Predicting the Most Likely Winner of the 2026 FIFA World Cup An Analytical Approach through a Multi-Dimensional Model
DOI:
https://doi.org/10.67603/jsrss.v6i2.10680Keywords:
Sports Prediction, 2026 World Cup, Multi-Dimensional Analytical Model, Squad Depth, Path Strength IndexAbstract
This study addresses the problem of predicting the team most likely to win the 2026 FIFA World Cup (hosted by the United States, Mexico, and Canada), drawing on quantitative data collected after the conclusion of the group stage.
The study adopts an analytical approach based on a composite, multi-dimensional model that assigns equal weight to four methodological axes: tracking performance trends across the first-round matches to assess consistency or fluctuation, classifying the pace of improvement or decline between the first and third matches, measuring squad depth across its various dimensions, and an index gauging the relative ease or difficulty of the anticipated knockout-stage path. The results indicate that Argentina leads the composite model owing to its balance of psychological momentum, squad depth, and a favorable path, followed by France—despite its stronger squad depth—due to a potentially more collision-prone path, and then Brazil, driven by the strongest upward performance trend. The study concludes that these findings represent statistical probabilities grounded in measurable quantitative indicators rather than deterministic predictions, since factors of chance and upset inherent to major tournaments remain beyond the model's scope
Downloads
Downloads
Published
Issue
Section
License
Copyright (c) 2026 ayoub Saoudi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes .
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
