Pre-match projection. Probabilities are frozen approximately 6 hours before kickoff to provide a stable pre-match reference.
Turnier-Torschützen
P(erzielt mindestens 1 Tor): WM 2026 Eine Modellschätzung je Spieler für die Wahrscheinlichkeit, dass der Spieler mindestens ein Tor in den Spielen erzielt, die sein Land beim WM-Turnier 2026 bestreitet. Berechnet aus der jüngsten Torquote auf Vereinsebene, einer geschätzten Einsatzminuten-Zahl abgeleitet aus Länderspiel-Einsätzen, einem positionsgewichteten Anteil der erwarteten Tore des Teams, einem Gegner-Abwehr-Multiplikator und der erwarteten Anzahl WM-Spiele des Teams aus der Monte-Carlo-Turnierbaum-Simulation. Methodik und bekannte v0-Einschränkungen sind dokumentiert unter /docs/methodology/ .
2,630 Spieler aus 48 Teams · Stand 2026-05-29
Top 50 nach P(erzielt mindestens 1) All 50 Forwards FW 50 Midfielders MF 50 Defenders DF 50 Goalkeepers GK 50
50 players
# Player · country Pos Form Team outlook P(scores ≥1) Rank 1 Raphinha PK Brazil Pos FW Form ↗ RisingTeam outlook Top-4 5.6 mp P(scores ≥1) 55.3% ? Rank 2 Jonathan David PK Canada Pos FW Form ↘ CoolingTeam outlook R16 4.7 mp P(scores ≥1) 47.5% ? Rank 3 Lionel Messi PK Argentina Pos FW Form → SteadyTeam outlook Final 5.8 mp P(scores ≥1) 46.9% ? Rank 4 Mikel Oyarzabal PK Spain Pos FW Form ↗ RisingTeam outlook Final 5.9 mp P(scores ≥1) 46.0% ? Rank 5 Erling Haaland PK Norway Pos FW Form → SteadyTeam outlook R16 4.5 mp P(scores ≥1) 45.1% ? Rank 6 Cristiano Ronaldo PK Portugal Pos FW Form ↘ CoolingTeam outlook Top-4 5.4 mp P(scores ≥1) 44.4% ? Rank 7 Folarin Balogun PK United States Pos FW Form → SteadyTeam outlook R16 4.3 mp P(scores ≥1) 43.3% ? Rank 8 Son Heung-min PK South Korea Pos FW Form ↘ CoolingTeam outlook R16 4.3 mp P(scores ≥1) 42.0% ? Rank 9 Chris Wood PK New Zealand Pos FW Form → SteadyTeam outlook Long shot 3.4 mp P(scores ≥1) 39.2% ? Rank 10 Marcus Thuram PK France Pos FW Form ↗ RisingTeam outlook Top-4 5.6 mp P(scores ≥1) 38.9% ? Rank 11 Ante Budimir PK Croatia Pos FW Form ↘ CoolingTeam outlook Top-8 4.9 mp P(scores ≥1) 38.8% ? Rank 12 James Rodríguez PK Colombia Pos MF Form ↘ CoolingTeam outlook Top-8 5.2 mp P(scores ≥1) 37.4% ? Rank 13 Raúl Jiménez PK Mexico Pos FW Form → SteadyTeam outlook R16 4.8 mp P(scores ≥1) 36.2% ? Rank 14 Alexander Sørloth Norway Pos FW Form ↘ CoolingTeam outlook R16 4.5 mp P(scores ≥1) 34.4% ? Rank 15 Kevin De Bruyne PK Belgium Pos MF Form → SteadyTeam outlook Top-8 5.2 mp P(scores ≥1) 33.1% ? Rank 16 Jordan Ayew PK Ghana Pos FW Form → SteadyTeam outlook Knockout 3.3 mp P(scores ≥1) 32.3% ? Rank 17 Harry Kane England Pos FW Form ↗ RisingTeam outlook Top-8 5.4 mp P(scores ≥1) 32.0% ? Rank 18 Gabriel Jesus Brazil Pos FW Form ↘ CoolingTeam outlook Top-4 5.6 mp P(scores ≥1) 31.7% ? Rank 19 Ferran Torres Spain Pos FW Form ↗ RisingTeam outlook Final 5.9 mp P(scores ≥1) 30.9% ? Rank 20 Ricardo