Comparisons Between European Leagues

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Shot Matrix International I: The Differences Between the European Leagues

The primary driver of England’s shot total, compared to the other largest continental leagues, is shots taken from the danger zone. That’s the old English directness right there.

If we look at shots from a wide angle inside the box, we see a very different picture. Despite the larger number of shots taken overall in the EPL, there’s no meaningful difference in total shots taken from wide areas inside the box. And when we narrow down just to shots from a difficult angle inside the box, the numbers really jump out. There are far more shots taken from those difficult angles in Spain and Italy than in England and Germany.

Shot Matrix International II: Shot and Pass Type

The zone-by-zone breakdown doesn’t teach us much. But the broader point here is that shots assisted by through-balls are attempted at massively differing rates. These tend to be extremely high-quality shots, converted at rates double or more regular shots from the same area. A successful through-ball by definition splits the defense and leaves an attacking player in space. We should expect higher rates of shot conversion in leagues where these sorts of attempts are more prevalent.

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Player Shooting Skill Studies

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Shot Matrix V: Identifying Player Finishing Skill

I could only identify an effect with samples of minimum 100 shots, and even then the effect is not overwhelming. (See Nerdery section below for more.) If a player has taken 20 or 30 shots but converted either a lot more or a lot fewer than you’d expect, you’re still best referring to the studies showing no y-to-y correlation in shot conversion. Probably it’s been a fluke. There’s a possibility that it isn’t, but the only good way to identify that statistically is with several seasons of data. So we need to be very careful about concluding that a player really has a significant shooting skill.

Player Finishing Skill is Real

When you aggregate data and collect groups of similar players, there emerges a clear tendency of higher-volume shooters and more advanced players to finish their chances more efficiently. I think this is a selection effect. Football managers recognize which of their players have the best striking skills and arrange tactics to get those players the most chances.

The Value of the Through-Ball

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Shot Matrix III: The Incredible Through-Ball:

The average shot taken in the English Premier League from 2009-2013 has an expected goals value on 8.7%. Arsenal’s shots have an expected goals value of 10.1%. Over a sample of 2693 shots, that is a difference highly unlikely to occur by chance. To put a number of it, there is less than a 1% chance of a club averaging a shot quality that far above league average, on that many shots, by pure random variation. It’s a real tactical effect.

Shots off Headers and Crosses

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Shot Matrix II: Shots off Headers and Crosses:

You can also see the multiplicative effect of crosses and headers. Headers not off crosses, perhaps from rebounds of shots or the occasional lofted pass, are better than headers off crosses but not as easy to convert as regular shots. Likewise with crosses—a cross played into feet in the six-yard box, if the player can manage to turn it goalward, will be a score nearly half the time. But it’s still harder to redirect that cross than to play a regular shot. This makes sense, of course. Getting a clean strike on a cross is hard, getting a clean strike using only your head is hard. Trying to do both at the same time, really hard. (Thus the title of the piece.)

How Expected Goals Predicted Liverpool’s Title Run

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The Statistics that Predicted Liverpool’s Title Run:

It isn’t precisely true that no one expected Liverpool to be title contenders. A number of statistical models viewed Liverpool as one of the best sides in the EPL last year. My expected goals ratio, a ratio of expected goals scored to expected goals allowed based on chance quality, rated Liverpool as the league’s best side last season.

The method behind expected goals, which you can read about in the “Shot Matrix” articles linked above or at the EPL Advanced Stats page, is pretty simple. For each chance, I estimate the average probability of a goal being scored, based on location on the pitch, whether the shot is taken with the head or the foot, whether it is a free kick or from open play, and the type of pass that assisted the shot. Over time, these estimates of chance quality are a better metric of team quality than raw goals scored or simple shot totals. And in the case of Liverpool, based on their expected goals in 2012-2013, the underlying stats expected this club to be among the best in the Premier League.

Tottenham Hotspur 2013-2014 Season Review

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How Tim Sherwood and Andre Villas-Boas Failed Tottenham:

What could have led a manager to look at Spurs on December 16th and decide that they should maintain the same, obviously failed defensive tactics of the previous month? Did Tim Sherwood think a disorganized press and a shifting block were the path to success? Perhaps, instead, the players just kept playing defense the same way because they weren’t instructed to change in the first place. Sherwood’s comments to the media after the Chelsea loss suggest a real failure to understand that his tactics had not been working previous to that defeat. So whether out of simple incompetence or a more complex misunderstanding of his club’s performance, Sherwood left Tottenham running a tactical set-up that was entirely doomed to fail.

Testing Expected Goals

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Part 1: What stats best predict goals scored and conceded?

The concept is pretty simple. For every shot, you assign an “expected goals” value based on characteristics like the location on the pitch, whether the shot is taken with the foot or the head, whether the shot is assisted by a cross or through-ball, and so on. This is in no way a comprehensive list of the characteristics of each shot, but it provides a reasonable estimate when dealing with larger samples. A club’s expected goals, then, is the sum of all their expected goals values for all their shots.

Part 2: What stats best predict wins and losses?

The relationship between goals and points is humongously complex. As Howard Hamilton showed in his work on the “soccer pythagorean”, having three unequal possible match results creates a weird, non-linear relationship. So instead of dealing with the math, I’m just simulating the games and comparing projected points to real points. There we should expect a simple linear relationship if the projections are good.