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Published byHarold Justin Waters Modified over 9 years ago
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Artificial Neural Network System to Predict Golf Score on the PGA Tour ECE 539 – Fall 2003 Final Project Robert Steffes ID: 901-685-8871
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Idea Use averages from seven of the major shot categories to predict scoring average. Inputs include: Driving Distance, Driving Accuracy (%), Greens in Regulation (%), Putting Average, Birdie Average, Sand Saves (%), and Putts per Round. The MLP is then tested using these inputs and a scoring average is predicted.
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Implementation Data gathered from top 188 players on the PGA Tour. Create training and testing files from this data. Run through MLP with several tests to get the optimum parameters: 3 Layers, 4 Hidden Neurons, Learning Rate=0.1, Momentum=0.3, 1000 Epochs.
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Results 77% average classification rate on multiple tests run. Compare to 17% random classification. No similar system implemented yet. Wide range of applications if used on the PGA Tour.
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Conclusion All players have access to their shot trends, averages, and statistics, but it is virtually impossible to draw a correlation just by looking at them. Potential applications beyond simply forecasting a player’s score –Eg. A player may hypothetically change one of his statistics and see whether the MLP predicts that that will change his scoring average
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Conclusion Professionals looking for every advantage they can get –A system to analyze their statistics and predict their scores could be extremely valuable if utilized I would like to look into actually developing a product from the concept of this project in the future
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