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English Information

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Authors
# Name
1 Matheus Melo(matheus.melo@aluno.cefet-rj.br)
2 Matheus Maia(matheus.vieira.2@aluno.cefet-rj.br)
3 Gabriel Padrão(gabriel.padrao@aluno.cefet-rj.br)
4 Diego Brandão(diego.brandao@cefet-rj.br)
5 Eduardo Bezerra(ebezerra@cefet-rj.br)
6 Juliano Spineti(juliano.spineti@fluminense.com.br)
7 Lucas Tavares(lucas.giusti@aluno.cefet-rj.br)
8 Jorge Soares(jorge.soares@cefet-rj.br)

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Reference
# Reference
1 Dandrieux, P.-E., Tondut, J., Nagahara, R., Mendiguchia, J., Morin, J.-B., Lahti, J., Ley, C., Edouard, P., and Navarro, L. (2023). Prédiction des blessures des ischiojambiers en football à l’aide d’apprentissage automatique: étude préliminaire sur 284footballeurs. Journal de Traumatologie du Sport, 40(2):69–73.
2 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).
3 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.
4 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).
5 Fiscutean, A. (2021). Data scientists are predicting sports injuries with an algorithm. Nature, 592(7852):S10–S11.
6 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).
7 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):738–42.
8 Jarrahi, M. H., Memariani, A., and Guha, S. (2023). The Principles of Data-Centric AI. Communications of the ACM, 66(8):84–92.
9 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
10 Kirkendall, D. T. and Dvorak, J. (2010). Effective injury prevention in soccer. Physician and Sportsmedicine, 38(1):147–157.
11 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.
12 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).
13 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).
14 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.
15 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.
16 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).
17 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.
18 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.
19 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.
20 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).