Acute chronic workload ratio (ACWR): how to calculate and read it
The acute chronic workload ratio (ACWR) compares an athlete's load over the last week with what they are used to from the weeks before. This guide shows how the ratio is calculated, where the well-known thresholds come from and what studies say about how much it can tell you.
The ACWR divides acute load from the last week by chronic load, the average of the last four weeks.
Rolling average or EWMA, coupled or uncoupled: the calculation method changes the value for the same week.
The thresholds of 0.8 to 1.3 and 1.5 come from a few team sports and are not universal.
In studies, a high ACWR was linked to more injuries at group level. For individual athletes, it does not work as a forecast.
It shows how current load compares with usual load: a prompt for a conversation between coach and athlete.
By default, Tletify uses an uncoupled EWMA over 7 and 28 days. Only coaches with Pro or Team see the value.
Definition
Acute chronic workload ratio: what the ACWR measures
The ACWR compares two periods of the same load measure, such as training load from duration times RPE in AU (arbitrary units). Acute load is the load of the last week. Chronic load is the average of the last four weeks and stands for what an athlete is used to. Dividing acute load by chronic load gives you the ACWR.
ACWR = acute load ÷ chronic load acute load = load of the last 7 days (AU) chronic load = average weekly load of the last 4 weeks (AU)
Hulin et al. (2014) introduced the calculation in a study of 28 cricket fast bowlers, with one week as acute load and the four-week rolling average as chronic load. Large spikes in acute load went along with more injuries.
Hulin et al. (2016) then examined the ratio in 53 rugby league players over two seasons. Values above 1 mean more acute than chronic load, values below 1 less. An ACWR of 1.5 means the last week was 50 percent above the four-week average.
Calculation methods
How to calculate the ACWR: rolling average, EWMA and coupling
There are two common ways to calculate chronic load. A rolling average weights every day in the window equally. Williams et al. (2017) proposed an exponentially weighted moving average (EWMA) instead: every day counts, but the further back it lies, the less it counts. In a study by Murray et al. (2017), the EWMA picked up the link between high values and injuries more sensitively.
Then there is coupling. Coupled means the current week is also part of the chronic average, so it sits in both the numerator and the denominator. Lolli et al. (2019a) showed that this creates spurious correlation. Uncoupled compares the current week only with the weeks before. In the review by Andrade et al. (2020), 95 percent of studies used the coupled calculation.
Item
Value
Calculation
Week 1
2,000 AU
chronic window
Week 2
2,200 AU
chronic window
Week 3
2,100 AU
chronic window
Week 4
2,300 AU
chronic window
Week 5 (current)
3,000 AU
acute load
Chronic load
2,150 AU
(2,000 + 2,200 + 2,100 + 2,300) ÷ 4
ACWR
1.40
3,000 ÷ 2,150 (rounded)
Worked example with a rolling average, uncoupled: the current week is not part of the chronic average. Weekly load in AU.
Calculated coupled, with the current week inside the chronic window (weeks 2 to 5), the chronic average would be 2,400 AU and the ACWR 1.25. The same week then lands once above and once below the 1.3 threshold covered in the next section. So only compare values that were calculated the same way.
Thresholds
Where the ACWR thresholds of 0.8, 1.3 and 1.5 come from
The best-known numbers go back to Gabbett (2016). He called values between 0.8 and 1.3 the training “sweet spot” and values of 1.5 and above the “danger zone.” He took the figure from Blanch and Gabbett (2016), and the data behind it come from cricket, Australian football and rugby league.
In the same paper, Gabbett argued that athletes with high loads they are used to have fewer injuries, and that excessive and rapid increases in load are the more likely problem. In line with this, players with a high chronic load in Hulin et al. (2016) were more resistant to injury at values between 0.85 and 1.35.
Gabbett added his own caveat: other sports may show different relationships between load and injuries, and until more data are available, the recommendations should be applied to athletes in individual sports with caution. The thresholds are observations from a few team sports, not universal limits.
Criticism
Criticism of the ACWR: statistics, method and missing evidence
Menaspà (2017) questioned early on whether rolling averages are a good way to assess training load with regard to injuries. Lolli et al. showed that the coupled calculation creates spurious correlation (2019a) and challenged the ratio as an inaccurate scaling index for an unnecessary normalization process (2019b).
According to Impellizzeri et al. (2020), there is no evidence supporting the use of the ACWR in load management systems or for training recommendations aimed at fewer injuries. In their view, the ratio adds noise and creates statistical artifacts. In the Journal of Athletic Training, the same group described ten methodological pitfalls and advised practitioners to keep relying on their expertise and experience.
