Epidemiology of Football Injuries of the German Bundesliga: A Media-Based, Prospective Analysis over 7 Consecutive Seasons

Abstract

Abstract Background This study describes the implementation of a standardised, prospective injury database covering the entire 1st male German football league (“Bundesliga”) based on publicly available media data. For the first time, various media sources were used simultaneously as the external validity of media-generated data was low in the past compared to data obtained by way of the “gold standard”, i.e. by the teams’ medical staffs. Methods The study covers 7 consecutive seasons (2014/15–2020/21). The primary data source was the online version of the sport-specific journal “kicker Sportmagazin™” complemented by further publicly available media data. Injury data collection followed the Fuller consensus statement on football injury studies. Results During the 7 seasons, 6653 injuries occurred, thereof 3821 in training and 2832 in matches. The injury incidence rates (IRs) per 1000 football hours were 5.5 [95% CI 5.3–5.6], 25.9 [25.0–26.9] per 1000 match, and 3.4 [3.3–3.6] per 1000 training hours. Twenty-four per cent of the injuries (n = 1569, IR 1.3 [1.2–1.4]) affected the thigh, 15% (n = 1023, IR 0.8 [0.8–0.9]) the knee, and 13% (n = 856, IR 0.7 [0.7–0.8]) the ankle. Muscle/tendon injuries contributed 49% (n = 3288, IR 2.7 [2.6–2.8]), joint/ligament injuries 17% (n = 1152, IR 0.9 [0.9–1.0]), and contusions 13% (n = 855, IR 0.7 [0.7–0.8]). Compared to studies using injury reports from the clubs’ medical staff, media data revealed similar proportional distributions of the injuries, but the IRs tended towards the lower end. Obtaining specific locations or diagnosis especially with regard to minor injuries is difficult. Conclusions Media data are convenient for investigating the quantity of injuries of an entire league, for identifying injuries for further subanalysis, and for analysing complex injuries. Future studies will focus on the identification of inter- and intraseasonal trends, players' individual injury histories, and risk factors for subsequent injuries. Furthermore, these data will be used in a complex system approach for developing a clinical decision support system, e.g. for return to play decisions.