Sports Analytics Weekly by kubeia.io - 24/2026
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đź“ť Sports Analytics
An Inverse Problem of Social Influence for Spatial Reference Positions in Soccer
springer.com - Ulrik Brandes
We present a complex network approach to depict representative spatial arrangements of association football (soccer) teams. It is designed to mitigate issues that arise with average locations, which are currently the most common choice. Our approach uses proximity networks, affiliation networks, and a network model of social influence to reduce spatial concentration bias and amplify the main positioning signals. Example applications include the display of enacted formations, and the placement of players in passing networks.
Find a Player's Statistical Twin
cannonstats.com - Scott Willis
Explaining and introducing the Cannon Stats Similar Player Tool
World Cup Projections: Introducing PADDLIN'
expectinggoals.com - Michael Caley
This model seeks an answer to all the problems that bedevil any objective measurement of international team quality. It uses an Elo rating based on actual results and, where available, an xElo rating based on the projected result of adjusted expected goals, to get a first view of team quality that can be compared among teams around the world. The model does in fact increase the weights of matches in which a team put up a significant margin in goal difference or xG difference, actually incorporating the question of whether one team or another did suffer a paddlin’. It makes use of player values from Transfermarkt.com to adjust these Elo ratings, giving teams credit for having better players available beyond the effects they may have had on results in the past. While these crowd-sourced player ratings are far from definitive, work by Paul Johnson among others has demonstrated their utility for statstical projections.