Stadion Miejski · ul. Słoneczna 1
Jagiellonia Bialystok2–1Finished
WKS Slask WroclawJagiellonia Bialystok 2 x 1 WKS Slask Wroclaw: prediction analysis
The model predicted correctly 4 of 5 settled markets.
Correct predictions
- 1x2: prediction Jagiellonia Bialystok; result Jagiellonia Bialystok 2–1.
- Double Chance: prediction Jagiellonia Bialystok or draw; result Jagiellonia Bialystok 2–1.
- Over 2.5 goals: prediction Over 2.5; result 3 goals.
- Both Teams to Score: prediction Yes; result Yes.
Incorrect predictions
- Over 9.5 corners: prediction Under 9.5; result 11 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 58.3% probability of a win for Jagiellonia Bialystok, 21.1% for a draw, and 20.6% for a win for WKS Slask Wroclaw.
The consensus of 11 bookmakers makes Jagiellonia Białystok a clear favorite, with odds ranging from 1.67 to 1.72. The draw and a Śląsk win are priced much higher, in a range from 4.00 to 4.47, reflecting a major difference in expectations. The deterministic signals also favor the home side, with 64.2% relative strength in the direct comparison, against 35.8% for the visitors. The market widens this advantage, assigning 71.6% to Jagiellonia after removing the draw. In other words, the data-based assessment and bookmakers’ pricing are aligned, indicating that the dominant view is consistent. On the other hand, the draw accounts for around 23% of the market’s implied probability, a figure that should not be ignored, as the match may have periods of balance. The uncertainty lies in Śląsk’s ability to be more efficient in attack than recent numbers suggest, especially if Jagiellonia cannot turn possession into goals.
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
1.72 / 58.3% 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)
1.73 / 58.0% 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)
+0.3 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
+0.5%Formula: (Estimated probability × Odds) − 1
Odds Comparison
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Probability Models
| Model | Jagiellonia Bialystok | Draw | WKS Slask Wroclaw |
|---|---|---|---|
| Predora Market Median | 58.3% / 1.72 | 21.1% / 4.73 | 20.6% / 4.85 |
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
1.26 / 79.4% 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)
1.22 / 82.0% 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)
-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
-3.1%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | 1X | X2 | 12 |
|---|---|---|---|
| Predora Market Median | 79.4% / 1.26 | 41.7% / 2.40 | 78.9% / 1.27 |
AI Analysis
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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 | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| 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. | 2-2-1 | 1-2-2 |
| 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%. | 40 % | 20 % |
Outcome Strength (Season)
Game context (Home/Away)
| Factor | Jagiellonia Bialystok Home | WKS Slask Wroclaw 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). | 1.5 pts/game | 0.33 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%. | 50 % | 0 % |
| Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%. | 0 % | 33.3 % |
| 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 | -0.75 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. | -0.09 xG/game | -0.23 xG/game |
Season baseline (Overall) Temp
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game. | 1.8 pts/game | 1 pts/game |
| Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%. | 60 % | 16.7 % |
| 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. | 0.5 goals/game | -0.29 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. | -0.17 xG/game | -0.2 xG/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| 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. | 3-1-1 | 1-3-1 |
| PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game. | 2 pts/game | 1.2 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
1.63 / 61.5% 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)
1.54 / 65.1% 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
-5.5%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Over | Under |
|---|---|---|
| Predora Market Median | 61.5% / 1.63 | 38.5% / 2.60 |
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. | 2.09 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 | 34 % |
| 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 | 67 % |
Season (baseline)
Game context (Home/Away)
| Factor | Jagiellonia Bialystok Home | WKS Slask Wroclaw 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. | 0.5 goals/game | 1 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. | 1 goals/game | 1.67 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. | 1.78 xG/game | 1.42 xG/game |
Overall (Season) Temp
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| 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 % | 60 % | 67 % |
| Goals scored (overall) Average goals scored per game this season (home + away). Example: 20 goals in 13 games = 1.54 goals/game. | 1.8 goals/game | 1.33 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. | 5.2 shots on target/game | 3.5 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. | 1.4 goals/game | 1.5 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) | 2.89 goals/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| 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. | 1.8 goals/game | 1.4 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. | 1.4 goals/game | 1.4 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 | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| 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
1.69 / 59.2% 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)
1.64 / 61.0% 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)
-1.8 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
-3.0%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Yes | No |
|---|---|---|
| Predora Market Median | 59.2% / 1.69 | 40.8% / 2.45 |
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 | 34 % |
Season baseline (Season)
Indicators associated with more BTTS outcomes
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%. | 60 % | 83 % |
| 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 % | 83 % |
| 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%. | 80 % | 83 % |
Indicators associated with fewer BTTS outcomes
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| 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%. | 20 % | 17 % |
| 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 % | 17 % |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| 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 % | 80 % |
| 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%. | 80 % | 80 % |
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
1.88 / 53.1% 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)
1.74 / 57.5% 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)
-4.4 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
-7.6%Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
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Probability Models
| Model | Over | Under |
|---|---|---|
| Predora Market Median | 46.9% / 2.13 | 53.1% / 1.88 |
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. | 8.83 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. | 59 % |
Season (baseline)
Game context (Home/Away)
| Factor | Jagiellonia Bialystok Home | WKS Slask Wroclaw 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. | 5.5 corners/game | 3.33 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. | 10 corners/game | 11.66 corners/game |
Overall (Season) Temp
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game. | 4.2 corners/game | 3.83 corners/game |
| Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game. | 8.8 corners/game | 9.5 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. | 10.21 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. | 3.67 cards/game |
Season baseline (Season) Temp
| Factor | Jagiellonia Bialystok | WKS Slask Wroclaw |
|---|---|---|
| Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game. | 2 cards/game | 1.83 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 % | 33 % |
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. | 3.82 cards/game |
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