Motocycle Grand Prix (MotoGP) Lap Time Models

• Activity Number: 671
• Type: Contributed
• Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Sports
• Author(s): Leanne Streja*+ and Robert E. Weiss and Catherine A. Sugar
• Companies: University of California, Los Angeles School of Public Health and University of California, Los Angeles School of Public Health and University of California, Los Angeles School of Public Health
• Keywords: Motorcycle Racing ; Motor Sports ; Spline Models

Abstract

I model lap times in Motorcycle Grand Prix (MotoGP) races as a function of lap, year, and rider characteristics such as grid position, years of racing experience, and prior race finish position. I develop graphs to identify how these characteristics influence lap times. Initially I fit an analysis of variance model with factors for lap, year, and rider. This model has many parameters which were not easily interpretable. Therefore I looked for a parsimonious lower-dimensional model with interpretable parameters that would capture the variability and provide a good explanation for the patterns of lap times. I simplified the model using cubic splines for the lap effect and continuous year. The model was fit to data from six different tracks.