URSAPHARM-Arena an der Kaiserlinde · Lindenstraße
SV 07 Elversberg1–2Finished
Bayern MunichSV 07 Elversberg 1 x 2 Bayern Munich: prediction analysis
The model predicted correctly 5 of 5 settled markets.
Correct predictions
- 1x2: prediction Bayern Munich; result Bayern Munich 1–2.
- Double Chance: prediction Draw or Bayern Munich; result Bayern Munich 1–2.
- Over 2.5 goals: prediction Over 2.5; result 3 goals.
- Both Teams to Score: prediction Yes; result Yes.
- Over 9.5 corners: prediction Over 9.5; result 12 corners.
Final assessment: For this game, most settled predictions matched the observed results.
View the analysis published before kickoff
Before the game, the model Predora Market Median estimated a 7.4% probability of a win for SV 07 Elversberg, 11.0% for a draw, and 81.6% for a win for Bayern Munich.
The market is quite clear: Bayern are strong favorites, with odds ranging from 1.14 to 1.19 across the 11 bookmakers analyzed. Elversberg’s odds range from 8.75 to 16, while the draw ranges from 7.9 to 9.5. In terms of consensus, the visitors account for around 82% of the implied probability, with the draw at around 11%.
The deterministic signals agree with the favoritism, but with much less intensity: the relative reading gives Bayern 53.6% against Elversberg’s 46.4%. This difference comes mainly from the home/away context and recent performance, which favor the home side. The chance-creation numbers (xG) and goal difference, meanwhile, reinforce the visitors’ superiority.
There is therefore a partial divergence: the data recognize Bayern as favorites, but not as strongly as the market suggests. The difference between the signal reading and the bookmakers’ prices may reflect factors not captured by the numbers, such as squad strength or the club’s institutional situation. The draw remains a significant source of uncertainty, especially if the game is more closely contested than the market expects.
Market 1x2
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.22 / 81.6% Formula: 1 ÷ Estimated probability
Best Market Odds The implied probability of the bookmaker offering the highest odds for this outcome. A reference for comparison with the model's estimate.
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
1.21 / 82.6% Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Edge (Mathematical Advantage) The difference between the model's probability and the implied probability of the highest-paying bookmaker. A positive value means the model estimates a higher probability than the odds imply; it does not guarantee a profit.
Formula: Probability − Implied probability (Book)
-1.0 p.p.Formula: Probability − Implied probability (Book)
EV (Expected Value) A statistical estimate calculated from the Active Model's probability and the highest observed odds. If the Active Model's estimate is closer to the true probability than the odds-implied probability, positive EV may indicate estimated value at those odds. It is not a proven advantage, promise of profit, or guarantee of a result.
Formula: (Estimated probability × Odds) − 1
-1.2%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | SV 07 Elversberg | Draw | Bayern Munich |
|---|---|---|---|
| Predora Market Median | 7.4% / 13.50 | 11.0% / 9.13 | 81.6% / 1.22 |
Market Double Chance
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.08 / 92.6% Formula: 1 ÷ Estimated probability
Best Market Odds The implied probability of the bookmaker offering the highest odds for this outcome. A reference for comparison with the model's estimate.
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
1.06 / 94.3% Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Edge (Mathematical Advantage) The difference between the model's probability and the implied probability of the highest-paying bookmaker. A positive value means the model estimates a higher probability than the odds imply; it does not guarantee a profit.
Formula: Probability − Implied probability (Book)
-1.7 p.p.Formula: Probability − Implied probability (Book)
EV (Expected Value) A statistical estimate calculated from the Active Model's probability and the highest observed odds. If the Active Model's estimate is closer to the true probability than the odds-implied probability, positive EV may indicate estimated value at those odds. It is not a proven advantage, promise of profit, or guarantee of a result.
