From cc750afa29798d4a38e89a12925a92c4e38e9f68 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Thu, 9 May 2019 20:32:01 -0700 Subject: [PATCH] Add files via upload --- ipynb/WWW.ipynb | 242 ++++++++++++++++++++++++++++++++++++------------ 1 file changed, 182 insertions(+), 60 deletions(-) diff --git a/ipynb/WWW.ipynb b/ipynb/WWW.ipynb index ed0e550..b6cde17 100644 --- a/ipynb/WWW.ipynb +++ b/ipynb/WWW.ipynb @@ -60,7 +60,7 @@ { "data": { "text/plain": [ - "0.61119" + "0.61201" ] }, "execution_count": 2, @@ -125,13 +125,13 @@ "text": [ "0 point differential = 50% win game = 50% win series\n", "1 point differential = 54% win game = 58% win series\n", - "2 point differential = 58% win game = 67% win series\n", + "2 point differential = 57% win game = 66% win series\n", "3 point differential = 61% win game = 73% win series\n", "4 point differential = 65% win game = 80% win series\n", "5 point differential = 68% win game = 85% win series\n", - "6 point differential = 72% win game = 90% win series\n", + "6 point differential = 72% win game = 89% win series\n", "7 point differential = 75% win game = 93% win series\n", - "8 point differential = 78% win game = 95% win series\n", + "8 point differential = 77% win game = 95% win series\n", "9 point differential = 80% win game = 97% win series\n" ] } @@ -165,8 +165,8 @@ "metadata": {}, "outputs": [], "source": [ - "diff = [d/10 for d in range(101)]\n", - "game = [win_game(d) for d in diff]\n", + "diff = [d/10 for d in range(101)]\n", + "game = [win_game(d) for d in diff]\n", "series = [win_series(p) for p in game]" ] }, @@ -177,7 +177,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -281,14 +281,14 @@ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Warriors vs Clippers | 80% Game; 97% Series; 97% All | 69% Game; 87% Series; 87% All\n", "Warriors vs Rockets | 65% Game; 80% Series; 77% All | 56% Game; 62% Series; 54% All\n", - "Warriors vs Nuggets | 70% Game; 87% Series; 68% All | 59% Game; 68% Series; 37% All\n", + "Warriors vs Nuggets | 70% Game; 87% Series; 68% All | 58% Game; 68% Series; 36% All\n", "Warriors vs Bucks | 52% Game; 54% Series; 37% All | 44% Game; 37% Series; 13% All\n" ] } ], "source": [ - "SRS = dict(Warriors=6.42, Nuggets=4.19, Rockets=4.96, Clippers=1.09, \n", - " Bucks=8.04, Raptors=5.49, Sixers=2.25, Celtics=3.90, Pistons=-0.56)\n", + "SRS = dict(Bucks=8.04, Warriors=6.42, Raptors=5.49, Rockets=4.96, Nuggets=4.19, \n", + " Celtics=3.90, Sixers=2.25, Clippers=1.09, Pistons=-0.56)\n", "\n", "playoffs('Warriors',\n", " ('Clippers', 0.80),\n", @@ -309,8 +309,8 @@ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Bucks vs Pistons | 80% Game; 97% Series; 97% All | 79% Game; 96% Series; 96% All\n", "Bucks vs Celtics | 67% Game; 83% Series; 80% All | 65% Game; 81% Series; 78% All\n", - "Bucks vs Raptors | 60% Game; 71% Series; 57% All | 59% Game; 70% Series; 54% All\n", - "Bucks vs Warriors | 48% Game; 46% Series; 26% All | 56% Game; 64% Series; 34% All\n" + "Bucks vs Raptors | 60% Game; 71% Series; 57% All | 60% Game; 70% Series; 54% All\n", + "Bucks vs Warriors | 48% Game; 46% Series; 26% All | 56% Game; 63% Series; 34% All\n" ] } ], @@ -326,11 +326,133 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So, my subjective probabilities favor the Warriors over the Bucks, 37% to 26%. But the SRS scores disagree, favoring the Bucks 34% to 13%.\n", + "Let's compare championship predictions for four methods: my subjective evaluations, the SRS point differentials, and two methods from [538](https://projects.fivethirtyeight.com/2019-nba-predictions/): ELO, which is similar to SRS, and their more complex CARM-ELO model:\n", "\n", - "Note that [538](https://projects.fivethirtyeight.com/2019-nba-predictions/) has two very different predictions. Their ELO model (similar to SRS) has the Bucks as favorites with a 23% chance of winning, with the Warriors and Rockets next at 16% each. But their CARMELO model has the Warriors with a staggering 61% chance, followed by the Raptors at 16% and the Bucks at 15%.