# Beating the NCAA Football Point Spread

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2 resulting in our raw data set. The raw data set is then processed with a Java program and used to generated training and testing examples. Finally, the examples are saved in a CSV format usable by our machine learning algorithms. Note that although we gather data for both the 2009 and 2010 seasons, we only use the 2010 season in experimenting with machine learning algorithms. The data from the 2009 season is used as initial values for computing examples for games that occur early in the 2010 season. One problem we came across in gathering our data set was missing values in the data. However, we noticed that all of the missing values came from events which occur very rarely in college football games. These rare events usually don t affect the outcome of a game. We simply disregarded these rarely occurring events since we suspect that they would ultimately have very low prediction power in our models. 3 Features The raw data set contains 94 fundamental college football statistics that captures each team s performance in a given game. Using the raw data set, we could easily compute new features and generate examples for feeding into our machine learning algorithms. Our initial examples were generated by first computing a cumulative average of the statistics in order from earliest games to most recent games. This gave us an average of a team s statistics up to a specific week in the season which could then be used in predicting future games the team plays in. This method of generating examples prevents us from making predictions on games using statistics generated in future games, which would distort our test error. For example, if we wanted to predict the outcome of a game at week k, we can use the teams cumulative averages from week k 1 (which we know doesn t incorporate future statistics) to train our model. For each game we create an example by concatenating the two teams cumulative statistics up to (but not including) the date of the game. This resulted in a total of 730 examples with 210 features per example (features besides the fundamental statistics were added). Each example is labeled 1 if the home team wins and 1 if the away team wins. Passes attempted Interceptions Points gained Passes completed Interception yardage Points allowed Passes intercepted Passing touchdowns allowed Red zone scoring percentage Pass completion percentage Passing yards allowed Red zone field goal percentage Pass rating Rushing touchdowns allowed Red zone touchdowns allowed Passing touchdowns Rushing yards allowed Red zone field goals allowed Passing yardage Sacks Third down conversion percentage Rushes attempted Sack yardage Third down conversion percentage allowed Rushing average Tackles for loss Quarterback hurries Rushing touchdowns Tackles for loss yardage Passes broken up Rushing yardage Forced fumbles Kicks or punts blocked 3.1 Momentum Figure 1: A subset of the fundamental statistics used as features One important aspect that can determine the outcome of a game is a team s momentum going into the game. Momentum shifts can occur abruptly and last for several games, e.g. a change in team strategy or an important player gets injured. We used two techniques to account for momentum in our models. First, instead of using a cumulative average of the fundamental statistics we use a k-game moving average of the fundamental statistics. Second, we computed 12 new features for each team that track momentum at different time horizons. 2

4 6 Results Train Error Test Error Always Home % Always Away % Random % Logistic 14.11% 38.52% SVM (Linear) 7.77% 34.43% SVM (Poly) 0% 35.66% SVM (RBF) 0% 47.54% GentleBoost 0% 27.46% ModestBoost 9.41% 30.74% (a) Results for straight bets Train Error Test Error Always Home % Always Away % Random % Logistic 23.72% 50.41% SVM (Linear) 23.52% 50% SVM (Poly) 0% 44.26% SVM (RBF) 0% 45.90% GentleBoost 0% 48.36% ModestBoost 0% 47.54% (b) Results for point spread bets 7 Analysis of Results Figure 3: Game outcome prediction results Our results show that we are able to successfully predict the outcome of straight bets with an accuracy of 73% using GentleBoost. This result is slightly more accurate than previous work that has been done in predicting outcomes of other sports games [4][6]. In the case of predicting point spread bets, SVM with a 3rd degree polynomial kernel performed the best. We were able to beat random guessing by 4% and achieve an accuracy of 56%. This accuracy, which may seem low, is competitive with professional college football bettors who consistently achieve accuracies of 58% in betting on college football point spread games [3]. It is interesting that were able to achieve much higher accuracy in predicting the straight outcomes of games compared to games with points spreads. Intuitively this makes sense because the purpose of the point spread is to create a market such that either team has roughly a 50% chance of winning. However, we wanted to investigate our data further to determine why this may be the case. We plotted the outcomes of games against the first two principal components in order to draw insight into why we were not seeing better performance in predicting the outcomes of games with point spreads. Axis 2 Home team loses Home team wins Axis 2 Home team loses Home team wins Axis 1 Axis 1 (a) Game outcomes without point spreads (b) Game outcomes with point spreads Figure 4: Game outcomes against top two principle components 4

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