I recently created a decent set of MLS possession data while working on another project, and I was curious if the patterns of the famous Reep analysis would hold for MLS. Thus, I attempted to replicate his result, and perhaps offer a couple new perspectives to the data.
I was first introduced to the legacy of Charles Reep while reading The Numbers Game (by Chris Anderson & David Sally). Reep was an early advocate for applying statistics to soccer, and was famous for tracking game events by hand over many seasons. According to his data, most goals were scored from possessions with three passes or fewer. And this was taken as empirical justification to play directly; minimizing the touches with longer passes in order to improve results.
Although Reep’s status as a pioneer in the sport is secure, many still debate the results and interpretation. Some critiques assert the underlying data was misinterpreted. Highlighting a simple majority of goals may not be the best analysis when most possessions had three or fewer passes anyway. Others suggest the structure of the analysis confuses correlation with causation; leading to misapplication of the results. In short, one can’t tell if the results were caused by the number of passes, or whether some other factors have causal roles. As I attempt to recreate the analysis; it’s worth stating the same criticisms and critiques apply to this replication effort as well. Read More
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. Read More
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: Read More
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. Read More
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).
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. Read More
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? Read More
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. Read More
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. Read More
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. Read More