How Long Will The Warriors' Streak Last?
December 9, 2015 ยท View on GitHub
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How Long Will The Warriors' Streak Last?\n", "\n", "The code below estimates the Golden State Warriors' odds of matching or beating the NBA's win-streak record of 33 games. To do so, it uses per-game odds produced by FiveThirtyEight's "CARM-Elo" projections.\n", "\n", "The longest regular-season win streak belongs to the '71-'72 Los Angeles Lakers, who won 33 games straight in a single season. The Warriors would tie the streak if they win their first 29 games this season, adding to a four-game streak carried over from the end of last regular season." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "import random\n", "import re\n", "import yaml\n", "import pandas as pd\n", "import matplotlib as mpl\n", "import seaborn as sb\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetch the projections from FiveThirtyEight" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "URL = "http://projects.fivethirtyeight.com/2016-nba-picks/js/bundle.js"\n", "js = requests.get(URL, params={"r": random.random()}).content.decode("utf-8")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fix_json(json_str):\n", " json_str = re.sub(r"([,:])", r"\1 ", json_str)\n", " json_str = re.sub(r"!1", "false", json_str)\n", " json_str = re.sub(r"!0", "true", json_str)\n", " return json_str" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "game_strings = re.findall(r"\{[^}[]+team1[^}[]+\}", fix_json(js))[:82 * 15]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "games = pd.DataFrame([ yaml.load(game) for game in game_strings ])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Make sure every team has 41 home games.\n", "assert((games["team1"].value_counts() == 41).sum() == 30)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n",
"<table border="1" class="dataframe">\n",
" \n",
" <tr style="text-align: right;">\n",
" \n",
" 0\n",
" 1\n",
" 2\n",
" 3\n",
" 4\n",
" \n",
" \n",
" \n",
" \n",
" carmelo1_post\n",
" 1570.47\n",
" 1521.81\n",
" 1734.34\n",
" 1442.24\n",
" 1467.79\n",
" \n",
" \n",
" carmelo1_pre\n",
" 1564.37\n",
" 1542.66\n",
" 1730.51\n",
" 1447.01\n",
" 1460.24\n",
" \n",
" \n",
" carmelo2_post\n",
" 1725.92\n",
" 1472.68\n",
" 1551.3\n",
" 1502.45\n",
" 1469.15\n",
" \n",
" \n",
" carmelo2_pre\n",
" 1732.03\n",
" 1451.83\n",
" 1555.13\n",
" 1497.68\n",
" 1476.71\n",
" \n",
" \n",
" carmelo_prob1\n",
" 0.403078\n",
" 0.755572\n",
" 0.833364\n",
" 0.567454\n",
" 0.612829\n",
" \n",
" \n",
" carmelo_prob2\n",
" 0.596922\n",
" 0.244428\n",
" 0.166636\n",
" 0.432546\n",
" 0.387171\n",
" \n",
" \n",
" carmelo_spread\n",
" 2.5\n",
" -7\n",
" -10\n",
" -1.5\n",
" -3\n",
" \n",
" \n",
" date\n",
" 2015-10-27\n",
" 2015-10-27\n",
" 2015-10-27\n",
" 2015-10-28\n",
" 2015-10-28\n",
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" \n",
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" 400827889\n",
" 400827888\n",
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" 400827891\n",
" 400827895\n",
" \n",
" \n",
" neutral\n",
" False\n",
" False\n",
" False\n",
" False\n",
" False\n",
" \n",
" \n",
" playoff\n",
" None\n",
" None\n",
" None\n",
" None\n",
" None\n",
" \n",
" \n",
" score1\n",
" 97\n",
" 94\n",
" 111\n",
" 87\n",
" 104\n",
" \n",
" \n",
" score2\n",
" 95\n",
" 106\n",
" 95\n",
" 88\n",
" 94\n",
" \n",
" \n",
" status\n",
" post\n",
" post\n",
" post\n",