Rodriguez PK Switzerland Pos DF Form ↘ CoolingTeam outlook Top-8 5.0 mp P(scores ≥1) 30.2% ? Rank 21 Gonçalo Ramos Portugal Pos FW Form — Team outlook Top-4 5.4 mp P(scores ≥1) 29.8% ? Rank 22 Franck Kessié PK Ivory Coast Pos MF Form ↘ CoolingTeam outlook R16 4.0 mp P(scores ≥1) 29.6% ? Rank 23 Marcus Rashford PK England Pos FW Form ↗ RisingTeam outlook Top-8 5.4 mp P(scores ≥1) 29.6% ? Rank 24 Yoane Wissa PK DR Congo Pos FW Form ↗ RisingTeam outlook Knockout 3.4 mp P(scores ≥1) 29.5% ? Rank 25 Niclas Füllkrug PK Germany Pos FW Form — Team outlook Top-8 5.3 mp P(scores ≥1) 28.6% ? Rank 26 Donyell Malen Netherlands Pos FW Form ↗ RisingTeam outlook Top-8 5.0 mp P(scores ≥1) 28.3% ? Rank 27 Vinícius Júnior Brazil Pos FW Form ↘ CoolingTeam outlook Top-4 5.6 mp P(scores ≥1) 28.0% ? Rank 28 Ousmane Dembélé France Pos FW Form ↗ RisingTeam outlook Top-4 5.6 mp P(scores ≥1) 27.9% ? Rank 29 Antonio Sanabria PK Paraguay Pos FW Form ↗ RisingTeam outlook R16 4.1 mp P(scores ≥1) 27.8% ? Rank 30 Amine Gouiri PK Algeria Pos FW Form ↗ RisingTeam outlook Knockout 3.9 mp P(scores ≥1) 27.7% ? Rank 31 Breel Embolo Switzerland Pos FW Form ↘ CoolingTeam outlook Top-8 5.0 mp P(scores ≥1) 27.7% ? Rank 32 Sofyan Amrabat PK Morocco Pos MF Form → SteadyTeam outlook R16 4.9 mp P(scores ≥1) 27.0% ? Rank 33 Omar Marmoush Egypt Pos FW Form ↗ RisingTeam outlook R16 4.1 mp P(scores ≥1) 26.9% ? Rank 34 Hakan Çalhanoğlu PK Turkey Pos MF Form → SteadyTeam outlook R16 4.3 mp P(scores ≥1) 26.5% ? Rank 35 Mostafa Mohamed Egypt Pos FW Form ↘ CoolingTeam outlook R16 4.1 mp P(scores ≥1) 25.4% ? Rank 36 Daichi Kamada PK Japan Pos MF Form ↘ CoolingTeam outlook R16 4.5 mp P(scores ≥1) 25.2% ? Rank 37 Jürgen Locadia PK Curaçao Pos FW Form — Team outlook Long shot 3.1 mp P(scores ≥1) 25.2% ? Rank 38 Luis Díaz Colombia Pos FW Form ↗ RisingTeam outlook Top-8 5.2 mp P(scores ≥1) 24.7% ? Rank 39 Marcel Sabitzer PK Austria Pos MF Form ↘ CoolingTeam outlook R16 4.1 mp P(scores ≥1) 24.3% ? Rank 40 Neymar Brazil Pos FW Form → SteadyTeam outlook Top-4 5.6 mp P(scores ≥1) 24.0% ? Rank 41 Lautaro Martínez Argentina Pos FW Form ↗ RisingTeam outlook Final 5.8 mp P(scores ≥1) 23.3% ? Rank 42 Ermedin Demirović Bosnia and Herzegovina Pos FW Form ↘ CoolingTeam outlook Knockout 3.6 mp P(scores ≥1) 22.8% ? Rank 43 Boulaye Dia PK Senegal Pos FW Form → SteadyTeam outlook R16 4.5 mp P(scores ≥1) 22.7% ? Rank 44 Memphis Depay PK Netherlands Pos FW Form ↘ CoolingTeam outlook Top-8 5.0 mp P(scores ≥1) 22.7% ? Rank 45 Bamba Dieng Senegal Pos FW Form ↘ CoolingTeam outlook R16 4.5 mp P(scores ≥1) 21.8% ? Rank 46 Lamine Yamal Spain Pos FW Form ↗ RisingTeam outlook Final 5.9 mp P(scores ≥1) 21.7% ? Rank 47 Darwin Núñez Uruguay Pos FW Form ↗ RisingTeam outlook R16 4.8 mp P(scores ≥1) 21.5% ? Rank 48 Cody Gakpo Netherlands Pos FW Form ↘ CoolingTeam outlook Top-8 5.0 mp P(scores ≥1) 21.4% ? Rank 49 Patrik Schick Czech Republic Pos FW Form → SteadyTeam outlook R16 4.1 mp P(scores ≥1) 21.4% ? Rank 50 Bradley Barcola France Pos FW Form → SteadyTeam outlook Top-4 5.6 mp P(scores ≥1) 20.5% ? ▸ How to read this table— 4 columns explained
Pos Broad position bucket — GK / DF / MF / FW.
