Neural NASCAR Networks Backpropagation Approach to Fantasy NASCAR Prediction Michael A. Hinterberg ECE 539 Project Presentation Wednesday, 10 May 2000 Overview of NNN Problem Description Data Gathering Data File Creation and Organization Network Inputs Neural Network Method
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Backpropagation Approach to Fantasy NASCAR Prediction
Michael A. Hinterberg
ECE 539 Project Presentation
Wednesday, 10 May 2000
Fantasy sports games have always been popular with fans, as they reward those with a vast knowledge of the sport and make it more fun to follow a sport. As NASCAR racing is becoming one of the most popular sports in America, so too has the emergence of fantasy NASCAR leagues, where players try to pick the most successful drivers each week. Although neural network approaches have been applied to many other fantasy sports, the presence of such analysis is relatively scarce in NASCAR. I believe a backpropagation implementation to this prediction will be relatively successful.
Data file separator
Data file directory
Track info file
Race results data
There are dual purposes to this project – first, to be able to predict NASCAR winners for fun, and second, to be able to compete in a NASCAR fantasy league
I will use the network to choose fantasy drivers for the first 11 races of this year and compare the results to the Bump and Grind NASCAR Pool:
I will consider the project a success if I do better than 50% of the human competitors. I hope to do much better than this.