" post\n",
" post\n",
" \n",
" \n",
" team1\n",
" CHI\n",
" ATL\n",
" GS\n",
" ORL\n",
" MIA\n",
" \n",
" \n",
" team2\n",
" CLE\n",
" DET\n",
" NO\n",
" WSH\n",
" CHA\n",
" \n",
" \n",
" time_et\n",
" 20: 00\n",
" 20: 00\n",
" 22: 30\n",
" 19: 00\n",
" 19: 30\n",
" \n",
" \n",
"\n",
"
"
],
"text/plain": [
" 0 1 2 3 4\n",
"carmelo1_post 1570.47 1521.81 1734.34 1442.24 1467.79\n",
"carmelo1_pre 1564.37 1542.66 1730.51 1447.01 1460.24\n",
"carmelo2_post 1725.92 1472.68 1551.3 1502.45 1469.15\n",
"carmelo2_pre 1732.03 1451.83 1555.13 1497.68 1476.71\n",
"carmelo_prob1 0.403078 0.755572 0.833364 0.567454 0.612829\n",
"carmelo_prob2 0.596922 0.244428 0.166636 0.432546 0.387171\n",
"carmelo_spread 2.5 -7 -10 -1.5 -3\n",
"date 2015-10-27 2015-10-27 2015-10-27 2015-10-28 2015-10-28\n",
"id 400827889 400827888 400827890 400827891 400827895\n",
"neutral False False False False False\n",
"playoff None None None None None\n",
"score1 97 94 111 87 104\n",
"score2 95 106 95 88 94\n",
"status post post post post post\n",
"team1 CHI ATL GS ORL MIA\n",
"team2 CLE DET NO WSH CHA\n",
"time_et 20: 00 20: 00 22: 30 19: 00 19: 30"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"games.head().T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select the Warriors' games\n",
"\n",
"... and add a few extra columns for ease of computation."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"\n",
"<table border="1" class="dataframe">\n",
" \n",
" <tr style="text-align: right;">\n",
" \n",
" date\n",
" status\n",
" team1\n",
" team2\n",
" carmelo_prob1\n",
" carmelo_prob2\n",
" opponent\n",
" \n",
" \n",
" game_no\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 1\n",
" 2015-10-27\n",
" post\n",
" GS\n",
" NO\n",
" 0.833364\n",
" 0.166636\n",
" NO\n",
" \n",
" \n",
" 2\n",
" 2015-10-30\n",
" post\n",
" HOU\n",
" GS\n",
" 0.445619\n",
" 0.554381\n",
" HOU\n",
" \n",
" \n",
" 3\n",
" 2015-10-31\n",
" post\n",
" NO\n",
" GS\n",
" 0.384816\n",
" 0.615184\n",
" NO\n",
" \n",
" \n",
" 4\n",
" 2015-11-02\n",
" post\n",
" GS\n",
" MEM\n",
" 0.812844\n",
" 0.187156\n",
" MEM\n",
" \n",
" \n",
" 5\n",
" 2015-11-04\n",
" post\n",
" GS\n",
" LAC\n",
" 0.735165\n",
" 0.264835\n",
" LAC\n",
" \n",
" \n",
"\n",
"
"
],
"text/plain": [
" date status team1 team2 carmelo_prob1 carmelo_prob2 opponent\n",
"game_no \n",
"1 2015-10-27 post GS NO 0.833364 0.166636 NO\n",
"2 2015-10-30 post HOU GS 0.445619 0.554381 HOU\n",
"3 2015-10-31 post NO GS 0.384816 0.615184 NO\n",
"4 2015-11-02 post GS MEM 0.812844 0.187156 MEM\n",
"5 2015-11-04 post GS LAC 0.735165 0.264835 LAC"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gs = games[\n",
" ((games["team1"] == "GS") |\n",
" (games["team2"] == "GS"))\n",
"][[ "date", "status", "team1", "team2", "carmelo_prob1", "carmelo_prob2" ]]\\n",
" .sort_values("date").copy()\n",
"gs["game_no"] = list(range(1, 83))\n",
"gs = gs.set_index("game_no")\n",
"gs.loc[(gs["team1"] == "GS"), "opponent"] = gs["team2"]\n",
"gs.loc[(gs["team2"] == "GS"), "opponent"] = gs["team1"]\n",
"gs.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gs["gs_prob"] = (gs["team1"] == "GS") * gs["carmelo_prob1"] \\n",
" + (gs["team2"] == "GS") * gs["carmelo_prob2"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Set all finished games' gs_prob to 1, because the Warriors won those\n",
