I have been working some more on passing data.
This time I have looked at
success percentage.
Considering every single pass recorded by Opta, EPL players succeed in reaching the target teammate 77% of the time.
Obviously not all the passes are created equal, as the success rate is 83% on short passes compared to 55% on long ones.
Similarly, teams are OK in letting their opponent move the ball in their defensive third (93%, that's also due to teams taking less risks when in front of their own net), but they make it harder as the opponents become more of a threat (84% in the middle third and 66% in the final third.)
Having data at an aggregate level (the Lite dataset), one cannot go into deeper details, such as combining the passes length and position.
My goal here is to find
factors related to the percentage of success.
While building my model I was interested in evaluating the effect of the passing
player and
team, the
opposing team and the
field of play.
I used a multilevel mixed model, as I do for baseball when evaluating the simultaneous effect of several players on the outcome (so that when I evaluate the ability of a catcher in preventing steals, I take into account his pitchers and the runners who attempt the steal against them.)
Here is the first table: percentage of success. Zero means an average performance, so Manchester United has a pass success six percentage points higher than the average team.
team %relative to average
Arsenal 3.3
Aston Villa -4.0
Blackburn Rovers -4.4
Bolton Wanderers -5.4
Chelsea 4.0
Everton -0.8
Fulham 2.6
Liverpool 2.7
Manchester City 5.5
Manchester United 6.0
Newcastle United -2.2
Norwich City -3.4
Queens Park Rangers -2.5
Stoke City -6.0
Sunderland -2.8
Swansea City 3.3
Tottenham Hotspur 4.6
West Bromwich Albion -1.4
Wigan Athletic 1.4
Wolverhampton Wanderers -0.5
Again, as could have been anticipated, the top teams are the better at making good passes. Like it was for passing attempts, Swansea City passes better than its position in the standings would indicate, while the opposite can be said for Newcastle United.
Let's look at the other side of the mirror.
opponent %relative to average
Arsenal -2.7
Aston Villa -0.5
Blackburn Rovers 2.5
Bolton Wanderers -0.8
Chelsea -0.3
Everton -0.8
Fulham 3.5
Liverpool -0.6
Manchester City -1.1
Manchester United -0.8
Newcastle United -0.5
Norwich City -0.4
Queens Park Rangers 0.6
Stoke City -1.1
Sunderland -0.1
Swansea City 0.6
Tottenham Hotspur -0.6
West Bromwich Albion 1.6
Wigan Athletic 1.3
Wolverhampton Wanderers 0.2
Here the variation is much smaller, suggesting (not surprisingly) that the team (and the player) attempting the pass has more impact on its success than the opposing team.
Fulham comes out again as a beast of its own. For some reason, if you are an average team at completing passes, when you face Fulham you suddenly become Arsenal!
I would like to hear from people who watch many EPL matches whether Fulham has a peculiar way of defending, as they allow both more passes and more successful ones than expected.
And here are the park effects.
stadium %relative to average
Arsenal 0.4
Aston Villa 0.3
Blackburn Rovers -0.6
Bolton Wanderers -1.0
Chelsea 0.9
Everton 0.5
Fulham -0.4
Liverpool -0.7
Manchester City 1.5
Manchester United 1.4
Newcastle United -0.3
Norwich City 1.2
Queens Park Rangers -1.6
Stoke City -2.3
Sunderland -0.1
Swansea City 0.3
Tottenham Hotspur 0.7
West Bromwich Albion 0.5
Wigan Athletic -0.2
Wolverhampton Wanderers -0.5
The baseball analyst in me made me throw the stadium variable in the model, but soccer fields are of roughly fixed dimension and surely fixed shape, thus they don't play much of a role. (Note that I referred to the stadium with the home team name. Sorry for not having bothered to attach the name of the venue.)
However small the park effect, I noted that the top teams play in pitches that make completing good passes easier (OK, the highest value is just +1.5%!)
This makes sense to me, as top clubs likely have better resources to take care of the field and, given they are stuffed with talented players, are more interested at keeping a fair surface of play.
I turn once more to EPL connoisseurs, asking them to share any info on the field of play of Stoke City, which appears as the most
good-pass-preventing venue.
Note that the stadium values are not influenced by the team calling it home. Thus the +1.4% of Old Trafford means that having accounted for the fact that the best passing team plays there, an increase of 1.4% in passing success is attributable to the venue itself.
I threw a couple more variables into the model.
One is
the zone where the pass was made. A pass in the middle part of the field is 8% less likely to reach the desired teammate than a pass in the defensive third. Going into the offensive zone the probability of success drops even more (-25% compared to the defensive third.)
The second one is an indicator of whether the team attempting the pass is
playing at home. In such cases the chances of a successful pass increase of about 1%. This also makes sense as players are certainly more familiar with the grounds the play on half of the time.
OK. You may say
"Wow, you told us that good teams are better at making passes, that playing at home is better and that once you get into the final third you have a tougher time—were advanced analyses needed for such obvious things?"
I agree, they weren't so much. However, when you want to evaluate individual passing ability, you have to make sure you remove other factors (such the ones presented here) from the equation. So that when you show the passing rate of a player, you have taken into account the fact that he plays (for example) for a good team, on an uneven field, and makes his passes mostly on the offensive zone.
Thus I'm happy that the model so far has spit out "obvious" results, because I'll be more comfortable when I look at individual ratings.
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