Soccer predictions: actual data vs predicted data
Slate 20260611 · 2 match(es)
How to read this report. Each match shows the model's headline prediction, the stage-by-stage lambda (goal-rate) build, both teams' standing/form inputs, and every starter's projected line next to their real season baseline. A moderate / large deviation badge flags a projection that strays far from the player's season form — a quick way to spot when the inputs look right but a projection does not. When the real result is known an actual-vs-predicted banner appears.
All matches overview
| Match | Score | Winner | H / D / A | O2.5 | BTTS | Conf | λ H/A | Actual | Result |
|---|
| South Korea vs Czechia FIFA World Cup | 1-1 | Draw | 25.1% / 25.8% / 49.1% | 47.0% | 50.3% | 0.74 | 1.02 / 1.53 | - | pending |
| Mexico vs South Africa FIFA World Cup | 1-1 | Draw | 44.0% / 27.7% / 28.3% | 42.1% | 47.4% | 0.72 | 1.34 / 1.02 | - | pending |
Match detail
South Korea vs Czechia
FIFA World Cup · 20260611 · 3-4-3 vs 3-4-2-1
Predicted 1-1Winner: DrawConfidence 0.74λ home/away 1.02 / 1.53Most-likely P 12.3%
Actual vs predicted: result not available yet (upcoming fixture).
Lambda decomposition (how the goal rates were built)
| Stage | South Korea λ | Czechia λ |
|---|
| Pre-competitive-floor | 0.910 | 1.535 |
| Pre-calibration | 0.910 | 1.535 |
| Final λ | 1.020 | 1.535 |
competition calibration scale 1.0competition observed total goals 0.0quality gap applied Falsemismatch score pressure applied Falsecompetitive underdog floor applied Truestage discount 1.0
Scoreline distribution & markets
| Top 5 scorelines | P |
|---|
| 1-1 | 12.3% |
| 0-1 | 11.8% |
| 1-2 | 9.3% |
| 0-2 | 9.2% |
| 0-0 | 7.9% |
| Home win | 25.1% |
| Draw | 25.8% |
| Away win | 49.1% |
| Over 2.5 | 47.0% |
| BTTS yes | 50.3% |
Projected players vs season baseline
Proj = projected for this match · Season = this season's per-game average from match data · deviation badge compares projected xG to the player's season xG/game.
South Korea — projected line vs season baseline (3-4-3)
| Player | Pos | Min | Proj G | Proj xG | Season xG/g | | Proj Sh | Season Sh/g | Proj Rtg | Season Rtg | Rec |
|---|
| Hwang Hee-chan | F | 89 | 0 | 0.07 | 0.04 | 70% | 2 | 0.7 | 81.5 | 65.3 | 6 |
| Son Heung-min | F | 90 | 1 | 0.15 | 0.31 | 50% | 2 | 2.2 | 92.0 | 42.0 | 8 |
| Kang-in Lee | M | 22 | 0 | 0.00 | 0.02 | 100% | 2 | 0.4 | 85.9 | 34.9 | 18 |
| Lee Tae-seok | M | 85 | 0 | 0.00 | 0.01 | 100% | 1 | 0.2 | 75.9 | 54.4 | 9 |
| Jae-sung Lee | M | 21 | 0 | 0.00 | 0.06 | 100% | 1 | 0.3 | 83.3 | 37.9 | 7 |
| Hwang In-beom | M | 90 | 0 | 0.05 | 0.13 | 62% | 1 | 1.3 | 83.8 | 74.7 | 3 |
| Young-woo Seol | D | 89 | 0 | 0.00 | 0.01 | 100% | 1 | 0.4 | 89.0 | 38.9 | 7 |
| Kim Tae-hyeon | D | 90 | 0 | - | - | n/a | | 0.0 | 88.6 | - | 4 |
| Kim Min-jae | D | 64 | 0 | 0.27 | 0.05 | 429% | 1 | 0.4 | 89.7 | 45.8 | 11 |
| Han-Beom Lee | D | 29 | 0 | 0.00 | 0.03 | 100% | 0 | 0.5 | 75.6 | 67.5 | 4 |
| Kim Seung-gyu | G | 90 | 0 | - | - | n/a | | 0.0 | 87.7 | 50.8 | 4 |
| Team totals | 1 | 1.02 | | 12 | | 84.8 | |
Czechia — projected line vs season baseline (3-4-2-1)
