Neural NASCAR NetworksNeural NASCAR Networks

Backpropagation Approach to Fantasy NASCAR Prediction

Michael A. Hinterberg

ECE 539 Project Presentation

Wednesday, 10 May 2000

Overview of NNNOverview of NNN

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

Problem DescriptionProblem 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.

Data GatheringData 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

Data File CreationData 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

Sample .ini FileSample .ini FileMark MartinTerry LabonteDale EarnhardtJeff GordonDale Jarrett<files>D:\Neural NASCAR\tracks.txtData 1999\race1.txtData 1999\race2.txt

Drivers

Data file separator

Data file directory

Track info file

Race results data

Network InputsNetwork 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

Network Inputs (continued)Network Inputs (continued) I will also implement driver information inputs

if time permits:– Total years racing– Total races– Total Wins– Total Top 5’s– Total Top 10’s

Neural Network MethodNeural Network Method

I will implement this using a multi-layer perceptron in Matlab with the backpropagation algorithm…– I will modify Professor Hu’s “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 Pardee’s NCAA Football Prediction

– If the project is successful, I will implement my own algorithm in Visual C++ in the future

Neural Network Method Neural Network Method (continued)(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.

Analysis / Baseline Analysis / Baseline ComparisonComparison

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.

ConclusionConclusion

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