Conformal Regression in Calorie Prediction for Team Jumbo-Visma

UCI WorldTour races, the premier men's elite road cycling tour, are grueling events that put riders' physical fitness and endurance to the test. The coaches of Team Jumbo-Visma have long been responsible for predicting the energy needs of each rider of the Dutch team for every race on the calendar. Those must be estimated to ensure riders have the energy and resources necessary to maintain a high level of performance throughout a race. This task, however, is both time-consuming and challenging, as it requires precise estimates of race speed and power output. Traditionally, the approach to predicting energy needs has relied on coaches' judgement and experience, but this method has its limitations and often leads to inaccurate predictions. In this paper, we propose a new, more effective approach to predicting energy needs for cycling races. By predicting the speed and power with regression models, we provide the coaches with calorie needs estimate for each individual rider per stage instantly. In addition, we compare methods to quantify uncertainty in estimating the speed and power of Team Jumbo-Visma riders for cycling races. The empirical analysis of the jackknife+, jackknife-minmax, jackknife-minmax-after-bootstrap, CV+, CV-minmax, conformalized quantile regression (CQR) and inductive conformal prediction (ICP) methods in conformal prediction reveals all methods except minmax based methods achieve valid prediction intervals while producing prediction intervals tight enough to be used for decision making. Furthermore, methods computing prediction intervals of fixed size produce significantly tighter intervals for low significance value. Among the methods computing intervals of varying length across the input space, namely the CQR and ICP methods, ICP computes tighter prediction intervals at larger significance level.