Wang et al. (2020) point to an initial load problem of the EWMA and consider it unsuitable for sports with tapering, the planned reduction of load before competitions. Their conclusion: these limitations should discourage use of the ACWR. Impellizzeri et al. (2021) even obtained similar results with made-up chronic values and suggested dismissing the ACWR as a framework and model.
Research
What systematic reviews say about the ACWR
Several systematic reviews and meta-analyses have summarized the individual studies. The picture is mixed: a link with injuries keeps showing up at group level, but calculation methods and categories differ so much that clear recommendations are hard to derive.
Review
Data
Main finding
Griffin et al. (2020)
22 studies
Link with non-contact injuries; EWMA the more suitable measure; use only as part of a broader monitoring system
Andrade et al. (2020)
20 studies, 1,234 athletes, men only
Mostly more injuries at higher values; 14 different categorizations limit the recommendations
Maupin et al. (2020)
27 studies
Trend toward the fewest injuries at 0.80 to 1.30; open issues with the method
Jiang et al. (2022)
Professional men's football
Possible link with non-contact injuries; no threshold could be identified
Ding et al. (2026)
16 studies, 797 athletes
Small to moderate link (g = 0.35), very heterogeneous studies; not suitable as a stand-alone model
Systematic reviews and meta-analyses on the ACWR, findings paraphrased
Ding et al. (2026) advise against using the ACWR as a stand-alone causal or predictive model and see it as a contextual indicator within individualized monitoring with several markers. Bahr (2016) explains why forecasts for individuals rarely work: even with a clear association, high- and low-risk groups overlap heavily.
Interpretation
What the ACWR can and cannot tell you
The ACWR describes how current load compares with usual load. At group level, team sport studies show a small to moderate link with injuries (Andrade et al., 2020; Ding et al., 2026). It can be used as one building block among several in athlete monitoring (Griffin et al., 2020).
For the individual athlete, the picture is different. The area under the curve (AUC) shows how well a value separates athletes with and without a later injury: 0.5 equals chance, 1.0 a perfect separation. In two studies in professional football, it was 0.53 to 0.60 (McCall et al., 2018; Fanchini et al., 2018). Impellizzeri et al. (2021) found a comparable c-statistic of 0.57 for the ACWR and 0.54 for acute load alone.
No forecast for individual athletes
No evidence that steering the value changes how many injuries occur
No universal thresholds, only observations from a few team sports
Calculation method and statistical artifacts of the ratio affect the value
Of little use during tapering and in the start-up phase of the EWMA
That leads to a sober reading: the ACWR shows whether the last week's load is clearly above what an athlete is used to. It is a prompt for a conversation between coach and athlete, not an injury forecast. What lies behind an unusual value is something you clarify in that conversation, for example with a look at the daily check.
How Tletify calculates
How Tletify calculates the acute:chronic workload ratio
The basis is daily load from the sRPE method: duration in minutes times RPE on a scale of 0 to 10, in AU. How to collect it properly is covered in our guide to session RPE. The default is the uncoupled EWMA model with 7 acute and 28 chronic days, and rest days count as 0.
Tletify smooths with the factor λ = 2/(N + 1), where N is the window length in days. The chronic value is taken from 7 days earlier, so the current week is not part of it. You can set the windows, the model (EWMA or rolling average) and the thresholds for each athlete.
No ratio while chronic load is below 30 AU per day
“Not enough data” with fewer than 8 sessions in 28 days or under half with a real RPE
After a break, the ratio stays hidden until the baseline is solid again
Only coaches with Pro or Team see the value, athletes never do
The default thresholds only place the value: below 0.8, load is below the usual range, up to 1.3 within it, up to 1.5 above it and beyond 1.5 clearly above it. That way you see when a week falls out of line, and you decide what follows. The metrics support decisions by coaches and do not replace a medical assessment.
In the macrocycle, Tletify also shows a planned ACWR. It comes from planned weekly volume times planned intensity and is deliberately not actual load but a look at the plan: you see how planned load develops compared with the weeks before.
ACWR calculator to download
The template calculates the way Tletify does: enter daily loads in AU, rest days as 0. It shows the uncoupled EWMA over 7 and 28 days, the rolling average for comparison, no ratio while the chronic value is below 30 AU per day, and the reading at the thresholds of 0.8, 1.3 and 1.5.
Which way of calculating the ACWR is the right one?
There is no single right one. The EWMA gives more weight to recent days, and one review rated it the more suitable measure (Griffin et al., 2020). The uncoupled calculation keeps the same week from sitting in both the numerator and the denominator (Lolli et al., 2019a). More important than the choice is to always calculate the same way.
Why does Tletify sometimes show no ACWR?
