1. Charles Rodenkirch December 11 th, 2013 ECE 539 – Introduction to Artificial Neural Networks PREDICTING INDIVIDUAL PLACEMENT IN COLLEGIATE WATERSKI TOURNAMENTS

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.comwww.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: 1 st -5 th, 6 th – 10 th, 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 http://www.usawaterski.org/rankings/view- tournamentsHQ.asp?sl=on&tr=on&ju=on&sTourLevel=Collegiate&sTourRange=5