Recent
Ever since we built goals added (g+) oh so many years ago, I’ve largely been unhappy with how we’ve treated goalkeepers here at ASA. ASA, the masterful puppeteer in the shadows crafting the rise of Matt Turner and Djordje Petrovic to the Premier League, letting them down! And so, my fellow analytics practitioners, ask not what your goalkeeper can do for you, but what you can do for your goalkeeper.
ASA and the analytics community at large has gotten to a pretty good place with goalkeeper shotstopping, at least with event only data. You scale the saves they make by the quality of chances they face, you accept that’s a pretty noisy metric season to season, it tracks directly to goals, it’s sort of easy. We have done a somewhat less good job looking at how the other parts of being a goalkeeper impact the game. Goals added does an okay job, assigning the value of their sweeping to the situations they interrupt. But it’s an imperfect picture, the whole point of sweeping is that you are preventing a much more dangerous situation from occurring further down the road, but where you are now is not actually that dangerous on its own. The ball playing side is similar, goalkeepers are so far from goal that aside from long kicks up the field, virtually all the passing they do is meaningless in the eye of a possession value model.
Today, though, we start with cross claiming. If you take the entire MLS dataset we have at ASA, the most productive cross claiming season by g+ is about +0.5 g+ across the entire season. Half a goal. Intercepting a cross in the 6 yard box off the head of a striker itself is worth half a goal! It’s wrong, and I won’t stand for this goalkeeper cross claiming erasure #GKUnion.
At the end of a season in soccer, the Golden Glove is awarded to the goalkeeper that has kept the most clean sheets. It seems intuitive, as being the last line of defense, their job is mainly to stop any shots that make it past the defense from entering the goal. However, a clean sheet is when the team prevents their opponent from scoring, not just the goalkeeper; it’s a team effort.
How scoring in the Men's World Cup compares to domestic leagues
By Jamon Moore
During the pandemic, when Carlon Carpenter and I researched the impact of certain types of soccer passes, we were blown away by how important they were to goal scoring. We wrote 10 articles about them throughout 2021, called the “Where Goals Come From” series. Even from those 10 articles, we never imagined the reach they would have in clubs across the world.
Now, we examine the world’s premier competition and compare it to our original and ongoing research on how shots are created and goals are scored in domestic league competitions.
MLS is back on Thursday, as we return from the World Cup break into a season finely poised to be one of the most fun we’ve had in quite some time. At the same time, our friends John Muller and Mike Imburgio have launched their new app, Futi. I’m sure they agonized over every word of their tagline, so I’ll copy it here:
The new app that makes football make sense. Follow your favorite teams and players with real-time scores, shareable data visuals and pro analytics made simple.
While ideologically we believe it’s called soccer, the app definitely does what it says on the tin. As such, I thought it’d be fun to dig in and see what Futi tells me to keep an eye on as we welcome MLS Saturday night’s back into our hearts. If you like what you see here (every image in here will be right out of the iOS app), head to futi.live and check it out for yourself.
Expected Threat (xT) is a model that estimates the value of a pass or carry based on its likelihood of leading to a shot and the danger associated with that shot. Unlike Karun Singh’s original xT framework, which relies entirely on historical transition probabilities, this version incorporates a logistic expected goals (xG) component to better capture shot quality. This analysis is inspired by similar work conducted by Chloe Sainsbury in 2025.
