Editorial

team sporting events, sports teams are dy-namic cohorts where athletes are exposed to game-time injury risk only when on the playing fi eld. Assuming no other problems with the study methodology, the unbiased estimate for injury rates is calculated us-ing individual-level data for injuries and exposure times,1–5 and a statistical model that is appropriate for the underlying data distribution and heterogeneity of the in-jury rates (eg, Poisson, negative binomial, quasi-Poisson).6 These data can be used to (1) answer general questions such as overall injury rates per game or per hour of game play, (2) investigate specifi c risk factors for injury by including individual-level covari-ates (eg, athlete’s age, previous injury) or (3) calculate quantile injury rates that answer questions like ‘what is the probability that a player will have three or fewer injuries?’6 Although calculations using individual-level exposure time is optimal, capturing exact minutes of play may be diffi cult or impossible, such as with investigations at the high school level or in large, central source databases. In such cases, risk factor analysis based on athlete-level characteris-tics becomes extremely problematic. How-ever, it remains possible to assess overall injury rates and risk factors if one estimates the time of exposure at the team level. Here, sports injury epidemiologists have used different methods to quantify expo-sure time. As with any study of incidence rates, different choices for the denomina-tor in the rate calculation may be a neces-sary consequence of the particular research question3 7 (eg, injuries per minute of play vs injuries per game of play). However, a commonly used method in sports injury surveillance studies leads to a systematic under-representation of game injury rates as it overestimates the time at risk. This bias is especially apparent when comparing injury rates across sports and may alter the comparison of injury rates between games and practices within single sports. The purpose of this study is to compare and contrast injury rates calculated via two common methods for estimating game exposure time when individual-level data are unavailable. This will be shown fi rst Injury rates in team sport events: tackling challenges in assessing exposure tim