Sports Analytics Weekly by kubeia.io - 8/2026

The real question is not how talent should be identified, but how it is identified when a recruiter has to make a decision.

This paper approaches that question by focusing on the people making those decisions. Instead of analysing players, the authors analyse experienced youth recruiters and ask them to explain, in detail, what they prioritise when selecting Under-13 players.

These judgements are not reconstructed later or framed through general development ideas. They are made at the moment selection actually happens.

What is Garbage Time? This is a concept that originate with NBA basketball, “garbage time” is the term that describes late game situations where the stats and performances aren’t really reflective of a normal competitive game. It is those final minutes of a blowout when the outcome is certain, coaches rest their stars, and the game is just played out to the final buzzer.

This has been adapted to American Football and baseball, but for the most part it has not been adopted very widely into soccer.

For most matches in the Premier League this is not something that will occur a lot, but it does happen often enough that it I believe that it is valuable to identify these situations because they alter the way that the match is played and resulting stats would reflect those same distortions.

What if the biggest problem with expected goals isn’t how accurate it is… but how little people trust it?

For years, we’ve built better and better models. More data. More features. More advanced models.

And yet, walk into many football departments, walk into X/Twitter conversations or football talk shows and you’ll still hear the same question:

“Why would I trust your numbers?”

This week’s Timeless Insights dives into a paper by Jan Van Haaren that tackles exactly that tension — not by chasing higher predictive performance, but by rethinking how we design models in the first place.

Every few months, the computer vision community prepares for a new YOLO release, typically faster, marginally lighter, and incrementally more accurate than the last. YOLOv26 (object detector), released by Ultralytics in January 2026, breaks that pattern.

Rather than increasing architectural complexity, YOLOv26 adopts an edge-first engineering approach. The focus shifts to latency, export paths, and hardware-friendly design. The result is a detector specifically designed for applications in robotics, drones, mobile devices, and embedded systems.

Why standard uncertainty methods break on time series data — and the production-grade fix you need to implement today.