Franco M Impellizzeri1, S Woodcock2, A J Coutts3, M Fanchini4, A McCall5, A D Vigotsky6,7. 1. Human Performance Research Centre, Faculty of Health, University of Technology Sydney (UTS), Driver Avenue, Moore Park, Sydney, NSW, 2021, Australia. franco.impellizzeri@uts.edu.au. 2. School of Mathematical and Physical Sciences, University of Technology Sydney (UTS), Sydney, NSW, Australia. 3. Human Performance Research Centre, Faculty of Health, University of Technology Sydney (UTS), Driver Avenue, Moore Park, Sydney, NSW, 2021, Australia. 4. AS Roma Performance Department, AS Roma Football Club, Roma, Italy. 5. Arsenal Performance and Research Team, Arsenal Football Club, London, UK. 6. Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA. 7. Department of Statistics, Northwestern University, Evanston, IL, USA.
Abstract
AIM: 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). METHODS: 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. RESULTS: 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). DISCUSSION: 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.
AIM: 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). METHODS: 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. RESULTS: 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). DISCUSSION: 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.
Authors: Travis R Pollen; David Ebaugh; Meghan Warren; Clare E Milner; Jennifer A Taylor; Sheri P Silfies Journal: J Athl Train Date: 2022-05-01 Impact factor: 3.824
Authors: Luis Suarez-Arrones; Borja De Alba; Mareike Röll; Ignacio Torreno; Sarah Strütt; Kathrin Freyler; Ramona Ritzmann Journal: Front Sports Act Living Date: 2020-07-08
Authors: Dhruv R Seshadri; Mitchell L Thom; Ethan R Harlow; Tim J Gabbett; Benjamin J Geletka; Jeffrey J Hsu; Colin K Drummond; Dermot M Phelan; James E Voos Journal: Front Sports Act Living Date: 2021-01-21
Authors: Rafael Oliveira; Luiz H Palucci Vieira; Alexandre Martins; João Paulo Brito; Matilde Nalha; Bruno Mendes; Filipe Manuel Clemente Journal: Medicina (Kaunas) Date: 2021-06-23 Impact factor: 2.430
Authors: Torstein Dalen-Lorentsen; Andreas Ranvik; John Bjørneboe; Benjamin Clarsen; Thor Einar Andersen Journal: BMJ Open Sport Exerc Med Date: 2021-06-22