</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Entry type C D X\n",
"Entry by Season \n",
"10WSH 2016 0.600000 0.161290 NaN\n",
" 2017 0.500000 0.304348 NaN\n",
"13WSH 2016 1.000000 0.000000 NaN\n",
" 2017 0.615385 0.000000 NaN\n",
"14WSH 2016 0.575758 0.292683 NaN\n",
"18WSH 2016 NaN 0.000000 NaN\n",
" 2017 1.000000 0.333333 NaN\n",
"19WSH 2016 0.592593 0.325581 NaN\n",
" 2017 0.547619 0.214286 NaN\n",
"20WSH 2016 0.537037 0.454545 NaN\n",
" 2017 0.575758 0.200000 NaN\n",
"22WSH 2016 0.250000 0.250000 NaN\n",
" 2017 1.000000 0.111111 NaN\n",
"25WSH 2016 1.000000 NaN NaN\n",
" 2017 0.500000 0.307692 NaN\n",
"26WSH 2016 0.533333 0.282609 NaN\n",
"27WSH 2016 0.500000 0.235294 NaN\n",
"29WSH 2016 NaN 1.000000 NaN\n",
" 2017 0.833333 0.272727 NaN\n",
"2WSH 2016 0.733333 0.428571 NaN\n",
" 2017 0.833333 0.333333 NaN\n",
"39WSH 2017 0.400000 0.130435 NaN\n",
"43WSH 2016 0.343750 0.323529 NaN\n",
" 2017 0.363636 0.187500 NaN\n",
"44WSH 2016 1.250000 0.178571 NaN\n",
" 2017 1.000000 0.266667 NaN\n",
"4WSH 2016 NaN 0.000000 NaN\n",
" 2017 NaN 0.444444 NaN\n",
"55WSH 2017 0.500000 0.000000 NaN\n",
"65WSH 2016 0.446809 0.190476 NaN\n",
" 2017 0.703704 0.181818 NaN\n",
"74WSH 2016 0.266667 0.294118 NaN\n",
" 2017 0.384615 0.333333 NaN\n",
"77WSH 2016 0.629630 0.258065 NaN\n",
" 2017 0.466667 0.545455 NaN\n",
"79WSH 2017 0.666667 0.600000 NaN\n",
"82WSH 2016 0.545455 0.083333 NaN\n",
"83WSH 2016 0.521739 0.250000 NaN\n",
" 2017 0.473684 0.083333 NaN\n",
"88WSH 2016 0.566667 0.269231 NaN\n",
"8WSH 2016 0.606061 0.214286 0.000000\n",
" 2017 0.500000 0.285714 NaN\n",
"90WSH 2016 0.576271 0.194444 NaN\n",
"91WSH 2017 0.000000 0.000000 NaN\n",
"92WSH 2016 0.541176 0.228571 NaN\n",
" 2017 0.539683 0.400000 NaN\n",
"93WSH 2017 0.000000 NaN NaN\n",
"9WSH 2016 0.531250 0.219512 NaN\n",
" 2017 0.625000 0.161290 NaN\n",
"WSH 2016 NaN NaN 0.510638\n",
" 2017 NaN NaN 0.439024"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Individual level, individual\n",
"\n",
"# Count by entry type\n",
"spe = entries2[pd.notnull(entries2['Entry by'])]\n",
"spe = spe[spe['Entry by'].str.contains('WSH')] \\\n",
" [['Season', 'Entry type', 'Team', 'Game', 'Period', 'Time', 'Entry by', 'Fen total']] \\\n",
" .drop_duplicates() \\\n",
" [['Season', 'Entry type', 'Team', 'Entry by', 'Fen total']] \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Entry type', 'Team', 'Entry by'], as_index=False) \\\n",
" .mean() \\\n",
" .pivot_table(index=['Season', 'Entry type', 'Entry by'], columns='Team', values='Fen total') \\\n",
" .reset_index() \\\n",
" .sort_values(['Entry by', 'Season', 'Entry type'])\n",
"\n",
"spe.pivot_table(index=['Entry by', 'Season'], columns='Entry type', values='WSH')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exits per 60\n",
"\n",
"Some of the relevant result codes:\n",
"\n",
"- P for pass\n",
"- C for carry\n",
"- D for dump\n",
"- M for clear\n",
"- F for fail"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:337: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[key] = _infer_fill_value(value)\n",
"/Users/muneebalam/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n"
]
},
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Period</th>\n",
" <th>Time</th>\n",
" <th>Attempt</th>\n",
" <th>Result</th>\n",
" <th>Pressured?</th>\n",
" <th>Pass Target</th>\n",
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" <th>Season</th>\n",
" <th>Time2</th>\n",
" <th>_Secs</th>\n",
" <th>Team</th>\n",
" <th>variable</th>\n",
" <th>Player</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2406</th>\n",
" <td>1</td>\n",
" <td>19:55:00</td>\n",
" <td>8PIT</td>\n",
" <td>I</td>\n",
" <td>N</td>\n",
" <td>13PIT</td>\n",
" <td>NaN</td>\n",
" <td>L</td>\n",
" <td>20007</td>\n",
" <td>2016</td>\n",
" <td>19:55</td>\n",
" <td>6</td>\n",
" <td>Opp</td>\n",
" <td>WSH1</td>\n",
" <td>2WSH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9068</th>\n",
" <td>1</td>\n",
" <td>19:55:00</td>\n",
" <td>8PIT</td>\n",
" <td>I</td>\n",
" <td>N</td>\n",
" <td>13PIT</td>\n",
" <td>NaN</td>\n",
" <td>L</td>\n",
" <td>20007</td>\n",
" <td>2016</td>\n",
" <td>19:55</td>\n",
" <td>6</td>\n",
" <td>Opp</td>\n",
" <td>WSH2</td>\n",
" <td>27WSH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15730</th>\n",
