Sportpark Ronhof Thomas Sommer · Laubenweg 60
Greuther Furth1–1Finished
1. FC MagdeburgGreuther Furth 1 x 1 1. FC Magdeburg: prediction analysis
The model predicted correctly 1 of 5 settled markets.
Correct predictions
- Both Teams to Score: prediction Yes; result Yes.
Incorrect predictions
- 1x2: prediction 1. FC Magdeburg; result Draw 1–1.
- Double Chance: prediction Greuther Furth or 1. FC Magdeburg; result Draw 1–1.
- Over 2.5 goals: prediction Over 2.5; result 2 goals.
- Over 9.5 corners: prediction Over 9.5; result 8 corners.
Final assessment: For this game, more predictions differed from the observed results than matched them.
View the analysis published before kickoff
Before the game, the model Predora Market Median estimated a 33.7% probability of a win for Greuther Furth, 23.2% for a draw, and 43.1% for a win for 1. FC Magdeburg.
Bookmaker consensus
The 11 bookmakers analyzed make 1. FC Magdeburg the favorite, with odds between 2.2 and 2.3 for the away side — well below the ranges for a draw (3.6–3.9) and a home win (2.6–2.9). This indicates that the market sees the away team as having a clear advantage, although the margin is not huge.
What the signals say
The deterministic data reinforce the preference for the away side, but more strongly than the odds suggest. Indicators such as PPG, recent form and home/away performance point to clear superiority for Magdeburg. In the head-to-head record and xG differential, however, the signals are more balanced, with little or no distinction between the sides.
Where there is agreement and where there is a difference
The market and the data agree on the direction: the away side is ahead. The difference is in the magnitude: while the odds consensus implies about a 55% chance for Magdeburg (after excluding the draw), the signals point to approximately 67% in the same comparison. In other words, the performance-based assessment suggests that the favorite's advantage could be even greater than the odds indicate.
Relevant uncertainty
The draw accounts for about 25% of the 1X2 consensus, a considerable percentage. In addition, the teams' recent head-to-head record is balanced (three draws in the last five matches), which calls for caution. Thus, the partial divergence between the data and the market indicates room for interpretation, not certainty that the bookmakers are wrong.
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
2.32 / 43.1% 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)
2.57 / 38.9% 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)
+4.2 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
+10.7%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Greuther Furth | Draw | 1. FC Magdeburg |
|---|---|---|---|
| Predora Market Median | 33.8% / 2.96 | 23.2% / 4.32 | 43.1% / 2.32 |
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
1.30 / 76.8% 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)
1.50 / 66.7% 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)
+10.2 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
+15.2%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | 1X | X2 | 12 |
|---|---|---|---|
| Predora Market Median | 56.9% / 1.76 | 66.3% / 1.51 | 76.8% / 1.30 |
AI Analysis
Market and data partially alignedAvailable with a free account
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Team Comparison
Head-to-head (H2H) These statistics use the teams' most recent head-to-head games, generally from the last 10 to 15 years.H2H Last 5
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| H2H: W/D/L (last 5) Results from the last 5 head-to-head games between the teams. Example: 3–1–1 = 3 wins, 1 draw, 1 loss for the team on the left. | 1-3-1 | 1-3-1 |
| H2H: win % (home vs. away) (last 5) Head-to-head win percentage (last 5), split by home and away team. Example: the home team won 3 of 5 = 60%. | 20 % | 20 % |
Outcome Strength (Season)
Game context (Home/Away)
| Factor | Greuther Furth Home | 1. FC Magdeburg 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). | 0 pts/game | 1.5 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%. | 0 % | 50 % |
| Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%. | 0 % | 0 % |
| 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. | -1 goals/game | 0.67 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. | -0.06 xG/game | 0.6 xG/game |
Season baseline (Overall) Temp
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game. | 1 pts/game | 1.8 pts/game |
| Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%. | 20 % | 60 % |
| Draw % (overall) Draw percentage this season (home + away). Example: 2 draws in 8 games = 25%. | 40 % | 0 % |
| 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 | 0.33 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. | -0.15 xG/game | 0 xG/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| 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. | 1-2-2 | 3-1-1 |
| PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game. | 1 pts/game | 2 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
1.57 / 63.5% 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)
1.51 / 66.2% 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)
-2.7 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
-4.1%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Over | Under |
|---|---|---|
| Predora Market Median | 63.5% / 1.57 | 36.5% / 2.74 |
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. | 3.25 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 | 75 % |
| 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 | 25 % |
Season (baseline)
Game context (Home/Away)
| Factor | Greuther Furth Home | 1. FC Magdeburg 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. | 1 goals/game | 2 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. | 2.5 goals/game | 1 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. | 1.65 xG/game | 1.84 xG/game |
Overall (Season) Temp
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| 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 % | 40 % | 80 % |
| Goals scored (overall) Average goals scored per game this season (home + away). Example: 20 goals in 13 games = 1.54 goals/game. | 1.6 goals/game | 2 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. | 6.2 shots on target/game | 5.2 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 goals/game | 1.6 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) | 3.47 goals/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| 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. | 1.6 goals/game | 2 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 goals/game | 0.6 goals/game |
Head-to-head (H2H) These statistics use the teams' most recent head-to-head games, generally from the last 10 to 15 years.H2H Last 5
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| H2H: Over 2.5 % (last 5) Percentage of the last 5 head-to-head games with over 2.5 goals. Example: 3 of 5 games = 60%.Recent H2H games with Over 2.5 | 60 % | 60 % |
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
1.53 / 65.4% 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)
1.45 / 69.0% 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)
-3.6 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
-5.2%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Yes | No |
|---|---|---|
| Predora Market Median | 65.4% / 1.53 | 34.6% / 2.89 |
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 | 75 % |
Season baseline (Season)
Indicators associated with more BTTS outcomes
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%. | 80 % | 40 % |
| 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%. | 80 % | 100 % |
| 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 % | 40 % |
Indicators associated with fewer BTTS outcomes
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| 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 % | 60 % |
| 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%. | 20 % | 0 % |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| 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%. | 80 % | 100 % |
| 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 % | 40 % |
Head-to-head (H2H) These statistics use the teams' most recent head-to-head games, generally from the last 10 to 15 years.H2H Last 5
| Factor | Head-to-head (H2H) |
|---|---|
| H2H: BTTS % (last 5) Percentage of the last 5 head-to-head games in which both teams scored. Example: 3 of 5 games = 60%. | 80 % |
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
1.71 / 58.5% 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)
1.65 / 60.6% 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)
-2.1 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
-3.5%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Over | Under |
|---|---|---|
| Predora Market Median | 58.5% / 1.71 | 41.5% / 2.41 |
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. | 14.5 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 | Greuther Furth Home | 1. FC Magdeburg 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. | 6 corners/game | 8.5 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. | 8.5 corners/game | 12.5 corners/game |
Overall (Season) Temp
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game. | 5.4 corners/game | 5.8 corners/game |
| Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game. | 10.8 corners/game | 9.6 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. | 11.19 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. | 3 cards/game |
Season baseline (Season) Temp
| Factor | Greuther Furth | 1. FC Magdeburg |
|---|---|---|
| Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game. | 0.8 cards/game | 3.2 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. | 20 % | 60 % |
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. | 4.49 cards/game |
1 FC Nuremberg
SV Darmstadt 98
Holstein Kiel
Arminia Bielefeld
VfL 1899 Osnabruck
1 FC Kaiserslautern
Hertha BSC
VFL Wolfsburg