Data-Centric AI for predicting non-contact injuries in professional soccer players
Resumo
One big concern in soccer professional teams is to search for preventive measures to reduce the frequency of harmful episodes in their athletes since these episodes greatly impact the sports industry and affect both the team’s performance and the association’s economic situation. Thus, the present work proposes a methodology to predict non-contact injury episodes that may affect them in a microcycle through Data-centric AI concepts. The prediction model is trained using a dataset related to professional soccer athletes. The most interesting result were with AUC-ROC of 79,8%. About the performance improvement strategies applied, the best undersampling ratio was 70/30, PCA with one or two principal components did best, and the Decision Tree algorithm excelled.
Palavras-chave:
Professional soccer, Injury prediction, Machine learning, Sports injuries, Data Science, Data-Centric AI
Referências
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Eetvelde, H., De Michelis Mendonça, L., Ley, C., Seil, R., and Tischer, T. (2021). Machine learning methods in sport injury prediction and prevention: a systematic review. Journal of Experimental Orthopaedics, 8(1).
Ekstrand, J., Spreco, A., Bengtsson, H., and Bahr, R. (2021). Injury rates decreased in men’s professional football: An 18-year prospective cohort study of almost 12 000 injuries sustained during 1.8 million hours of play. British Journal of Sports Medicine, 55(19):1084–1091.
Fernández Cuevas, I., Carmona, P., Quintana, M., Salces, J., Arnaiz-Lastras, J., and Barrón, A. (2010). Economic costs estimation of soccer injuries in first and second spanish division professional teams. In Proceedings of the 15th Annual Congress of the European College of Sport Sciences (ECSS).
Fiscutean, A. (2021). Data scientists are predicting sports injuries with an algorithm. Nature, 592(7852):S10–S11.
Giusti, L., Carvalho, L., Gomes, A. T. A., Coutinho, R., de Abreu Soares, J., and Ogasawara, E. S. (2022). Analyzing flight delay prediction under concept drift. Evolving Systems, (0123456789).
Hägglund, M., Waldén, M., Hedevik, H., Kristenson, K., Bengtsson, H., and Ekstrand, J. (2013). Injuries affect team performance negatively in professional football: An 11-year follow-up of the UEFA Champions League injury study. British Journal of Sports Medicine, 47(12):738–742.
Jarrahi, M. H., Memariani, A., and Guha, S. (2023). The Principles of Data-Centric AI. Communications of the ACM, 66(8):84–92.
Jauhiainen, S., Kauppi, J.-P., Krosshaug, T., Bahr, R., Bartsch, J., and Äyrämö, S. (2022). Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes. American Journal of Sports Medicine, 50(11):2917–2924.
Kirkendall, D. T. and Dvorak, J. (2010). Effective injury prevention in soccer. Physician and Sportsmedicine, 38(1):147–157.
Kolodziej, M., Groll, A., Nolte, K., Willwacher, S., Alt, T., Schmidt, M., and Jaitner, T. (2023). Predictive modeling of lower extremity injury risk in male elite youth soccer players using least absolute shrinkage and selection operator regression. Scandinavian Journal of Medicine and Science in Sports, (February 2022):1–13.
Majumdar, A., Bakirov, R., Hodges, D., Scott, S., and Rees, T. (2022). Machine Learning for Understanding and Predicting Injuries in Football. Sports Medicine - Open, 8(1).
Martins, F., Przednowek, K., França, C., Lopes, H., Nascimento, M., Sarmento, H., Marques, A., Ihle, A., Henriques, J., and Gouveia, E. (2022). Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players. Journal of Clinical Medicine, 11(16).
Page, M., Mckenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., Mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S., Chou, R., Glanville, J., Grimshaw, J., Hróbjartsson, A., Lalu, M., Li, T., Loder, E., Mayo-Wilson, E., Mcdonald, S., and Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ, 372.
Pfirrmann, D., Herbst, M., Ingelfinger, P., Simon, P., and Botzenhardt, S. (2016). Analysis of injury incidences in male professional adult and elite youth soccer players: A systematic review. Journal of Athletic Training, 51(5):410–424.
Pilka, T., Grzelak, B., Aleksandra, S., Górecki, T., and Dyczkowski, K. (2023). Predicting injuries in football based on data collected from gps-based wearable sensors. Sensors, 23(3).
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F., Fernández, J., and Medina, D. (2018). Effective injury forecasting in soccer with gps training data and machine learning. PloS one, 13(7):e0201264.
Rossi, A., Pappalardo, L., Filetti, C., and Cintia, P. (2022). Blood sample profile helps to injury forecasting in elite soccer players. Sport Sciences for Health, 19(1):285–296.
Studnicka, A. (2020). The emergence of wearable technology and the legal implications for athletes, teams, leagues and other sports organizations across amateur and professional athletics. DePaul J. Sports L., 16:i.
