Group K · Matchday 1
UzbekistanvsColombia
2026-06-17·20:00 localPredictions finalised
The forecast
Match-outcome probability
- Uzbekistan win4.5%
- Draw22.6%
- Colombia win73.0%
A clash of identities: Uzbekistan's balanced approach meets Colombia's pragmatic style in a fixture the model gives to Colombia at 73%.
Why the model says this
Favoring Uzbekistan
- ·Uzbekistan has conceded fewer goals in their last six matches (5 goals) compared to Colombia (6 goals) in the same period.
- ·Uzbekistan has suffered only one loss in their last six matches (2W, 3D, 1L), whereas Colombia has two losses (3W, 1D, 2L) in the same period.
Favoring Colombia
- ·Colombia's FIFA ranking is significantly higher at 13th, compared to Uzbekistan's 50th.
- ·The Elo rating system indicates a substantial gap, with Colombia favoured by 248 points.
- ·The expected goals model predicts Colombia to score significantly more (1.64 xG) than Uzbekistan (0.53 xG).
- ·Individual model probabilities consistently favour Colombia, with Elo giving them a 69.7% chance and Stacking 68.0%.
What the model can't fully price
- ·The model does not account for the fitness doubt of one player across the squads, as its lineup channel currently contributes zero.
- ·Uzbekistan is making their debut in this competition, a factor that might influence performance but is not explicitly priced by the model.
- ·The match venue is Mexico City Stadium, which could imply specific environmental conditions not fully captured by standard models.
Form check
Uzbekistan
SteadyUzbekistan's recent form shows resilience with only one loss in their last six matches, securing two wins and three draws. They have maintained a solid defensive record, conceding just 5 goals in this period.
1 loss in last 6 matches
Colombia
DecliningColombia's recent form is mixed, with three wins, one draw, and two losses in their last six fixtures. While they have shown attacking prowess, scoring 11 goals, they have also conceded 6 goals in this period, including two recent friendly defeats.
2 losses in last 2 matches
Analysis
How it plays out
Neither side has a rigid tactical identity. Both adapt to the opponent, so the first 15 minutes will reveal who imposes their plan first. Colombia will expect to hold 53% possession. Uzbekistan need their shape to stay compact without the ball and be clinical when they win it back.
What decides it
Colombia adjust shape to the opponent. That flexibility is an asset, but it takes longer to settle into a game. James Rodríguez's 8.8% scoring probability is the highest in this fixture. Containing that output is Uzbekistan's primary defensive task.
Off the pitch
Uzbekistan travel 13,062km, 4x Colombia's journey. Second-half fatigue is a real factor at that differential. Néstor Lorenzo (4 years in charge of Colombia) vs Fabio Cannavaro (1 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
The model gives Uzbekistan just 14.2% to win. Every World Cup produces group-stage upsets; the question is whether this fixture is one of them.
▸Goals & scorelines
Likeliest score 0–1 (17.3%) · xG 0.5 - 1.9
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 0–117.3%
- 0–216.7%
- 0–310.4%
- 0–010.0%
- 1–19.1%
From the Dixon-Coles joint Poisson with the low-score correction. Scorelines are listed in probability order; this is a description of the model's distribution, not a recommendation.
Most likely half-time scorelines
- 0–031.2%
- 0–128.4%
- 0–213.5%
- 1–17.4%
- 1–07.0%
Same Dixon-Coles fit as the full-time list above, with rates halved to a 45-minute window and the low-score correction applied to that 1st-half block. The 0-0 row sits higher here than at full-time because fewer minutes have elapsed.
Goal totals
- More than 0.5 goals90.0%
- More than 1.5 goals68.7%
- More than 2.5 goals41.9%
- More than 3.5 goals21.2%
- More than 4.5 goals9.0%
- More than 5.5 goals3.3%
- Both teams score32.8%
Each row is the probability the match finishes with more than the listed number of goals. Both-teams-to-score is the probability each side scores at least once. All values are marginals of the Dixon-Coles joint goal grid that produces the scoreline list above — not market lines or any other operator construct.
Event-typed probabilities
- Uzbekistan clean sheetOpposing team scores zero15.3%
- Colombia clean sheetOpposing team scores zero61.9%
Derived from the same Dixon-Coles joint distribution as the scoreline list. These are descriptive event probabilities — see CLAUDE.md §3/§4 (formerly COMPLIANCE.md §4.2.7) for the framing the project uses.
