Sport

Abstract Modelling the behaviour and performance of cyclists during competition ahead of time has several useful applications to professional cycling teams. Although existing physics-based approaches perform reasonably well in this regard, they generally require several assumed parameters. Performance is also dependant on several variables which may be hard to quantify, such as a rider’s cornering ability and pacing strategy. The work presented in this thesis is a fully data-driven approach to speed pre- diction in time trials performed by elite cyclists.