" <td>1</td>\n",
" <td>19:55:00</td>\n",
" <td>8PIT</td>\n",
" <td>I</td>\n",
" <td>N</td>\n",
" <td>13PIT</td>\n",
" <td>NaN</td>\n",
" <td>L</td>\n",
" <td>20007</td>\n",
" <td>2016</td>\n",
" <td>19:55</td>\n",
" <td>6</td>\n",
" <td>Opp</td>\n",
" <td>WSH3</td>\n",
" <td>92WSH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22392</th>\n",
" <td>1</td>\n",
" <td>19:55:00</td>\n",
" <td>8PIT</td>\n",
" <td>I</td>\n",
" <td>N</td>\n",
" <td>13PIT</td>\n",
" <td>NaN</td>\n",
" <td>L</td>\n",
" <td>20007</td>\n",
" <td>2016</td>\n",
" <td>19:55</td>\n",
" <td>6</td>\n",
" <td>Opp</td>\n",
" <td>WSH4</td>\n",
" <td>8WSH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29054</th>\n",
" <td>1</td>\n",
" <td>19:55:00</td>\n",
" <td>8PIT</td>\n",
" <td>I</td>\n",
" <td>N</td>\n",
" <td>13PIT</td>\n",
" <td>NaN</td>\n",
" <td>L</td>\n",
" <td>20007</td>\n",
" <td>2016</td>\n",
" <td>19:55</td>\n",
" <td>6</td>\n",
" <td>Opp</td>\n",
" <td>WSH5</td>\n",
" <td>77WSH</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Period Time Attempt Result Pressured? Pass Target Entry? Direction \\\n",
"2406 1 19:55:00 8PIT I N 13PIT NaN L \n",
"9068 1 19:55:00 8PIT I N 13PIT NaN L \n",
"15730 1 19:55:00 8PIT I N 13PIT NaN L \n",
"22392 1 19:55:00 8PIT I N 13PIT NaN L \n",
"29054 1 19:55:00 8PIT I N 13PIT NaN L \n",
"\n",
" Game Season Time2 _Secs Team variable Player \n",
"2406 20007 2016 19:55 6 Opp WSH1 2WSH \n",
"9068 20007 2016 19:55 6 Opp WSH2 27WSH \n",
"15730 20007 2016 19:55 6 Opp WSH3 92WSH \n",
"22392 20007 2016 19:55 6 Opp WSH4 8WSH \n",
"29054 20007 2016 19:55 6 Opp WSH5 77WSH "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exits = wsh['exits']\n",
"\n",
"# Drop extra team cols\n",
"colnames = [x for x in exits.columns if not x.upper() == x or x[:3] == 'WSH']\n",
"exits = exits[colnames]\n",
"\n",
"# Flag exits as WSH or Opp\n",
"exits.loc[:, 'Team'] = exits['Attempt'].apply(lambda x: 'WSH' if str(x)[-3:] == 'WSH' else 'Opp')\n",
"\n",
"# Melt to long\n",
"idvars = [x for x in exits.columns if x not in ['WSH1', 'WSH2', 'WSH3', 'WSH4', 'WSH5']]\n",
"exits2 = exits.melt(id_vars=idvars, value_name='Player').sort_values(['Season', 'Game', '_Secs', 'variable'])\n",
"\n",
"exits2.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Season</th>\n",
" <th>Result</th>\n",
" <th>Opp</th>\n",
" <th>WSH</th>\n",
" <th>TOI</th>\n",
" <th>WSH60</th>\n",
" <th>Opp60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>369.0</td>\n",
" <td>344.0</td>\n",
" <td>60976</td>\n",
" <td>20.309630</td>\n",
" <td>21.785621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>171.0</td>\n",
" <td>207.0</td>\n",
" <td>37848</td>\n",
" <td>19.689283</td>\n",
" <td>16.265060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>60976</td>\n",
" <td>NaN</td>\n",
" <td>0.059040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017</td>\n",
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" <td>NaN</td>\n",
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" <td>37848</td>\n",
" <td>0.095117</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016</td>\n",
" <td>CD</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>2016</td>\n",
" <td>D</td>\n",
" <td>168.0</td>\n",
" <td>157.0</td>\n",
" <td>60976</td>\n",
" <td>9.269221</td>\n",
" <td>9.918657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2017</td>\n",
" <td>D</td>\n",
" <td>115.0</td>\n",
" <td>111.0</td>\n",
" <td>37848</td>\n",
" <td>10.558022</td>\n",
" <td>10.938491</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2016</td>\n",
" <td>F</td>\n",
" <td>402.0</td>\n",
" <td>362.0</td>\n",
" <td>60976</td>\n",
" <td>21.372343</td>\n",
" <td>23.733928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2017</td>\n",
" <td>F</td>\n",
" <td>199.0</td>\n",
" <td>222.0</td>\n",
" <td>37848</td>\n",
" <td>21.116043</td>\n",
" <td>18.928345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2016</td>\n",
" <td>I</td>\n",
" <td>67.0</td>\n",
" <td>34.0</td>\n",
" <td>60976</td>\n",
" <td>2.007347</td>\n",
" <td>3.955655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2017</td>\n",
" <td>I</td>\n",
" <td>32.0</td>\n",
" <td>35.0</td>\n",
" <td>37848</td>\n",
" <td>3.329106</td>\n",