"gs.loc[gs["status"] == "post", "gs_prob"] = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate the odds that the Warriors extend their streak through a given game"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gs["cumulative_prob"] = gs["gs_prob"].cumprod()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"23"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SEASON_WINS = (gs["status"] == "post").sum()\n",
"SEASON_WINS"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"PREV_WINS = 4"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"STREAK_WINS = SEASON_WINS + PREV_WINS"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"STREAK_RECORD = SEASON_RECORD = 33"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"29"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"STREAK_TIE_GAME = SEASON_WINS + (STREAK_RECORD - STREAK_WINS)\n",
"STREAK_TIE_GAME"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"STREAK_BREAK_GAME = STREAK_TIE_GAME + 1"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"SEASON_TIE_GAME = SEASON_RECORD\n",
"SEASON_BREAK_GAME = SEASON_TIE_GAME + 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The previous game, plus the next 10 games:\n",
"\n",
"Note: game_no refers to the sequence of Warriors games this regular season, not the game number in the streak."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<table border="1" class="dataframe">\n",
" \n",
" <tr style="text-align: right;">\n",
" \n",
" date\n",
" status\n",
" team1\n",
" team2\n",
" carmelo_prob1\n",
" carmelo_prob2\n",
" opponent\n",
" gs_prob\n",
" cumulative_prob\n",
" \n",
" \n",
" game_no\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 23\n",
" 2015-12-08\n",
" post\n",
" IND\n",
" GS\n",
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" 0.747491\n",
" IND\n",
" 1.000000\n",
" 1.000000\n",
" \n",
" \n",
" 24\n",
" 2015-12-11\n",
" pre\n",
" BOS\n",
" GS\n",
" 0.304816\n",
" 0.695184\n",
" BOS\n",
" 0.695184\n",
" 0.695184\n",
" \n",
" \n",
" 25\n",
" 2015-12-12\n",
" pre\n",
" MIL\n",
" GS\n",
" 0.127148\n",
" 0.872852\n",
" MIL\n",
" 0.872852\n",
" 0.606793\n",
" \n",
" \n",
" 26\n",
" 2015-12-16\n",
" pre\n",
" GS\n",
" PHX\n",
" 0.927174\n",
" 0.072826\n",
" PHX\n",
" 0.927174\n",
" 0.562602\n",
" \n",
" \n",
" 27\n",
" 2015-12-18\n",
" pre\n",
" GS\n",
" MIL\n",
" 0.954676\n",
" 0.045324\n",
" MIL\n",
" 0.954676\n",
" 0.537103\n",
" \n",
" \n",
" 28\n",
" 2015-12-23\n",
" pre\n",
" GS\n",
" UTA\n",
" 0.913321\n",
" 0.086679\n",
" UTA\n",
" 0.913321\n",
" 0.490547\n",
" \n",
" \n",
" 29\n",
" 2015-12-25\n",
" pre\n",
" GS\n",
" CLE\n",
" 0.815078\n",
" 0.184922\n",
" CLE\n",
" 0.815078\n",
" 0.399834\n",
" \n",
" \n",
" 30\n",
" 2015-12-28\n",
" pre\n",
" GS\n",
" SAC\n",
" 0.950854\n",
" 0.049146\n",
" SAC\n",
" 0.950854\n",
" 0.380184\n",
" \n",
" \n",
" 31\n",
" 2015-12-30\n",
" pre\n",
" DAL\n",
" GS\n",
" 0.248551\n",
" 0.751449\n",
" DAL\n",
" 0.751449\n",
" 0.285689\n",
" \n",
" \n",
" 32\n",
" 2015-12-31\n",
" pre\n",
" HOU\n",
" GS\n",
" 0.248239\n",
" 0.751761\n",
" HOU\n",
" 0.751761\n",
" 0.214770\n",
" \n",
" \n",
" 33\n",
" 2016-01-02\n",
" pre\n",
" GS\n",
" DEN\n",
" 0.960504\n",
" 0.039496\n",
" DEN\n",
" 0.960504\n",
" 0.206288\n",
" \n",
" \n",
" 34\n",
" 2016-01-04\n",
" pre\n",
" GS\n",
" CHA\n",
" 0.915446\n",
" 0.084554\n",
" CHA\n",
" 0.915446\n",
" 0.188845\n",
" \n",
" \n",
"\n",
"
"
],
"text/plain": [
" date status team1 team2 carmelo_prob1 carmelo_prob2 opponent \\n",
"game_no \n",
"23 2015-12-08 post IND GS 0.252509 0.747491 IND \n",
"24 2015-12-11 pre BOS GS 0.304816 0.695184 BOS \n",
