1 18 CHAPTER 1 In Chapter 3, we studied the predictability of physical performance in elite soccer matches using various physical performance measures and machine learning techniques. Match data was collected from 302 matches in elite soccer throughout one season. Semi-automatic multiple-camera video technology, the SportsVU optical tracking system, recorded each player’s position over time. The individual positions in time translated into three increasingly sensitive physical performance measures, i.e. distance covered, distance in speed category, and energy expenditure in power category. These physical performance measures were used in different machine learning models to identify and predict the physical performance of individual players throughout an elite soccer match. In Chapter 4, we investigated the effect of substitution in soccer on a team’s physical performance and the suitability of following the causal roadmap and a causal model in this context. A causal model of the relationships between substitution variables and the soccer team’s physical performance was created as an essential step in following the causal roadmap. The causal model included variables such as the number of substitutions made by a team, the moment of the substitution and the soccer team’s total distance covered. We used the causal model to identify the assumptions needed to infer causality from the data and the potential sources of bias and confounding that may affect the causal effect estimates. We also provided an in-depth analysis of statistical methods. We evaluated the accuracy of estimating the impact of substitutions on a football team’s physical performance using synthetically generated position and substitution data and data from the SportsVU optical tracking system. We compared the accuracy of the TMLE and generalized linear model using the complete data set versus the accuracy of these models when a crucial variable was removed from the data set. The difference in accuracy between the two methods indicated whether a more robust statistical method such as TMLE could provide more accurate insight into the effect of substitutions on football team physical performance when a crucial variable is absent, compared to a traditional generalized linear model. In Chapter 5, we investigated the relationship between training characteristics and injuries in competitive runners while evaluating the feasibility of using statistical analysis and exploring the potential of machine learning. The dataset comprised test, training, and injury log data collected from individual competitive runners and their coach. We used the log data on multiple physical load measures, such as training intensity and rate of perceived exertion, to construct the physical performance measure acute:chronic workload ratio. Traditionally running injuries have a complex origin, and datasets lack relevant confounding variables that can provide insight into injury risk
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