Sports Analytics Weekly by kubeia.io - 25/2026
- Week 25
- Week 24
- Week 23
- Week 22
- Week 21
- Week 20
- Week 19
- Week 17
- Week 16
- Week 15
- Week 13
- Week 12
- Week 11
- Week 10
- Week 9
- Week 8
- Week 7
- Week 6
- Week 5
- Week 3
- Week 2
📝 Sports Analytics
World Cup Update June 18: England, Colombia, and Explaining Adjusted xG
expectinggoals.com - Michael Caley
Uzbekistan is credited with 0.6 “Adjusted xG” while Opta’s published xG rates their chances at 1.2 xG. While my xG system will differ from others at times,1 this is not the case here. In unadjusted xG I had Uzbekistan with 1.1 xG.
The reason that adjusted xG differs so much from published xG here is that Uzbekistan’s equalizing goal was a header into an open net by Abbosbek Fayzullaev off a saved shot from wide in the penalty area. The chance is credited at 0.98 xG by Opta’s system and 0.92 xG by mine.2 If the job of xG is to tell you how many goals are likely to be scored in the match, based on the shooting chances, then this is correct. Uzbekistan were going to score a goal in this match barring an Eric Choupo-Moting situation.
But if the job of xG is to evaluate the quality of the two teams in the match and how well they played, that massive xG is misleading.
👁️ Computer Vision
TrackNetV4/TrackNetV4
github.com
This repository contains the official TensorFlow implementation of our ICASSP 2025 paper: TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps.We provide TensorFlow code demonstrating our Motion-Aware Fusion framework, integrated with TrackNetV2, for training, testing, and visual prediction of sports object trajectories. Our implementation supports three datasets: tennis, badminton, and newly introduced badminton dataset. Additionally, we provide model code for integrating the framework with TrackNetV3.Additionally, feel free to explore the project website for more visualizations, dataset details, and experimental results.
🏛️ Economics
The World Cup and Equity Markets
hulltactical.com
No-one now really believes humans are rational, either individually or in aggregate. And the entire field of behavioral finance is predicated on this fact.
The effects that relate to the market have often been strange. Researchers have linked stock returns to holidays, Netflix, the doomsday clock, cloud cover, seasonal affective disorder, and lunar cycles. Much of this literature is entertaining but not especially convincing. Many of the effects are small, difficult to replicate, or vulnerable to accusations of data mining.
Then there is football (“soccer” if you insist on being wrong).
In 2007, Alex Edmans, Diego Garcia, and Øyvind Norli published one of the most famous papers in behavioral finance: Sports Sentiment and Stock Returns. Their findings were simple, surprising, and difficult to explain away. When a national soccer team loses an important match, the country’s stock market tends to fall the next trading day.