Neural NASCAR Networks

1
Neural NASCAR Networks

• Backpropagation Approach to Fantasy NASCAR
  Prediction

Michael A. Hinterberg ECE 539 Project Presentatio
n
Wednesday, 10 May 2000
2
Overview of NNN

• Problem Description
• Data Gathering
• Data File Creation and Organization
• Network Inputs
• Neural Network Method
• Analysis / Baseline Comparison
• Conclusion

3
Problem Description

• 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.

4
Data Gathering

• Use data from NASCAR Online
• http//www.nascar.com
• Download data for each race from 1996 through the
  current races in 2000 (over 140 races total)
  
• Download driver data information for all main
  drivers
  
• Download track information for all NASCAR tracks
• Strip raw text from HTML using HTML Stripper

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Data File Creation

• Create an .ini file that stores a list of
  drivers, data file directories, data files, and
  track info file
  
• Parse data files using Visual C
• Create output data files for each driver
• Parse data files for driver results
• Store driver results information in
  comma-separated variable format per each race

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Sample .ini File

• Mark Martin
• Terry Labonte
• Dale Earnhardt
• Jeff Gordon
• Dale Jarrett
• D\Neural NASCAR\
• tracks.txt
• Data 1999\race1.txt
• Data 1999\race2.txt

Drivers
Data file separator
Data file directory
Track info file
Race results data
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Network Inputs

• For each track (per driver), I will store the
  inputs for the following information
  
• End position
• Start position
• Track length (encoded short, medium, long)
• Car make (encoded Chevy, Ford, Pontiac, Dodge)
• Restrictor Plate track (binary)
• Bonus points (for leading a lap or leading most
  laps)
  
• Total points for race

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Network Inputs (continued)

• I will also implement driver information inputs
  if time permits
  
• Total years racing
• Total races
• Total Wins
• Total Top 5s
• Total Top 10s

9
Neural Network Method

• I will implement this using a multi-layer
  perceptron in Matlab with the backpropagation
  algorithm
  
• I will modify Professor Hus bp.m
• I am most familiar with the backpropagation
  algorithm
  
• I am impressed with the success of
  backpropagation in other sports prediction, such
  as Mike Pardees NCAA Football Prediction
  
• If the project is successful, I will implement my
  own algorithm in Visual C in the future
  

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Neural Network Method (continued)

• Implementation Details
• I will run a separate net on each driver and try
  to predict his performance in a given race.
  
• I will scale analog data based off of maximum for
  that category to prevent statistical bias.
  
• To predict a race, I will use all previous data,
  and fill in all known inputs, namely driver, car,
  and track information. All unknown information
  will be averaged.

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Analysis / Baseline Comparison

• 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
  
• http//home.earthlink.net/johnet1/
• 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.
  

12
Conclusion

• I believe NASCAR is a well-chosen sport for ANN
  analysis, and that this network will outperform
  most human prediction for NASCAR races
  
• ANN remembers more data than a human
• ANN is free from driver bias
• ANN considers current driving trends, streaks,
  and success for each track
  
• NASCAR contains less dependent variables than
  most other sports, since it is an individual sport