| Player | Pos | Min | Proj G | Proj xG | Season xG/g | | Proj Sh | Season Sh/g | Proj Rtg | Season Rtg | Rec |
|---|
| David Jurásek | D | 58 | 0 | 0.00 | - | n/a | 1 | 0.0 | 79.7 | 39.3 | 6 |
| Vladimír Darida | M | 88 | 0 | 0.33 | 0.21 | 57% | 1 | 1.2 | 86.1 | 48.8 | 6 |
| Tomáš Souček | M | 79 | 0 | 0.10 | 0.18 | 46% | 2 | 0.7 | 85.1 | 67.5 | 6 |
| Vladimír Coufal | D | 88 | 0 | 0.04 | 0.01 | 223% | 1 | 0.3 | 79.8 | 45.8 | 9 |
| Ladislav Krejčí | D | 81 | 0 | 0.06 | 0.09 | ok | 1 | 1.2 | 85.1 | 57.2 | 6 |
| Robin Hranáč | D | 55 | 0 | 0.00 | - | n/a | 1 | 0.0 | 77.7 | 51.4 | 5 |
| Štěpán Chaloupek | D | 71 | 0 | 0.36 | 0.18 | 99% | 1 | 0.8 | 76.5 | 45.5 | 6 |
| Matěj Kovář | G | 90 | 0 | - | - | n/a | | 0.0 | 82.2 | 72.6 | 5 |
| Pavel Šulc | F | 90 | 0 | 0.18 | 0.13 | 39% | 3 | 0.6 | 84.1 | 56.4 | 8 |
| Lukáš Provod | F | 90 | 0 | 0.31 | 0.05 | 492% | 3 | 0.8 | 86.3 | 47.2 | 6 |
| Patrik Schick | F | 74 | 1 | 0.38 | 0.52 | ok | 2 | 1.9 | 92.0 | 59.6 | 8 |
| Team totals | 1 | 1.53 | | 16 | | 83.2 | |
Antagonist / tactical summary
Midfield is evenly contested — Kang-in Lee vs Tomáš Souček will be the pivotal duel. Total antagonist xG — Home: 0.56, Away: 1.27. Home danger zone: Shadow Striker vs Centre Back — Half-Space Threat. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.
Midfield — possession 50.7% / 49.3%, dominance ratio 0.55, key duel: Kang-in Lee vs Tomáš Souček
Jae-sung Lee xG 0.06Kang-in Lee xG 0.15Lee Tae-seok xG 0.08Vladimír Darida xG 0.30Tomáš Souček xG 0.10Pavel Šulc xG 0.18
Summary
Antagonist matchup analysis for South Korea vs Czechia. Most likely scoreline: 1-1. Lambdas derived from 6 home and 5 away individual player duels.
Tactical parity between 3-4-3 and 3-4-2-1. Match outcome driven primarily by individual player quality. Midfield is evenly contested — Kang-in Lee vs Tomáš Souček will be the pivotal duel. Total antagonist xG — Home: 0.56, Away: 1.27. Home danger zone: Shadow Striker vs Centre Back — Half-Space Threat. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.
Mexico vs South Africa
FIFA World Cup · 20260611 · 4-1-2-3 vs 4-2-3-1
Predicted 1-1Winner: DrawConfidence 0.72λ home/away 1.34 / 1.02Most-likely P 13.0%
Actual vs predicted: result not available yet (upcoming fixture).
Lambda decomposition (how the goal rates were built)
| Stage | Mexico λ | South Africa λ |
|---|
| Pre-competitive-floor | 1.344 | 0.731 |
| Pre-calibration | 1.344 | 0.731 |
| Final λ | 1.344 | 1.020 |
competition calibration scale 1.0competition observed total goals 0.0quality gap applied Truemismatch score pressure applied Falsecompetitive underdog floor applied Truestage discount 1.0
Scoreline distribution & markets
| Top 5 scorelines | P |
|---|
| 1-1 | 13.0% |
| 1-0 | 12.5% |
| 0-0 | 9.5% |
| 0-1 | 9.5% |
| 2-1 | 8.7% |
| Home win | 44.0% |
| Draw | 27.7% |
| Away win | 28.3% |
| Over 2.5 | 42.1% |
| BTTS yes | 47.4% |
Projected players vs season baseline
Proj = projected for this match · Season = this season's per-game average from match data · deviation badge compares projected xG to the player's season xG/game.
