You choose to attack me personally. Not very professional.
I don’t hide that I believe in working a solution to this problem using AI-based methods. You found that out pretty easily.
It makes sense for me to defend methods that I believe in. (even if I haven’t published them yet,).
There’s no shame in working to solve this problem outside the academic framework.
Anyway, if you can’t have a discussion without being offensive. I’m done here.
I never said anything about publishing only studies with a positive outcome. Therefore saying my comment is ignorant and harmful is out of place.
I said that the extent of the dataset made it impossible to have a positive outcome in the first place, therefore I don’t find a reason to report that there was indeed no positive outcome.
Your practical applications section has a direct and decisive statement about the methodology not being useful, not about the data. The data limitations are discussed afterwards and aren’t put front and center as the main cause of low predictive power. In my opinion, that statement and the way it’s structured is wrong and conveys a message that creates distrust in using advanced data science in sport.
Writing a sentence like “it can be concluded that the non-contact hamstring injuries could not be predicted by using training load data with features engineered, as described previously” is misleading and essentially wrong.
It’s obvious a model trained on 20 injuries will not predict anything.
Writing this before describing the limitations of the data is even more misleading.
It doesn’t make sense to design a study in a way that is bound to fail and then publish it just to say it indeed failed.
Honestly, better to retract the whole thing.