Economic resources, sporting efficiency and uncertainty: a global test on the 2026 FIFA world cup
Abstract
A related paper (arXiv:2610.06967) developed a stochastic optimization framework in which football performance combines GDP/population-based potential with a latent mean-reverting efficiency process. The framework was applied to a cross-section of the 24 national teams participating in AFCON 2025. This paper applies the same methodology to the full 48-team 2026 FIFA World Cup, extending the analysis beyond Africa without modifying the underlying model. Using GDP, population, FIFA ranking data, and complete tournament outcomes, it evaluates the framework against a global sample spanning all six confederations. Two findings emerge. First, GDP and population explain only 9 percent of single-tournament performance variance, compared with 33 percent for FIFA points. This gap is consistent with the model's distinction between systematic performance and match-specific idiosyncratic shocks. Second, using the less noisy FIFA-points measure, the African pattern persists globally: Morocco, Senegal, Argentina, Croatia, Portugal, and Uruguay emerge as leading GDP-relative over-performers. Conversely, several major economies, including the United States, Germany, Japan, and South Korea, underperform their GDP-implied potential. As in the related paper, we interpret the residual as a symmetric, non-structurally identified conditional performance residual, rather than an SFA-consistent efficiency estimate. We also quantify the extent to which its cross-country variance mechanically reflects the underlying performance measure. The GDP-rank/simulated-performance-rank correlation is robust to the choice of the model's unidentified dynamic parameters across a wide grid.
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BibTeX
@misc{thioune2026economic,
title = {{Economic resources, sporting efficiency and uncertainty: a global test on the 2026 FIFA world cup}},
author = {Thierno Thioune and Babacar Mbaye Ndiaye},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.28344},
eprint = {2610.28344},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.28344},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Economic resources, sporting efficiency and uncertainty: a global test on the 2026 FIFA world cup
AU - Thierno Thioune
AU - Babacar Mbaye Ndiaye
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2610.28344
UR - https://arxiv.org/abs/2610.28344
AB - A related paper (arXiv:2610.06967) developed a stochastic optimization framework in which football performance combines GDP/population-based potential with a latent mean-reverting efficiency process. The framework was applied to a cross-section of the 24 national teams participating in AFCON 2025. This paper applies the same methodology to the full 48-team 2026 FIFA World Cup, extending the analysis beyond Africa without modifying the underlying model. Using GDP, population, FIFA ranking data, and complete tournament outcomes, it evaluates the framework against a global sample spanning all six confederations. Two findings emerge. First, GDP and population explain only 9 percent of single-tournament performance variance, compared with 33 percent for FIFA points. This gap is consistent with the model's distinction between systematic performance and match-specific idiosyncratic shocks. Second, using the less noisy FIFA-points measure, the African pattern persists globally: Morocco, Senegal, Argentina, Croatia, Portugal, and Uruguay emerge as leading GDP-relative over-performers. Conversely, several major economies, including the United States, Germany, Japan, and South Korea, underperform their GDP-implied potential. As in the related paper, we interpret the residual as a symmetric, non-structurally identified conditional performance residual, rather than an SFA-consistent efficiency estimate. We also quantify the extent to which its cross-country variance mechanically reflects the underlying performance measure. The GDP-rank/simulated-performance-rank correlation is robust to the choice of the model's unidentified dynamic parameters across a wide grid.
ER -