Back in 2017, Vox published a video summarizing research from Michael Mauboussin’s book The Success Equation, which ranked the major team sports on a scale of luck to skill using a formula that included games played, player size, number of possessions, chances, and various other factors. This research wasn’t intended to measure player skill—surprise! professional athletes tend to be very skillful at their chosen sport—but rather how well their sports “capture” that skill. in other words, the study sought to show how well results in those sports could be predicted by player skills. Soccer—specifically, the Premier League—came out as the second most “skill-based” of the major sports, ranking behind only basketball in terms of its non-randomness. Still, as anyone who’s watched any CONCACAF matches can attest, luck is an, um, “relevant” factor in the outcome of a match.
Still, beyond the obvious instances of human fallibility (and the question of if and how much the introduction of VAR has reduced this “luck factor” is a question that should be explored in more depth) the video brings up the question of what aspects of the sport are “lucky” vs. “skilled”, and whether the existing balance of those two is the most desirable. Read More
With our most recent app update, you might notice that some numbers in the xGoals tables have changed for past years where it wouldn’t normally make sense to see changes. As an example, Josef Martinez had 29.2 xG in 2018, but updated app shows 28.7 (-1.7%). No, this is not an Atlanta effect, though I can understand why you might support such an effect. Gyasi Zardes lost 0.5 xG as well (-2.4%), and no one dislikes Columbus.
We have updated our xGoal models with the 2018 season’s data, and that is the culprit of all the discrepancies since the last version of the app. I have already cited the largest two discrepancies by magnitude, so this isn’t some major overhaul of the model. In fact, only 2018’s xG values have been materially adjusted.* The new model estimated 35.6 fewer xGoals in 2018 than it did before, equivalent to a 2.8% drop. Read More
Coaching the New York Red Bulls must be a dream for most managers in North America's soccer circle, but Chris Armas also has had one of the toughest tasks in MLS. A mid-season takeover is never easy, let alone the takeover of a contender from the legendary Jesse Marsch. The Red Bulls organization may have boasted that they focus on the same pressing style starting from the academy, but everyone has their own unique ideas they want to implement. Armas is treading a fine line: he is introducing new elements while also keeping what was working for Marsch. The Red Bulls are still playing a similar style of soccer, so it appears Armas has been making quantitative, rather than qualitative, changes. Deciphering those changes will require some analytics techniques.
I first look at how New York has fared under the two managers using different variants of Expected Possession Goal (xPG). I recommend you read that full article, but in short it’s a score that measures the risks a team bears vs the rewards it creates. In short, Negative xPG measures the risks a team bears, while Mistake xPG measures the amount of turnovers a team commits from those risks. Read More
Using xPG variants to assess risk-and-reward of the game
We introduced Expected Possession Goals (xPG) in two recent articles. xPG groups and rates the outcome of a possession and began from an idea that every action in the possession connects to create a shot. Here, we’re introducing new xPG variants, extensions to the original xPG definition to assess the risks and rewards inherent in a soccer possession.
xPG rates a group of uninterrupted events - or when an interruption lasts fewer than two seconds - based on where the ball travels. It assumes the purpose of the possession is to move the ball within shooting distance. Read More
Josef Martinez is a man on fire, and, as of writing this, he currently sits on 28 goals in 2018, having just broken the all time scoring record of 27 first set by Roy Lassiter in MLS’ inaugural season and matched by Chris Wondolowski in 2012 and Bradley Wright Phillips in 2014.
But I want to take this opportunity to look at how goal scorers score goals, and compare Wondolowski, Bradley Wright-Phillips and Martinez (we don’t have data on Lassiter, sadly) on their march to 27. Yes, Martinez has broken the record, but this article is going to deal with his stats on the way to 27. For a more complete breakdown of his data and where he lands, I’m sure someone at ASA (let’s say, Harrison) will write you that article at the end of the year. Read More
Expected goals (xG) has finally made it, the Times of London are including an alternate table for the English Premier League based upon per game xG for this season. While using only which team had the highest xG in a game for determining a winner is problematic, it is still a step in the right analytical direction. Read More
How do you analytically measure a high defensive line and defensive pressing (see StatsBomb pressing index and Jamon's piece from a couple weeks ago)? Do we have enough data and information to analyze this behavior? If we do, how do these tactics impact the performance of a team? Read More
Jesse Marsch’s New York Red Bulls play a style unlike any other team in Major League Soccer. They employ a frenzied, but organized high press that is a staple of Red Bull teams all over the soccer world. RBNY usually set up in a somewhat fluid 4-2-3-1. Bradley Wright-Phillips leads the line, often occupying the space between opposing center backs and shrinking the field. Right behind BWP sits Argentinian playmaker Kaku. Flanking Kaku is usually a combination of Florian Valot, Daniel Royer, and Derrick Etienne Jr.; these wingers are tasked with pressuring the ball in wide areas and occasionally dropping to help the pair of deeper midfielders. Who are those deeper mids? USMNT starlet Tyler Adams and fellow American Sean Davis are instructed to patrol the entire center of the field, acting as a pair of disrupters, intercepting passes, marking opposing playmakers, and shutting down attacks. Read More
Expected goals (xG), love ‘em, or hate ‘em, are increasingly being accepted across the soccer world, with misguided notable exceptions. While there are multiple xG models in the soccer analytics world, the concept basically boils down to quantifying the likelihood of a shot being scored based upon where and how the shot was taken. xG quantifies what you may understand intuitively, a shot taken close to goal is more likely to be scored than a shot taken 30 yards away. There are many ways to misinterpret expected goals, one of the most common is that xG tells you exactly how many goals a team will score in a game. Obviously, this cannot be the case, as the sum of xG values of shots in a game is rarely a round number. A team cannot score 1.62 goals in a game, but it can score 1 or 2. xG gives the most likely outcome for goals scored in a game. But since goals come in discrete units of 1, and no more than 1 goal can be scored per shot, calculating the probability of goals scored in a game gets a bit complex. The number and quantity of shots that go into a team’s overall xG for a game matter, it’s not just the sum of xG. Read More
Starting today, if you go to our xG by Game page (also listed at the bottom of this post), you'll notice that there are now two expected goals totals for each team. That's because we have multiple xG models, and they give different results. Crazy, we know. One is called the team expected goals model, and the other the player expected goals model. There are only two simple differences between these models, but they are significant.
- Penalty kicks are worth less in the team model.
- Sequential shots get their value diminished in the team model.