Formula: (Estimated probability × Odds) − 1
-1.9%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | 1X | X2 | 12 |
|---|---|---|---|
| Predora Market Median | 18.4% / 5.45 | 92.6% / 1.08 | 89.1% / 1.12 |
AI Analysis
Market and data partially alignedAvailable with a free account
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Team Comparison
Outcome Strength (Season)
Game context (Home/Away)
| Factor | SV 07 Elversberg Home | Bayern Munich Away |
|---|---|---|
| PPG (home/away) Average points per game in the relevant context (home team at home, away team away). Example: 6 points in 4 games = 1.5 points/game.Points per game in context (home team at home / away team away). | 3 pts/game | 1 pts/game |
| Win % (home/away) Win percentage in the relevant context (home for the home team, away for the away team). Example: 2 wins in 4 games = 50%. | 100 % | 0 % |
| Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%. | 0 % | 100 % |
| Goal difference (home/away) Average goal difference in context: goals scored minus goals conceded per game. Example: scored 6 and conceded 4 in 4 games → (6−4)/4 = +0.5. | 0 goals/game | 0.5 goals/game |
| xG difference (home/away) Average xG difference in context: xG for minus xG against per game. Example: 1.4 − 1.1 = +0.3 xG/game. | -1.58 xG/game | 2.05 xG/game |
Season baseline (Overall) Temp
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game. | 3 pts/game | 2 pts/game |
| Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%. | 100 % | 50 % |
| Draw % (overall) Draw percentage this season (home + away). Example: 2 draws in 8 games = 25%. | 0 % | 50 % |
| Goal difference (overall) Average goal difference this season: (goals scored − goals conceded) / games. Example: 10−6 in 8 games → +0.5 per game. | 0.33 goals/game | 1.67 goals/game |
| xG difference (overall) Average xG difference this season: xG for minus xG against per game. Example: 1.6 − 1.2 = +0.4 xG/game. | -1.26 xG/game | 1.76 xG/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| Form (last 5): W/D/L Results from the last 5 games: wins, draws, and losses. Example: 2–2–1 = 2 wins, 2 draws, 1 loss. | 4-0-1 | 4-1-0 |
| PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game. | 2.4 pts/game | 2.6 pts/game |
Market Over 2.5 goals
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.23 / 81.6% Formula: 1 ÷ Estimated probability
Best Market Odds The implied probability of the bookmaker offering the highest odds for this outcome. A reference for comparison with the model's estimate.
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
1.18 / 84.7% Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Edge (Mathematical Advantage) The difference between the model's probability and the implied probability of the highest-paying bookmaker. A positive value means the model estimates a higher probability than the odds imply; it does not guarantee a profit.
Formula: Probability − Implied probability (Book)
-3.2 p.p.Formula: Probability − Implied probability (Book)
EV (Expected Value) A statistical estimate calculated from the Active Model's probability and the highest observed odds. If the Active Model's estimate is closer to the true probability than the odds-implied probability, positive EV may indicate estimated value at those odds. It is not a proven advantage, promise of profit, or guarantee of a result.
