What Role Do Chronic Workloads Play in the Acute to Chronic Workload Ratio? Time to Dismiss ACWR and Its Underlying Theory



The aim of this study was to examine the associations between the injury risk and the acute (AL) to chronic (CL) workload ratio (ACWR) by substituting the original CL with contrived values to assess the role of CL (i.e., the presence and implications of statistical artefacts).


Using previously published data, we generated a contrived ACWR by dividing the AL by fixed and randomly generated CLs, and we compared these results to real data. We also reproduced previously reported subgroup analyses, including dichotomising players’ data above and below the median CL. Our analyses follow the same, previously published modelling approach.


The analyses with original data showed effects compatible with higher injury risk for ACWR only (odd ratios, OR: 2.45, 95% CI 1.28–4.71). However, we observed similar effects by dividing AL by the “contrived” fixed and randomly generated CLs: OR 1.95 (1.18–3.52) dividing by 1510 (average CL); and OR ranging from 1.16 to 2.07, using random CL 1.53 (mean). Random ACWRs reduced the variance relative to the original AL and further inflated the ORs (mean OR 1.89, from 1.42 to 2.70). ACWR causes artificial reclassification of players compared to AL alone. Finally, neither ACWR nor AL alone confer a meaningful predictive advantage to an intercept-only model, even within the training sample (c-statistic 0.574/0.544 vs. 0.5 in both ACWR/AL and intercept-only models, respectively).


ACWR is a rescaling of the explanatory variable (AL, numerator), in turn magnifying its effect estimates and decreasing its variance despite conferring no predictive advantage. Other ratio-related transformations (e.g., reducing the variance of the explanatory variable and unjustified reclassifications) further inflate the OR of AL alone with injury risk. These results also disprove the etiological theory behind this ratio and its components. We suggest ACWR be dismissed as a framework and model, and in line with this, injury frameworks, recommendations, and consensus be updated to reflect the lack of predictive value of and statistical artefacts inherent in ACWR models.

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Fig. 1


  1. 1.

    Maupin D, Schram B, Canetti E, Orr R. The relationship between acute: chronic workload ratios and injury risk in sports: a systematic review. Open Access J Sports Med. 2020;11:51–75.

    Article  Google Scholar 

  2. 2.

    Griffin A, Kenny IC, Comyns TM, Lyons MJSM. The association between the acute: chronic workload ratio and injury and its application in team sports: a systematic review. Sports Med. 2019;50:561–80.

    Article  Google Scholar 

  3. 3.

    Drew MK, Blanch P, Purdam C, Gabbett TJ. Yes, rolling averages are a good way to assess training load for injury prevention. Is there a better way? Probably, but we have not seen the evidence. Br J Sports Med. 2017;51(7):618–9.

    CAS  Article  Google Scholar 

  4. 4.

    Eckard TG, Padua DA, Hearn DW, Pexa BS, Frank BS. The relationship between training load and injury in athletes: a systematic review. Sports Med. 2018;48(8):1929–61.

    Article  Google Scholar 

  5. 5.

    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):471–5.

    Article  Google Scholar 

  6. 6.

    Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50(5):273–80.

    Article  Google Scholar 

  7. 7.

    Gabbett TJ, Hulin BT, Blanch P, Whiteley R. High training workloads alone do not cause sports injuries: how you get there is the real issue. Br J Sports Med. 2016;50(8):444–5.

    Article  Google Scholar 

  8. 8.

    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):231–6.

    Article  Google Scholar 

  9. 9.

    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):708–12.

    Article  Google Scholar 

  10. 10.

    Gabbett TJ, Whiteley R. Two training-load paradoxes: can we work harder and smarter, can physical preparation and medical be teammates? Int J Sports Physiol Perform. 2017;12(Suppl 2):S250–4.

    Article  Google Scholar 

  11. 11.

    Bourdon PC, Cardinale M, Murray A, Gastin P, Kellmann M, Varley MC, et al. Monitoring athlete training loads: consensus statement. Int J Sports Physiol Perform. 2017;12(Suppl 2):S2161–70.

    Article  Google Scholar 

  12. 12.

    Soligard T, Schwellnus M, Alonso JM, Bahr R, Clarsen B, Dijkstra HP, et al. How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury. Br J Sports Med. 2016;50(17):1030–41.

    Article  Google Scholar 

  13. 13.

    Pluim BM, Drew MK. It’s not the destination, it’s the “road to load” that matters: a tennis injury prevention perspective. Br J Sports Med. 2016;50(11):641–2.

    Article  Google Scholar 

  14. 14.

    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):749–54.

    Article  Google Scholar 

  15. 15.

    Williams S, West S, Cross MJ, Stokes KA. Better way to determine the acute:chronic workload ratio? Br J Sports Med. 2017;51(3):209–10.

    Article  Google Scholar 

  16. 16.

    Gabbett TJ, Hulin B, Blanch P, Chapman P, Bailey D. To couple or not to couple? For acute: chronic workload ratios and injury risk, does it really matter? Int J Sports Med. 2019;40(9):597–600.

