Where Goals Come From: Using past goals to create future goals

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Where Goals Come From: Using past goals to create future goals

The outline for this article is going to be:

  • If you’ve heard about or looked at xG in the past but either 1) didn't see its utility or 2) didn't know how to make it useful, we want to help with these scenarios in this article and upcoming articles.

  • xG is always improving, so regardless of what you saw or read about a few years ago, it is much better now at evaluating individual shots because of better and more data.

  • Not all xG values from various sources are equal because there is not equal access to the data points and data volume, and because data providers, clubs, and analysts have varying ideas on how to value shots and optimize their models.

  • There are other stats and metrics that are not talked about as much as xG but can also be very useful in addition to or along with xG. Some may be better suited to your audience.

  • xG helps us answer the quality question about a shot, and we'll be talking about improving shot quality utilizing xG and other tools throughout this season. Without xG, shot quality becomes highly subjective and experiential.

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Single Game xG is here to stay - is it useful?

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Single Game xG is here to stay - is it useful?

It’s been documented for a while that Expected Goals is the best single metric for understanding performance of a soccer team and predicting it’s future. It outperforms possession, total shots ratio, goal difference, points scored, and other fun but inferior statistics. The word on expected goals has been slowly on the rise since ASA’s first model was released in 2013. For kicks, below is an indexed view of times the metric has been searched for in the United States since that grand moment in time. Searches reached an all-time high this past February.

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The Evolution of MLS Penalty Kicks (and How to Fix Them)

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The Evolution of MLS Penalty Kicks (and How to Fix Them)

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.

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Model Update: Coefficient Blending

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Model Update: Coefficient Blending

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.

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Chris Armas’ transformation of the New York Red Bulls

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Chris Armas’ transformation of the New York Red Bulls

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.

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The Next Level of xG: Expected Possession Goals

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The Next Level of xG: Expected Possession Goals

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.

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The Legend of Josef Martinez and what it takes to get to 27 (and more) goals

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The Legend of Josef Martinez and what it takes to get to 27 (and more) goals

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.

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The good, the bad, and the unlucky: What Expected Points tell us about the 2018 MLS season

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The good, the bad, and the unlucky: What Expected Points tell us about the 2018 MLS season

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.

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Pressing, Defensive Lines, and What Defensive Actions Correlate with Goals

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Pressing, Defensive Lines, and What Defensive Actions Correlate with Goals

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?

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What Makes the Red Bulls' High Press Work?

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What Makes the Red Bulls' High Press Work?

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.

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