Passing

Using k-means to learn what soccer passing tells us about playing styles by ASA Staff

When you talk about a soccer team, you almost always talk about its style: high-pressing, possession-heavy, parking-the-bus, etc. A team’s style not only signifies how they play on the field but also reflects its coaching. Since there aren't guidelines on how the style of the team should be defined, everyone uses their own rules and we can't directly compare each other's descriptions.

An accurate quantitative description of the style is needed. It can help one to properly analyze not only the opponent's team but also his/her own team. With an accurate method to describe the style, one can scientifically evaluate if a training exercise is efficient at serving its purpose. We previously have used dimension reduction technique, t-SNE, to find MLS teams with similar styles based on the spatial distribution of activities and pass networks. This time we use a different method, k-means clustering of pass types, to quantitatively measure style, tactical specialization, and the influence of coaching on a team’s system.

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Passing Model Update by Matthias Kullowatz

We updated our xGoals model a few weeks ago, as well as our process for continuously updating it throughout the season. Naturally, we’ve done the same for the xPassing model, which estimates the probability of any given pass being completed based on a number of details about the pass. You can read more about the original model here, but here’s the summary of the new model:

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A Feast for Throws by Eliot McKinley

In Game of Throw-Ins, I characterized and introduced an expected throw-in possession retention model (xRetain) for MLS. Go read the whole thing, but it showed that throw-ins are more likely to be completed and possession retained when they are thrown backwards, quickly, and outside a team’s defensive third. But what are MLS teams and players doing with their throw-ins?

To help differentiate teams’ throw-in styles, I turned to hierarchical clustering (see the graph below). I won’t get into mathematical details, but you can think of it sort of like an evolutionary tree. However, instead of the branches separating species, they are separating different throw-in angle frequencies. Kind of like how humans and chimpanzees are near each other on the branches of an evolutionary tree but far away from birds, teams which always throw the ball backwards and short will be far away from those that always take throw-ins forward and long.

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Game of Throw-ins by Eliot McKinley

Much has been written and studied about set pieces in soccer. Penalty kicks have been Bayesed multiple times, I’ve analyzed free kicks in MLS and at the World Cup, corner kicks have been rigorously studied. But what about the humble throw-in? Aside from when teams develop a long throw-in program (see Delap, Rory) they are largely ignored or even ridiculed, in the case of Liverpool hiring a throw-in coach (see the first comment here).


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You Down with t-SNE? by ASA Staff

We all know that some teams play a certain style, Red Bulls play with high pressure and direct attacks, Vancouver crosses the ball, Columbus possesses the ball from the back. Although we know these things intuitively, we can use analytical methods to group teams as well. Doing so seems unnecessary when we have all these descriptors like press-resistance, overload, trequartista-shadow striker hybrid, gegenthrowins, mobile regista, releasing, Colorado Countercounter gambits...etc (we actually don’t know what some of these terms mean and may have made some up, but the real ones are popular so just google them yourself). Those terms are nice, but no qualitative descriptor can tell us how the styles of New York City and Columbus differ from each other. We need to measure, compare, and model two teams’ playing styles and efficiencies. If we are able to do these things we may be in a position to answer what style really is.

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Shots Not Taken: Exploring the propensity of teams to shoot from good positions by DMP

Do you ever find yourself yelling “JUST SHOOT THE BALL!” at the TV screen? Of course you do, you watch soccer! Sometimes it can be maddening to see your star striker make his/her way into the box, only to futz around with a pass or dribble. At times it doesn’t even matter whether that pass or dribble was successful. Does it seem like your team does it particularly bad? You’re probably not alone.

Psychologists will be quick to point out a thing called negativity bias. Basically, we probably all think our team dilly-dallies in the box more than others because we remember it better. The existence of this bias, by the way, is supported by a convincing amount of experimental evidence. But it begs the question, who is empirically more likely to shoot when they can?

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Examining Pace in MLS by Kevin Minkus

There’s been a decent amount of discussion this week about how the pace of play in MLS looked quicker in week one than it typically does. Teams like Atlanta, New York Red Bulls, Kansas City, and Houston all came flying out of the gate, with fairly up-tempo styles of play both with the ball and without the ball.

Unfortunately, coming up with a metric for pace is pretty tricky, and it depends specifically on what type of pace you’re talking about. Going all the way back to 2013, Ted Knutson looked at pace as the total number of shots taken in a game. More recently, Thom Lawrence looked at pace as the distance covered over time within a team’s possessions. Both of these definitions speak to a certain amount of directness of play that I don’t think meshes with what people currently mean when they say MLS is playing ‘faster’ so far this year.

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Forget Everything You Think You Know About Goal Kicks by Jared Young

The ball is placed on painted grass and a tall bright goalkeeper strides backward toward the goal. Now lunging forward the leg swings like a pendulum through the ball sending it bravely toward the sky. The onlookers lift their gaze as the ball reaches the peak of its flight. Two gladiators lock limbs below, jostling for position. They leap together in an effort to possess the falling ball. There is a deflection and the gladiators separate. They rejoin the play.

Long kicks by goalkeepers are a staple in soccer matches and they are a beautiful sight to behold, but that doesn't mean they are a good idea. By the end of this article I hope to convince you of that fact, even if the data isn't entirely perfect.

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Is Steve Clark a Good Fit for Columbus? by Bill Reno

Over the course of a year, the Columbus Crew have gone from runner-ups in the 2015 MLS Cup to bottom dwellers, finishing ninth in the Eastern Conference in 2016. After losing a number of games off conceding late goals and tying over a third of their games, Crew fans are right to be polarized when evaluating their team, especially the defense. In 2015 the Crew conceded 53 goals and another 58 in 2016, both getting close to most in the league. Currently all eyes are on Steve Clark, with debate on both sides on if he is the right goalkeeper for Columbus.

Every team needs a goalkeeper that fits their style of play. For example, if a defense is bleeding crosses, they may want a strong goalkeeper to handle the dangerous lobs into the box. When looking at Columbus, it’s easy to look at the number of goals they’re giving up and think they need a better shot stopper, when in reality, Clark is above average on saving shots in both expected goals and save percentage. The Crew don’t need a spectacular shot stopper. Instead, there are two main things they require from their goalkeeper.

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Who is the best passer in MLS? Debuting an Expected Pass Completion Model by Jared Young

Javier Morales of Real Salt Lake had completed 82.3% of his passes before last weekend. Chad Marshall completed 84.4% of his passes for the Seattle Sounders. Gyasi Zardes has a 66.1% pass completion percentage. Is any of this good? Which player is a better passer? Fans are smart enough to know they can’t compare those numbers directly because Morales and Zardes play attacking roles and Marshall is a defender and will face pressure less often. 

Looking at pass completion percentages without context is a fool's errand. To address this issue I built a model that predicts the likelihood that a given pass will be completed. The effect is that the difficulty of the pass - including position on the field, part of body used, whether it was backwards, etc – can be factored into this expectation. With this model we can observe whether or not a player passes more or less effectively than an average passer, adjusting for the difficulty of the pass.

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