Welcome to Complementary Training Community! › Forums › Complementary Training › Blog Posts and Articles › Predicting non-contact hamstring injuries by using training load data and machine learning models › Reply To: Predicting non-contact hamstring injuries by using training load data and machine learning models
I do not know if my transparency about the method, data, limitations, assumptions can be more transparent than reported. Thus either you have issues reading or you have malevolent tendencies. You have many other studies with a similar data sample size, claiming ‘prediction’ yet not testing predictive performance on unseen data nor cross-validating. One of the purposes of this report is to showcase that predictive performance must be evaluated on the hold-out data. My conclusion is that GIVEN this data set and models used, prediction who will get injured within 7-15 day span is not possible. I do not see any issues with such transparent reporting and CONDITIONAL statement. I provided a potential method for fellow sports scientist to use as a tool on their data set.
On the flip side, even if we could predict who is going to get injured from the observational data it doesn’t give us CAUSAL interpretation, which is discussed at the end of this report and in my other paper published in Aspetar. So yes, I am leaning in the Judea Pearl’s stance that those ‘advanced data science in sport’ is simple ‘curve fitting’ and that they need to be taken with a grain of salt. It also serves as a critique of ‘intellectuals yet idiots’ (see works by Nassim Taleb) working and being paid at the high-level sports organizations and providing neat graphs and dashboards, but jack-shit interventional forum for action.
Having said that, I highly welcome your study showing that we can predict injuries and we can INTERVENE on those predictions to avoid them. I’ll wait…