Neural NASCAR NetworksOverview of NNNProblem DescriptionData GatheringData File CreationSample .ini FileNetwork InputsNetwork Inputs (continued)Neural Network MethodNeural Network Method (continued)Analysis / Baseline ComparisonConclusionNeural NASCAR NetworksNeural NASCAR NetworksBackpropagation Approach to Fantasy NASCAR PredictionMichael A. HinterbergECE 539 Project PresentationWednesday, 10 May 2000Overview of NNNOverview of NNNProblem DescriptionData GatheringData File Creation and OrganizationNetwork InputsNeural Network MethodAnalysis / Baseline ComparisonConclusionProblem DescriptionProblem DescriptionFantasy 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 GatheringUse data from NASCAR Online:http://www.nascar.comDownload data for each race from 1996 through the current races in 2000 (over 140 races total)Download driver data information for all main driversDownload track information for all NASCAR tracksStrip raw text from HTML using HTML StripperData File CreationData File CreationCreate an .ini file that stores a list of drivers, data file directories, data files, and track info fileParse data files using Visual C++Create output data files for each driverParse data files for driver resultsStore driver results information in comma-separated variable format per each raceSample .ini FileSample .ini FileMark MartinTerry LabonteDale EarnhardtJeff GordonDale Jarrett<files>D:\Neural NASCAR\tracks.txtData 1999\race1.txtData 1999\race2.txtDriversData file separatorData file directoryTrack info fileRace results dataNetwork InputsNetwork InputsFor 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 raceNetwork 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’sNeural Network MethodNeural Network MethodI 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 futureNeural 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 ComparisonComparisonThere 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 leagueI 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.ConclusionConclusionI 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
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