{
  "abstract": "We build and evaluate a pre-game win-probability forecaster for individual maps (``games'') in professional \\emph{League of Legends} (LoL). The proposed model is a one-stage logistic regression fit end-to-end on the win/loss log-loss: each team's exponentially-weighted moving average of past same-side results, a ridge-shrunk stable strength that is the maximum-a-posteriori estimate of a logistic mixed model, and a first-pick draft covariate, natively calibrated out of sample (walk-forward slope $0.995$). It augments a purely dynamic Bradley--Terry specification with stable team strengths. A second, independently built two-stage composite mixed model under restricted maximum likelihood (REML) and best linear unbiased prediction (BLUP) shrinkage, with Platt calibration, serves as the strongest rival the authors could build. On $5{,}135$ games across six regional leagues and three international events (2024--2026), under paired per-game Diebold--Mariano inference, the two architectures are statistically indistinguishable on every protocol and window (global holdout $0.2230$ vs.\\ $0.2257$; walk-forward $0.2207$ vs.\\ $0.2215$), so the simpler model is preferred on parsimony, not accuracy; both improve on the classical dynamic benchmark ($0.2351$) by a clear margin and on the static fits ($0.2301$/$0.2268$) more modestly. Against Polymarket on $928$ matched maps, the forecasts are statistically indistinguishable from the market on its own per-game contracts, with a modest market edge concentrated on cross-region Worlds and series-decider maps.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.08060",
  "author_total": 2,
  "authors": [
    {
      "name": "Min-Ren Guan"
    },
    {
      "name": "Shen-Ning Tung"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1vnbxc35zpj7a.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.08060",
  "fulltext_url": null,
  "id": "1vnbxc35zpj7a",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.08060",
  "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",
      "q-fin.ST"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2002|Sports Analytics and Performance"
    ]
  ],
  "openalex": "W7212054902",
  "pdf_url": "https://arxiv.org/pdf/2609.08060",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1vnbxc35zpj7a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:07Z",
      "native_id": "2609.08060",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.08060"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Pre-game paired-comparison modeling of professional League of Legends map outcomes",
  "title_text": "Pre-game paired-comparison modeling of professional League of Legends map outcomes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/1vnbxc35zpj7a",
  "venue": "arXiv",
  "versions": []
}