Monday, November 26, 2007

Predictions 2007 Week 13 and Accuracy Report

The predictions are based on yards per play on rushing and passing plays (sacks and sack yards included), sack rates, third down conversion rates, penalty yards per game, and fumble and interception rates. The models are trained on the 1996-2006 seasons. Linear regression predicts the final score margin (home team points - away team points). Logistic regression estimates the probability that the home team will win.

Quick Summary of Accuracy
LAST WEEK
Linear regression, opponent adjusted: 12 out of 16
Logistic regression, opponent adjusted: 12 out of 16
Linear regression, unadjusted: 12 out of 16
Logistic regression, unadjusted: 10 out of 16

YEAR SO FAR
Linear regression, opponent adjusted: 67.3611%
Logistic regression, opponent adjusted: 65.9722%
Linear regression, unadjusted: 63.8889%
Logistic regression, unadjusted: 62.5000%

Full accuracy numbers after the jump...





















Predictions based on opponent-adjusted stats
GamePredicted Final Score MarginP(Home Team Wins)
NYJ @ MIA6.961073.5104
NE @ BAL-7.667427.6416
CIN @ PIT9.142577.4754
JAX @ IND8.622877.0392
HOU @ TEN4.113663.4817
SD @ KC0.427952.3628
DEN @ OAK-0.039844.1519
GB @ DAL6.737771.4024
SEA @ PHI2.994462.0521
BUF @ WAS5.875570.7703
NYG @ CHI-1.195045.3762
DET @ MIN5.704767.4535
SF @ CAR4.346764.5604
TB @ NO-2.520341.0657
CLE @ ARI2.627360.2216
ATL @ STL0.957153.7558





















Predictions based on unadjusted stats
GamePredicted Final Score MarginP(Home Team Wins)
NYJ @ MIA6.023870.8583
NE @ BAL-6.127132.5650
CIN @ PIT10.281580.7850
JAX @ IND7.237074.1194
HOU @ TEN3.782862.2564
SD @ KC1.334654.0065
DEN @ OAK1.026146.2019
GB @ DAL6.413371.2740
SEA @ PHI1.201955.2600
BUF @ WAS6.621872.0127
NYG @ CHI-1.723741.8105
DET @ MIN5.435965.9035
SF @ CAR4.134965.0194
TB @ NO-2.370842.1331
CLE @ ARI3.281262.7295
ATL @ STL1.145555.5421



OPPONENT-ADJUSTED
Linear regression
Win prediction accuracy: 67.3611%
Mean absolute error: 11.3790 points
Correlation with result: 0.3939
% of games predicted as home team wins: 72.2222%
Logistic regression
Win prediction accuracy: 65.9722%
Correlation with result: 0.3910
% of games predicted as home team wins: 75.0000%
Win prediction accuracy when 20%≤P(home team wins)<30%: 100.0000
Win prediction accuracy when 30%≤P(home team wins)<40%: 78.5714
Win prediction accuracy when 40%≤P(home team wins)<50%: 70.0000
Win prediction accuracy when 50%≤P(home team wins)<60%: 50.0000
Win prediction accuracy when 60%≤P(home team wins)<70%: 70.5882
Win prediction accuracy when 70%≤P(home team wins)<80%: 65.3846
Win prediction accuracy when 80%≤P(home team wins)<90%: 87.5000


UNADJUSTED
Linear regression
Win prediction accuracy: 63.8889%
Mean absolute error: 11.3918 points
Correlation with result: 0.3834
% of games predicted as home team wins: 70.1389%
Logistic regression
Win prediction accuracy: 62.5000%
Correlation with result: 0.3780
% of games predicted as home team wins: 71.5278%
Win prediction accuracy when 10%≤P(home team wins)<20%: 100.0000
Win prediction accuracy when 20%≤P(home team wins)<30%: 66.6667
Win prediction accuracy when 30%≤P(home team wins)<40%: 64.2857
Win prediction accuracy when 40%≤P(home team wins)<50%: 61.9048
Win prediction accuracy when 50%≤P(home team wins)<60%: 46.8750
Win prediction accuracy when 60%≤P(home team wins)<70%: 58.8235
Win prediction accuracy when 70%≤P(home team wins)<80%: 80.7692
Win prediction accuracy when 80%≤P(home team wins)<90%: 60.0000
Win prediction accuracy when 90%≤P(home team wins)<100%: 100.0000

11 comments:

Brian Burke said...

It looks like the opponent-adjustments make a big difference, especially early in the season.

I noticed your home team win prediction rate is over 70%. Since it's really around 55% or so this year, I'm wondering how accurate your model could be if you de-tune the home team coefficients to better reflect the actual home team win rate. I bet it would be over 70%.

Brian Burke said...

Also, I'd like your comments on my recent post about running on the road. I think the model I used for the research is similar to yours, and I'm wondering if you also see low coefficients for the visiting team's running efficiency.

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