"25 2015-12-12 pre MIL GS 0.127148 0.872852 MIL \n",
"26 2015-12-16 pre GS PHX 0.927174 0.072826 PHX \n",
"27 2015-12-18 pre GS MIL 0.954676 0.045324 MIL \n",
"28 2015-12-23 pre GS UTA 0.913321 0.086679 UTA \n",
"29 2015-12-25 pre GS CLE 0.815078 0.184922 CLE \n",
"30 2015-12-28 pre GS SAC 0.950854 0.049146 SAC \n",
"31 2015-12-30 pre DAL GS 0.248551 0.751449 DAL \n",
"32 2015-12-31 pre HOU GS 0.248239 0.751761 HOU \n",
"33 2016-01-02 pre GS DEN 0.960504 0.039496 DEN \n",
"34 2016-01-04 pre GS CHA 0.915446 0.084554 CHA \n",
"\n",
" gs_prob cumulative_prob \n",
"game_no \n",
"23 1.000000 1.000000 \n",
"24 0.695184 0.695184 \n",
"25 0.872852 0.606793 \n",
"26 0.927174 0.562602 \n",
"27 0.954676 0.537103 \n",
"28 0.913321 0.490547 \n",
"29 0.815078 0.399834 \n",
"30 0.950854 0.380184 \n",
"31 0.751449 0.285689 \n",
"32 0.751761 0.214770 \n",
"33 0.960504 0.206288 \n",
"34 0.915446 0.188845 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gs.ix[SEASON_WINS:SEASON_WINS + 11]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.39983449666561749"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gs.ix[STREAK_TIE_GAME]["cumulative_prob"]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
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"data": {
"text/plain": [
"0.38018407428889595"
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},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gs.ix[STREAK_BREAK_GAME]["cumulative_prob"]"
]
},
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"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
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"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"gs.ix[SEASON_TIE_GAME]["cumulative_prob"]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
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"execution_count": 23,
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"source": [
"gs.ix[SEASON_BREAK_GAME]["cumulative_prob"]"
]
},
{
"cell_type": "code",
"execution_count": 24,
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"outputs": [
{
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8LSLmV+tExGxgncy8svdnJEmSJEmSeqGfWm5sDSxsJTYqtwCbAnOAm6rERssV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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114f2f278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"next_20 = gs.ix[SEASON_WINS:SEASON_WINS+21]\n",
"ax = next_20["cumulative_prob"].plot(kind="bar", figsize=(18, 6), width=0.9,\n",
" color=[ "#006BB6", "#FDB927" ],\n",
" lw=0)\n",
"\n",
"ax.set_title("How Long Will The Warriors' Streak Last?\n", linespacing=0.5, fontsize=24, fontweight="bold")\n",
"\n",
"ax.set_xlabel("\nStreak Length and Opponent", fontsize=16)\n",
"ax.set_xticklabels([ "{0}\n{1}".format(wins, opp)\n",
" for wins, opp in zip(next_20.index + PREV_WINS, next_20["opponent"]) ],\n",
" rotation=0, fontsize=14)\n",
"ax.set_ylabel("Probability", fontsize=16)\n",
"ax.set_yticks(pd.np.arange(0, 1.1, 0.1))\n",
"ax.set_yticklabels([ "{0:.0f}%".format(y * 100) for y in ax.get_yticks() ], fontsize=14)\n",
"ax.figure.text(0.5, -0.1,\n",
" s="Per-game odds: FiveThirtyEight's \"CARM-Elo\" model. / Chart: BuzzFeed News",\n",
" fontsize=14,\n",
" ha="center")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Color codes from teamcolorcodes.com."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"---\n",
"\n",
"---"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}