[Research Review] Training Load–Injury Paradox

In the following video I am reviewing very interesting article by Windt, Gabbett et al.: “Training load–injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players?”. Tim Gabbett is leading researcher in the field of training load and injuries, and this paper represents continuation of his work on this topic. All papers (co)authored by Tim are highly recommended reading for coaches in general and sports scientists in particular.

The purpose of this video is to provide some of critiques and recommendations for future authors of similar studies, reviewer and journal editors. Please bear in mind that I am not an expert in this field nor expert in statistics/machine learning, so take my opinions with grain of salt.

Some of my main critiques are the following:

  • Predictive vs. retrodictive models (no hold-out data sets, no cross-validation)
  • Lack of any reporting of the overall model fit (deviance, AIC, BIC, WAIC, ROC)
  • Lack of tuning of the model parameters: ACT, CTL and injury lag, as well as no description/rationale of using weekly binning vs. rolling windows
  • Pooled injuries and not differentiation between types and locations – for example building multiple models for estimating likelihood of hamstring strain, groin strain and so forth
  • No reporting of model comparison (univariate vs. multivariate). No comparison/use of model models such as random forest, LASSO, ElasticNet, SVM and so forth
  • No discussion regarding practical significance of predictors association (statistical significance is not practical significance). Plus, hard to judge coefficients from logistic regression intuitively and without intercept
  • More counterfactual graphs needed
  • No discussion of random effects
  • No code or data available – the need for reproducible research
  • And much more

Click Here to Download the R Script

Related Articles

Thoughts on Injury Prediction

In the following article, I am discussing the famous “J” curve in injury prediction, as well as simulate some data to show how that curve is estimated. I also show the distinction between association and prediction, as well as how to make training decisions based on the different costs of committing false positive and false negative errors.

Uncertainty, Heuristics and Injury Prediction

I was asked by Rod Whiteley and Nicol van Dyk to contribute to the Aspetar Journal targeted topic issue that just got released off the press. I tried to combine my knowledge of predictive analytics, machine learning, philosophy of science, heuristics and practical experience as coach & sport scientist into one article. Hopefully I managed to create readable narrative.


Your email address will not be published. Required fields are marked *

Cancel Membership

Please note that your subscription and membership will be canceled within 24h once we receive your request.