Sport is evolving continuously and has taken great strides over the past few decades, from games played principally for enjoyment, to a competitive professionalised industry . Athletes participating in elite sports are exposed to increasingly higher training-loads, saturated competition calendars, and very short periods of rest and recovery . In a study conducted by Bengtsson et al , match congestion was associated with increased injury rates. Further studies have also shown that higher injury rates have a major effect on a team’s overall performance and final log standings [4, 5]. As such, reducing the amount of time lost through injury is extremely important.
Given the importance of player availability and overall team performance, there has been a surge in training-load and monitoring research in recent years . Evidence from this research suggests that poor training-load management and prescription is a major risk factor for injury . In fact, it is commonly viewed that training-load related injuries are, for the most part, preventable, and thus, sport science and sports medicine practitioners should address these issues by implementing monitoring protocols . These monitoring protocols need to address the issues of training-load management and prescription to improve performance, track readiness, and most importantly, to prevent injury.
One such protocol/method of training-load monitoring which has gained popularity over the last decade due to its versatility, is the ‘acute: chronic workload ratio’. This ratio allows practitioners to view a snapshot of an athlete’s training- and match-load history, thus, allowing practitioners to gauge their athletes’ readiness to compete, improve training periodisation, and act as a flagging value for injury risk.
What is the Acute: Chronic Workload Ratio?
In 1975, Banister et al  proposed that “the performance of an athlete in response to training can be estimated from the difference between a negative function (‘fatigue’) and a positive function (‘fitness’).” Building on Banister’s model, it was later suggested that the ideal training stimulus is one that maximises performance by utilising an appropriate training load, whilst simultaneously limiting the negative consequences of training (i.e. injury and fatigue) . Therefore, it is important for practitioners to understand and monitor training-load so that they can gauge their athletes past and present fitness levels. Or, in other words, what they have previously done and are prepared for. The relationship between what they have done, and what they are prepared for, can be examined via the use of the acute: chronic workload ratio (ACWR).
Typically, this is the workload performed by an athlete in 1-week (7 days) . This value contains both training- and match-load information over this 7-day period. It is this figure which is represented as the ‘fatigue’ aspect of the ACWR.
For example, a common method for calculating workload is by multiplying the session-rating of perceived exertion (sRPE) by session duration. Thus, if an athlete reported a sRPE of 6 and trained for 100 minutes, the athlete’s workload for the day would be 600 arbitrary units (AU) (6 * 100 = 600). If the athlete trained twice in one day (e.g. a technical session and a gym session), then the workload for both of these sessions would be added together to calculate the acute workload for that given day (e.g. 600 + 800 = 1400 AU).
This process would need to be replicated for each athlete, and for every training and match day. The final ‘acute workload’ value and interpretation of the data will vary according to type of ACWR model the practitioner wishes to use (e.g. The Rolling Average Model or the Exponentially Weight Moving Average Model).
The chronic workload is typically the 4-week (28 day) average acute workload . This value is important as it provides a clear indication of what an athlete has done leading up to the present training or match day. Therefore, it is commonly viewed as an indication of an athlete’s ‘fitness’.
For example, let’s suggest an athlete had a weekly average (acute) workload consisting of the following:
- Week 1 = 1400 AU
- Week 2 = 1200 AU
- Week 3 = 1800 AU
- Week 4 = 1600 AU
In this case, the 4-week chronic workload value would be the average of these four workloads (1400 + 1200 + 1800 + 1600 / 4 = 1500 AU). This is a simple example of what the 4-week chronic workload represents, but it is important to understand that a 3-week (21 day) chronic workload value is also commonly used .
Similarly, to the acute workload, the exact calculation of the chronic workload, and its dynamic day-to-day value, will depend on the type of ACWR model that is being utilised by the practitioner (e.g. the Rolling Average Model or the Exponentially Weight Moving Average Model).
Acute: Chronic Workload Ratio
The ratio itself is calculated by dividing the acute workload (fatigue) by the chronic workload (fitness). For example, an acute workload of 1400 AU may be divided by a chronic workload of 1500 AU, providing an ACWR of 0.93 (1400 / 1500 = 0.93).
Generally, in team sports such as soccer, which has regular fixtures (Saturday to Saturday), the acute workload is the training load performed by an athlete in 1 week, and the chronic workload is the 4-week average acute workload (as stated above) . Having said this, it is important to note that these periods can be altered according to the calendar associated with that sport.
For example, this relationship was recently investigated in AFL, and it was found that a 3:21 day ratio best described non-contact injury occurrence . However, the investigators concluded by stating that “the best choices of acute and chronic time windows may need to be identified sport by sport, or team by team, and it may depend on the specific structure of an athlete’s competition and training schedule.” Thus, further research is required for this aspect of the ACWR.
Comparison of the acute workload to the chronic workload as a ratio, is therefore, a dynamic representation of a player’s preparedness . This ratio allows practitioners to consider the training-load the athlete has performed recently (within the last training week) relative to the training-load the athlete has prepared for (within the last four weeks).