Formula: (Estimated probability × Odds) − 1
-3.8%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Over | Under |
|---|---|---|
| Predora Market Median | 81.6% / 1.23 | 18.4% / 5.42 |
Team Comparison
Predictions (AI) Prev
| Factor | Game |
|---|---|
| Potential: average goals Pregame estimate of total goals, based on historical data and AI, combining both teams. Example: 2.8 goals/game ≈ an estimate of 3 goals. | 2.5 goals |
| Over 2.5 potential Pregame estimated probability of 3+ goals, based on historical data and AI. Example: 52% estimates Over 2.5 in about 52 of 100 similar games.Pregame probability of over 2.5 goals | 50 % |
| Under 2.5 potential Pregame estimated probability of under 2.5 goals, based on historical data and AI. Example: 55% estimates Under 2.5 in about 55 of 100 similar games.Pregame probability of under 2.5 goals | 50 % |
Season (baseline)
Game context (Home/Away)
| Factor | SV 07 Elversberg Home | Bayern Munich Away |
|---|---|---|
| Goals scored (home/away) Average goals scored per game in the relevant context (home team at home, away team away). Example: 6 goals in 4 games = 1.5 goals/game. | 3 goals/game | 0 goals/game |
| Goals conceded (home/away) Average goals conceded per game in the home/away context. Example: 5 goals conceded in 4 games = 1.25 goals/game. | 2 goals/game | 0 goals/game |
| xG for (home/away) Average chance quality created per game (xG) in the home/away context. Example: 1.6 xG per game means the team’s shots add up to 1.6 expected goals per game on average. | 1.32 xG/game | 2.24 xG/game |
Overall (Season) Temp
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| Over 2.5 (season) Percentage of season games with over 2.5 goals. Example: 7 of 13 games = 54%.Season games with Over 2.5 % | 100 % | 50 % |
| Goals scored (overall) Average goals scored per game this season (home + away). Example: 20 goals in 13 games = 1.54 goals/game. | 3.5 goals/game | 2.5 goals/game |
| Shots on target / game Average shots on target per game this season (home + away). Example: 48 shots on target in 10 games = 4.8 per game. | 8 shots on target/game | 7 shots on target/game |
| Goals conceded (overall) Average goals conceded per game this season (home + away). Example: 15 goals in 13 games = 1.15 goals/game. | 2.5 goals/game | 0.5 goals/game |
League (baseline) League
| Factor | League |
|---|---|
| League average goals Average goals per game in the league this season (competition baseline). Example: 2.68 goals/game ≈ 2–3 goals per game.Average league goals per game (baseline) | 3.85 goals/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| Goals scored (last 5) Average goals scored per game in the last 5 games (recent form). Example: 9 goals in 5 games = 1.8 goals/game. | 2.67 goals/game | 2.33 goals/game |
| Goals conceded (last 5) Average goals conceded per game in the last 5 games (recent form). Example: 6 goals conceded in 5 games = 1.2 goals/game. | 2.33 goals/game | 0.67 goals/game |
Market Both Teams to Score
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.61 / 61.9% Formula: 1 ÷ Estimated probability
Best Market Odds The implied probability of the bookmaker offering the highest odds for this outcome. A reference for comparison with the model's estimate.
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
1.55 / 64.5% Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Edge (Mathematical Advantage) The difference between the model's probability and the implied probability of the highest-paying bookmaker. A positive value means the model estimates a higher probability than the odds imply; it does not guarantee a profit.
Formula: Probability − Implied probability (Book)
-2.6 p.p.Formula: Probability − Implied probability (Book)
EV (Expected Value) A statistical estimate calculated from the Active Model's probability and the highest observed odds. If the Active Model's estimate is closer to the true probability than the odds-implied probability, positive EV may indicate estimated value at those odds. It is not a proven advantage, promise of profit, or guarantee of a result.
Formula: (Estimated probability × Odds) − 1
-4.0%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Yes | No |
|---|---|---|
| Predora Market Median | 61.9% / 1.61 | 38.1% / 2.63 |
Team Comparison
Predictions (AI) Prev
| Factor | Game |
|---|---|
| Potential: BTTS (%) Pregame estimated probability of both teams scoring, based on historical data and AI. Example: 58% estimates that about 58 of 100 similar games would have goals from both teams. | 50 % |
| Over 2.5 potential Pregame estimated probability of 3+ goals, based on historical data and AI. Example: 52% estimates Over 2.5 in about 52 of 100 similar games.Pregame probability of over 2.5 goals | 50 % |
Season baseline (Season)
Indicators associated with more BTTS outcomes
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%. | 100 % | 50 % |
| Games scoring 1+ goal % (season) Percentage of season games in which the team scored at least 1 goal. Example: scored in 10 of 12 games = 83%. | 100 % | 50 % |
| Games conceding 1+ goal % (season) Percentage of season games in which the team conceded at least 1 goal. Example: conceded in 8 of 12 games = 67%. | 100 % | 50 % |
Indicators associated with fewer BTTS outcomes
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| Clean sheet % (season) Percentage of clean sheets, a factor associated with fewer Both Teams to Score outcomes. Example: 4 clean sheets in 12 games = 33%. | 0 % | 50 % |
| Failed to score % (season) Percentage of games without scoring, a factor associated with fewer Both Teams to Score outcomes. Example: no goals scored in 3 of 12 games = 25%. | 0 % | 50 % |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| Games scoring 1+ goal % (last 5) Percentage of the last 5 games in which the team scored at least 1 goal. Example: scored in 4 of 5 = 80%. | 100 % | 66.7 % |
| Games conceding 1+ goal % (last 5) Percentage of the last 5 games in which the team conceded at least 1 goal. Example: conceded in 3 of 5 = 60%. | 100 % | 66.7 % |
Market Over 9.5 corners
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.88 / 53.3% Formula: 1 ÷ Estimated probability
Best Market Odds The implied probability of the bookmaker offering the highest odds for this outcome. A reference for comparison with the model's estimate.