Form Current-form momentum: the player's most recent full club season vs their own multi-season baseline (Rising / Steady / Cooling). Big-5 club data only, so it's blank for players without recent top-five-league history.
Team outlook Contender tier — the deepest bracket stage the player's team has at least a 25% chance to reach — plus the team's expected number of WC matches. Drives the per-player total: a player on a deeper-running team gets more shots at scoring.
P(scores ≥1) Total probability the player scores at least one goal across their team's WC matches. Derived from npxG/90, expected minutes, team xG share, opponent defence, and the team's expected match count. Jede Zeile zeigt die Wahrscheinlichkeit, im gesamten Turnier mindestens 1 Tor zu erzielen, zusammen mit dem Teamausblick (Kontrahenten-Stufe und erwartete Spiele).
Nach Land Jede Karte zeigt den bestplatzierten Spieler des Landes nach P(erzielt mindestens 1). Klicken Sie durch für die vollständige Länderliste.
Was das ist. v0 of the per-player tournament-scorer model. npxG/90 from each player's most recent ≥500-minute Big-5 season; E[minutes] is a two-state mixture of starter (75 min) and substitute (20 min) weighted by caps relative to the team's last-2-year fixture count; team_xG_share is position-weighted within a notional 4-3-3 (FW 4× / MF 2× / DF 1× / GK 0); opp_def_factor is the team-average xG-against multiplier across the other 47 WC nations; E[matches] is the team's expected number of WC matches from the bracket Monte Carlo. Known v0 simplifications: no penalty-taker designation, no set-piece-taker bonus, no fatigue / rotation, constant within-position xG share. See /docs/methodology/ for the full write-up.
Was das nicht ist. Diese Seite veröffentlicht eine Modellausgabe als Wahrscheinlichkeit. Es ist keine Empfehlung, keine Prognose für ein bestimmtes Spiel und kein Leitfaden für ein kommerzielles Produkt. Siehe /docs/ für die Forschungsrahmung des Projekts.
onthepitch
Eine Forschungsplattform zur FIFA Fussball-Weltmeisterschaft 2026.
Methodik
Forecasts come from a calibrated Ensemble Elo Dixon-Coles Hierarchical Poisson /docs/methodology/ for the full write-up. Tap any abbreviation in a data table for its definition.
How the forecasts score against results — Brier log-loss calibration scoreboard .
Built 10 Jun 22:56 UTC · 1 days to kickoff · Methodology
Sportanalytik und statistische Forschung. Modellausgaben sind Wahrscheinlichkeiten, keine Empfehlungen. Vergangene Kalibrierung garantiert keine zukünftige Genauigkeit. Diese Website bewirbt oder vermittelt kein Glücksspiel. Terms .