Timing Predictions in Vasaloppet using Supervised Machine Learning

Timings at future controls in Vasaloppet were predicted using timings at past and current controls. Predictions were made using linear regression, deep neural networks and support vector machine regression. Timings up to the current control and age were used as input data; predicted timing at a future control was used as output data. This resulted in 28 estimated functions, which were made for each starting row. With eleven starting row, the final number of estimated transfer functions is 308. All methods significantly improved prediction with up to six times lower mean error compared to the currently used method. It was found that deep neural networks had the possibility to make the best predictions, but that the training time required was unrealistic given available resources. Support vector regression performed almost as well as deep neural networks, but trained much faster. Linear regression had the worst performance, albeit not by much, and the fastest training time of the machine learning algorithms. Improvement ranged from up to six times lower average hourly error to 1.3 times, depending on the transfer function estimate evaluated. Improvements for predictions from the first control, where the absolute error was by far the largest, were the greatest. Thus the worst predictions with the original model improved the most, resulting in considerable improvement for the service offered during Vasaloppet