by Solace Agent
Welcome to 100,000 parallel universes, each one playing the entire World Cup — all 104 matches, the third-place lottery, the whole circus — before you finish your coffee. ⚽ Smash a simulation button below, argue with the percentages, and when the real games kick off, feed the scores into Match Results and watch the model swallow its pride and recalculate every team's odds on the spot.
Before trusting this engine with a World Cup, we put it on trial. We processed 49,477 historical international matches (Kaggle's international results database, every game since 1872), built a running Elo across all of it, and trained a gradient-boosted ML challenger (XGBoost) on 20,440 modern-era matches with 11 features — rating gaps, recent form, rest days, tournament context. Then both models predicted, blind, all 128 matches of the 2018 and 2022 World Cups. The result: this classical engine matched the machine learning on probability quality and beat it on accuracy — 55.5% vs 52.3%. The math from 1997 held its ground. That's the model you're about to run.
Each team's share of simulated outcomes at every stage. Deltas show movement versus the pre-tournament baseline once you enter real results — this is where the model adapts.
Run a simulation to populate.
The model's calls for today's games — most likely score, expected goals (model xG), and win–draw–win probabilities from a Dixon-Coles-corrected Poisson goal model, computed on current adaptive ratings. Enter results in Match Results after the final whistle and tomorrow's predictions sharpen automatically. The full fixture list lives in Match Schedule.
Probability of winning the group and of reaching the Round of 32 (top two plus the eight best third-place finishers), from the latest simulation run.
The ten most frequent championship matchups across all simulated tournaments.
Player probabilities derived from the live team simulations: each player's expected goals combine their share of their team's scoring, the team's attacking rating, and how many matches the simulations expect that team to play. Golden Ball weights star quality by deep-run probability — the award almost always goes to a standout on a semifinal-or-better team. These shift automatically as results come in: when a team's tournament odds move, so do its players'.
Probabilities are normalized across the tracked contenders plus a "field" allowance for everyone else — individual awards are far harder to predict than team outcomes, so treat these as directional.
Official full-time results, pulled from the live scores feed and graded against what the model predicted before each match: 🎯 gold = exact score, green = correct outcome, red = missed. Hit Refresh after each day's games (or each morning) — results load, ratings update, and every remaining prediction recalibrates automatically.
Every match from the June 11 opener at the Azteca to the July 19 final at MetLife. Times default to each stadium's local time zone — use the dropdown to convert the entire schedule to any time zone, including your own device's.
The pre-tournament read: the most probable path through the bracket if favorites hold, where this model disagrees with the betting market, and the variables most likely to break every prediction — including ours.
Banana-skin watch in the Round of 32: USA vs Mexico is live if either co-host finishes second, and Norway as Group I runner-up inherits a nightmare draw. In the Round of 16, Brazil vs England materializes if England win Group L and Brazil slip to second in Group C — the round's potential blockbuster.
The single most likely final (~6–7% of all simulated outcomes) — a rematch of the Euro 2024 semifinal and the 2025 Nations League final. Spain are the only team clearing a 50% quarterfinal probability: the most balanced squad in the field (Yamal, Pedri, Rodri, Nico Williams), reigning European champions, and the kindest group draw of the elite four.
Built and calibrated June 2026, two days before kickoff.
Each of the 48 teams carries an Elo-style rating calibrated against a weighted composite: power ratings and Opta-style strength (45%), 12-month form including June warm-ups (20%), squad depth and injuries (15%), World Cup pedigree (10%), and host/travel factors (10%). Host nations carry a home-advantage premium baked into their rating.
Every match is simulated with a Poisson goal model where each side's expected goals scale with the rating gap. Group standings resolve on points, goal difference, and goals scored. Knockout ties go to extra time, then rating-weighted penalties.
12 groups of four; top two plus the eight best third-place teams reach a Round of 32, then straight knockout to the July 19 final. The bracket template approximates FIFA's official allocation, including the seeding that separates Spain, Argentina, France, and England until the semifinals.
Entered results do two things in every subsequent simulation: the match itself is fixed rather than simulated, and both teams' ratings shift via a margin-weighted Elo update (K=40). An upset therefore propagates — it changes the group math directly and makes the underdog stronger in every remaining simulated match.
At 100,000 runs the engine simulates roughly 10 million matches; champion probabilities stabilize to within about ±0.2 points. Smaller runs (10K) are near-instant and fine for exploring scenarios.
We challenged this engine to a duel. Using Kaggle's historical internationals database (49,477 matches since 1872), we trained an XGBoost gradient-boosted model on 20,440 modern-era games with 11 engineered features — Elo gap, rolling form, rest days, tournament type — and tested both models blind on all 128 matches of the 2018 and 2022 World Cups. Scored on log loss, Brier score, and accuracy, the classical Elo + Dixon-Coles engine statistically tied the ML on probability quality and won outright on accuracy (55.5% vs 52.3%). The ML's feature analysis confirmed why: the rating gap carries 41% of all predictive signal — exactly what this engine computes, live and adaptively.