" <td>3.043754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2016</td>\n",
" <td>M</td>\n",
" <td>568.0</td>\n",
" <td>544.0</td>\n",
" <td>60976</td>\n",
" <td>32.117554</td>\n",
" <td>33.534505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2017</td>\n",
" <td>M</td>\n",
" <td>348.0</td>\n",
" <td>267.0</td>\n",
" <td>37848</td>\n",
" <td>25.396322</td>\n",
" <td>33.100824</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2016</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>60976</td>\n",
" <td>0.059040</td>\n",
" <td>0.059040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2017</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>37848</td>\n",
" <td>NaN</td>\n",
" <td>0.095117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2016</td>\n",
" <td>P</td>\n",
" <td>560.0</td>\n",
" <td>640.0</td>\n",
" <td>60976</td>\n",
" <td>37.785358</td>\n",
" <td>33.062188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>321.0</td>\n",
" <td>353.0</td>\n",
" <td>37848</td>\n",
" <td>33.576411</td>\n",
" <td>30.532657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2017</td>\n",
" <td>PN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>37848</td>\n",
" <td>NaN</td>\n",
" <td>0.095117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2017</td>\n",
" <td>p</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>37848</td>\n",
" <td>0.095117</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Season Result Opp WSH TOI WSH60 Opp60\n",
"0 2016 C 369.0 344.0 60976 20.309630 21.785621\n",
"1 2017 C 171.0 207.0 37848 19.689283 16.265060\n",
"2 2016 C 1.0 NaN 60976 NaN 0.059040\n",
"3 2017 C8 NaN 1.0 37848 0.095117 NaN\n",
"4 2016 CD NaN 1.0 60976 0.059040 NaN\n",
"5 2016 D 168.0 157.0 60976 9.269221 9.918657\n",
"6 2017 D 115.0 111.0 37848 10.558022 10.938491\n",
"7 2016 F 402.0 362.0 60976 21.372343 23.733928\n",
"8 2017 F 199.0 222.0 37848 21.116043 18.928345\n",
"9 2016 I 67.0 34.0 60976 2.007347 3.955655\n",
"10 2017 I 32.0 35.0 37848 3.329106 3.043754\n",
"11 2016 M 568.0 544.0 60976 32.117554 33.534505\n",
"12 2017 M 348.0 267.0 37848 25.396322 33.100824\n",
"13 2016 N 1.0 1.0 60976 0.059040 0.059040\n",
"14 2017 N 1.0 NaN 37848 NaN 0.095117\n",
"15 2016 P 560.0 640.0 60976 37.785358 33.062188\n",
"16 2017 P 321.0 353.0 37848 33.576411 30.532657\n",
"17 2017 PN 1.0 NaN 37848 NaN 0.095117\n",
"18 2017 p NaN 1.0 37848 0.095117 NaN"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Season level\n",
"\n",
"# Count by exit type\n",
"exits60 = exits2[['Season', 'Result', 'Team', 'Game', 'Period', 'Time']] \\\n",
" .drop_duplicates() \\\n",
" [['Season', 'Result', 'Team']] \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Result', 'Team'], as_index=False) \\\n",
" .count() \\\n",
" .pivot_table(index=['Season', 'Result'], columns='Team', values='Count') \\\n",
" .reset_index() \\\n",
" .sort_values(['Result', 'Season'])\n",
"\n",
"# Add TOI\n",
"toi = dfs['toi']['WSH'] \\\n",
" [['Season']].assign(TOI=1) \\\n",
" .groupby('Season', as_index=False).count()\n",
"\n",
"exits60 = exits60.merge(toi, how='left', on='Season')\n",
"exits60.loc[:, 'WSH60'] = exits60.WSH / (exits60.TOI / 3600)\n",
"exits60.loc[:, 'Opp60'] = exits60.Opp / (exits60.TOI / 3600)\n",
"\n",
"exits60"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Season</th>\n",
" <th>Result</th>\n",
" <th>Player</th>\n",
" <th>Opp</th>\n",
" <th>WSH</th>\n",
" <th>TOI</th>\n",
" <th>WSH60</th>\n",
" <th>Opp60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>10WSH</td>\n",
" <td>70.0</td>\n",
" <td>72.0</td>\n",
" <td>10314</td>\n",
" <td>25.130890</td>\n",
" <td>24.432810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>12WSH</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>5321</td>\n",
" <td>27.062582</td>\n",
" <td>27.062582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>13WSH</td>\n",
" <td>12.0</td>\n",
" <td>11.0</td>\n",
" <td>1433</td>\n",
" <td>27.634334</td>\n",
" <td>30.146546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>14WSH</td>\n",
" <td>88.0</td>\n",
" <td>80.0</td>\n",
" <td>14716</td>\n",
" <td>19.570535</td>\n",
" <td>21.527589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>18WSH</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>466</td>\n",
