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Measuring and Predicting UW Badgers’s performance by quarterback and running back stats

By: Tyler Chu ECE 539 Fall 2013. Measuring and Predicting UW Badgers’s performance by quarterback and running back stats. Reasons to Predict. Millions of Badgers Fans who want to know how their team is going to do Immense amounts of money go into the NCAA football programs.

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Measuring and Predicting UW Badgers’s performance by quarterback and running back stats

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  1. By: Tyler Chu ECE 539 Fall 2013 Measuring and Predicting UW Badgers’s performance by quarterback and running back stats

  2. Reasons to Predict • Millions of Badgers Fans who want to know how their team is going to do • Immense amounts of money go into the NCAA football programs

  3. Main Problem & Goal • Problem: • Most predictions available have a human bias in it which stems from personal opinions that could result in errors with the predictions. • Goal: • Eliminate the human error by having a Multi-layer Perceptron to perform the prediction

  4. Why MLP • Teams can win in a variety of ways • No linear mapping exists to determine the outcome • No one piece of the data always correlates to a win or loss as there are many ways in which a team can win or lose.

  5. Why MLP • MLPs • Multi-Layer Perpceptrons are capable of predicting outcomes of non-linear data. • Multi-Layer Perceptrons reduce the problem to a Neural Network prediction problem and remove the human personal bias of a teams performance from the prediction.

  6. Data Collection • Data was to be available the web’s many different sport statistic sites. • A large data set was required to represent the large number of ways to win • Used Sports References’s website • Used Excel’s web query feature to acquire tabular data

  7. Data Collection • Many feature vectors were collected • Passing Completions, Attempts • Yards per attempt • Touchdowns • Interceptions • Passer Ratings • Rushing equivalents for RB’s

  8. Preliminary Results • Data was formatted in Matlab and then fed into a modified MLP Matlab program provided from the class website. • Multiple tests run using the same variables for alpha and momentum set to default values of 0.1 and 0.8 respectively • Average of initial results on the data with one hidden layer and neuron was a 73.6842 classification rate

  9. Initial Test

  10. Secondary Test

  11. Results • Additional hidden layers and neurons eventually converged to a 95% classification rate • Decided to predict future seasons based upon if the current quarterback and running back stay – generally large difference if they do not

  12. Results • Use a linear formula between each consecutive season • Found that UW would improve to a 9 win season if Stave and Ball both stayed • Currently at 9 wins with one game to go

  13. References • Newman, M. E. J., and Park, Juyong; A network-based ranking system for US college Football. Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109. arXiv:physics/0505169 v4 31 Oct 2013 • ESPN, ESPN College Football. 8 Dec. 2013 http://espn.go.com/college-football/team/_/id/275/ • Sports References. SR College Football. 8 Dec. 2013 http://www.statfox.com/nfl/nfllogs.htm

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