Group J · Matchday 1
ArgentinavsAlgeria
2026-06-16·20:00 localPredictions finalised
The forecast
Match-outcome probability
- Argentina win69.9%
- Draw22.0%
- Algeria win8.1%
A 370-point Elo gap frames this as a significant mismatch, yet the model still gives Algeria a 7% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Argentina
- ·Argentina holds a significant Elo advantage of 370 points over Algeria.
- ·Argentina is ranked 2nd globally by FIFA, significantly higher than Algeria's 35th position.
- ·The model projects Argentina to generate 1.8 expected goals compared to Algeria's 0.68 xG.
- ·In their sole historical encounter, Argentina secured a 4-3 victory.
Favoring Algeria
- ·Algeria exhibits a more intense pressing style, with a PPDA percentile of 98.8 compared to Argentina's 66.2.
- ·Algeria shows a higher reliance on set pieces for xG creation, with 20.0% of their xG from set pieces (90.8 percentile) compared to Argentina's 17.1% (80.3 percentile).
What the model can't fully price
- ·The model does not account for squad availability, with 3 players across both squads carrying fitness doubts, 2 of whom are projected starters.
Form check
Argentina
ImprovingArgentina enters this match in strong form, having won 5 of their last 6 fixtures. During this period, they scored 16 goals and conceded only 2, with their sole loss being a narrow 0-1 defeat.
5 consecutive wins
Algeria
SteadyAlgeria's recent form includes 4 wins, 1 draw, and 1 loss in their last 6 matches. They have scored 11 goals and conceded 3, featuring a notable 7-0 victory and a 0-0 draw.
4 wins in last 6 matches
Analysis
How it plays out
Both sides run a possession dominant system, so this becomes a test of who executes the same ideas better on the day. Algeria's aggressive press (PPDA 11.1) against Argentina's deeper build-up (PPDA 19.1) creates a clear territory question: can Algeria force errors high up, or will Argentina play through the press and find space behind it?
What decides it
Both sides run the same system (possession dominant), so execution quality separates them, not tactical asymmetry. Lionel Messi carries the marginally higher scoring probability (10.7% vs 6.5%).
Off the pitch
Lionel Scaloni (8 years in charge of Argentina) vs Vladimir Petković (2 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
The model gives Algeria just 12.6% to win. Every World Cup produces group-stage upsets; the question is whether this fixture is one of them.
▸Goals & scorelines
Likeliest score 1–0 (15.3%) · xG 1.8 - 0.6
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–015.3%
- 2–014.3%
- 1–110.6%
- 0–09.4%
- 2–19.0%
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
- 0–030.3%
- 1–026.3%
- 2–012.0%
- 1–18.9%
- 0–18.8%
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 goals90.5%
- More than 1.5 goals70.3%
- More than 2.5 goals43.7%
- More than 3.5 goals22.7%
- More than 4.5 goals9.9%
- More than 5.5 goals3.7%
- Both teams score39.4%
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
- Argentina clean sheetOpposing team scores zero53.5%
- Algeria clean sheetOpposing team scores zero16.5%
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
- Argentina by 4+7.1%
- Argentina by 3+18.4%
- Argentina by 2+39.0%
- Argentina by 1+65.1%
- Draw23.2%
- Algeria by 1+11.7%
- Algeria by 2+3.0%
- Algeria by 3+0.5%
- Algeria by 4+0.1%
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 43.7% · BTTS 39.4%
Game state through the match
- Argentina ahead65.7%
- Level22.0%
- Algeria ahead12.3%
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
- 0–1533.3%
- 15–3022.2%
- 30–4514.8%
- 45–609.9%
- 60–756.6%
- 75–904.4%
- No goal8.8%
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 → | HArgentina win | DDraw | AAlgeria win |
|---|---|---|---|
| HArgentina ahead | 44.3% | 3.4% | 0.6% |
| DLevel | 19.4% | 15.4% | 5.0% |
| AAlgeria ahead | 1.9% | 3.3% | 6.7% |
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
- Argentina trail at HT, avoid defeat at FT5.3%
- Algeria trail at HT, avoid defeat at FT4.0%
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: Messi (10.7%)
Match detail
Argentina
Model-rated key players: Lionel Messi (FW) — P(scores) 10.7%; Lautaro Martínez (FW) — P(scores) 6.2%; Nicolás González (FW) — P(scores) 4.8%.
Argentina under Lionel Scaloni play a possession dominant game, holding 59% of the ball — among the highest in the tournament field. Their likely shape is a 4-3-3, though they have also used 3-5-2 and 4-4-2. They apply moderate pressing intensity (PPDA 19.1) and build patiently through midfield with 7.8 passes per attacking sequence. They favour high-quality chances (xG/shot 0.163, among the best in the field).
To succeed, Argentina must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match. Managing minutes for Lionel Messi across what could be seven matches will test the coaching staff's rotation planning.
Algeria
Model-rated key players: Amine Gouiri (FW) — P(scores) 7.1%; Riyad Mahrez (FW) — P(scores) 2.0%; Mohamed Amoura (FW) — P(scores) 1.5%.
Algeria under Vladimir Petković play a possession dominant game, holding 68% of the ball — among the highest in the tournament field. They press intensely (PPDA 11.1, highest in the field). They generate a high volume of shots (14.1 per 90) and rely heavily on set pieces (20% of their xG).
To succeed, Algeria must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match. Managing minutes for Riyad Mahrez across what could be seven matches will test the coaching staff's rotation planning.
Argentina's predicted XI averages 1,997 club minutes over the 2024-25 season (moderate load).