Mexico — projected line vs season baseline (4-1-2-3)
| Player | Pos | Min | Proj G | Proj xG | Season xG/g | | Proj Sh | Season Sh/g | Proj Rtg | Season Rtg | Rec |
|---|
| Julián Quiñones | F | 86 | 1 | 0.47 | 0.53 | ok | 2 | 3.2 | 86.8 | 68.8 | 9 |
| Raúl Jiménez | F | 20 | 0 | 0.03 | 0.42 | 92% | 2 | 3.0 | 83.3 | 59.3 | 7 |
| Roberto Alvarado | F | 12 | 0 | 0.22 | 0.05 | 327% | 2 | 0.9 | 83.7 | 63.0 | 8 |
| Álvaro Fidalgo | M | 43 | 0 | 0.00 | 0.02 | 100% | 0 | 0.3 | 85.7 | 36.8 | 15 |
| Brian Gutiérrez | M | 90 | 0 | 0.05 | 0.08 | 36% | 2 | 1.3 | 74.8 | 60.9 | 7 |
| Erik Lira | D | 90 | 0 | - | 0.00 | n/a | | 0.1 | 90.8 | 32.0 | 11 |
| Jesús Gallardo | D | 90 | 0 | - | 0.03 | n/a | | 1.2 | 89.6 | 69.7 | 6 |
| Johan Vásquez | D | 91 | 0 | 0.13 | 0.04 | 226% | 2 | 0.2 | 82.1 | 45.0 | 8 |
| César Montes | D | 75 | 0 | 0.11 | 0.02 | 517% | 1 | 0.2 | 93.9 | 56.4 | 5 |
| Israel Reyes | D | 45 | 0 | 0.00 | 0.02 | 100% | 0 | 0.6 | 82.7 | 59.6 | 7 |
| Raúl Rangel | G | 90 | 0 | - | - | n/a | | 0.0 | 88.7 | 51.4 | 7 |
| Team totals | 1 | 1.34 | | 13 | | 85.6 | |
South Africa — projected line vs season baseline (4-2-3-1)
| Player | Pos | Min | Proj G | Proj xG | Season xG/g | | Proj Sh | Season Sh/g | Proj Rtg | Season Rtg | Rec |
|---|
| Aubrey Modiba | D | 90 | 0 | - | 0.07 | n/a | | 1.7 | 87.6 | 68.3 | 7 |
| Mbekezeli Mbokazi | D | 90 | 0 | - | 0.01 | n/a | | 0.4 | 89.6 | 46.9 | 9 |
| Ime Okon | D | 90 | 0 | - | 0.00 | n/a | | 0.2 | 73.6 | 68.0 | 4 |
| Khuliso Mudau | D | 90 | 0 | - | - | n/a | | 0.0 | 87.6 | 66.7 | 6 |
| Ronwen Williams | G | 90 | 0 | - | - | n/a | | 0.0 | 93.7 | 67.4 | 7 |
| Jayden Adams | M | 90 | 0 | 0.05 | - | n/a | 2 | 0.0 | 88.7 | 51.2 | 5 |
| Teboho Mokoena | M | 90 | 0 | 0.05 | 0.14 | 65% | 2 | 1.4 | 89.7 | 61.3 | 7 |
| Themba Zwane | M | 90 | 0 | 0.05 | - | n/a | 2 | 0.0 | 87.7 | 16.4 | 7 |
| Lyle Foster | F | 21 | 1 | 0.24 | 0.01 | 2664% | 2 | 0.2 | 78.4 | 24.4 | 5 |
| Relebohile Mofokeng | M | 90 | 0 | 0.05 | 0.08 | 36% | 1 | 1.8 | 88.7 | 45.7 | 9 |
| Oswin Appollis | M | 90 | 0 | 0.05 | 0.16 | 69% | 1 | 1.8 | 89.7 | 53.6 | 9 |
| Team totals | 1 | 1.02 | | 12 | | 86.8 | |
Antagonist / tactical summary
Midfield is evenly contested — Álvaro Fidalgo vs Teboho Mokoena will be the pivotal duel. Total antagonist xG — Home: 0.79, Away: 0.38. Home danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.
Midfield — possession 51.3% / 48.7%, dominance ratio 0.51, key duel: Álvaro Fidalgo vs Teboho Mokoena
Brian Gutiérrez xG 0.06Julián Quiñones xG 0.47Raúl Jiménez xG 0.03Jayden Adams xG 0.05Teboho Mokoena xG 0.05Themba Zwane xG 0.05
Summary
Antagonist matchup analysis for Mexico vs South Africa. Most likely scoreline: 1-1. Lambdas derived from 4 home and 4 away individual player duels.
Tactical parity between 4-1-2-3 and 4-2-3-1. Match outcome driven primarily by individual player quality. Midfield is evenly contested — Álvaro Fidalgo vs Teboho Mokoena will be the pivotal duel. Total antagonist xG — Home: 0.79, Away: 0.38. Home danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.
Generated 2026-09-04 22:27:55 UTC · Soccer prediction reporting