Investigation: Atlanta United’s Historically High Finishing Rate

Finishing!

I think this is the first time I’ve discussed this topic over here at DirtySouth, but those of you who read my blog might have just audibly groaned. As we drive further forward into the season, it’s an increasingly difficult topic to ignore for Atlanta. While it’s easy to lament individual missed chances and their impact on points dropped in the table (think of the DC home match), the truth is that Atlanta United are a statistical outlier, finishing an exceptionally high percentage of their chances relative to the rest of the league, and outperforming their expected goals by the widest margins we’ve seen in any MLS season (that I’m aware of). It begs the question…

When it comes to converting shots into goals, are Atlanta luckier than most or better than most? Stick around. I have charts.

Background: Shot conversion percentages are fickle

Evidence has shown that in soccer, consistently creating good chances and limiting your opponent’s chances is a key success factor. The rate at which your chances are converted into goals may come and go, but whatever it is that you do that creates more and better chances and limits the quantity and quality of your opponent’s chances is the secret recipe, the valuable IP that makes a team good. In fact when trying to predict a team’s future performance, the number and quality of its past goal scoring chances compared to those of its opponents is more often a better predictor than its past goal differential.

And it’s because of this that when you see a team with an exceptionally high shot conversion rate, you are supposed to be worried that the team will perform worse in the future than it has in the past — that its past results are due in large part to something that won’t be repeated in the future (chance). This fear could be comforted if the team were taking higher probability shots than the rest of the field (the sort of thing you might measure by looking at the team’s expected goals per shot - an important concept covered beautifully by your own Rob Usry and Joe Patrick in THIS VIDEO), but generally speaking high conversion percentages are red flags.

The data tells us Atlanta United is amazing at shooting

The Five Stripes have an exceptionally high shot conversion rate, which has been sustained basically all season long. Atlanta are on top of almost every flavor of shot conversion chart (shots, shots on target, open play shots, etc). As an example, immediately below is the shot on target conversion metric through the 21 games or so in 2017 (you won’t have to scroll too far to find your team):

It is not only the highest SOT conversion rate in MLS at the moment, but it is historically high (only 2014 Dallas and 2013 Red Bulls top Atlanta’s 2017 figure), although curiously a few teams this year are also very high. Does Atlanta have a high shot quality per shot (xG/shot) number to support this high of a conversion percentage? No, they don’t, at least for the models that are published on the internet. It’s middling at best. See a helpful graphic of MLS shot quality and volumes from AmericanSoccerAnalysis here. Another way of looking at this is to look at Atlanta’s goals scored compared to their expected goals. Atlanta are averaging 1.9 goals per game against an expected goals per game figure of 1.3. They’ve scored 15 more goals (41 vs 26) than MLS teams historically have based on the volume and location and other attributes of the shots they have taken (based on publicly available data). And again to prove the point, this will look familiar, but here’s how Atlanta looks historically in terms of their goals scored above expected goals per game (it is the highest on record):

Again, woa. What gives?

In Search of an Answer

So what do we say about this? As I’ve posited before, the possibilities seem to be as follows, and a combination of these is likely:

1. Atlanta are luckier than most.
2. Atlanta are more skilled at shooting than most.
3. Atlanta are taking better shots than most, which is propping up the high conversion number; however, the models we have for measuring shot quality (expected goals) are struggling to properly value the particular shots Atlanta are taking.

Shot Accuracy (also historically high)

Quickly, on #2: Skill, I’d like to point out that not only are Atlanta putting a higher percentage of their on-target shots past the goalkeeper, they are also putting a higher percentage of their shots … on target — testing the keeper at a higher rate than anyone else. And while, like shot conversion percentages, this isn’t a perfectly repeatable stat (repeatability suggests skill is involved), it is more repeatable than conversion. First, here’s the shot accuracy of 2017 MLS teams (shots on target / shots). Atlanta kills it.

Once again, that’s historically high. Only the 2015 Sounders put a higher percentage of their shots on target. And again curiously, another 2017 team is right there with Atlanta, this time the Fire.

