Beyond xG: Using PFF Shooting Grades to improve goalscoring predictions in MLS

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Expected goals (xG) have become the most widely used metric in football analytics in the last decade. In short, xG models, such as the one developed by American Soccer Analysis, calculate how many goals a team should have scored based on the characteristics of the shots they have taken. Penalties provide the easiest example: roughly 76% of penalties are converted, so each penalty has an xG value of 0.76. Many articles have shown that using expected goals is preferred to actual goals when evaluating performance, because the metric is generally much more stable due to the rare nature of actual goals.

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Does Passing Matter?

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Does Passing Matter?

I really want passing to matter. I watch, on average, 968 passes in a soccer game, and I’d like to think that completing them actually means something more than launching the ball into the first row.

Watching a beautiful through ball unhinge a defense is like watching a sun set on the bay. But that beauty doesn’t mean it matters, at least not to the data. Not if you care about winning.

Let me show you the correlation between a team’s pass completion rate and their expected goal difference over the last three years in Major League Soccer:

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2023 MLS Analytics Survey

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2023 MLS Analytics Survey

Every year, we update the State of MLS Analytics by putting teams into tiers based upon how many analytics staff they have. However, the number of analytics staff members doesn’t necessarily say anything about the quality of work that a club is producing or if analytics is being incorporated into team decision making. And unfortunately, we can never really know what is going on inside a club’s analytics department. This year, though, we decided to do the best we could to get behind the scenes and asked club analytics staff for their input.

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State of MLS Analytics: May 2023

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State of MLS Analytics: May 2023

Over the last few years, analytics in MLS has been turned on its head. Five years ago, if a team had one person “doing analytics,” it was a big deal. Now, questions are asked if a club doesn’t - and it is not uncommon for clubs to have multiple people on staff. The Tiers of MLS Analytics are now based upon the number of full time analytics staff members a club employs.

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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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Replication Project: Are shots from free kicks useless or good?

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Replication Project: Are shots from free kicks useless or good?

Way back in 2011 a somewhat remarkable thing occurred in the nascent soccer analytics blogosphere. On May 20, Chris Anderson posted the provocatively titled “The Uselessness of Free Kicks in the Premier League,” which argued that since free kicks are rare and not often converted they are “not particularly effective devices for scoring in the Premier League”. A few weeks later, Anderson lent space on his own blog, Soccer by the Numbers, to Ian Graham, now the outgoing Director of Research at Liverpool but then at Decision Technology, to argue the opposite. “Why Shots From Free Kicks Are A Good Idea, Or At Least Not A Bad One” showed that while free kick shots rarely result in a goal, the correct comparison is to other shots outside of the box. In that comparison, free kick shots are about twice as valuable as open play shots from outside the box. So with an additional decade of data, have things changed since then using MLS data and who was right?

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