" <td>30.901288</td>\n",
" <td>7.725322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>19WSH</td>\n",
" <td>85.0</td>\n",
" <td>96.0</td>\n",
" <td>17399</td>\n",
" <td>19.863211</td>\n",
" <td>17.587218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>20WSH</td>\n",
" <td>103.0</td>\n",
" <td>107.0</td>\n",
" <td>15087</td>\n",
" <td>25.531915</td>\n",
" <td>24.577451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>22WSH</td>\n",
" <td>17.0</td>\n",
" <td>17.0</td>\n",
" <td>2676</td>\n",
" <td>22.869955</td>\n",
" <td>22.869955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>26WSH</td>\n",
" <td>72.0</td>\n",
" <td>64.0</td>\n",
" <td>11752</td>\n",
" <td>19.605174</td>\n",
" <td>22.055820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>27WSH</td>\n",
" <td>124.0</td>\n",
" <td>110.0</td>\n",
" <td>20882</td>\n",
" <td>18.963701</td>\n",
" <td>21.377263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>2WSH</td>\n",
" <td>147.0</td>\n",
" <td>123.0</td>\n",
" <td>21875</td>\n",
" <td>20.242286</td>\n",
" <td>24.192000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>43WSH</td>\n",
" <td>74.0</td>\n",
" <td>60.0</td>\n",
" <td>12407</td>\n",
" <td>17.409527</td>\n",
" <td>21.471750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>44WSH</td>\n",
" <td>116.0</td>\n",
" <td>110.0</td>\n",
" <td>18880</td>\n",
" <td>20.974576</td>\n",
" <td>22.118644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>4WSH</td>\n",
" <td>3.0</td>\n",
" <td>7.0</td>\n",
" <td>789</td>\n",
" <td>31.939163</td>\n",
" <td>13.688213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>65WSH</td>\n",
" <td>78.0</td>\n",
" <td>78.0</td>\n",
" <td>11876</td>\n",
" <td>23.644325</td>\n",
" <td>23.644325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>74WSH</td>\n",
" <td>104.0</td>\n",
" <td>100.0</td>\n",
" <td>19699</td>\n",
" <td>18.275039</td>\n",
" <td>19.006041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>77WSH</td>\n",
" <td>88.0</td>\n",
" <td>81.0</td>\n",
" <td>16742</td>\n",
" <td>17.417274</td>\n",
" <td>18.922470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>83WSH</td>\n",
" <td>82.0</td>\n",
" <td>70.0</td>\n",
" <td>12722</td>\n",
" <td>19.808206</td>\n",
" <td>23.203899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>88WSH</td>\n",
" <td>90.0</td>\n",
" <td>92.0</td>\n",
" <td>14731</td>\n",
" <td>22.483199</td>\n",
" <td>21.994434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>8WSH</td>\n",
" <td>102.0</td>\n",
" <td>87.0</td>\n",
" <td>18229</td>\n",
" <td>17.181414</td>\n",
" <td>20.143727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>90WSH</td>\n",
" <td>103.0</td>\n",
" <td>95.0</td>\n",
" <td>16914</td>\n",
" <td>20.219936</td>\n",
" <td>21.922668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>92WSH</td>\n",
" <td>109.0</td>\n",
" <td>85.0</td>\n",
" <td>17422</td>\n",
" <td>17.564000</td>\n",
" <td>22.523246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2016</td>\n",
" <td>C</td>\n",
" <td>9WSH</td>\n",
" <td>137.0</td>\n",
" <td>131.0</td>\n",
" <td>22548</td>\n",
" <td>20.915381</td>\n",
" <td>21.873337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>10WSH</td>\n",
" <td>23.0</td>\n",
" <td>41.0</td>\n",
" <td>6894</td>\n",
" <td>21.409922</td>\n",
" <td>12.010444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>13WSH</td>\n",
" <td>47.0</td>\n",
" <td>59.0</td>\n",
" <td>10468</td>\n",
" <td>20.290409</td>\n",
" <td>16.163546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>18WSH</td>\n",
" <td>19.0</td>\n",
" <td>24.0</td>\n",
" <td>4723</td>\n",
" <td>18.293458</td>\n",
" <td>14.482321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>19WSH</td>\n",
" <td>58.0</td>\n",
" <td>57.0</td>\n",
" <td>11360</td>\n",
" <td>18.063380</td>\n",
" <td>18.380282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>20WSH</td>\n",
" <td>28.0</td>\n",
" <td>52.0</td>\n",
" <td>8071</td>\n",
" <td>23.194152</td>\n",
" <td>12.489159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>22WSH</td>\n",
" <td>37.0</td>\n",
" <td>40.0</td>\n",
" <td>7878</td>\n",
" <td>18.278751</td>\n",
" <td>16.907845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2017</td>\n",
