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# The Technical Area:StatDNA's Blog

We analyze the world's most advanced soccer statistics to better understand the game.

# Why players, teams are undifferentiated on "passing skill"

4. May 2011 17:26

The statistic of pass completion % is one that I have discussed in the past as having limited relevance - see the post here for example. The key issue is that pass completion % does not tell you anything about who won the game or who scored the most goals, because its very situation specific (at certain times of the game a team may be ceding possession and allowing a team to complete a large number of non-threatening passes, for example).  One key factor that pass completion % does not take into account at all is pass difficulty.  Whether a team is banging the ball around in the defensive backfield or its making quick one-touch passes in traffic, pass completion % treats them all equally.  About the best you might be able to do at the player level is to compare players at similar positions on this metric, but as we know, no two players (even in the same position) play exactly the same role.

We decided therefore to try to partially address the problem by running a regression to determine pass difficulty and then adjust passing skill based on the difficulty of passes attempted.  We ran a regression on over 100,000 passes from the Brazil Serie A, and also looked at several subsamples (passes in attacking 1/3, only passes on the ground in the attacking 1/3) and all roads lead to one conclusion: after adjusting for difficulty, pass completion % is nearly equal among all players and teams. Said another way, the skill in executing pass is almost equal across all players and teams, as pass difficulty and pass completion % is nearly completely correlated.

Before summarizing or concluding any further, we let's discuss a bit more the analysis that was done. We took completed pass as the dependent variable in a logistic regression that included the independent variables of level of defensive pressure on the passer, pass distance, direction passer is facing, whether the pass was one-timed, and if the pass was with the head or foot, and if the pass was hit on the ground or in the air.  We also used the field zone of the next touch (whether the pass was complete or incomplete) as a proxy for the level of pressure on the recipient of the pass, because we know that defensive pressure tends to increase as you move up the field and towards the goal. We needed to do this because we can't really measure pressure on the intended recipient on an incomplete pass.  All of the coefficients we tried were extremely significant and the regression had a very strong fit.
Here is a summary of the most important coefficients and impact on likelihood of completing a pass:

Pass Distance: --
Pressure: --
Pressure on recipient (proxied by field zone): --
Forward pass: -
Air pass: -
One-timed pass: -

The fit on this model is incredibly strong (we use something called the Hosemer-Lemeshow test to judge the fit of the logistic regression and the model is significant at the .000 level).

Using the model, we can then compute two things. Expected completion percentage of the pass can be thought of as pass difficulty (or more appropriately inverse pass difficulty, because the closer to 1 it is, the easier the pass).  We can also calculate a measure of passing skill, which is completed passes/expected completed passes, with 1 being neutral passing skill and figures above one being above average passing skill.

What we find on this front is interesting. Firstly, if we take actual pass completion % and compare it to pass difficulty we have a correlation of 0.94 across the entire sample.  What this says is that pass difficulty basically completely determines a player's pass completion %.  Stated another way, if you look at completed passes/expected completed passes, its almost always near 1.  So viewed this way, differentiated passing skill is non-existent at this level of play, at least in terms of executing a pass.  The characteristics of the pass in terms of pressure, distance, etc, will in the long-run determine the completion %.  When starting this analysis, I felt pretty confident that we would find that when you adjusted pass completion % for difficulty, you would find interesting things - for example that "passing skill" in the attacking 1/3 would correlate strongly to goals or assists.  When I first saw that passing skill was non-differentiating when adjusted for difficulty - I was a bit surprised, but when I thought a bit more about it, it didn't seem that off-base. Here is why:

The problem with general pass completion % is that it does not take into account difficulty. And now we have a new problem with the "passing skill statistic"; while it does adjust for pass difficulty it does not adjust for  a couple of other factors: (1) actions prior to strking the pass and (2) danger created by the pass. Firstly, how much does a passer increase his pass completion % by the actions he takes prior to taking the pass? For example is Xavi an "excellent passer" because he can place a pass on a dime or is it more his ability to find pockets of space where no defensive pressure exist to receive the ball in and his miraculous ball control allows him to continue to avoid pressure and hit higher value passes for an equal level of difficulty?  Many players put themselves in difficult passing situations because they dwell on the ball too long and upon receiving the ball are not able to reposition their bodies in a way that opens up the field.  In order to look at this we need to understand better the situation a player receives the ball in, and whether he reduces or improves his relative abilty to complete a pass (E.g. pass difficulty) with his actions between then and the time of the pass. Since we do keep detailed statistics on situations upon a player receiving the ball, this is something we plan to analyze.

Another very important factor is potential danger created by a pass.  We have a statistic for this which is called Pval (pass value) and measure the % increase that a team's chance of scoring a goal increases with each pass.  We believe pval is where players' values in passing should be measured, because that's where they do create value differentially.  A player creating more pval is increasing value by creating situations that have higher yield for equal pass difficulty - this could be by getting himself open by eluding defenders (with the dribble or off ball movement) and also by using his vision to select the highest value pass for a fixed difficulty. We also cannot dismiss the overall contribution of the team to each players pval - with consecutive passes and ball movements that help continuously create more pval (by creating space, penetration into the defensive and upfield progress) being related to many players and just not the one or two responsible for the last two touches.  We need to find a way to properly distribute credit, and it's no simple task. We will be having a first look at pval in a blog post later this week.

The next time you see a statistic on straight pass completion % - you'll have a new way to understand it. This number simply roughly reflects the average difficulty of the pass that team attempted (though in a small sample - this becomes a little less binding).  The difficulty in turn may be a reflection of a whole host of things having to with the tactics each team were pursuing in the game (e.g. who and where to pressure, how direct to attack, etc).  Given this, its very hard to judge pass completion % on face value as any indicator of team or player performance.

passing

jhunt
5/8/2011 4:21:52 PM #

Is it possible that (and I'm saying this having watched little Brazilian soccer recently) that a homogenous style played by teams in the league is the cause of this?  Perhaps looking at different leagues (say the Premier League in England, where the gulf in quality, and subsequent styles played are different) would yield different results.

That said, great work you guys are doing.  Have you ever thought of doing game analysis in a similar vein to (though on a higher statistical level) zonalmarking.net?

5/9/2011 11:03:23 PM #

Thanks for your comments.  I'm going to run the numbers on the EPL and post what I find later this week. We have a smaller sample size, but still over 100,000 passes I believe, so I expect some robust results.

I think we will probably start doing some analysis for the coming season.  We're so busy right now doing analysis that we are integrating into our product offering to teams, that it doesn't leave alot of time for match analysis.  Also, we plan to do pre-match rather than post match analysis I think, as our data takes quite a while to collect post-match and by the team we post something, it might be a bit stale I guess. On the pre-match side, we can do analysis of the two teams strengths and weaknesses based on the last 10 or 20 games and I think that might be more interesting.

jhunt
5/10/2011 12:04:22 AM #

That sounds great.  It'll perhaps give the interested but neutral party something to look for during the course of a game.  Best of luck with this, it's great the work you are doing.

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8/5/2011 12:02:12 PM #

So understand and interest article!!! Because much players haven't good pass))

Max
11/21/2011 8:20:12 PM #

Isn't it possible the players with higher skill are the ones that are able to get the more difficult pass out anyway?

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