Jeff Harrison & Philip Tan

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Presentation transcript:

Jeff Harrison & Philip Tan An Iterative Strength Based Model for the Prediction of NCAA Basketball Games Jeff Harrison & Philip Tan

Motivations Money Pattern Prediction Extrapolation to Future Betting Economic Scientific Extrapolation to Future

Research Questions & Method What method of predicting college basketball games should we use to obtain the best results? Can we alter the basic algorithm to produce more accurate predictions of the NCAA tournament? Methods: Ranking Systems ISR System Tweaking the Standard Determining the Winner "Winner Takes All"- Higher Ranking = Better Team Problem: Does not consider how close the rankings are. Markov Chain Determining win probability as a function of difference in ranking If the rankings are close, there is a probability that the lower ranked team will win

Iterative Strength Ranking Overview Set all teams to an initial ranking. Go through every game of the season. Give winner their opponents ranking + a constant bonus Give the loser their opponents ranking - a constant bonus Use the ranking generated by this iteration as the starting point for another iteration. (Recursion!) When two successive iterations yield the same ranking, You're Done!

Results Winner Takes All: Smart Winner: Biases: Applicability: Close game 79.37% Smart Winner: Standard 82.54% Biases: Data already known Applicability: Difficult to apply to future NCAA basketball volatile

End