" <td>C</td>\n",
" <td>25WSH</td>\n",
" <td>41.0</td>\n",
" <td>45.0</td>\n",
" <td>7975</td>\n",
" <td>20.313480</td>\n",
" <td>18.507837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>285</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>19WSH</td>\n",
" <td>95.0</td>\n",
" <td>125.0</td>\n",
" <td>11360</td>\n",
" <td>39.612676</td>\n",
" <td>30.105634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>20WSH</td>\n",
" <td>69.0</td>\n",
" <td>72.0</td>\n",
" <td>8071</td>\n",
" <td>32.114980</td>\n",
" <td>30.776855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>287</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>22WSH</td>\n",
" <td>77.0</td>\n",
" <td>68.0</td>\n",
" <td>7878</td>\n",
" <td>31.073877</td>\n",
" <td>35.186596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>288</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>25WSH</td>\n",
" <td>67.0</td>\n",
" <td>68.0</td>\n",
" <td>7975</td>\n",
" <td>30.695925</td>\n",
" <td>30.244514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>289</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>29WSH</td>\n",
" <td>73.0</td>\n",
" <td>86.0</td>\n",
" <td>8784</td>\n",
" <td>35.245902</td>\n",
" <td>29.918033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>2WSH</td>\n",
" <td>84.0</td>\n",
" <td>99.0</td>\n",
" <td>10013</td>\n",
" <td>35.593728</td>\n",
" <td>30.200739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>291</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>39WSH</td>\n",
" <td>59.0</td>\n",
" <td>49.0</td>\n",
" <td>6624</td>\n",
" <td>26.630435</td>\n",
" <td>32.065217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>292</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>43WSH</td>\n",
" <td>71.0</td>\n",
" <td>81.0</td>\n",
" <td>7687</td>\n",
" <td>37.934175</td>\n",
" <td>33.250943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>293</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>44WSH</td>\n",
" <td>113.0</td>\n",
" <td>119.0</td>\n",
" <td>12994</td>\n",
" <td>32.969063</td>\n",
" <td>31.306757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>294</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>4WSH</td>\n",
" <td>33.0</td>\n",
" <td>22.0</td>\n",
" <td>3533</td>\n",
" <td>22.417209</td>\n",
" <td>33.625814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>295</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>55WSH</td>\n",
" <td>24.0</td>\n",
" <td>21.0</td>\n",
" <td>2908</td>\n",
" <td>25.997249</td>\n",
" <td>29.711142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>296</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>65WSH</td>\n",
" <td>51.0</td>\n",
" <td>74.0</td>\n",
" <td>7199</td>\n",
" <td>37.005140</td>\n",
" <td>25.503542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>297</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>74WSH</td>\n",
" <td>112.0</td>\n",
" <td>154.0</td>\n",
" <td>14439</td>\n",
" <td>38.396011</td>\n",
" <td>27.924371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>298</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>77WSH</td>\n",
" <td>70.0</td>\n",
" <td>99.0</td>\n",
" <td>9137</td>\n",
" <td>39.006238</td>\n",
" <td>27.580169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>299</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>79WSH</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>1696</td>\n",
" <td>21.226415</td>\n",
" <td>21.226415</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>83WSH</td>\n",
" <td>57.0</td>\n",
" <td>57.0</td>\n",
" <td>7677</td>\n",
" <td>26.729191</td>\n",
" <td>26.729191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>301</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>8WSH</td>\n",
" <td>100.0</td>\n",
" <td>115.0</td>\n",
" <td>11351</td>\n",
" <td>36.472557</td>\n",
" <td>31.715267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>91WSH</td>\n",
" <td>12.0</td>\n",
" <td>8.0</td>\n",
" <td>1441</td>\n",
" <td>19.986121</td>\n",
" <td>29.979181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>303</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>92WSH</td>\n",
" <td>101.0</td>\n",
" <td>105.0</td>\n",
" <td>11133</td>\n",