I’m tempted to suggest this very high accuracy number is evidence of either #2 (shooting skill) or #3 (Atlanta’s chances being better than the models suggest). I can’t really prove that first idea but it seems plausible. The second one — I don’t know — I think I remember something about shots on target including some bit of embedded information about the quality of the chances (since its easier to put a high quality chance on target than a low quality one). To my eye, watching the team, it doesn’t feel like they are lucking into a high percentage of shots on target. The team really seems to prefer to not shoot until they’ve set up an open look. Many good opportunities end before a shot is generated as the team works to find a better shot by passing or dribbling — this sounds a little bit like *gaming xG* but I can’t be too sure.

Digging deeper: A study in goals

I haven’t looked at every single shot taken this year by the Five Stripes, but if we look at all the goals (yep all of them, many of them here: thanks Whitecaps), a very high percentage of these are 1v1 against the keeper (and several are empty net opportunities), or there are very few opposition outfield players between the shooter and the keeper. This type of “openness” of a shot has a big impact on its chance conversion, but most expected goals models can only “guess” at the the openness of a shot based on other event data context. They don’t “see” the openness, but they might see that the shot was assisted by a through ball or that the buildup was defined as a “fast break.” I tried to record a bunch of admittedly judgmental shot quality attributes for all these goals. See below for a summary of what I compiled watching every goal, looking out for certain “openness” attributes and then I’ll compare it to another team:

I haven’t done this for all MLS teams because of time, but what jumps out to me are the 26 1v1 chances, and the 35 chances with 1 defender or fewer between the shooter and the keeper/goal. My gut is that these figures are high compared to the league average (which I do not know). For comparisons sake, I picked another team, New York Red Bulls and gave them the similar treatment of watching every goal scored and capturing “openness data.” I chose them for two reasons, first they are currently running neck and neck with Atlanta for the 4th seed in the playoffs (and a first round home match), and secondly (and more importantly), they are an example of a very good team that’s not crushing its expected goal numbers like Atlanta is. In fact they are slightly under-performing. So I was interested to see if there’s a significant difference in the composition of the goals scored. There is.

Across the board NYRB’s goals feature slightly fewer “openness” attributes, except they do a great job of finding BWP in the 6 yard box. If we compare them side by side it’s somewhat easier to see that Atlanta’s goals are coming from mostly cleaner chances.

Is this the difference between a team over-performing its expected goals by historic margins (Atlanta’s +0.6/game) and a team slightly under-performing its expected goals (RBNY’s -0.11/game)? Could be. To zoom in on one of those attributes, here are the distributions of defenders in between the shooter and the keeper for Atlanta vs Red Bulls goals in 2017. Atlanta first (so many chances piling up with very few defenders) then Red Bulls (more evenly distributed).

Again, we can feel confident it’s either 1) chance/luck and Atlanta really aren’t as good in attack as the results show, 2) Shooting skill with Atlanta being better at finishing than most other teams, or 3) the quality of Atlanta’s chances (primarily related to their “openness”) not being picked up in the event data based expected goals models.

I would suggest it is a mixture of all three with the above “goal quality attributes” hinting gently towards #3. And perhaps Atlanta’s three designated players being signed into the attacking front 4 hinting gently towards #2.

Let me know your thoughts. What am I missing?

I should note that watching all of a team’s goals is a fairly weak attempt at trying to identify other shooting attributes that might be contributing to outliers in shooting efficiency and that looking at every single shot would be a much better exercise (and even still a flawed one). But I don’t know how to get the footage and honestly I don’t know if I could do it. This is at least something. I think it’s better than throwing one’s hands up and saying “welp, Atlanta are lucky” or “welp, Atlanta are master finishers.”

Expected goals data comes from AmericanSoccerAnalysis.

If you made it this far and you actually enjoyed it, why not follow me on twitter at @tiotalfootball and check out my blog at atlutdinsight.wordpress.com where I try to post stuff like this most weeks.