Win-margin probability
- Uzbekistan by 4+<0.1%
- Uzbekistan by 3+0.3%
- Uzbekistan by 2+1.7%
- Uzbekistan by 1+8.1%
- Draw21.2%
- Colombia by 1+70.7%
- Colombia by 2+44.1%
- Colombia by 3+21.9%
- Colombia by 4+8.8%
Each row is the probability the match ends with the listed margin or larger in that direction. Marginal of the Dixon-Coles joint goal grid; the “by 1+” rows plus the draw row sum to 1.
▸How the match unfolds
Over 2.5 goals 41.9% · BTTS 32.8%
Game state through the match
- Uzbekistan ahead8.6%
- Level20.1%
- Colombia ahead71.2%
Probability of each game state at minutes 0, 15, 30, 45, 60, 75, 90 — derived from two independent thinned-Poisson processes with the Dixon-Coles per-team rates. The three lines always sum to 1 at each minute. The right column shows the state at the match's closing minute.
When the first goal arrives
- 0–1532.5%
- 15–3021.9%
- 30–4514.8%
- 45–6010.0%
- 60–756.8%
- 75–904.6%
- No goal9.5%
Probability the match's first goal arrives in each 15-minute window. Homogeneous Poisson with combined rate λ = λh + λa from the Dixon-Coles fit; the seven rows (six windows + no-goal tail) sum to 1.
Half-time / full-time grid
| HT ↓ / FT → | HUzbekistan win | DDraw | AColombia win |
|---|---|---|---|
| HUzbekistan ahead | 4.5% | 2.7% | 1.7% |
| DLevel | 3.6% | 14.8% | 20.5% |
| AColombia ahead | 0.3% | 2.8% | 49.0% |
Each cell is P(half-time result, full-time result). All nine cells sum to 1. Derived from a halved-λ Dixon-Coles fit for the first half plus an independent-Poisson second-half convolution.
Comeback probability
- Uzbekistan trail at HT, avoid defeat at FT3.1%
- Colombia trail at HT, avoid defeat at FT4.3%
Joint probability — P(side trailing at half-time AND avoiding defeat at full-time). NOT conditional on trailing at HT. Derived from the same half-time / full-time decomposition that produces the HT/FT grid above; a tied first half is neither a home nor an away comeback opportunity.
Cards
- Expected yellow cardsMean of the Poisson on total yellow cards.3.45
- Total yellows over 2.567.0%
- Total yellows over 3.545.3%
- Total yellows over 4.526.5%
- Any red cardP(at least one red card in the match).9.5%
Referee not yet assigned. Using the 2026 pool-mean per-match rate as a placeholder; the model picks up the referee's personal rate once the assignment is published. Total yellow cards modelled as a Poisson with mean equal to two team baselines plus the referee's deviation from the pool mean. Reds are modelled the same way, independently. See /docs/methodology/.
▸Teams & players
Top scorer: Rodríguez (8.8%)
Match detail
Uzbekistan
Model-rated key players: Eldor Shomurodov (FW) — P(scores) 3.2%; Abbosbek Fayzullaev (FW) — P(scores) 2.3%; Dostonbek Khamdamov (FW) — P(scores) 2.3%.
Limited recent tournament data is available for Uzbekistan's tactical profile. Early indicators suggest a balanced approach.
Uzbekistan will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries.
Colombia
Model-rated key players: James Rodríguez (MF) — P(scores) 8.8%; Luis Díaz (FW) — P(scores) 7.3%; Jhon Córdoba (FW) — P(scores) 4.0%.
Colombia under Néstor Lorenzo play a pragmatic game with 53% possession. They apply moderate pressing intensity (PPDA 18.9).
Colombia play a pragmatic, results-oriented game that adapts shape to the opposition. Tactical flexibility is their strength. The risk is inconsistency — without a default identity, a poor result can cascade if the team struggles to find a Plan B.
Colombia converts 12.4% from set-pieces (0.23 expected). Combined, the model expects 0.23 set-piece goals across the 90 minutes.
- P(Colombia scores set-piece goal) 20.8%
- P(set-piece goal in match) 20.8%
Colombia: James Rodríguez on corners (58 corners) (per fbref 2020 21)
If a penalty is awarded to Uzbekistan, the model gives 76.0% conversion, 71.4% for Colombia.
