SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Gabriel Padrão(gabriel.padrao@aluno.cefet-rj.br)
2 Matheus Melo(matheus.melo@aluno.cefet-rj.br)
3 Ana Gabriela de Araújo(ana.araujo.4@aluno.cefet-rj.br)
4 Juliano Spineti(juliano.spineti@fluminense.com.br)
5 Lucas Tavares(lucas.giusti@aluno.cefet-rj.br)
6 Diego Brandão(diego.brandao@cefet-rj.br)
7 Jorge Soares(jorge.soares@cefet-rj.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Andrade, R. et al. [2020]. “Is the Acute: Chronic Workload Ratio (ACWR) Associated with Risk of Time-Loss Injury in Professional Team Sports? A Systematic Review of Methodology, Variables and Injury Risk in Practical Situations”. Em: Sports Medicine 50.9, pp. 1613–1635.
2 Bengtsson, H. et al. [2013]. “Muscle injury rates in professional football increase with fixture congestion: An 11-year follow-up of the UEFA Champions League injury study”. Em: British Journal of Sports Medicine 47.12, pp. 743–747.
3 Bowen, L. et al. [2020]. “Spikes in acute:chronic workload ratio (ACWR) associated with a 5–7 times greater injury rate in English Premier League football players: a comprehensive 3-year study”. Em: British Journal of Sports Medicine, pp. 731–738.
4 Carey, D. et al. [2016]. “Training loads and injury risk in Australian football-differing acute: Chronic workload ratios influence match injury risk”. Em: British Journal of Sports Medicine.
5 Delecroix, B. et al. [2018]. “Workload and non-contact injury incidence in elite football players competing in European leagues”. Em: European Journal of Sport Science 18.9, pp. 1280–1287.
6 Di Credico, A. et al. [2021]. “The prediction of running velocity during the 30–15 intermittent fitness test using accelerometry-derived metrics and physiological parameters: A machine learning approach”. Em: Intern. J. of Environmental Research and Public Health.
7 Dwyer, D. B. e Gabbett, Tim J. [2012]. “Global positioning system data analysis: velocity ranges and a new definition of sprinting for field sport athletes”. Em: Journal of Strength and Conditioning Research 26.3, pp. 818–824.
8 Eetvelde, H. V. et al. [2021]. “Machine learning methods in sport injury prediction and prevention: a systematic review”. Em: Journal of Experimental Orthopaedics 8.1.
9 Gabbett, T. J. [2016]. “The training-injury prevention paradox: should athletes be training smarter and harder?” Em: British Journal of Sports Medicine 50, pp. 273–280.
10 Jiang, Z. et al. [2022]. “A Systematic Review of the Relationship between Workload and Injury Risk of Professional Male Soccer Players”. Em: Inter. J. of Environmental Research and Public Health, p. 13237.
11 Malone, J. J. et al. [2015]. “Seasonal training-load quantification in elite English Premier League soccer players”. Em: Inter. J. of Sports Physiology and Performance, pp. 489–497.
12 Melo, M. et al. [2024]. “Data-Centric AI for predicting non-contact injuries in professional soccer players”. Em: XXXIX Simp´osio Brasileiro de Bancos de Dados, pp. 167–180.
13 Piłka, T. et al. [2023]. “Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors”. Em: Sensors 23.3.
14 Rossi, A. et al. [2018]. “Effective injury forecasting in soccer with GPS training data and machine learning”. Em: PLoS ONE 13.7.
15 Salces, J.N. et al. [2014]. “An examination of injuries in Spanish Professional Soccer League”. Em: The Journal of sports medicine and physical fitness 6, pp. 765–771.
16 Vallance, E. et al. [2020]. “Combining internal- and external-training-loads to predict non-contact injuries in soccer”. Em: Applied Sciences 10.15.