Why data-driven approach to soccer is slow to catch on?

Question

A lot of Baseball teams are using data-driven approach and computer modelling to help them better succeed in games. The most famous example being Billy Beane of Oakland Athletics (2001-2003), popularized by the film Moneyball.

My question is why soccer or football is very slow to warm up to this approach? In an interview Billy Beane said that :

“It’s a very dynamic sport. Baseball is very stop-start and it lends itself to measurement, but on the flip side there are a lot more events going on during a football match than there are in a baseball game - and anybody who is well versed in modelling, whether from a computer science background or a mathematics background, will tell you the more data you have, the better you are able to put the models together.

“So it may be different and there’s certainly a correlation of an interaction between players that may not exist in baseball – but there are a lot of events, and the more events you have the better you should be at predicting things.”

I've no idea what these words mean. On one moment he said that football has a lot more events, and on the other moment he said that the more data the better for data driven approach. So I guess more events should lead to more data which means that football should be amenable to data-driven approach, right?

In addition to whatever he was saying, is there any other reasons why football is slower to warm up to data-driven approach?

Edit: the question How are football players are objectively rated is related, but not necessarily a duplicate as suggested. Reason being, data-driven approach is not necessarily limited to sports where individual players can be objectively rated. In fact football is a collective sport where individual talents are less important than team dynamics. I can easily imagine that we find other team-related metrics, as opposed to individual metrics, that are more amenable to data-driven analysis.

So no, it's not a duplicate at all.

Answer 1

The simple answer is because baseball is much easier to analyze than soccer: baseball very easily breaks down into a series of separate events (pitches), each of which have an clear outcome (strike, ball, base hit, home run, etc) which move the game from one clearly defined state to another (e.g. "team down by 2 runs in the top of the 7th, runners on first and third, 2-2 count" to "scores level in the top of the 7th, runner on second, 0-0 count"). And then to make it even easier, each pitch effectively just involves two players, the pitcher and the batter - while the rest of the defenders and the runners do have an effect, it's been shown to be minimal.

Soccer doesn't have any of those advantages: it doesn't break down into separate events, and individual plays don't clearly just involve a small number of players in the same way. This just makes it much harder to analyze, so it's not surprising that it's behind baseball in the level of modelling which is used.

Answer 2

According to Nate Silver in The Signal and the Noise a major reason that Baseball is so amenable to analysis is that it's played a lot more.

Topflight baseball schedules have 162 games a season whereas topflight football has far fewer. For example, in the English Premier League each team plays just 38 games a year in the league. There are additional games from various knock-our tournaments - and some players, obviously, take international duty for additional games - but this does not come close to reaching the number of games in baseball.

Because player's performances vary between matches and over time, the sample size of different matches is extremely important in the ability to distinguish genuinely good players from luckier ones.