" <td>33.953112</td>\n",
" <td>32.659660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>304</th>\n",
" <td>2017</td>\n",
" <td>P</td>\n",
" <td>9WSH</td>\n",
" <td>128.0</td>\n",
" <td>139.0</td>\n",
" <td>15255</td>\n",
" <td>32.802360</td>\n",
" <td>30.206490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>305</th>\n",
" <td>2017</td>\n",
" <td>PN</td>\n",
" <td>20WSH</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>8071</td>\n",
" <td>NaN</td>\n",
" <td>0.446041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306</th>\n",
" <td>2017</td>\n",
" <td>PN</td>\n",
" <td>25WSH</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>7975</td>\n",
" <td>NaN</td>\n",
" <td>0.451411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>307</th>\n",
" <td>2017</td>\n",
" <td>PN</td>\n",
" <td>39WSH</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>6624</td>\n",
" <td>NaN</td>\n",
" <td>0.543478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308</th>\n",
" <td>2017</td>\n",
" <td>PN</td>\n",
" <td>55WSH</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>2908</td>\n",
" <td>NaN</td>\n",
" <td>1.237964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>309</th>\n",
" <td>2017</td>\n",
" <td>PN</td>\n",
" <td>9WSH</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>15255</td>\n",
" <td>NaN</td>\n",
" <td>0.235988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>310</th>\n",
" <td>2017</td>\n",
" <td>p</td>\n",
" <td>22WSH</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>7878</td>\n",
" <td>0.456969</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311</th>\n",
" <td>2017</td>\n",
" <td>p</td>\n",
" <td>43WSH</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>7687</td>\n",
" <td>0.468323</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>312</th>\n",
" <td>2017</td>\n",
" <td>p</td>\n",
" <td>44WSH</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>12994</td>\n",
" <td>0.277051</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>313</th>\n",
" <td>2017</td>\n",
" <td>p</td>\n",
" <td>8WSH</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>11351</td>\n",
" <td>0.317153</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>314</th>\n",
" <td>2017</td>\n",
" <td>p</td>\n",
" <td>92WSH</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>11133</td>\n",
" <td>0.323363</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>315 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Season Result Player Opp WSH TOI WSH60 Opp60\n",
"0 2016 C 10WSH 70.0 72.0 10314 25.130890 24.432810\n",
"1 2016 C 12WSH 40.0 40.0 5321 27.062582 27.062582\n",
"2 2016 C 13WSH 12.0 11.0 1433 27.634334 30.146546\n",
"3 2016 C 14WSH 88.0 80.0 14716 19.570535 21.527589\n",
"4 2016 C 18WSH 1.0 4.0 466 30.901288 7.725322\n",
"5 2016 C 19WSH 85.0 96.0 17399 19.863211 17.587218\n",
"6 2016 C 20WSH 103.0 107.0 15087 25.531915 24.577451\n",
"7 2016 C 22WSH 17.0 17.0 2676 22.869955 22.869955\n",
"8 2016 C 26WSH 72.0 64.0 11752 19.605174 22.055820\n",
"9 2016 C 27WSH 124.0 110.0 20882 18.963701 21.377263\n",
"10 2016 C 2WSH 147.0 123.0 21875 20.242286 24.192000\n",
"11 2016 C 43WSH 74.0 60.0 12407 17.409527 21.471750\n",
"12 2016 C 44WSH 116.0 110.0 18880 20.974576 22.118644\n",
"13 2016 C 4WSH 3.0 7.0 789 31.939163 13.688213\n",
"14 2016 C 65WSH 78.0 78.0 11876 23.644325 23.644325\n",
"15 2016 C 74WSH 104.0 100.0 19699 18.275039 19.006041\n",
"16 2016 C 77WSH 88.0 81.0 16742 17.417274 18.922470\n",
"17 2016 C 83WSH 82.0 70.0 12722 19.808206 23.203899\n",
"18 2016 C 88WSH 90.0 92.0 14731 22.483199 21.994434\n",
"19 2016 C 8WSH 102.0 87.0 18229 17.181414 20.143727\n",
"20 2016 C 90WSH 103.0 95.0 16914 20.219936 21.922668\n",
"21 2016 C 92WSH 109.0 85.0 17422 17.564000 22.523246\n",
"22 2016 C 9WSH 137.0 131.0 22548 20.915381 21.873337\n",
"23 2017 C 10WSH 23.0 41.0 6894 21.409922 12.010444\n",
"24 2017 C 13WSH 47.0 59.0 10468 20.290409 16.163546\n",
"25 2017 C 18WSH 19.0 24.0 4723 18.293458 14.482321\n",
"26 2017 C 19WSH 58.0 57.0 11360 18.063380 18.380282\n",
"27 2017 C 20WSH 28.0 52.0 8071 23.194152 12.489159\n",
"28 2017 C 22WSH 37.0 40.0 7878 18.278751 16.907845\n",
"29 2017 C 25WSH 41.0 45.0 7975 20.313480 18.507837\n",
".. ... ... ... ... ... ... ... ...\n",
