NCAA FBS Projections

REIFF - Regression Estimated Iterative Football Forecaster

Quick, dirty, and very rough experiments with Python, projection models, machine learning, and NCAA FBS football. Models implemented with Jupyter/IPython, Pandas, NumPy, and scikit-learn.

Models

2015 NCAA FBS Bowl Projections

Overview

Head-to-head points

Accuracy: 0.560975609756
Against Spread: 0.317073170732

Point differential

Accuracy: 0.634146341463
Against Spread: 0.512195121951

Head-to-head points

What if only offense mattered? This model explores this what if scenario using a Monte Carlo projected point model simulated with Kernel density estimation and Lasso regression. Output is projected winning team, projected spread is median head-to-head point margin after simulating 10,000 games, followed by odds of each team winning.

Arizona, 0.5796, New Mexico, 0.4204
-4.70832746107
Utah, 0.5178, BYU, 0.4822
-1.15592238906
Appalachian State, 0.7103, Ohio, 0.2895
-13.3554004411
San Jose State, 0.55, Georgia State, 0.45
-1.61190897552
Louisiana Tech, 0.5065, Arkansas State, 0.4932
-0.425221271289
Western Kentucky, 0.6782, South Florida, 0.3218
-9.70518851225
Utah State, 0.6105, Akron, 0.3893
-5.43898401827
Toledo, 0.6502, Temple, 0.3498
-5.85408234028
Boise State, 0.5546, Northern Illinois, 0.4454
-3.46417015238
Bowling Green, 0.7567, Georgia Southern, 0.2433
-9.23205410155
Western Michigan, 0.5182, Middle Tennessee, 0.48
-1.18105545451
Cincinnati, 0.5188, San Diego State, 0.4812
-0.904666620114
Marshall, 0.6683, Connecticut, 0.3273
-8.77809630324
Washington State, 0.6059, Miami (Florida), 0.3941
-5.10600932899
Southern Mississippi, 0.6713, Washington, 0.3277
-12.8832489116
Indiana, 0.5696, Duke, 0.4304
-2.70289211059
Tulsa, 0.6677, Virginia Tech, 0.3323
-7.34450675929
UCLA, 0.5758, Nebraska, 0.4241
-4.75186681086
Navy, 0.8169, Pittsburgh, 0.1831
-11.4341089584
Central Michigan, 0.5831, Minnesota, 0.4169
-3.45498669754
California, 0.5788, Air Force, 0.4212
-3.85473195556
Baylor, 0.6038, North Carolina, 0.3962
-6.30183683627
Colorado State, 0.7471, Nevada, 0.2529
-8.25830224046
Texas Tech, 0.7309, LSU, 0.269
-12.1460670302
Memphis, 0.693, Auburn, 0.2943
-19.7671662595
Mississippi State, 0.5258, North Carolina State, 0.4742
-1.18370687576
Texas A&M, 0.5066, Louisville, 0.4934
-0.27183639462
USC, 0.6636, Wisconsin, 0.3364
-8.10956161707
Houston, 0.6387, Florida State, 0.3613
-8.20379030129
Oklahoma, 0.6773, Clemson, 0.3227
-8.64367001802
Alabama, 0.6037, Michigan State, 0.3963
-2.84366964145
Tennessee, 0.7224, Northwestern, 0.2776
-14.0202172743
Notre Dame, 0.5476, Ohio State, 0.4524
-1.98945414198
Michigan, 0.5983, Florida, 0.4016
-5.83115662033
Stanford, 0.654, Iowa, 0.346
-6.57790304208
Oklahoma State, 0.5293, Mississippi, 0.4707
-1.77293666195
Penn State, 0.5091, Georgia, 0.4907
-0.360030329768
Arkansas, 0.5623, Kansas State, 0.4377
-3.35681345504
TCU, 0.57, Oregon, 0.43
-3.98094296189
West Virginia, 0.543, Arizona State, 0.457
-2.24814843635
Clemson, 0.5877, Alabama, 0.4123
-3.56158002366
Accuracy: 0.560975609756
Against Spread: 0.317073170732

Point differential

"Defense wins championships" and clearly there is room for improvement by accounting for a defense's contribution to winning. Projecting margin of victory instead of total point production yields a 5% improvement in win-loss accuracy (63.41% > 56.1%). This model is also a Monte Carlo model simulated with KDE and Lasso regression, but projects point margin instead of total points. Spreads are the median margin of victory. A tie breaking strategy is currently not implemented; ties contribute toward each team's win probability, which is why probabilities do not equal 1.

