Stoter, M. (Matthijs) (2020) Are they in sync? Studying the alignment of coaches and athletes using machine learning in short track speed skating. thesis, Sport Sciences.
Full text available on request.Abstract
Objective. Maximizing performance is a key determinant for athletes to be victorious. Consequently, coaches design training programs consisting of a variety of training types to optimally prepare their athletes. However, previous studies have shown an apparent misalignment between coaches’ intentions and athletes’ perceptions. In a highly intermittent sport such as short track speed skating, minimizing this misalignment is of vital importance to ensure maximum performance levels. To investigate this misalignment, Machine Learning (ML) techniques were used to compare physiological responses between different training types, possibly revealing unique training type characteristics and allowing coaches to optimize their training programs design. Therefore, training types were predicted by classification models using training load parameters. Methods. Heart rate data, lap times and session Rating of Perceived Exertion (sRPE) were collected from 17 elite short track speed skaters during the 2019-2020 season. A Kruskal-Wallis test with Dunn post hoc test was performed to analyze differences between the short track speed skating specific TRaining IMPulse Short Track (TRIMPST) and the speed zone-based Speed Score between training types. Using TRIMPST and Speed Score along additional training load parameters, a K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Random Forest Classifier (RFC) and Logistic Regression (LR) were computed. These classification models were used to predict training type using internal and external training load parameters. The models were evaluated using accuracy, precision, recall and F1 scores. Results. A Kruskal-Wallis test showed significant differences in TRIMPST and Speed Score between training types, 2(6) = 86,37, p<0.001 and 2(6) = 126,71, p<0.001, respectively. Accuracy scores for the classification models for external load were 0.75, 0.75, 0.71 and 0.49 for the KNN, RFC, DTC and LR models, respectively. Accuracy scores for the classification models for internal load were 0.37, 0.44, 0.38 and 0.46 for the KNN, RFC, DTC and LR models, respectively. The classification models using external load parameters performed well in predicting the training types extensive interval (EXT INT) and intensive interval (INT INT), whereas the classification models using internal load parameters performed well in predicting the training type extensive endurance (EXT END). Conclusion. Our approach opens a novel perspective on training program evaluation and training load monitoring, and supports coaches in their alignment with their athletes in short track speed skating. Using external training load parameters, training types can be accurately predicted. However, our findings show that training types cannot be accurately predicted using internal load parameters. Furthermore, since there was high internal intraclass variance in the internal load parameters, physiological responses to training load appear to be highly individual. Since the internal and external load classification models are unable to predict training types in a similarly accurate fashion, there appears to be a misalignment between coaches and athletes. It appears more intense training sessions cannot be accurately predicted using internal load measures. Coaches’ intentions might therefore not be in sync with their athletes’ perceptions. Keywords: Machine learning, short track speed skating, internal load, external load, classification
| Item Type: | Thesis (UNSPECIFIED) |
|---|---|
| Supervisor name: | Kempe, dr. M. and Laat, B. de |
| Faculty: | Medical Sciences |
| Date Deposited: | 20 May 2022 09:46 |
| Last Modified: | 20 May 2022 09:48 |
| URI: | https://umcg.studenttheses.ub.rug.nl/id/eprint/3378 |
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