During team time trials in road cycling changing schemes are used to spread the workload over the cyclists in the team. Models that provide predictions of race performance already exist for individual time trials. It is proposed that with a performance model for team time trials, the performance of different strategies can be compared and optimised. In literature combinations of mechanical resistance models and physiological models are used to determine the performance of individual time trials. The aerodynamic interaction between cyclists is very important to the effectiveness of a strategy. Coefficients of drag reduction between cyclists in a team time trial are presented in several studies, however most studies use groups of only four cyclists, which is not useful for a team time trial with eight cyclists. Only two studies report data for groups up to eight cyclists. These two models show different behaviour and are both used to asses the performance of strategies. Also two physiological models were used. In the model provided in this study the resistances are calculated from the kinematics resulting from the evaluated strategy. The mechanical resistance model, including the aerodynamic interaction model calculates the power required to perform the strategy. The physiological model calculates the physiology during the race, which determines if the cyclists are able to sustain the prescribed strategy. Genetic algorithm optimisation is used to optimise the strategy parameters, such as initial position and times spend in first position. The velocity is optimised for each evaluated strategy configuration. A convergence test was performed to determine the parameters for the genetic algorithm, which are used in the optimisation of strategy. Using the standard strategy, where cyclists only change from first to last position, different orders are compared. From this study it was determined that the mean velocity over a 30 km team time trial could be raised by a maximum of 0.228 m/s by improving the order, depending on the model configuration. It was found that the best performing orders were those where the mean performance difference of following cyclists was lowest. Two different strategies have been assessed where cyclists still always change from first, but not necessarily last position have been assessed on their performance. With those more complex strategies the mean velocity could be increased with 0.358 m/s over a 30 km team time trial. The model still lacks validity, but gives a relevant insight in the performance of different team time trial strategies. The validity can either improved by using track test to validate the drag reduction coefficients or by using power data from a team time trial to show that the model predicts realistic physiology. Of those two methods the last is preferred.

Both coaches and cyclists nowadays determine the training schedules for important races based on experience and gut-feeling. Data are available for the evaluation of training schedules, but a sound scientific method of accomplishing this does not yet exist. The biggest hurdle in developing such a method for cycling is the absence of an objective performance metric. This report uses the data of six cyclists in order to introduce and analyze a new performance metric: the cycled critical power. Subsequently, this metric is used to fit the parameters of the Fitness-Fatigue model; a model that is successfully applied in other sports to model the performance of an athlete. Several training attributes are included in the analysis, such as duration, intensity, and load; with which the performance of the cyclists is modeled. The model, applied to the data, leads to correlations for the six cyclists with R2-values within the range of 0.13 - 0.56. Compared to other scientific research, this range indicates that the Fitness-Fatigue model could be a valuable tool for elite cyclists and coaches when constructing their training schedules, but further research into the performance metric and the application of the Fitness-Fatigue model is still required.

This exploration study focuses on the braking behaviour of World Tour cyclists during a descent. For this study, 8 riders descended over 6 trials using a ‘sensor bike’. As part of this particular study, a novel brake sensor is developed and validated in practice. The sensor package can be transferred between bicycles, allowing each rider to use his own bicycle. Results of the exploration study indicate that for the chosen descent, braking and cornering skills are not decisive for overall time over the descent. However, significant time differences are found for the corner which is analysed. Time difference reported over this corner are approximately one second. With a shortest cornering time of 10.14 seconds this is a significant difference. The brake behaviour of theWorld Tour cyclists could be split into two distinct braking strategies: • ’stop brake late’ ( where braking is stopped late and well into the turn). • ’stop brake early’ (where braking is stopped before or early in the turn). It was found in this study that the best brake strategy is ’stop brake late’. The peak brake forces measured during the descent are much lower than the maximum brake forces reported in literature. This implies, that performance can still be improved by increasing brake forces at the first part of the braking action, so the braking can start later. The results found in this study can contribute to optimise braking and cornering skills of riders by explaining and showing where improvements can be found. An additional result is that the brake sensor revealed unexpected brake rub at certain parts of the descent, especially when riders are accelerating. Eliminating this brake rub, can improve rider performance. The riders also shows clear signs of learning behaviour as can be observed in the obtained data. For future research it is recommended to add a speed sensor to the sensor package. Recommended small improvements for the brake sensor are identified. Using this improved sensor package, trials could be rehearsed on a steeper descent, such that pedalling actions have less influence, exploring braking and cornering strategies further.