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Predicting Individual placement in collegiate Waterski Tournaments

Predicting Individual placement in collegiate Waterski Tournaments. Charles Rodenkirch December 11 th , 2013 ECE 539 – Introduction to Artificial Neural Networks. Project History. Project is of personal interest President of UW-Madison Waterski/Wakeboard Team (est. 1999)

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Predicting Individual placement in collegiate Waterski Tournaments

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  1. Predicting Individual placement in collegiate Waterski Tournaments Charles Rodenkirch December 11th, 2013 ECE 539 – Introduction to Artificial Neural Networks

  2. Project History • Project is of personal interest • President of UW-Madison Waterski/Wakeboard Team (est. 1999) • Could be used as a prediction tool to determine future standings • Could be used as a training tool to see which events a skier should focus on to best improve his overall ranking

  3. NCWSA Ski Tournaments • How Tournaments are Scored • Separate Score For Each Event • Slalom (buoy count), Trick (points), Jump (distance in feet) • Overall Placement Calculation • Awarded points based on ranking in each event, sum of every events points used for final rankings

  4. Skier’s Dilemmas predicting performance • Information a skier can predict before a tournament • Weather conditions • # of competitors • Their scores in Trick, Slalom, and Jump • Difficulties with calculating final placement • Placement is not based off our scores in each event, it is instead based off relative performance in each event compared to the other competitors • Hard to predict what other competitors will scores

  5. Data Collection • Past Tournament Scores and Resulting Placement • Data for last 4 years of tournaments available on www.NCWSA.com • Only using data from tournaments in our conference to reduce size • Data needed to be collected/processed • Results split up by event, loaded into excel for formating • Used MatLab to combine all scores into vector for each skier • Weather Data • Archived data available from www.Weather.org

  6. Encoding Data for MLP • Directly Scale Data to Features • Overall Placement • Split into groups ex: 1st -5th, 6th – 10th, etc. • Number of Competitors • Precipitation on Saturday and Sunday • Wind speed on Saturday and Sunday • Groups created using K Nearest Neighbor Classification • For data that has nonlinear grouping • Trick, Jump, and Slalom scores all have unpredictable groupings around score points due to differences in techniques and tricks as skiers improve • Classification of these groups helps properly encode data for MLP

  7. Input and output features • Input Features • 11 Features • Scores in jump, trick, and slalom • Temperature, wind speed and precipitation for Saturday and Sunday • Month of the year • Number of competitors • Output Features • 1 Feature • Overall Ranking

  8. Processing with MLP • Back Propagation Multi Layer Perceptron Used • Learning Type = Supervised • Trained with previous tournament results • Cross Testing Performed • Each iteration one tournament will be removed from training data and used as a testing set • Momentum and Learning Rate • Will be varied over trials to determine best performance • Types of activation • Sigmoidal, Radial Basis, and Tangent functions to be tested • Number of Layers and Hidden Neurons • Multiple configurations will be tested

  9. Future Work • Expand Data Set • Continue Testing MLP with different configurations • Vary Number of Hidden Neurons • Vary Number of Hidden Layers • Vary Momentum and Learning Rate • Vary Activation Functions

  10. References • http://weather.org/weatherorg_records_and_averages.htm • http://www.usawaterski.org/rankings/view-tournamentsHQ.asp?sl=on&tr=on&ju=on&sTourLevel=Collegiate&sTourRange=5

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