JSM 2011 Online Program

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Abstract Details

Activity Number: 624
Type: Contributed
Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Sports
Abstract - #302948
Title: Predicting Race Times for Motorcycle Grand Prix Racing
Author(s): Leanne Streja*+ and Robert E. Weiss and Catherine Ann Sugar
Companies: University of California at Los Angeles and University of California at Los Angeles and University of California at Los Angeles
Address: School of Public Health Department of Biostatistics, Los Angeles, CA, 90095,
Keywords: motorcycle racing ; prediction model ; sports statistics ; analysis of variance
Abstract:

Motorcycle Grand Prix (MotoGP) is the world premier class of motorcycle road racing. In this analysis I build statistical models of lap times and race times from MotoGP 2002-2007 race seasons to make predictions for 2008 races. Predictors included in the models are lap number, year, rider's finish rank in his most recent prior race, and rider's position on the starting grid. Predictions are made for all riders competing at six tracks. A cubic spline for lap effect is used in the lap time model and quadratic year effect is used for the race time model. Cumulative predicted lap times from the lap time models are used to predict race times. Model fits and predictions are compared through analysis of the root mean square error (RMSE) and root mean square error of predictions (RMSEP). Predictions are accurate on average to within 20-30 seconds of observed race times. Predicting race times can be useful for teams and fans for better understanding of race outcomes.


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