It’s that time of year. Decision day has come and gone, the playoff matchups are set. It’s awards time. Here is our ballot at ASA.

By Nate Gilman

Without publicly available pressing data (RIP pressing data on FBref), understanding how much and where teams pressured opponents has become more and more difficult. We’re left with less precise measures like the raw counts of standard defensive actions in the final third, PPDA (passes per defensive action), and the average height of defensive actions, to try to piece together a more complete picture of how much teams are pressing. All of these metrics can inform how we analyze pressing and pressure, but is there a better way to quantify it?

For reasons unbeknownst to me, the algorithm decided to surface a lot of people talking about the upcoming change in the MLS schedule for the 2027/28 season. Maybe it’s the crisp fall air, the departure from the hottest summer I’ve ever experienced, people excited for a super El Nino, I don’t know.

By Kieran Doyle

Finishing is a fairly contentious topic when it comes to the world of soccer analytics. The somewhat mainstream analytics practitioner opinion can be summed up as follows: finishing is objectively a skill that exists, but the majority of players possess a skill level that impacts the underlying finishing rate to a much smaller degree than the random noise of sometimes ball go in. This is the thing with soccer as a sport as a whole. Feet are just so much worse than hands. One topic that this has made me think about is the whole hot hand fallacy. Do feet, which are much less good at manipulating balls than hands, get hot? I’m going to try and avoid being too academic and stiff here, but bear with me.

By Kieran Doyle

In 2022, Daniel Dinsdale and Joe Gallagher of Stats Perform (the artist formerly known as Opta) released an arXiv of a paper titled “Transfer Portal: Accurately Forecasting the Impact of a Player Transfer in Soccer”. The paper is a worthwhile read, but the idea can sort of be summed up as the three approaches. 

1. Identify how the player is currently doing on a per 90’ basis, for the metrics you care about.

2. Identify how their team and league fits into the global hierarchy, in relation to other teams and leagues.

3. For players with insufficient data, weight the average of their small data sample and some prior for the league/age/position etc.

Then take all those things, stick it in some neural networks, and try to predict the impact if that player moved from team A to team B. And it works! It’s a pretty good predictor of how players do when they move clubs, reducing mean squared error by 50% compared to assuming their performance translated over one-to-one. This is a cool result, and league translation/transfer projection is an extremely difficult task. 

Where my brain went when I read this paper six years ago was, wow, this would be a great approach to trying to figure out how good young players might be. Three months after this, MLS Next Pro started its inaugural season, in March of 2022. While Opta’s work on this was based on some 26,000 samples and 2600 transfers, after four seasons of MLS Next Pro we certainly don’t have 2600 players graduating to their first team, but we might have enough to try.