Distinguishing Runner Profiles Through Change Points in Training
Abstract
Street running has been attracting more practitioners worldwide. Concurrently, there is a noticeable spread of approaches based on time series and event detection in enhancing sports performance. In this context, this article analyzes and compares the points of change in the time series of individual training for more and less experienced runners. The results indicate a significant difference (p < 0.05) between the proportions of change points for the two experience levels, providing an alternative indicator capable of differentiating athlete profiles, which can support increasingly specialized recommendation models by offering personalized feedback for each goal.
Keywords:
sports statistics, running, time series, change points
References
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Corbett, D., Sweeting, A., and Robertson, S. (2019). A change point approach to analysing the match activity profiles of team-sport athletes. Journal of Sports Sciences, 37.
El-Kassabi, H. T., Khalil, K., and Serhani, M. A. (2020). Deep learning approach for forecasting athletes’ performance in sports tournaments. In ACM International Conference Proceeding Series, pages 203 – 208.
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Hespanhol Junior, L., Pillay, J., and van Mechelen, W. (2015). Meta-analyses of the effects of habitual running on indices of health in physically inactive adults. Sports Medicine, 45:1455–1468.
Komitova, R., Raabe, D., Rein, R., and Memmert, D. (2022). Time Series Data Mining for Sport Data: a Review. IJCSS, 21(2):17 – 31.
Mo, S. and Chow, D. H. (2019). Reliability of the fluctuations within the stride time series measured in runners during treadmill running to exhaustion. Gait and P., 74:1 – 6.
Parra-Camacho, D., González-Serrano, M. H., González-García, R. J., and Calabuig Moreno, F. (2019). Sporting habits of urban runners: Classification according to their motivation. International Journal of Environmental Research and Public Health, 16(24).
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Smyth, B., Lawlor, A., Berndsen, J., and Feely, C. (2022). Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners. User Modeling and User-Adapted Interaction, 32(5):787 – 838.
Takeuchi, J. and Yamanishi, K. (2006). A unifying framework for detecting outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering, 18(4):482–492.
Teune, B., Woods, C., Sweeting, A., Inness, M., and Robertson, S. (2022). A method to inform team sport training activity duration with change point analysis. PLOS ONE, 17(3):1–11.
Thuany, M., Vieira, D., Villiger, E., Gomes, T. N., Weiss, K., Nikolaidis, P. T., Sousa, C. V., Scheer, V., and Knechtle, B. (2023). An analysis of the São Silvestre race between 2007–2021: An increase in participation but a decrease in performance. SMHS, 5(4):277 – 282.
Van den Berghe, P., Gosseries, M., Gerlo, J., Lenoir, M., Leman, M., and De Clercq, D. (2020). Change-point detection of peak tibial acceleration in overground running retraining. Sensors, 20(6).
Yong, W., Lingyun, P., and Jia, W. (2020). Statistical analysis and ARMA modeling for the big data of marathon score. Science and Sports, 35(6):375 – 385.
Corbett, D., Sweeting, A., and Robertson, S. (2019). A change point approach to analysing the match activity profiles of team-sport athletes. Journal of Sports Sciences, 37.
El-Kassabi, H. T., Khalil, K., and Serhani, M. A. (2020). Deep learning approach for forecasting athletes’ performance in sports tournaments. In ACM International Conference Proceeding Series, pages 203 – 208.
Ely, M. R., Martin, D. E., Cheuvront, S. N., and Montain, S. J. (2008). Effect of ambient temperature on marathon pacing is dependent on runner ability. MSSE, 40(9):1675 – 1680.
Gómez-Molina, J., Ogueta-Alday, A., Camara, J., Stickley, C., Rodríguez-Marroyo, J. A., and García-López, J. (2017). Predictive variables of half-marathon performance for male runners. Journal of Sports Science and Medicine, 16(2):187 – 194.
Hespanhol Junior, L., Pillay, J., and van Mechelen, W. (2015). Meta-analyses of the effects of habitual running on indices of health in physically inactive adults. Sports Medicine, 45:1455–1468.
Komitova, R., Raabe, D., Rein, R., and Memmert, D. (2022). Time Series Data Mining for Sport Data: a Review. IJCSS, 21(2):17 – 31.
Mo, S. and Chow, D. H. (2019). Reliability of the fluctuations within the stride time series measured in runners during treadmill running to exhaustion. Gait and P., 74:1 – 6.
Parra-Camacho, D., González-Serrano, M. H., González-García, R. J., and Calabuig Moreno, F. (2019). Sporting habits of urban runners: Classification according to their motivation. International Journal of Environmental Research and Public Health, 16(24).
Salles, R., Escobar, L., Baroni, L., Zorrilla, R., Ziviani, A., Kreischer, V., Delicato, F., Pires, P. F., Maia, L., Coutinho, R., Assis, L., and Ogasawara, E. (2020). Harbinger: Um framework para integração e análise de métodos de detecção de eventos em séries temporais. In Anais do Simpósio Brasileiro de Banco de Dados, pages 73–84. SBC.
Smyth, B., Lawlor, A., Berndsen, J., and Feely, C. (2022). Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners. User Modeling and User-Adapted Interaction, 32(5):787 – 838.
Takeuchi, J. and Yamanishi, K. (2006). A unifying framework for detecting outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering, 18(4):482–492.
Teune, B., Woods, C., Sweeting, A., Inness, M., and Robertson, S. (2022). A method to inform team sport training activity duration with change point analysis. PLOS ONE, 17(3):1–11.
Thuany, M., Vieira, D., Villiger, E., Gomes, T. N., Weiss, K., Nikolaidis, P. T., Sousa, C. V., Scheer, V., and Knechtle, B. (2023). An analysis of the São Silvestre race between 2007–2021: An increase in participation but a decrease in performance. SMHS, 5(4):277 – 282.
Van den Berghe, P., Gosseries, M., Gerlo, J., Lenoir, M., Leman, M., and De Clercq, D. (2020). Change-point detection of peak tibial acceleration in overground running retraining. Sensors, 20(6).
Yong, W., Lingyun, P., and Jia, W. (2020). Statistical analysis and ARMA modeling for the big data of marathon score. Science and Sports, 35(6):375 – 385.
Published
2024-10-14
How to Cite
TITO, Nathália; PAIXÃO, Balthazar; TAVARES, Lucas G.; OGASAWARA, Eduardo; AMORIM, Glauco F..
Distinguishing Runner Profiles Through Change Points in Training. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 834-840.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.243205.