"285 2017 P 19WSH 95.0 125.0 11360 39.612676 30.105634\n",
"286 2017 P 20WSH 69.0 72.0 8071 32.114980 30.776855\n",
"287 2017 P 22WSH 77.0 68.0 7878 31.073877 35.186596\n",
"288 2017 P 25WSH 67.0 68.0 7975 30.695925 30.244514\n",
"289 2017 P 29WSH 73.0 86.0 8784 35.245902 29.918033\n",
"290 2017 P 2WSH 84.0 99.0 10013 35.593728 30.200739\n",
"291 2017 P 39WSH 59.0 49.0 6624 26.630435 32.065217\n",
"292 2017 P 43WSH 71.0 81.0 7687 37.934175 33.250943\n",
"293 2017 P 44WSH 113.0 119.0 12994 32.969063 31.306757\n",
"294 2017 P 4WSH 33.0 22.0 3533 22.417209 33.625814\n",
"295 2017 P 55WSH 24.0 21.0 2908 25.997249 29.711142\n",
"296 2017 P 65WSH 51.0 74.0 7199 37.005140 25.503542\n",
"297 2017 P 74WSH 112.0 154.0 14439 38.396011 27.924371\n",
"298 2017 P 77WSH 70.0 99.0 9137 39.006238 27.580169\n",
"299 2017 P 79WSH 10.0 10.0 1696 21.226415 21.226415\n",
"300 2017 P 83WSH 57.0 57.0 7677 26.729191 26.729191\n",
"301 2017 P 8WSH 100.0 115.0 11351 36.472557 31.715267\n",
"302 2017 P 91WSH 12.0 8.0 1441 19.986121 29.979181\n",
"303 2017 P 92WSH 101.0 105.0 11133 33.953112 32.659660\n",
"304 2017 P 9WSH 128.0 139.0 15255 32.802360 30.206490\n",
"305 2017 PN 20WSH 1.0 NaN 8071 NaN 0.446041\n",
"306 2017 PN 25WSH 1.0 NaN 7975 NaN 0.451411\n",
"307 2017 PN 39WSH 1.0 NaN 6624 NaN 0.543478\n",
"308 2017 PN 55WSH 1.0 NaN 2908 NaN 1.237964\n",
"309 2017 PN 9WSH 1.0 NaN 15255 NaN 0.235988\n",
"310 2017 p 22WSH NaN 1.0 7878 0.456969 NaN\n",
"311 2017 p 43WSH NaN 1.0 7687 0.468323 NaN\n",
"312 2017 p 44WSH NaN 1.0 12994 0.277051 NaN\n",
"313 2017 p 8WSH NaN 1.0 11351 0.317153 NaN\n",
"314 2017 p 92WSH NaN 1.0 11133 0.323363 NaN\n",
"\n",
"[315 rows x 8 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Individual level, on-ice\n",
"\n",
"# Count by exit type\n",
"exits60 = exits2[['Season', 'Result', 'Team', 'Game', 'Period', 'Time', 'Player']] \\\n",
" [['Season', 'Result', 'Team', 'Player']] \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Result', 'Team', 'Player'], as_index=False) \\\n",
" .count() \\\n",
" .pivot_table(index=['Season', 'Result', 'Player'], columns='Team', values='Count') \\\n",
" .reset_index() \\\n",
" .sort_values(['Result', 'Season', 'Player'])\n",
"\n",
"# Add TOI\n",
"toi = dfs['toi']['WSH'] \\\n",
" [['Season', 'WSH1', 'WSH2', 'WSH3', 'WSH4', 'WSH5']] \\\n",
" .melt(id_vars='Season', value_name='Player') \\\n",
" .drop('variable', axis=1) \\\n",
" .assign(TOI=1) \\\n",
" .groupby(['Season', 'Player'], as_index=False).count()\n",
"toi.loc[:, 'Player'] = toi['Player'].apply(lambda x: str(wsh_players[x]) + 'WSH' if x in wsh_players else x)\n",
"\n",
"exits60 = exits60.merge(toi, how='left', on=['Season', 'Player'])\n",
"exits60.loc[:, 'WSH60'] = exits60.WSH / (exits60.TOI / 3600)\n",
"exits60.loc[:, 'Opp60'] = exits60.Opp / (exits60.TOI / 3600)\n",
"\n",
"exits60"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Result</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th>P</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Attempt</th>\n",
" <th>Season</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10WSH</th>\n",
" <th>2017</th>\n",
" <td>0.522193</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13WSH</th>\n",
" <th>2017</th>\n",
" <td>0.343905</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.343905</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19WSH</th>\n",
" <th>2017</th>\n",
" <td>0.316901</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.633803</td>\n",
" <td>1.267606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20WSH</th>\n",
" <th>2017</th>\n",
" <td>0.446041</td>\n",
" <td>NaN</td>\n",
" <td>0.892083</td>\n",
" <td>NaN</td>\n",
" <td>0.892083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22WSH</th>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.913938</td>\n",
" <td>0.456969</td>\n",
" <td>0.913938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25WSH</th>\n",
" <th>2017</th>\n",
" <td>0.451411</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.902821</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29WSH</th>\n",
" <th>2017</th>\n",
" <td>0.409836</td>\n",
" <td>NaN</td>\n",
" <td>0.409836</td>\n",
" <td>0.409836</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2WSH</th>\n",
