Sports Analytics Weekly by kubeia.io - 16/2026

What happens when the same player performs differently… simply because his position changes?

At first, the answer seems obvious. Of course a fullback runs more than a centre back, and of course a wide midfielder behaves differently from a central one. Positional differences are one of the most established findings in football analytics.

But that is not the most interesting question.

The paper begins by situating football analytics within the rise of probabilistic models such as expected goals (xG), which assign likelihoods to actions like shots based on contextual features. These models typically rely on event data and include predictors such as shot distance, angle, body part, and more recently contextual elements like defender positioning or goalkeeper location.However, a key limitation is highlighted: most models do not explicitly include the player as a predictor. As the authors note, “two separate shots that have the same measures for the model predictors will be assigned the exact same xG regardless of who is taking the shot”. This omission contradicts the fundamental assumption that players differ in skill and execution ability.

Over the past decade, elite football has experienced a steady increase in physical demands, largely driven by the intensification of competitions and the growing number of matches within short timeframes. Congested fixture periods, typically defined as matches played with less than 96 hours of recovery, have become increasingly common and are associated with fluctuations in external load.

These periods impose both physiological and psychological strain, with top teams sometimes playing up to six matches in 18 days. This accumulation of load is linked to neuromuscular fatigue, reduced recovery, and increased injury risk. While prior research has documented general declines or adaptations in physical output during congested schedules, it has largely treated these periods as homogeneous, without considering the type of competition involved.

The paper highlights that different competitions, such as domestic leagues, national cups, and the UEFA Champions League, may impose distinct contextual demands. These include differences in opponent quality, travel, tactical priorities, and psychological pressure. As a result, understanding external load requires a more granular approach that integrates both competition type and individual playing time.

Whether you love long attacking throw-ins or hate them, there is no denying that they’ve become both a key feature and flashpoint in men’s soccer in the past year. John Muller likely sparked a renaissance of the tactic (and a soon-to-be Arsenal title) with his 2023 article for The Athletic, and Joe Lowery and I borrowed his method for Backheeled when Minnesota United started longthrowmaxxing in 2025 (Editor’s note: Minnesota work with Mike Imburgio through ASA’s firewalled consulting arm). But while each game has about 40 throw-ins on average, only about 10 of those throws happen close enough to reach the box. But apart from Formerly Called Twitter jokes about consultant Thomas Grønnemark, there hasn’t been much commentary about all the other ones in popular media or public analytics circles. The only exceptions I’m aware of are Eliot McKinley’s 2018 two-part opus on this very website, and some recent academic work on the top 5 European leagues that, if you like in-text citations and interpreting regressions, is an excellent spoiler for the rest of this article.Eliot did that work almost a decade ago (before Game of Thrones jumped the shark), and I thought it was time to replicate and extend those findings with all the amazing infrastructure that ASA has built since the days of CSV files on Dropbox. In addition to models estimating throw completions and retained possession, I also analyze the goals added for possessions following throws to assess the value of throw choices. This allows me to find the MLS throw-in MVPs and offer an expanded set of (very general) rules for approaching these overlooked moments of play.

One of the main uses of simulation and forecasting in designed feedback systems is for deciding how to act. If I can map what will happen next, I can choose actions that steer me toward good outcomes. This mindset seems perfectly sensible, and it’s the backbone of statistical decision theory, tree search in game play, optimal control, and model predictive control. Moreover, people who are good at prediction get clout. You can even win money in markets. It seems like forecasting is a skill and talent, and one that requires deep knowledge of how the world works. And yet, in class on Monday, I discussed how you can make excellent forecasts by simple, strategic accounting.