Goals Subtracted (g-): Set piece edition

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This past summer we introduced a framework for evaluating the individual contributions of defenders, goals subtracted (g-). We concluded that–while it was a tricky problem, and not one that was obviously made easier even with tracking data–the framework had potential to help control for otherwise unmitigated interrupting g+ value. We observed players on bad defensive teams getting a lot of interrupting value largely because the ball is coming at them all the time, and g- hinted that it might be able to control for this. That article was about measuring open-play defending, and this article is about measuring set-piece defending. If you are unfamiliar with goals added (g+), I would start here with our primer.

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Introducing Goals Subtracted: Where you aren’t but you oughta be

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Introducing Goals Subtracted: Where you aren’t but you oughta be

While valuations of offensive actions in soccer are, by no means, perfect, they are still significantly more accurate and meaningful than how we evaluate defensive actions and players’ defensive contributions. In a challenge-accepted moment of weakness, we took a stab at better assigning a Goals Added (g+) equivalent for defense: g- (“g minus”). What we’re about to share will blow your mind reinforce just how hard it is to quantify the value of an individual’s defensive actions, but hopefully I can also entertain you down this rabbit hole we’ve been playing around in for more than a year.

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Valuing goalkeepers with goals added

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Valuing goalkeepers with goals added

We have updated our goals added (g+) methodology to produce g+ components for goal keepers. You can find these new metrics on the Goals Added window in the app under the Goalkeepers tab (MLS, NWSL). Up to this point, we had not published g+ metrics for goal keepers. We recognize that goalkeepers perform many unique tasks on the field, and the first version of our expected possession value models and g+ framework missed a lot of those. Below I’ll explain the specific keeper g+ components and what they try to measure, share a few examples, and then wrap up with some nitty gritty details.

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Rolling out USL and NASL data in the web application

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Rolling out USL and NASL data in the web application

In an effort to continue to bring you the best American soccer analysis, we have added three new leagues to the web application: USL Championship (2017 - ), USL League One (2019 - ), and the now-defunct NASL (2016 – 17). These new leagues join MLS and the NWSL in our app, representing more than 4,000 players across nearly 6,000 games. In the app, you can select which league you want to view in the upper right, and then navigate through the tabs to explore shooting, passing, and goals added (g+) statistics for players and teams.

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Explaining our 2020 MLS playoffs projections

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Explaining our 2020 MLS playoffs projections

Predicting playoff outcomes in MLS has always been particularly difficult. While about 400 regular season games may seem like a lot, it is still not even close to enough of a sample size to home in on fine differences between teams through the data alone. And now, with a COVID-shortened season and fake home games, it’s even more difficult. With that said, here are our model’s predicted probabilities of each team making it to each stage of the MLS Cup Playoffs, along with the implied championship probabilities from the Bovada sportsbook and championship probability differences between the two. I’ll go over what we did to produce these predictions, and what missing information could make them better.

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Goals Added: Deep Dive Methodology

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Ever since we founded American Soccer Analysis in 2013, I’ve hoped to construct a metric that credits players for actions all over the field, not just for goals and assists. I’ve always wanted something that could be used to ascribe values to players in a currency all soccer fans could understand. In the pre-ASA era, I got my fix analyzing baseball statistics, where analysts are spoiled by tons of publicly available data and a sport that, by rule, creates distinct plays largely independent of all other plays.

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This is the Very Model of a Modern Major Soccer League -

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Our model gives LAFC a 65% chance to win MLS Cup, which is admittedly an absurdly high figure. Such a figure requires that LAFC have, on average, greater than 85% chances of winning each of three games on their way to the championship. Despite getting all three of those games at home, 85% still feels almost impossible against some good teams. So I’m here to break down what factors are giving LAFC such good chances in our model, and why that model is “wrong.”

Let’s do it like this. LAFC is most likely to play Minnesota in the second round, a team that adequately represents the caliber of a typical MLS playoff team that LAFC will face. For reference, Minnesota was second in the conference in goal differential (GD) and third in the conference in expected goal differential (xGD). I’ll start as though Minnesota were playing itself on a neutral field, and then I’ll layer in the various factors that make LAFC different, and that get us to more than 85% chances of winning a knockout matchup.

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Updated Playoff Seeding Projections

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Updated Playoff Seeding Projections

We have updated our playoff seeding projections on our web application, which show the probability that each team finishes in each playoff seed postion within its conference. We have done this in years past, but dedicated fans of the site will recognize that this is a bit earlier in the season than usual. Some tweaks to the predictive model have allowed us to publish meaningful predictions sooner! I’m here to tell you about those tweaks.

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Passing Model Update

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Passing Model Update

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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