GitHub - fabio-ricardo/worldcup-forecasting-model: Hybrid Prior-Performance Monte Carlo model for forecasting FIFA World Cup knockout outcomes (paper companion code). · GitHub
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World Cup Knockout Forecasting Model
Preprint: SSRN Working Paper No. 7013338 — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7013338
Companion code for:
F. R. Araujo da Silva, A Hybrid Prior-Performance Monte Carlo Model for<br>Forecasting FIFA World Cup Knockout Outcomes: Methodology and Retrospective<br>Validation 1986–2026.<br>ORCID: 0000-0003-4938-5856
A transparent Monte Carlo model that forecasts the FIFA World Cup champion from<br>the first knockout round. Each team gets a strength rating blending season-long<br>priors (FIFA ranking, squad market value), group-stage performance (expected<br>goals, goals, individual production), and a goalkeeper/defensive-pedigree term.<br>Strength differences drive a Poisson goal engine with extra-time and penalty<br>resolution; the fixed bracket is simulated many times to estimate championship<br>probabilities.
No knockout-stage result is ever used as a model input.
Requirements
Python 3.8+ (standard library only — no external packages).
Usage
python worldcup_model.py # backtests (1986–2022) + 2026 prediction<br>python worldcup_model.py --loocv # also run leave-one-out cross-validation
Files
File<br>Description
worldcup_model.py<br>Model, simulation engine, backtests, prediction
data.py<br>Per-tournament inputs and fixed brackets
Method summary
Strength rating:
R_i = 1500 + 250 * (Attack_i - Defense_i)
Attack_i = 0.70*rank + 0.20*value + 0.13*xGF + 0.05*goals + 0.03*star<br>Defense_i = 0.55*xGA - 0.35*gk_pedigree
(z-scored signals; for pre-2002 tournaments rank+value are replaced by a single<br>pedigree rating carrying their combined weight.)
Match: expected goals lambda = clip(1.35 * exp(0.5*tanh((R_a - R_b)/400)), 0.25, 3.5),<br>independent Poisson draws, reduced-rate extra time, then a coin-flip shootout.
Validation
Champion in the model's top two in 10/10 tournaments, 1986–2022.
Out-of-sample (1986–1998, never used for tuning): top-two 4/4 .
Leave-one-tournament-out CV (2002–2022): top-two in 5/6 held-out folds.
Data sources
FIFA/Coca-Cola Men's World Ranking; Transfermarkt squad valuations; FIFA official<br>match statistics; published xG trackers (2010–2026, estimated for ≤1998);<br>RSSSF archive and ESPN for historical brackets and group-stage results.
License
MIT (code). Statistical data are public domain / cited to their sources.
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Hybrid Prior-Performance Monte Carlo model for forecasting FIFA World Cup knockout outcomes (paper companion code).
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