Vallance, E., Sutton-Charani, N., Imoussaten, A., Montmain, J., and Perrey, S. (2020). Combining internal- and external-training-loads to predict non-contact injuries in soccer. Applied Sciences (Switzerland), 10(15).
Eetvelde, H., De Michelis Mendonça, L., Ley, C., Seil, R., and Tischer, T. (2021). Machine learning methods in sport injury prediction and prevention: a systematic review. Journal of Experimental Orthopaedics, 8(1).
Ekstrand, J., Spreco, A., Bengtsson, H., and Bahr, R. (2021). Injury rates decreased in men’s professional football: An 18-year prospective cohort study of almost 12 000 injuries sustained during 1.8 million hours of play. British Journal of Sports Medicine, 55(19):1084–1091.
Fernández Cuevas, I., Carmona, P., Quintana, M., Salces, J., Arnaiz-Lastras, J., and Barrón, A. (2010). Economic costs estimation of soccer injuries in first and second spanish division professional teams. In Proceedings of the 15th Annual Congress of the European College of Sport Sciences (ECSS).
Fiscutean, A. (2021). Data scientists are predicting sports injuries with an algorithm. Nature, 592(7852):S10–S11.
Giusti, L., Carvalho, L., Gomes, A. T. A., Coutinho, R., de Abreu Soares, J., and Ogasawara, E. S. (2022). Analyzing flight delay prediction under concept drift. Evolving Systems, (0123456789).
Hägglund, M., Waldén, M., Hedevik, H., Kristenson, K., Bengtsson, H., and Ekstrand, J. (2013). Injuries affect team performance negatively in professional football: An 11-year follow-up of the UEFA Champions League injury study. British Journal of Sports Medicine, 47(12):738–742.
Jarrahi, M. H., Memariani, A., and Guha, S. (2023). The Principles of Data-Centric AI. Communications of the ACM, 66(8):84–92.
Jauhiainen, S., Kauppi, J.-P., Krosshaug, T., Bahr, R., Bartsch, J., and Äyrämö, S. (2022). Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes. American Journal of Sports Medicine, 50(11):2917–2924.
Kirkendall, D. T. and Dvorak, J. (2010). Effective injury prevention in soccer. Physician and Sportsmedicine, 38(1):147–157.
Kolodziej, M., Groll, A., Nolte, K., Willwacher, S., Alt, T., Schmidt, M., and Jaitner, T. (2023). Predictive modeling of lower extremity injury risk in male elite youth soccer players using least absolute shrinkage and selection operator regression. Scandinavian Journal of Medicine and Science in Sports, (February 2022):1–13.
Majumdar, A., Bakirov, R., Hodges, D., Scott, S., and Rees, T. (2022). Machine Learning for Understanding and Predicting Injuries in Football. Sports Medicine - Open, 8(1).
Martins, F., Przednowek, K., França, C., Lopes, H., Nascimento, M., Sarmento, H., Marques, A., Ihle, A., Henriques, J., and Gouveia, E. (2022). Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players. Journal of Clinical Medicine, 11(16).
Page, M., Mckenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., Mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S., Chou, R., Glanville, J., Grimshaw, J., Hróbjartsson, A., Lalu, M., Li, T., Loder, E., Mayo-Wilson, E., Mcdonald, S., and Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ, 372.
Pfirrmann, D., Herbst, M., Ingelfinger, P., Simon, P., and Botzenhardt, S. (2016). Analysis of injury incidences in male professional adult and elite youth soccer players: A systematic review. Journal of Athletic Training, 51(5):410–424.
Pilka, T., Grzelak, B., Aleksandra, S., Górecki, T., and Dyczkowski, K. (2023). Predicting injuries in football based on data collected from gps-based wearable sensors. Sensors, 23(3).
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F., Fernández, J., and Medina, D. (2018). Effective injury forecasting in soccer with gps training data and machine learning. PloS one, 13(7):e0201264.
Rossi, A., Pappalardo, L., Filetti, C., and Cintia, P. (2022). Blood sample profile helps to injury forecasting in elite soccer players. Sport Sciences for Health, 19(1):285–296.
Studnicka, A. (2020). The emergence of wearable technology and the legal implications for athletes, teams, leagues and other sports organizations across amateur and professional athletics. DePaul J. Sports L., 16:i.
Vallance, E., Sutton-Charani, N., Imoussaten, A., Montmain, J., and Perrey, S. (2020). Combining internal- and external-training-loads to predict non-contact injuries in soccer. Applied Sciences (Switzerland), 10(15).
Publicado
14/10/2024
Como Citar
MELO, Matheus; MAIA, Matheus; PADRÃO, Gabriel; BRANDÃO, Diego; BEZERRA, Eduardo; SPINETI, Juliano; GIUSTI, Lucas; SOARES, Jorge.
Data-Centric AI for predicting non-contact injuries in professional soccer players. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 167-180.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240518.