Argentina coverage: 85.0% (11/11 XI matched against the FBref Big-5) · Algeria: 33.0% (6/11).
Argentina historically converts 17.1% of xG from set-pieces, contributing 0.31 expected set-piece goals in this fixture. Algeria converts 20.0% from set-pieces (0.13 expected). Combined, the model expects 0.43 set-piece goals across the 90 minutes.
- P(Argentina scores set-piece goal) 26.4%
- P(Algeria scores set-piece goal) 11.8%
- P(set-piece goal in match) 35.1%
Argentina: Lionel Messi on corners (32 corners), Guido Rodríguez on free kicks (per fbref 2022 23) · Algeria: Ilan Kebbal on corners (30 corners), Nabil Bentaleb on free kicks (per fbref 2021 22)
If a penalty is awarded to Argentina, the model gives 77.0% conversion, 71.4% for Algeria.
Argentina primary PK: Lionel Messi (3/5 in 2020-21, per fbref 2022 23) · Algeria primary PK: Amine Gouiri (3/5 in 2021-22, per fbref 2021 22).
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
- PPDA
- 19.1
- Possession
- 59%
- Directness (yds/pass)
- 4.3
- Long balls/90
- 27
- Set-piece xG
- 17%
- PPDA
- 11.1
- Possession
- 68%
- Directness (yds/pass)
- 6.1
- Long balls/90
- 32
- Set-piece xG
- 20%
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
Argentina
- Giovani Lo CelsoAttacking midfieldNo natural backup0.30gap
- Lautaro MartínezStrikerCover: José Manuel López · 0.670.30gap
- Leandro ParedesDefensive midfieldNo natural backup0.26gap
Algeria
- Mohamed AmouraStrikerCover: Amin Chiakha · 0.160.64gap
- Amine GouiriStrikerCover: Amin Chiakha · 0.160.59gap
- Rayan Aït-NouriFull-backCover: Mehdi Dorval · 0.530.45gap
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
- AltitudeNear sea level229 m
- Avg temperatureFive-year mean over the tournament window25.8 °C
- Avg humidity69%
- Heat stressShade WBGT ~27.5 °CLow heat stress
- Pitch surfacenatural grass
Natural-grass NFL stadium; FIFA-standard hybrid pitch for 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)
- Lionel MessiPKFW10.7%
- Lautaro MartínezFW6.2%
- Nicolás GonzálezFW4.8%
- Amine GouiriPKFW7.1%
- Riyad MahrezFW2.0%
- Mohamed AmouraFW1.5%
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
Argentina
vs Cape Verde · avg 6.6
Algeria
vs Switzerland · avg 6.2
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.
6Lautaro MartinezRegistered one shot on target that was saved, but otherwise had limited impact on the match events.
Registered one shot on target that was saved, but otherwise had limited impact on the match events.
6Julian AlvarezEntered the game as a substitute but had no specific actions mentioned in the match notes.
Entered the game as a substitute but had no specific actions mentioned in the match notes.
6Nicolas GonzalezEntered the game as a substitute but had no specific actions mentioned in the match notes.
Entered the game as a substitute but had no specific actions mentioned in the match notes.
6E. MartinezWas part of team celebrations and reacted to the opponent's goal, but had no direct on-field actions mentioned.
Was part of team celebrations and reacted to the opponent's goal, but had no direct on-field actions mentioned.
7Luca Zidane36'–50'Made multiple crucial saves against Messi and Martinez, preventing Algeria from conceding more goals.
3saves▼
Made multiple crucial saves against Messi and Martinez, preventing Algeria from conceding more goals.
Match timeline
6Fares ChaibiScored a goal that was subsequently disallowed due to a foul, limiting his overall positive impact.
Scored a goal that was subsequently disallowed due to a foul, limiting his overall positive impact.
4Rafik Belghali35'–35'Received a yellow card for a foul, which was his only notable action and a negative contribution.
1 yellow▼
Received a yellow card for a foul, which was his only notable action and a negative contribution.
Match timeline
▸Under the hood
Model-by-model comparison
Argentina vs Algeria
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 79.6% | 20.4% | 0.0% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 65.0% | 23.2% | 11.8% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 62.7% | 23.4% | 13.9% |
| Bayesian stackingLearned-weight combination | — | 78.1% | 19.9% | 1.9% |
| Ensemble (published)Uniform average + isotonic calibration | — | 69.2% | 23.4% | 7.4% |
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(Argentina win)69.9%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Argentina win)69.9%
Decomposition of the published P(Argentina 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 |
|---|---|---|---|---|---|
| 16 Jun 2026 | FIFA World Cup | NKansas City | 3–0 | W | — |
| 5 Jun 2007 | Friendly | NBarcelona | 4–3 | W | — |
Argentina vs Algeria, every senior international meeting in the martj42 results dataset (score from Argentina's perspective; H/A/N = home/away/neutral).
Latest news & match context
- Stage:
- Group J · Matchday 1
- Date:
- 16 Jun
Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.
- 1.Squad availability: 1 carrying a fitness doubt across the two squads, 1 of them projected starters. The forecast does not adjust for who is missing: its lineup channel currently contributes zero, so this is context the probabilities do not include.
Argentina
Argentina come in at close to full strength.
Algeria
Algeria: 1 carrying a fitness doubt.
- DoubtAnthony Mandrea, the first-choice goalkeeper, is recovering from Shoulder injury and is a fitness watch item; if unavailable the projected XI shifts.
Availability runs in Argentina's favour here: Algeria are managing a fitness concern over Anthony Mandrea, while Argentina's projected XI looks intact.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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