There's been much more data-driven decision making / data science applied to the sport, but no huge watershed moment like in baseball or basketball.

I think that has to do with the fact that compared to the mentioned sports, soccer doesn't really have any single pivotal roles, that can make or break a match, which you can then optimize for/around.

Fundamentally speaking, soccer is a pretty slow moving game as far as scoring goes. One goal can be enough, and teams either play defensive or offensive games - while the other team tries to find weaknesses around their plays.

From what I've seen, a lot of the data science around soccer goes on things like player condition, predicting what players to swap out around games, analyzing weak / strong patterns in the strategy / playing style, and similar. I've seen this been done via camera usage and machine learning, analyzing ball position, player position - player performance on and off the field, and similar stuff. But this is 5-6-7 years ago, so probably outdated information...the info came from a guy that worked for a ML research group, who in turn had people that consulted for certain European clubs.

Even at the semi-professional soccer level, coaches are making use of analytics. For example, they get information about where other teams are - or who is - losing the ball. Then, the game was pushed toward extreme (and boring) control of ball possession largely by analytics. When I played at a decent level, it was considered "criminal" to pass the ball in close proximity (up to 20 yards) to the net while being "pressured" by the other team. Nowadays, it is very rare to see a team that gives up possession easily.

AC Milan had been using "Milan Lab" since the early 2000s and it was whispered that it used neural networks to predict the probability of injury. It was a largely unsuccessful experiment at the time-players were very often injured (also, Milan's head of the medical/performance team, Meersseman https://www.jpmchiropratica.it/en/studio/jeann-pierre-meerss..., was pulling players' teeth because he believed that some teeth could give postural problems and thus increase the likelihood of injury, see Seedorf).

The analytics/data science revolution has already arrived in soccer, but the reasonably large effects have been, due to the nature of the game, less visible than those in American football and baseball.

I don't think it is quite the same and I don't have any hard data to point to, but it seems players are being judged by the number of game speed minutes on their legs more than they have in the past. IE, a 26 year old who has been playing since he was 19 might be a star, but will not be worth as much as a he might have been 10 years ago when it was assumed he was just entering his prime.

I would say that the Pep Guardiola led Barcelona already had this watershed moment, with their possession and passing focused play, built around tiki taka and dependence on phenomenonal playmakers like Xavi, Iniesta and Messi. No other team has managed to replicate this success so far though, not even Barcelona itself after Guardiola left and Xavi/Iniesta retired.

Basketball now uses SportVU tracking data. A camera system records the game, and computer vision algorithms extract the locations of the players, and the ball, at high space and time resolution.

https://en.wikipedia.org/wiki/SportVU

https://arxiv.org/abs/1703.07030

SportVU was originally developed with European football in mind, but I don't know whether it has found use there or not.