Blanch and Gabbett (2016) demonstrated that the acute:chronic workload ratio explains 53% of likelihood of subsequent injury via a curved relationship. They stated that each type of loading for different sports will most likely require its own specific model. Their modelling clearly demonstrates that if athletes perform greater workloads than they are prepared for, they are more likely to sustain an injury, while acknowledging the strong probability that other factors such as age, training history and injury history will shift the curve up or down.
As with any modelling, the model will become more accurate with more data over time and may be specific to the players who provided the data. This will benefit a team that retains a core of players and fitness coaches over a number of years (as many successful teams do) as they should have a rather precise estimate of risk relative to training loads. The authors advocate that the acute:chronic workload ratio should be applied to variables that are specific to the athlete, sport and injury mechanism, i.e., simple and valid metrics should be examined before complicating the analysis.
Blanch P, Gabbett TJ. Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player’s risk of subsequent injury. Br J Sports Med 2016; 50: 471–475.