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
1.76 / 57.0% Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Edge (Mathematical Advantage) The difference between the model's probability and the implied probability of the highest-paying bookmaker. A positive value means the model estimates a higher probability than the odds imply; it does not guarantee a profit.
Formula: Probability − Implied probability (Book)
-3.7 p.p.Formula: Probability − Implied probability (Book)
EV (Expected Value) A statistical estimate calculated from the Active Model's probability and the highest observed odds. If the Active Model's estimate is closer to the true probability than the odds-implied probability, positive EV may indicate estimated value at those odds. It is not a proven advantage, promise of profit, or guarantee of a result.
Formula: (Estimated probability × Odds) − 1
-6.5%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Over | Under |
|---|---|---|
| Predora Market Median | 53.3% / 1.88 | 46.7% / 2.14 |
Team Comparison
Predictions (AI) Prev
| Factor | Game |
|---|---|
| Potential: average corners Pregame estimate of total corners, based on historical data and AI. Example: 10.2 corners ≈ an estimate of 10 corners. | 15 corners |
| Potential Over 9.5 corners (%) Pregame estimated probability of 10+ corners, based on historical data and AI. Example: 58% estimates Over 9.5 in about 58 of 100 similar games. | 50 % |
Season (baseline)
Game context (Home/Away)
| Factor | SV 07 Elversberg Home | Bayern Munich Away |
|---|---|---|
| Corners for (home/away) Average corners won per game in context (home team at home, away team away). Example: 24 corners in 6 games = 4.0 per game. | 4 corners/game | 11 corners/game |
| Total corners (home/away) Average total corners per game in context (both teams combined). Example: 60 corners in 6 games = 10.0 per game. | 7 corners/game | 11 corners/game |
Overall (Season) Temp
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game. | 4 corners/game | 10 corners/game |
| Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game. | 12 corners/game | 11.5 corners/game |
League (baseline) League
| Factor | League |
|---|---|
| League average corners (corners/game) Average corners per game in the league this season (competition baseline). Example: 9.3 corners/game ≈ 9–10 corners per game. | 11.41 corners/game |
Cards
Team Comparison
Predictions (AI) Prev
| Factor | Game |
|---|---|
| Potential: average cards Pregame estimate of total cards, based on historical data and AI. Example: 4.8 cards ≈ an estimate of 5 cards. | 5 cards/game |
Season baseline (Season) Temp
| Factor | SV 07 Elversberg | Bayern Munich |
|---|---|---|
| Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game. | 2 cards/game | 1.5 cards/game |
| Over 4.5 cards % Probability (or observed percentage, depending on the source) of 5+ cards in the game. Example: 60% estimates Over 4.5 in about 60 of 100 similar games. | 50 % | 0 % |
League (baseline) League
| Factor | League |
|---|---|
| League average cards Average cards per game in the league this season (competition baseline). Example: 4.3 cards/game ≈ 4–5 cards per game. | 3.33 cards/game |
Borussia Dortmund
RB Leipzig
FC Augsburg
FSV Mainz
SC Paderborn 07
Eintracht Frankfurt