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An Iterative Strength Based Model for the Prediction of NCAA Basketball Games

An Iterative Strength Based Model for the Prediction of NCAA Basketball Games. Jeff Harrison & Philip Tan. Motivations. Money Betting Pattern Prediction Economic Scientific Extrapolation to Future. Research Questions & Method. Questions:

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An Iterative Strength Based Model for the Prediction of NCAA Basketball Games

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

  2. Motivations • Money • Betting • Pattern Prediction • Economic • Scientific • Extrapolation to Future

  3. Research Questions & Method Questions: • 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

  4. 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!

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

  6. End

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