It is increasingly important for professional sports teams to monitor player fitness in order to optimize performance. Models have been put forward linking fitness in training to performance in competition but rely on regular measurements of player fitness. As formal tests for measuring player fitness are typically time-consuming and inconvenient, measurements are taken infrequently. As such, it may be challenging to accurately predict performance in competition as player fitness is unknown. Alternatively, other data, such as how the players are feeling, may be measured more regularly. This data, however, may be biased as players may answer the questions differently and these differences may dominate the data. Linear Mixed Methods and Support Vector Machines were used to estimate player fitness from available covariates at times when explicit measures of fitness are unavailable. Using data provided by a professional rugby club, a case study was used to illustrate the application and value of these models. Both models performed well with R^2 values ranging from 60% to 85%, demonstrating that the models largely captured the biases introduced by individual players.
|Journal||International Journal of Sports Science and Coaching|
|Early online date||27 Feb 2017|
|Publication status||E-pub ahead of print - 27 Feb 2017|
- machine learning
- predictive modelling