New Mexico, 0.6207, Arizona, 0.5818
-1.50076420858
BYU, 0.5765, Utah, 0.4908
-13.694616364
Appalachian State, 0.6263, Ohio, 0.5068
-5.70054296494
Georgia State, 0.6227, San Jose State, 0.6095
-2.0641339626
Louisiana Tech, 0.6135, Arkansas State, 0.525
-5.89564595176
Western Kentucky, 0.6597, South Florida, 0.3754
-11.1402723041
Akron, 0.608, Utah State, 0.6
-2.28628833357
Toledo, 0.5798, Temple, 0.4794
-13.0647490854
Boise State, 0.5936, Northern Illinois, 0.5041
-12.9984286942
Bowling Green, 0.6939, Georgia Southern, 0.3729
-8.870420153
Middle Tennessee, 0.6593, Western Michigan, 0.5444
-2.61620899932
San Diego State, 0.6584, Cincinnati, 0.4737
-6.18491066511
Marshall, 0.6926, Connecticut, 0.4839
-0.0
Miami (Florida), 0.6173, Washington State, 0.5665
-5.83007595297
Southern Mississippi, 0.6656, Washington, 0.4779
-7.69936127371
Duke, 0.7419, Indiana, 0.4808
-0.0
Virginia Tech, 0.7821, Tulsa, 0.401
-0.0
UCLA, 0.6535, Nebraska, 0.4354
-5.54369386922
Navy, 0.6782, Pittsburgh, 0.4119
-6.86723703292
Central Michigan, 0.6439, Minnesota, 0.5206
-0.0
Air Force, 0.5851, California, 0.5312
-7.75348294082
Baylor, 0.5431, North Carolina, 0.4716
-18.6296368544
Colorado State, 0.7056, Nevada, 0.4563
-1.40535406857
LSU, 0.6164, Texas Tech, 0.6156
-0.0
Memphis, 0.8033, Auburn, 0.3361
-0.0
Mississippi State, 0.5898, North Carolina State, 0.4962
-9.19639083209
Louisville, 0.6055, Texas A&M, 0.598
-3.24225062753
Wisconsin, 0.583, USC, 0.5331
-9.54816175024
Houston, 0.6085, Florida State, 0.4413
-10.1300014409
Oklahoma, 0.5994, Clemson, 0.4178
-21.7693619435
Alabama, 0.5865, Michigan State, 0.4585
-13.0676772769
Tennessee, 0.5802, Northwestern, 0.4557
-12.6207704135
Ohio State, 0.7174, Notre Dame, 0.2838
-12.8248790971
Michigan, 0.6778, Florida, 0.4247
-7.72340743036
Stanford, 0.523, Iowa, 0.5175
-14.399476052
Mississippi, 0.6242, Oklahoma State, 0.4654
-8.30384406221
Penn State, 0.5477, Georgia, 0.5253
-11.0597488147
Arkansas, 0.7145, Kansas State, 0.564
-0.0
TCU, 0.656, Oregon, 0.4359
-7.44151994921
West Virginia, 0.7239, Arizona State, 0.404
-1.92454994014
Clemson, 0.5643, Alabama, 0.4513
-18.4814595865
Accuracy: 0.634146341463
Against Spread: 0.512195121951

Footnotes

College football is notoriously difficult to project due to the small sample of data to draw from. While not incorporated into these models, for bowl games especially, strength of schedule, and team based rankings (e.g. FPI, Elo) have been shown to be fairly effective. Jupyter notebook has a working name in honor of 2012 NFL first-round selection Riley Reiff. You can probably guess what team I root for.

Author

This project has been a fun weekend learning experience by Fredrick Galoso. Team data is from /r/CFBAnalysis. Odds data is from TeamRankings.com.