\n", "\n", - "I have low confidence in the [SRS ratings](https://www.basketball-reference.com/leagues/NBA_2019.html), because the Warriors seemed like they were coasting for parts of the regular season and are capable of \"flipping the switch\" in the playoffs, and because the Bucks have significant injuries to Brogdon, Mirotic and Gasol, all of whom contributed to the Bucks' great record in the season but will miss parts of the playoffs." + "|Method|Warriors|Bucks|\n", + "|------|--------|-----|\n", + "|Subjective| 37% | 26% |\n", + "|SRS | 13% | 35% |\n", + "| ELO | 16% | 23% |\n", + "| CARM-ELO| 61% | 15% |\n", + "\n", + "\n", + "\n", + "Which prediction method is best? I have low confidence in the SRS ratings, because the Warriors seemed like they were coasting for parts of the regular season and are capable of \"flipping the switch\" in the playoffs, and because the Bucks have significant injuries to Brogdon, Mirotic and Gasol, all of whom contributed to the Bucks' great record in the season but will miss parts of the playoffs.\n", + "\n", + "# 1 May, 2019\n", + "\n", + "The first round of playoffs was pretty uneventful—the favored team won in each of the eight matchups. Here's where we are today:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Team Opponent | Subjective Probabilities | SRS Differential\n", + "Warriors vs Clippers | 80% Game;100% Series;100% All | 69% Game;100% Series;100% All\n", + "Warriors vs Rockets | 65% Game; 95% Series; 95% All | 56% Game; 88% Series; 88% All\n", + "Warriors vs Nuggets | 70% Game; 87% Series; 83% All | 59% Game; 68% Series; 60% All\n", + "Warriors vs Bucks | 52% Game; 54% Series; 45% All | 44% Game; 37% Series; 22% All\n" + ] + } + ], + "source": [ + "playoffs('Warriors',\n", + " ('Clippers', 0.80, 4, 2),\n", + " ('Rockets', 0.65, 2, 0),\n", + " ('Nuggets', 0.70),\n", + " ('Bucks', 0.52))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Team Opponent | Subjective Probabilities | SRS Differential\n", + "Bucks vs Pistons | 80% Game;100% Series;100% All | 79% Game;100% Series;100% All\n", + "Bucks vs Celtics | 67% Game; 80% Series; 80% All | 65% Game; 77% Series; 77% All\n", + "Bucks vs Raptors | 60% Game; 71% Series; 56% All | 60% Game; 70% Series; 54% All\n", + "Bucks vs Warriors | 48% Game; 46% Series; 26% All | 56% Game; 64% Series; 34% All\n" + ] + } + ], + "source": [ + "playoffs('Bucks',\n", + " ('Pistons', 0.80, 4, 0),\n", + " ('Celtics', 0.67, 1, 1),\n", + " ('Raptors', 0.60),\n", + " ('Warriors', 0.48))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 8 May, 2019\n", + "\n", + "The favored teams keep winning: three of them are ahead 3-2, and the fourth, the Bucks, won their series 4-1. But the Warriors suffered the loss of a second starter, Keven Durant, to injury, and it is unclear how long he'll be out,\n", + "so I'm uncertain how to adjust the subjective probabilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Team Opponent | Subjective Probabilities | SRS Differential\n", + "Warriors vs Clippers | 80% Game;100% Series;100% All | 70% Game;100% Series;100% All\n", + "Warriors vs Rockets | 50% Game; 75% Series; 75% All | 56% Game; 80% Series; 80% All\n", + "Warriors vs Nuggets | 60% Game; 71% Series; 53% All | 59% Game; 68% Series; 55% All\n", + "Warriors vs Bucks | 50% Game; 50% Series; 27% All | 44% Game; 37% Series; 20% All\n" + ] + } + ], + "source": [ + "playoffs('Warriors',\n", + " ('Clippers', 0.80, 4, 2),\n", + " ('Rockets', 0.50, 3, 2),\n", + " ('Nuggets', 0.60),\n", + " ('Bucks', 0.50))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Team Opponent | Subjective Probabilities | SRS Differential\n", + "Bucks vs Pistons | 80% Game;100% Series;100% All | 79% Game;100% Series;100% All\n", + "Bucks vs Celtics | 67% Game;100% Series;100% All | 65% Game;100% Series;100% All\n", + "Bucks vs Raptors | 60% Game; 71% Series; 71% All | 60% Game; 71% Series; 71% All\n", + "Bucks vs Warriors | 50% Game; 50% Series; 36% All | 56% Game; 63% Series; 45% All\n" + ] + } + ], + "source": [ + "playoffs('Bucks',\n", + " ('Pistons', 0.80, 4, 0),\n", + " ('Celtics', 0.67, 4, 1),\n", + " ('Raptors', 0.60),\n", + " ('Warriors', 0.50))" ] }, { @@ -348,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -358,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -367,9 +489,9 @@ "text": [ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Rockets vs Wolves | 75% Game; 93% Series; 93% All | 71% Game; 89% Series; 89% All\n", - "Rockets vs Jazz | 70% Game; 87% Series; 81% All | 64% Game; 78% Series; 70% All\n", - "Rockets vs Warriors | 55% Game; 61% Series; 49% All | 59% Game; 69% Series; 48% All\n", - "Rockets vs Raptors | 60% Game; 71% Series; 35% All | 53% Game; 57% Series; 28% All\n" + "Rockets vs Jazz | 70% Game; 87% Series; 81% All | 64% Game; 79% Series; 70% All\n", + "Rockets vs Warriors | 55% Game; 61% Series; 49% All | 59% Game; 69% Series; 49% All\n", + "Rockets vs Raptors | 60% Game; 71% Series; 35% All | 54% Game; 58% Series; 28% All\n" ] } ], @@ -390,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -399,9 +521,9 @@ "text": [ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Warriors vs Spurs | 75% Game; 93% Series; 93% All | 61% Game; 73% Series; 73% All\n", - "Warriors vs Blazers | 65% Game; 80% Series; 74% All | 62% Game; 74% Series; 54% All\n", + "Warriors vs Blazers | 65% Game; 80% Series; 74% All | 62% Game; 75% Series; 54% All\n", "Warriors vs Rockets | 45% Game; 39% Series; 29% All | 41% Game; 31% Series; 17% All\n", - "Warriors vs Raptors | 55% Game; 61% Series; 18% All | 45% Game; 38% Series; 6% All\n" + "Warriors vs Raptors | 55% Game; 61% Series; 18% All | 45% Game; 39% Series; 6% All\n" ] } ], @@ -439,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -470,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -480,7 +602,7 @@ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Spurs vs Memphis | 83% Game; 98% Series; 98% All | 88% Game; 99% Series; 99% All\n", "Spurs vs Thunder | 62% Game; 75% Series; 73% All | 62% Game; 74% Series; 74% All\n", - "Spurs vs Warriors | 42% Game; 33% Series; 24% All | 50% Game; 50% Series; 37% All\n", + "Spurs vs Warriors | 42% Game; 33% Series; 24% All | 50% Game; 49% Series; 37% All\n", "Spurs vs Cavs | 67% Game; 83% Series; 20% All | 68% Game; 84% Series; 31% All\n" ] } @@ -495,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -506,7 +628,7 @@ "Cavs vs Pistons | 83% Game; 98% Series; 98% All | 68% Game; 85% Series; 85% All\n", "Cavs vs Hawks | 60% Game; 71% Series; 70% All | 57% Game; 66% Series; 56% All\n", "Cavs vs Raptors | 55% Game; 61% Series; 42% All | 55% Game; 61% Series; 34% All\n", - "Cavs vs Warriors | 33% Game; 17% Series; 7% All | 32% Game; 15% Series; 5% All\n" + "Cavs vs Warriors | 33% Game; 17% Series; 7% All | 32% Game; 16% Series; 5% All\n" ] } ], @@ -540,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -549,8 +671,8 @@ "text": [ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Warriors vs Rockets | 70% Game; 97% Series; 97% All | 83% Game;100% Series;100% All\n", - "Warriors vs Blazers | 55% Game; 61% Series; 59% All | 72% Game; 89% Series; 89% All\n", - "Warriors vs Spurs | 55% Game; 61% Series; 36% All | 50% Game; 50% Series; 45% All\n", + "Warriors vs Blazers | 55% Game; 61% Series; 59% All | 71% Game; 89% Series; 89% All\n", + "Warriors vs Spurs | 55% Game; 61% Series; 36% All | 50% Game; 51% Series; 45% All\n", "Warriors vs Cavs | 60% Game; 71% Series; 26% All | 68% Game; 85% Series; 38% All\n" ] } @@ -572,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -581,9 +703,9 @@ "text": [ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Spurs vs Memphis | 83% Game;100% Series;100% All | 88% Game;100% Series;100% All\n", - "Spurs vs Thunder | 62% Game; 75% Series; 75% All | 62% Game; 75% Series; 75% All\n", - "Spurs vs Warriors | 45% Game; 39% Series; 29% All | 49% Game; 49% Series; 37% All\n", - "Spurs vs Cavs | 67% Game; 83% Series; 24% All | 68% Game; 84% Series; 31% All\n" + "Spurs vs Thunder | 62% Game; 75% Series; 75% All | 62% Game; 74% Series; 74% All\n", + "Spurs vs Warriors | 45% Game; 39% Series; 29% All | 49% Game; 49% Series; 36% All\n", + "Spurs vs Cavs | 67% Game; 83% Series; 24% All | 68% Game; 85% Series; 31% All\n" ] } ], @@ -597,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -641,7 +763,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -650,9 +772,9 @@ "text": [ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Warriors vs Rockets | 70% Game;100% Series;100% All | 83% Game;100% Series;100% All\n", - "Warriors vs Blazers | 67% Game; 96% Series; 96% All | 72% Game; 98% Series; 98% All\n", + "Warriors vs Blazers | 67% Game; 96% Series; 96% All | 71% Game; 98% Series; 98% All\n", "Warriors vs Spurs | 60% Game; 71% Series; 68% All | 50% Game; 51% Series; 50% All\n", - "Warriors vs Cavs | 55% Game; 61% Series; 42% All | 68% Game; 85% Series; 42% All\n" + "Warriors vs Cavs | 55% Game; 61% Series; 42% All | 68% Game; 84% Series; 42% All\n" ] } ], @@ -666,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -675,7 +797,7 @@ "text": [ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Spurs vs Memphis | 83% Game;100% Series;100% All | 88% Game;100% Series;100% All\n", - "Spurs vs Thunder | 60% Game; 36% Series; 36% All | 62% Game; 38% Series; 38% All\n", + "Spurs vs Thunder | 60% Game; 36% Series; 36% All | 62% Game; 39% Series; 39% All\n", "Spurs vs Warriors | 40% Game; 29% Series; 10% All | 50% Game; 49% Series; 19% All\n", "Spurs vs Cavs | 50% Game; 50% Series; 5% All | 68% Game; 84% Series; 16% All\n" ] @@ -691,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -702,7 +824,7 @@ "Thunder vs Dallas | 83% Game;100% Series;100% All | 78% Game;100% Series;100% All\n", "Thunder vs Spurs | 40% Game; 64% Series; 64% All | 38% Game; 62% Series; 62% All\n", "Thunder vs Warriors | 40% Game; 29% Series; 19% All | 38% Game; 25% Series; 15% All\n", - "Thunder vs Cavs | 45% Game; 39% Series; 7% All | 56% Game; 63% Series; 10% All\n" + "Thunder vs Cavs | 45% Game; 39% Series; 7% All | 56% Game; 64% Series; 10% All\n" ] } ], @@ -716,7 +838,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -761,7 +883,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -770,7 +892,7 @@ "text": [ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Warriors vs Rockets | 70% Game;100% Series;100% All | 83% Game;100% Series;100% All\n", - "Warriors vs Blazers | 67% Game;100% Series;100% All | 71% Game;100% Series;100% All\n", + "Warriors vs Blazers | 67% Game;100% Series;100% All | 72% Game;100% Series;100% All\n", "Warriors vs Thunder | 63% Game; 61% Series; 61% All | 62% Game; 59% Series; 59% All\n", "Warriors vs Cavs | 55% Game; 61% Series; 37% All | 68% Game; 85% Series; 50% All\n" ] @@ -797,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -822,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -847,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -857,8 +979,8 @@ "Team Opponent | Subjective Probabilities | SRS Differential\n", "Thunder vs Dallas | 83% Game;100% Series;100% All | 77% Game;100% Series;100% All\n", "Thunder vs Spurs | 40% Game;100% Series;100% All | 38% Game;100% Series;100% All\n", - "Thunder vs Warriors | 45% Game; 83% Series; 83% All | 37% Game; 76% Series; 76% All\n", - "Thunder vs Cavs | 55% Game; 61% Series; 51% All | 56% Game; 64% Series; 48% All\n" + "Thunder vs Warriors | 45% Game; 83% Series; 83% All | 37% Game; 75% Series; 75% All\n", + "Thunder vs Cavs | 55% Game; 61% Series; 51% All | 56% Game; 63% Series; 48% All\n" ] } ], @@ -881,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -936,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -945,7 +1067,7 @@ "0.9088750000000001" ] }, - "execution_count": 29, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -956,16 +1078,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.967354726464" + "0.966948562677312" ] }, - "execution_count": 30, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -985,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1012,8 +1134,8 @@ " if W == 4 or L == 4:\n", " return Counter({(W, L): weight})\n", " else:\n", - " return (series_results(p, weight * p, W + 1, L) +\n", - " series_results(p, weight * (1 - p), W, L+1))\n", + " return (series_results(p, weight * p, W + 1, L)\n", + " + series_results(p, weight * (1 - p), W, L+1))\n", " \n", "def series_results_table(pcts=pcts):\n", " outcomes = [(4, 0), (4, 1), (4, 2), (4, 3), (3, 4), (2, 4), (1, 4), (0, 4)]\n",