Because a number based on little data looks just as certain as one based on a lot. With fewer than 8 completed sessions in 28 days, or if fewer than half of them have a real RPE, you see “Not enough data.” If chronic load is below 30 AU per day, for example after a break, Tletify forms no ratio until the baseline is solid again.
Do athletes see their ACWR?
No. Only coaches with Pro or Team see the ACWR, for example in Analysis and in the ACWR card on the dashboard. Athletes never see it in their interface, neither in the web app nor in the app. You only see an athlete's data with their explicit permission. More on the training load management page.
At what ACWR should I change the load?
There is no universal cut-off. The thresholds of 0.8 to 1.3 and 1.5 come from a few team sports (Gabbett, 2016), and Andrade et al. (2020) found 14 different ways of grouping the values. An unusual value is a reason to talk with the athlete and review the plan. The decision is yours.
References
Andrade R, Wik EH, Rebelo-Marques A, et al. 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. Sports Med. 2020;50(9):1613-1635. doi:10.1007/s40279-020-01308-6
Bahr R. Why screening tests to predict injury do not work—and probably never will…: a critical review. Br J Sports Med. 2016;50(13):776-780. doi:10.1136/bjsports-2016-096256
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(8):471-475. doi:10.1136/bjsports-2015-095445
Ding L, Weldon A, Xu J, et al. Acute:chronic workload ratio and load management for team sports: a multilevel meta-analysis. Front Public Health. 2026;14:1896651. doi:10.3389/fpubh.2026.1896651
Fanchini M, Rampinini E, Riggio M, Coutts AJ, Pecci C, McCall A. Despite association, the acute:chronic work load ratio does not predict non-contact injury in elite footballers. Sci Med Football. 2018;2(2):108-114. doi:10.1080/24733938.2018.1429014
Gabbett TJ. The training—injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50(5):273-280. doi:10.1136/bjsports-2015-095788
Griffin A, Kenny IC, Comyns TM, Lyons M. The association between the acute:chronic workload ratio and injury and its application in team sports: a systematic review. Sports Med. 2020;50(3):561-580. doi:10.1007/s40279-019-01218-2
Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JW. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2014;48(8):708-712. doi:10.1136/bjsports-2013-092524
Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA. The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med. 2016;50(4):231-236. doi:10.1136/bjsports-2015-094817
Impellizzeri FM, McCall A, Ward P, Bornn L, Coutts AJ. Training load and its role in injury prevention, part 2: conceptual and methodologic pitfalls. J Athl Train. 2020;55(9):893-901. doi:10.4085/1062-6050-501-19
Impellizzeri FM, Tenan MS, Kempton T, Novak A, Coutts AJ. Acute:chronic workload ratio: conceptual issues and fundamental pitfalls. Int J Sports Physiol Perform. 2020;15(6):907-913. doi:10.1123/ijspp.2019-0864
Impellizzeri FM, Woodcock S, Coutts AJ, Fanchini M, McCall A, Vigotsky AD. What role do chronic workloads play in the acute to chronic workload ratio? Time to dismiss ACWR and its underlying theory. Sports Med. 2021;51(3):581-592. doi:10.1007/s40279-020-01378-6
Jiang Z, Hao Y, Jin N, Li Y. A systematic review of the relationship between workload and injury risk of professional male soccer players. Int J Environ Res Public Health. 2022;19(20):13237. doi:10.3390/ijerph192013237
Lolli L, Batterham AM, Hawkins R, et al. Mathematical coupling causes spurious correlation within the conventional acute-to-chronic workload ratio calculations. Br J Sports Med. 2019;53(15):921-922. doi:10.1136/bjsports-2017-098110
Lolli L, Batterham AM, Hawkins R, et al. The acute-to-chronic workload ratio: an inaccurate scaling index for an unnecessary normalisation process? Br J Sports Med. 2019;53(24):1510-1512. doi:10.1136/bjsports-2017-098884
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McCall A, Dupont G, Ekstrand J. Internal workload and non-contact injury: a one-season study of five teams from the UEFA Elite Club Injury Study. Br J Sports Med. 2018;52(23):1517-1522. doi:10.1136/bjsports-2017-098473
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Murray NB, Gabbett TJ, Townshend AD, Blanch P. Calculating acute:chronic workload ratios using exponentially weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages. Br J Sports Med. 2017;51(9):749-754. doi:10.1136/bjsports-2016-097152
Wang C, Vargas JT, Stokes T, Steele R, Shrier I. Analyzing activity and injury: lessons learned from the acute:chronic workload ratio. Sports Med. 2020;50(7):1243-1254. doi:10.1007/s40279-020-01280-1
Williams S, West S, Cross MJ, Stokes KA. Better way to determine the acute:chronic workload ratio? Br J Sports Med. 2017;51(3):209-210. doi:10.1136/bjsports-2016-096589
