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I keep seeing these articles about the introduction of artificial intelligence/data science to football and basketball strategy. What's crazy to me is that it's happening now instead of much much earlier. The book Moneyball was published in 2003 (the movie in 2011) spreading the story of how use of statistics changed the game when it came to every aspect of managing a baseball team. After reading it, I and many others thought to ourselves "this would be cool to do in other sports" - using data would be interesting in every area of every sport (drafting, play calling, better coaching, clock management, etc). But I guess I assumed - if I thought of it, why wouldn't other people?
It's kind of a wild example of the idea that "if something works a little, you should do more of it and see if it works a lot, and keep doing that until you see evidence that it's running out of incremental benefit." My assumption that the "Moneyball" space was saturated back in 2011 was completely off given that in the time between 2011 and now, one could have trained themselves from scratch in the relevant data science methods and pushed for such jobs (my intuition is that 8 years of training could get you there). So, it's not even a "right place, right time" story given the timeline. It's just - when you saw the obvious trend, did you assume that everyone else was already thinking about it, or did you jump in yourself?
Part of the problem was that doing the work to apply those insights and doing so in a way that beats trained humans is hard because until recently those models couldn't handle all the variables and data humans could and so ignored many things that made a difference. Now that more data can be fed into the models they can make the same or better predictions that humans can make and thus stand a chance of outperforming them rather than making "correct" but poorly-informed decisions that, in the real world, would have lost games.