“All models are wrong, some are useful.” — George Box, famous British statistician
Just like in stock trading, sports can’t be broken down into purely numbers. There’s a human and behavioral component to life, sports, the economy, etc that AI, algorithms, models, etc haven’t been able to account for and likely won’t be able to account for. It’s why momentum funds and quant funds aren’t perfect and why there are still market crashes.
Statisticians, economists, weathermen, and stock brokers are the best professionals in the world at being wrong about telling you what’s going to happen but being really good about telling you why they were wrong and why it wasn’t their fault. There’s a common element to all of those professions.
One of the problems with this discussion is the idea that only one group can be right. Just like with everything in life there is nuance and context that need to be considered. You can’t run a team based solely on models. If you did, and analytics were perfect, there would never be a draft bust. You’d never pay a dollar more than a player was worth. You’d know the exact moment you should get rid of a player. There would also be no advantage to using models because everyone would have them and there would be no arbitrage opportunities.
It’s pretty clear that using analytics in your decision making process is crucial. Data helps you uncover opportunities, avoid potential landmines, and make better decisions. Teams that use data as part of their process aren’t perfect but they’re definitely better off than teams that don’t. That said, to say a player doesn’t have value because one specific measurement says he’s not good at one thing is just as misinformed as not using data at all.