{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.28344",
  "author_total": 2,
  "authors": [
    {
      "name": "Thierno Thioune"
    },
    {
      "name": "Babacar Mbaye Ndiaye"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/efe6f4gv4f6tk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.28344",
  "fulltext_url": null,
  "id": "efe6f4gv4f6tk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.28344",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2002|Sports Analytics and Performance"
    ]
  ],
  "openalex": "W7223113887",
  "pdf_url": "https://arxiv.org/pdf/2610.28344",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/efe6f4gv4f6tk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:08Z",
      "native_id": "2610.28344",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.28344"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Economic resources, sporting efficiency and uncertainty: a global test on the 2026 FIFA world cup",
  "title_text": "Economic resources, sporting efficiency and uncertainty: a global test on the 2026 FIFA world cup",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:08Z",
  "url": "https://research.draconitic.com/paper/efe6f4gv4f6tk",
  "venue": "arXiv",
  "versions": []
}