Predicting College Football Outcomes

[Pages:41]+

Kernel Methods in Data Analytics: Predicting College Football Outcomes by Logistic Regression

Theodore Trafalis, School of Industrial and Systems Engineering , University of Oklahoma Panel in Sports Analytics, International Conference in Sports Management Athens, Greece May 9, 2016

+ Agenda

Background Existing Approaches Model Development Parameters Used

Offensive Defensive Interactions Other

Results and Interpretation Validation Conclusions and Recommendations Works Cited

+ Background

College Football Post Season

30-40 Bowls College Football Playoffs

Huge Viewership

6 Million per game in 2014 33 Million watched Championship Game

Millions bet on outcomes of Bowl Games

Uncommon matchups Lots of Uncertainty

+ Existing Approaches

Las Vegas Oddsmakers

Massey Ratings

Conglomerate of Computer Polls History of Computer Polls in BCS era

ELO Ratings

Developed by Arpad Elo to rank Chess players Adapted to sports, videogames, programming, etc.

S&P+ Ratings

Derived from Play by Play data Efficiency Explosiveness Field Position Finishing Drives Turnovers

Football Power Index

Developed by ESPN Predictive and Simulation based

+ Model Development

Binomial Logistic Regression

Fits dependent variable into one of two non-overlapping sets Can utilize many different types of input variable

Nominal Ordinal Interval

Without Interactions

With Interactions

+ Model Development

Parameter Fitting

36 Games from 2014 used as training data Method of Least Squares

Program Used

Excel's Solver GRG package

+ Parameters Used

Offensive Metrics

8 Team Statistics Consider both teams

Defensive Metrics

8 Team Statistics Consider both teams

Quarterback Rating Conversion Metrics Disruptive Metrics Penalty Metrics Outside Rankings Conference Interaction Effects

+ Offensive Metrics

Yardage Metrics

Total Yards Total Yards per Game Passing Yards Passing Yards per Game Rushing Yards Rushing Yards per Game

Scoring Metrics

Total Points Scored Points Scored per Game

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