The analytics revolution wasn't about short players specifically, the 3-point revolution aspect was mainly about selecting shots based on the expected value of the shot. The warriors in this aspect was very far behind the curve compared to Houston. The main takeaway for them was taking shots from farther behind the three point line, but they didn't have an emphasis on corner 3s (which are closer to the basket) or de-emphasis on mid-range shots (between the 3 point line and outside of 8 to 10ish feet from the basket). They (along with the rest of the league) were trending towards Moreyball during their championship years (particularly in 2015-16 if I remember correctly) but there were years where they mainly had extraordinary shooters.

Hockey has proven somewhat stubborn. Coaches will pull the goalie earlier now, and there is more of an emphasis on skilled players rather than nasty players, but I'm not convinced the latter wouldn't have happened in the absence of analytics. There was a wave of excitement when Kyle Dubas and a few other young GMs started hyping up analytics and new stats like expected goals and possession metrics and stuff, but as far as I can tell, it hasn't had a huge impact on the game.

I think hockey fans should count their blessings!

As a lifelong baseball fan and someone who works with data for a living the analytics revolution is killing the sport. People discovered that sacrifice bunts weren't worthwhile, but they were really fun to watch in a way that walks and strikeouts aren't. There wasn't enough respect for baseball as a cultural game.

I'm surprised that the league hasn't pushed harder for optimizing fan enjoyment in the same way that they optimized for winning.

I know less about basketball but I hear the same thing there, that 3 point shots have replaced a lot of interesting dynamics at mid range.

Interestingly, in football the analytics playstyle is more dynamic and exciting. A lot riskier and more explosive offensive plays compared to the traditional style. I wonder whether the league guided that (in terms of what data was made available, or by learning from the mistakes of baseball) or if they just got lucky.

I find high skill hockey more entertaining than bruiser hockey, so I'm happy for now! Hockey has had terrible eras in the past, like the advent of the neutral zone trap. There was an infamous game in which a defenseman just held on to the puck and skated around his own end for minutes at a time rather than try to get through the trap.

happening in American Football as well with more aggressive play calling on 4th down and in the red zone due to analytics. There are also teams using tools to calculate "game speed" vs traditional testing to help scouting be more accurate

What's crazy to me is that the more modern 4th down play-calling statistics has been known for at least two decades now; I first heard about it from my stats teacher in high school. The issue is that making the right call and it not working out would reflect more poorly on a coach than making the more traditional call and playing worse. It reminds me of the saying "No one ever got fired for buying IBM".

Young Sheldon in 2017: "Statistically always punting on 4th down makes no sense."

https://www.youtube.com/watch?v=zZffa_Z_Cxs#t=17s

Five Thirty Eight has written a bit about this in the past. That's pretty much what they said. No one remembers the risky play that paid off or the safe/expected play; they remember the risky play that didn't pay off.

Good discussion around the topic with Daryl Morey who, as you linked, started that at the Huston Rockets. I know absolutely nothing about basketball and found it very interesting, nevertheless.

https://www.preposterousuniverse.com/podcast/2021/03/15/138-...

Has this revolution happened in European football yet? It seems like a harder to quantify sport (less episodic, more flowing, so harder to quantify positions)

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.

Previously (same author):

http://danluu.com/programmer-moneyball/

This section in that piece about undervaluing training ("trainingball") is priceless.

I worked somewhere where someone constantly brought up Moneyball as a way of getting an edge in hiring. They later became head of the unit, and it became clear they had absolutely zero ability to manage other people, because they thought of them as consumable goods. They would level scathing reviews of people working under them that they were "poorly mentored" as if this was an attribute of the employee in question, not at all recognizing the irony in what they were saying.

Moneyball was an interesting observation, but it's also telling because it is implicitly based on a number of assumptions that plague modern workplaces, such as treating people as fixed, unchangeable objects rather than as persons that can learn and adapt.

The comment by that author about "trainingball" is noteworthy because it points to the importance of training but also potential as a selection factor. And not just "potential" in the sense of "new and undeveloped" but in the broader sense of "this person has the attributes and background to be what we need if we just work with them." I think that attitude was much more common in the past maybe, but I wasn't alive then.

There's also a different and many understudied problem, which is institutions failing to acknowledge and/or recognize when there have been significant changes in the institution, and when the employees aren't communicated with about these things. Sometimes training isn't so much training as it is just communicating with people.

Happening in golf now too.

How is it applied to golf?

Not just this, but the prevalence of strokes gained being the key metric to focus on for players has opened up a new understanding of what it takes to score well and win tournaments.