Title: A Bayesian nonparametric approach for functional regression with application to sport data
Authors: Alessandro Lanteri - University of Turin (Italy) [presenting]
Raffaele Argiento - University of Torino (Italy)
Silvia Montagna - University of Turin (Italy)
James Hopker - University of Kent (United Kingdom)
Abstract: In sport analytics, there is often interest in predicting elite athletes performance at a future sporting event given his/her competitive results tracked throughout the athlete's career and other (time-varying) covariates. Such predictions can be useful both for scouting purposes, and to build red flag indicators of unexpected increases in athlete performance for targeted anti-doping testing. We propose a predictive model for the longitudinal trajectory of athletes performance where we characterize the curve with a sparse basis expansion allowing individual time-dependant covariates to impact the shape of the estimated trajectories. Moreover, we introduce random intercepts, distributed according to a nonparametric hierarchical process, in order to induce clustering while borrowing statistical information across curves. In particular, we assume a hierarchical normalized generalized gamma process to grants great flexibility in clustering and accuracy in prediction. We apply our model to a longitudinal study on shot put athletes, where their competitive results are tracked throughout their career.