2026 World Cup · Data Hub | Attack/Defense Stats, Goal Distribution, xG, Head-to-Head, Model Analysis

📊 2026 World Cup · Data Hub Attack/Defense | Goal Distribution | xG | H2H | Model Analysis

⚽ Total simulated goals: 156 (2.44 avg)
📈 Avg xG per game: 2.68 (above actual)
🎯 Most lethal attack: France (xG 2.1/game)
🛡️ Best defense: Brazil (0.7 conceded/game)
⚔️ Attack & Defense 🥅 Goal Distribution 📐 Expected Goals (xG) 📜 Head-to-Head 🧠 Model Analysis
⚔️ Attack & Defense · Core Team Metrics Per game avg | Efficiency | Defensive solidity
🇫🇷 France
2.1
Goals scored/game
1.2
Goals conceded/game
Shot conversion 14%
🏴󠁧󠁢󠁥󠁮󠁧󠁿 England
1.9
Goals scored/game
0.9
Goals conceded/game
Shot conversion 12%
🇦🇷 Argentina
2.0
Goals scored/game
0.8
Goals conceded/game
Shot conversion 16%
🇧🇷 Brazil
2.3
Goals scored/game
0.7
Goals conceded/game
Shot conversion 18%
📌 Core insight: Brazil leads in both attack and defense balance, averaging 6.2 shots on target per game. France's attack relies heavily on Mbappé's left-sided cuts; right side contribution limited. England's low conversion rate suggests struggles against compact defenses.
🥅 Goal Distribution · Time Intervals & Scoring Methods Halves | Set pieces | Open play
⏱️ Goal time segments
0-15' 16-30' 31-45' 45+ 46-60' 61-75' 76-90+'
🎯 Goal method breakdown
64%
Open play goals
22%
Set pieces
14%
Penalties / counters
🌍 Top teams goal share
73%
1st half goals (Brazil/France)
27%
Injury-time winners
📌 Distribution insight: Last 15 minutes goal share rises to 31% in knockout stage. Set-piece goal ratio climbs to 28% after quarter-finals; England and Germany are set-piece specialists. Group stage first-half goals account for 58%, dropping to 49% in knockouts — reflecting cautious approach.
📐 Expected Goals (xG) · Actual vs Expected Performance deviation | Chance conversion
TeamActual goals/gAvg xG/gDiff (actual - xG)Conversion rating
🇫🇷 France2.12.3-0.2Below expectation
🏴󠁧󠁢󠁥󠁮󠁧󠁿 England1.92.2-0.3Inefficient
🇦🇷 Argentina2.01.9+0.1At par
🇧🇷 Brazil2.32.1+0.2Above expectation
🇩🇪 Germany1.82.0-0.2Underperforming
📐 xG insight: England's actual goals lag xG by 0.3 — attacking conversion needs urgent improvement. Brazil is the only top team consistently outperforming xG, thanks to Vinícius Jr. & Rodrygo's finishing. Knockout stage xG model accuracy improves by 12%.
📜 Head-to-Head · Classic Matchups & Psychological Edge Last 10 meetings | Win rate | Avg goals
🇫🇷 France vs 🇩🇰 Denmark
40%
Draw rate (last 5 comps)
2.1
Avg total goals
🏴󠁧󠁢󠁥󠁮󠁧󠁿 England vs 🇺🇸 USA
30%
Draw rate (World Cup history)
2.4
Avg total goals
🇪🇸 Spain vs 🇩🇪 Germany
35%
Draw rate (last 10 years)
2.7
Avg total goals
📜 H2H data: France-Denmark posted 40% draws in last 5 competitive meetings, far exceeding market expectations. England never beaten USA by more than 1 goal in World Cup history; psychological edge minimal. Spain-Germany saw red cards or penalties in last 3 major tournament clashes — high variable count.
🧠 Model Analysis · Machine Learning Predictions & 1X2 Weights ELO rating | Attack weight | Defensive stability
🤖 1X2 model accuracy
71%
Last 10 simulated matches
📊 High-value factor weights
34%
Handicap anomalies
25%
Key player absence
18%
Knockout psychology
⚡ ELO change (pre-knockout)
+37
Brazil (biggest rise)
-28
Germany (title defense pressure)
🧠 Model core conclusion: Combining attacking xG, defensive PPDA, set-piece efficiency, and historical psychological factors, the highest-rated teams are Brazil (92.4), France (89.1), England (87.3). Draw probability is systematically underestimated by ~7% — model suggests raising draw weight to above 32%.
📈 Data-driven strategy: Based on xG discrepancy & handicap abnormality, "France vs Denmark" draw yields highest model value score; "England vs USA" away odds deviate from model by 4.5%, creating positive expected value betting window.
📈 Data Dashboard · Key Indicators Summary Dynamic update
⚽ Tournament avg goals: 2.44 (sim)
📐 Avg xG per game: 2.68 (chances created > actual)
🎯 Best finishing efficiency: Brazil (+0.2 xG diff)
🛡️ Lowest xGA: Brazil (0.9/game)
✅ Data hub trend summary: Brazil leads both attack and defensive metrics with positive xG differential; France and England underperform xG expectations, leaving room for efficiency correction. The historically high draw rate in knockouts has been factored into the model — focus on "draw + under" combo.
※ Data based on 2026 simulated season + last 5 World Cups historical stats. xG model uses machine learning dynamic calibration.
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