Colombia primary PK: James Rodríguez (2/2 in 2013-14, per fbref 2020 21).
Derived from the model's per-fixture forecast joint and supporting reference data (predicted squads, set-piece xG share, PK posteriors, club minutes). See /docs/methodology/ for the full methodology.
Tactical forecast
Partial coverage from FotMob match stats (recent qualifiers and friendlies): possession and shot volume only. Press and build-up metrics are not available for this side.
- PPDA
- —
- Possession
- 44%
- Directness (yds/pass)
- —
- Long balls/90
- —
- Set-piece xG
- —
- PPDA
- 18.9
- Possession
- 53%
- Directness (yds/pass)
- 6.6
- Long balls/90
- 41
- Set-piece xG
- 12%
Style profile per side from StatsBomb open-data aggregation across recent international tournaments (Euro 2020/2024, Copa America 2024, AFCON 2023, World Cup 2018/2022). The tactical-fingerprint badge maps each team’s observed style vector into one of eight canonical archetypes via a rule-based classifier; teams with fewer than three matches of qualifying coverage carry an “insufficient-data” label rather than being forced into a default. Sides outside the StatsBomb-open corpus use FotMob team match stats from recent qualifiers and friendlies instead (possession and shot volume only), marked as partial coverage. PPDA = passes the side allows per defensive action (lower = more intense press). Formation distributions are not yet produced — that head of the §2.7 classifier is pending its own data pull. See /docs/methodology/.
Squad depth
Most irreplaceable starters
Uzbekistan
- Abdukodir KhusanovCentre-backCover: Umar Eshmurodov · 0.280.53gap
- Eldor ShomurodovStrikerNo natural backup0.22gap
- Odiljon HamrobekovDefensive midfieldCover: Abdulla Abdullaev · 0.310.03gap
Colombia
- Luis DíazWingerCover: Jaminton Campaz · 0.630.31gap
- Cucho HernándezStrikerCover: Luis Suárez · 0.570.20gap
- Jhon AriasWingerCover: Jaminton Campaz · 0.630.17gap
Gap = how far a side's rating at the position falls from the starter to his likely in-squad replacement (named under each name). Larger = harder to replace. Descriptive metric, does not feed the published probabilities. Methodology →
Match conditions
High-altitude venue. Mexico City sits at 2,240 m above sea level — thinner air affects stamina and ball flight.
- AltitudeHigh altitude2,240 m
- Avg temperatureFive-year mean over the tournament window17.7 °C
- Avg humidity70%
- Heat stressShade WBGT ~19.5 °CLow heat stress
- Pitch surfacenatural grass
Natural-grass football stadium; a new pitch was laid during the stadium's renovation ahead of the tournament.
Heat stress is a shade Wet-Bulb Globe Temperature proxy from the venue's climatology mean temperature and humidity; FIFA mandates cooling breaks at WBGT 32 °C. Evening kickoff (local time). These are long-window averages, not a match-day forecast, and they are not inputs to the forecast.
Top scorers · P(scores in this match)
- Eldor ShomurodovFW3.2%
- Abbosbek FayzullaevFW2.3%
- Dostonbek KhamdamovFW2.3%
- James RodríguezPKMF8.8%
- Luis DíazFW7.3%
- Jhon CórdobaFW4.0%
Per-player scoring rate from Model #5 (`p_score_per_match`). Reflects each player's npxG/90, expected minutes, team xG share, and the average opposing-team defence. See /docs/methodology/.
Recent match form
Last match player ratings
Uzbekistan
vs DR Congo · avg 6.8
Colombia
vs Ghana · avg 7.0
Worked well: Their counter-attacking movements were effective, leading to the opening goal and several other clear-cut chances. The interplay between their attacking players, particularly Luis Suarez, was a key strength.
Struggled: Despite creating many opportunities, their finishing was not always clinical, allowing Ghana's goalkeeper to make several important stops and keeping the scoreline tight.
Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →
Video analysis: player performance
Per-player ratings and event breakdowns from official highlights analysis. Tap a player to see their full match timeline.
7Utkir YusupovDespite conceding three goals, he made several crucial saves that kept Uzbekistan in the game for extended periods.
Despite conceding three goals, he made several crucial saves that kept Uzbekistan in the game for extended periods.
9Luis Díaz4'–64'Delivered a man-of-the-match performance, constantly threatening the opposition goal and scoring a crucial goal.
