Diferenciando Perfis de Corredores por Meio de Pontos de Mudança nos Treinos
Resumo
A corrida de rua vem atraindo cada vez mais praticantes em todo o mundo. Em paralelo, nota-se uma disseminação de abordagens baseadas em séries temporais e detecção de eventos na potencialização de desempenho no âmbito do esporte. Nesse contexto, esse artigo analisa e compara os pontos de mudança nas séries temporais de treinos individuais de corredores mais e menos experientes. Os resultados encontrados indicam uma diferença significativa (p < 0,05) entre as proporções de pontos de mudança para os dois níveis de experiência, fornecendo uma alternativa de indicador capaz de diferenciar perfis de atletas, que pode apoiar modelos de recomendação cada vez mais especialistas ao fornecer comentários personalizados para cada objetivo.
Palavras-chave:
estatísticas esportivas, corrida, séries temporais, pontos de mudança
Referências
Berndsen, J., Lawlor, A., and Smyth, B. (2017). Running with recommendation. In CEUR Workshop Proceedings, volume 1953, pages 18 – 21.
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.
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.
Publicado
14/10/2024
Como Citar
TITO, Nathália; PAIXÃO, Balthazar; TAVARES, Lucas G.; OGASAWARA, Eduardo; AMORIM, Glauco F..
Diferenciando Perfis de Corredores por Meio de Pontos de Mudança nos Treinos. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (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.