" <th>2017</th>\n",
" <td>0.719065</td>\n",
" <td>0.359533</td>\n",
" <td>0.719065</td>\n",
" <td>0.719065</td>\n",
" <td>2.157196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39WSH</th>\n",
" <th>2017</th>\n",
" <td>0.543478</td>\n",
" <td>NaN</td>\n",
" <td>0.543478</td>\n",
" <td>0.543478</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43WSH</th>\n",
" <th>2017</th>\n",
" <td>0.468323</td>\n",
" <td>NaN</td>\n",
" <td>0.468323</td>\n",
" <td>0.936646</td>\n",
" <td>1.404969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44WSH</th>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>0.277051</td>\n",
" <td>0.277051</td>\n",
" <td>0.277051</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65WSH</th>\n",
" <th>2017</th>\n",
" <td>1.000139</td>\n",
" <td>NaN</td>\n",
" <td>0.500069</td>\n",
" <td>NaN</td>\n",
" <td>0.500069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70WSH</th>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74WSH</th>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.249325</td>\n",
" <td>0.997299</td>\n",
" <td>0.498649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83WSH</th>\n",
" <th>2017</th>\n",
" <td>0.468933</td>\n",
" <td>NaN</td>\n",
" <td>0.468933</td>\n",
" <td>0.468933</td>\n",
" <td>1.406800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8WSH</th>\n",
" <th>2017</th>\n",
" <td>0.317153</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.317153</td>\n",
" <td>0.634305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92WSH</th>\n",
" <th>2017</th>\n",
" <td>0.323363</td>\n",
" <td>0.646726</td>\n",
" <td>NaN</td>\n",
" <td>0.323363</td>\n",
" <td>0.970089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9WSH</th>\n",
" <th>2017</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.235988</td>\n",
" <td>0.943953</td>\n",
" <td>0.707965</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Result C D F M P\n",
"Attempt Season \n",
"10WSH 2017 0.522193 NaN NaN NaN NaN\n",
"13WSH 2017 0.343905 NaN NaN 0.343905 NaN\n",
"19WSH 2017 0.316901 NaN NaN 0.633803 1.267606\n",
"20WSH 2017 0.446041 NaN 0.892083 NaN 0.892083\n",
"22WSH 2017 NaN NaN 0.913938 0.456969 0.913938\n",
"25WSH 2017 0.451411 NaN NaN 0.902821 NaN\n",
"29WSH 2017 0.409836 NaN 0.409836 0.409836 NaN\n",
"2WSH 2017 0.719065 0.359533 0.719065 0.719065 2.157196\n",
"39WSH 2017 0.543478 NaN 0.543478 0.543478 NaN\n",
"43WSH 2017 0.468323 NaN 0.468323 0.936646 1.404969\n",
"44WSH 2017 NaN 0.277051 0.277051 0.277051 NaN\n",
"65WSH 2017 1.000139 NaN 0.500069 NaN 0.500069\n",
"70WSH 2017 NaN NaN NaN NaN NaN\n",
"74WSH 2017 NaN NaN 0.249325 0.997299 0.498649\n",
"83WSH 2017 0.468933 NaN 0.468933 0.468933 1.406800\n",
"8WSH 2017 0.317153 NaN NaN 0.317153 0.634305\n",
"92WSH 2017 0.323363 0.646726 NaN 0.323363 0.970089\n",
"9WSH 2017 NaN NaN 0.235988 0.943953 0.707965"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Individual level, individual\n",
"\n",
"# Count by exit type\n",
"exits60 = exits2[exits2.Attempt.str.contains('WSH')] \\\n",
" .query('Game == 20502') \\\n",
" [['Season', 'Result', 'Team', 'Game', 'Period', 'Time', 'Attempt']] \\\n",
" .drop_duplicates() \\\n",
" [['Season', 'Result', 'Team', 'Attempt']] \\\n",
" .assign(Count=1) \\\n",
" .groupby(['Season', 'Result', 'Team', 'Attempt'], as_index=False) \\\n",
" .count() \\\n",
" .pivot_table(index=['Season', 'Result', 'Attempt'], columns='Team', values='Count') \\\n",
" .reset_index() \\\n",
" .sort_values(['Result', 'Season', 'Attempt'])\n",
"\n",
"# Add TOI\n",
"toi = dfs['toi']['WSH'] \\\n",
" [['Season', 'WSH1', 'WSH2', 'WSH3', 'WSH4', 'WSH5']] \\\n",
" .melt(id_vars='Season', value_name='Attempt') \\\n",
" .drop('variable', axis=1) \\\n",
" .assign(TOI=1) \\\n",
" .groupby(['Season', 'Attempt'], as_index=False).count()\n",
"toi.loc[:, 'Attempt'] = toi['Attempt'].apply(lambda x: str(wsh_players[x]) + 'WSH' if x in wsh_players else x)\n",
"\n",
"exits60 = exits60.merge(toi, how='left', on=['Season', 'Attempt'])\n",
"exits60.loc[:, 'WSH60'] = exits60.WSH / (exits60.TOI / 3600)\n",
"\n",
"exits60.pivot_table(index=['Attempt', 'Season'], columns='Result', values='WSH60')"
]
}
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