1goals1shots1fouls won▼
Delivered a man-of-the-match performance, constantly threatening the opposition goal and scoring a crucial goal.
Match timeline
8Daniel Muñoz40'–40'Scored a crucial opening goal with a well-taken volley, demonstrating excellent attacking instincts from his defensive role.
1goals▼
Scored a crucial opening goal with a well-taken volley, demonstrating excellent attacking instincts from his defensive role.
Match timeline
7Jaminton Campaz90'–90'Made an immediate impact by scoring a late goal to seal the victory after coming on as a substitute.
1goals▼
Made an immediate impact by scoring a late goal to seal the victory after coming on as a substitute.
Match timeline
Match observations
- The match was played at Mexico City Stadium.
- Uzbekistan were making their debut in the competition.
- Colombia showed strong attacking intent throughout the match, particularly through Luis Diaz.
▸Under the hood
Model-by-model comparison
Uzbekistan vs Colombia
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 4.3% | 22.0% | 73.7% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 8.6% | 21.4% | 70.0% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 9.8% | 22.1% | 68.1% |
| Bayesian stackingLearned-weight combination | — | 2.1% | 17.5% | 80.5% |
| Ensemble (published)Uniform average + isotonic calibration | — | 4.5% | 22.6% | 73.0% |
How each model works
- Elo
- Each team carries a single strength rating updated after every match by a margin-aware K-factor. Match probabilities come from the logistic function of the rating gap. Elo is fast-adapting but coarse — it sees only who won and by how much, not how the goals were scored.
- Dixon-Coles
- A Poisson regression on team-level attack and defence parameters, fitted via maximum likelihood with an exponential time-decay weighting. The Dixon-Coles correction adjusts the four low-score cells (0-0, 1-0, 0-1, 1-1) where independent Poisson underestimates dependence. Produces full scoreline distributions, not just H/D/A.
- Hierarchical Poisson
- A Bayesian Poisson model fitted via MCMC (PyMC) with hierarchical priors that pool attack and defence parameters within confederations. Shrinks small-sample teams toward their confederation mean — helpful for nations with few recent competitive fixtures. Slower to fit but better-calibrated on the tails.
- Bayesian stacking
- Optimises simplex weights (w_elo, w_dc, w_hp) to maximise the leave-one-out log-score across a walk-forward backtest (Yao et al. 2018). The result is a weighted average of the three component models' probabilities, then isotonic-calibrated. Adds no extra features — just learns which component to trust more from historical accuracy.
- Ensemble (published)
- Equal-weight average of all three component models, followed by per-class isotonic regression calibration fitted on 24 months of walk-forward out-of-fold predictions. This is the probability published on the site. The uniform mean is deliberately simple — it avoids overfitting to the stacking weights' training window.
Three independent component models feed two combination strategies. The uniform ensemble is the published probability; Bayesian stacking uses learned weights. Amber bars flag >5pp divergence from the published number. Full methodology
Probability decomposition (transparency surface)
- Baseline ensemble — P(Uzbekistan win)12.4%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Uzbekistan win)12.4%
Decomposition of the published P(Uzbekistan win) into the calibrated- baseline plus contributions from the §2.3 expected-XI lineup delta and the §2.7 style-matchup interaction. The §2.7 roadmap is explicit that style effects are second-order to team strength — single-digit-percentage P(win) shifts on extreme style matchups, near-zero on balanced ones. We surface the decomposition for transparency even when the contributions are small; the baseline carries the prediction. Methodology: /docs/methodology.
For this fixture both contributions round to under 0.05pp — the fitted style-matchup pair effect is in the small-magnitude regime the model expects to dominate.
Head-to-head history
| Date | Competition | Venue | Score | Result | xG |
|---|---|---|---|---|---|
| 17 Jun 2026 | FIFA World Cup | NMexico City | 1–3 | L | — |
Uzbekistan vs Colombia, every senior international meeting in the martj42 results dataset (score from Uzbekistan's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for Uzbekistan or Colombia.
- Stage:
- Group K · Matchday 1
- Date:
- 17 Jun
Uzbekistan
Uzbekistan come in at close to full strength.
Colombia
Colombia come in at close to full strength.
Uzbekistan and Colombia both come in at close to full strength, so the forecast rests on baseline team strength rather than late team-news swings.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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