    Article  Google Scholar 

  17. 17.

    Carey DL, Crow J, Ong KL, Blanch P, Morris ME, Dascombe BJ, et al. Optimizing preseason training loads in australian football. Int J Sports Physiol Perform. 2018;13(2):194–9.

    Article  Google Scholar 

  18. 18.

    Malone S, Owen A, Mendes B, Hughes B, Collins K, Gabbett TJ. High-speed running and sprinting as an injury risk factor in soccer: can well-developed physical qualities reduce the risk? J Sci Med Sport. 2018;21(3):257–62.

    Article  Google Scholar 

  19. 19.

    Lolli L, Batterham AM, Hawkins R, Kelly DM, Strudwick AJ, Thorpe RT, et al. The acute-to-chronic workload ratio: an inaccurate scaling index for an unnecessary normalisation process? Br J Sports Med. 2018;53:1510–3.

    Article  Google Scholar 

  20. 20.

    Curran-Everett D. Explorations in statistics: the analysis of ratios and normalized data. Adv Physiol Educ. 2013;37(3):213–9.

    Article  Google Scholar 

  21. 21.

    Kronmal RA. Spurious correlation and the fallacy of the ratio standard revisited. J R Stat Soc. 1993;156(3):379–92.

    Article  Google Scholar 

  22. 22.

    Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Ann Intern Med. 2007;147(8):W163–94.

    Article  Google Scholar 

  23. 23.

    Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1-73.

    Article  Google Scholar 

  24. 24.

    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):108–14.

    Article  Google Scholar 

  25. 25.

    Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc. 2004;36(6):1042–7.

    Article  Google Scholar 

  26. 26.

    Fuller CW, Ekstrand J, Junge A, Andersen TE, Bahr R, Dvorak J, et al. Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Scand J Med Sci Sports. 2006;16(2):83–92.

    CAS  Article  Google Scholar 

  27. 27.

    Wilks DS. Sampling distributions of the Brier score and Brier skill score under serial dependence. QJR Meteorol Soc. 2010;136(653):2109–18.

    Article  Google Scholar 

  28. 28.

    Bowen L, Gross AS, Gimpel M, Bruce-Low S, Li FX. Spikes in acute:chronic workload ratio (ACWR) associated with a 5–7 times greater injury rate in English Premier League football players: a comprehensive 3-year study. Br J Sports Med. 2019;54:731–8.

    Article  Google Scholar 

  29. 29.

    Frishman F. On the arithmetic means and variances of products and ratios of random variables. In: Patil GP, Kotz S, Ord JK, editors. A modern course on statistical distributions in scientific work. Dordrecht: Springer; 1975.

    Google Scholar 

  30. 30.

    Curran-Everett D, Williams CL. Explorations in statistics: the analysis of change. Adv Physiol Educ. 2015;39(2):49–54.

    Article  Google Scholar 

  31. 31.

    Colby MJ, Dawson B, Peeling P, Heasman J, Rogalski B, Drew MK, et al. Multivariate modelling of subjective and objective monitoring data improve the detection of non-contact injury risk in elite Australian footballers. J Sci Med Sport. 2017;20(12):1068–74.

    Article  Google Scholar 

  32. 32.

    Coyne JOC, Nimphius S, Newton RU, Haff GG. Does mathematical coupling matter to the acute to chronic workload ratio? A case study from elite sport. Int J Sports Physiol Perform. 2019;30:1–8.

    Google Scholar 

  33. 33.

    Delgado-Rodriguez M, Llorca J. Bias. J Epidemiol Community Health. 2004;58(8):635–41.

    Article  Google Scholar 

  34. 34.

    Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2012.

    Google Scholar 

  35. 35.

    Windt J, Ardern CL, Gabbett TJ, Khan KM, Cook CE, Sporer BC, et al. Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies. BMJ Open. 2018;8(10):e022626.

    Article  Google Scholar 

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The Authors would like to thank Lorenzo Lolli for the suggestions, Marco Riggio for the support in the data extraction and preparation, and Ermanno Rampinini for the reuse of data.

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Correspondence to Franco M. Impellizzeri.

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The Authors affirm that they have no financial affiliation (including research funding) or involvement with any commercial organization that has a direct financial interest in any matter included in this manuscript.

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Data cannot be shared since there is no permission from the team owning the data, but sharing of a synthetic dataset may be considered upon request.

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This study was conducted reanalysing previously published data with the permission of the authors and the management of the team.

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We declared that all the authors made substantial contributions to the manuscript: conception, acquisition, interpretation of data, drafted the work, revised it critically for important intellectual content, approved the version to be published, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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The original article has been updated: Due to Table 2 update.

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Impellizzeri, F.M., Woodcock, S., Coutts, A.J. et al. What Role Do Chronic Workloads Play in the Acute to Chronic Workload Ratio? Time to Dismiss ACWR and Its Underlying Theory. Sports Med 51, 581–592 (2021). https://doi.org/10.1007/s40279-020-01378-6

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