Championship

Molineux Stadium · Waterloo Road

Wolverhampton Wanderers crestWolverhampton Wanderers
1–0Finished
West Bromwich Albion crestWest Bromwich Albion

Wolverhampton Wanderers 1 x 0 West Bromwich Albion: prediction analysis

The model predicted correctly 3 of 5 settled markets.

Correct predictions

  • 1x2: prediction Wolverhampton Wanderers; result Wolverhampton Wanderers 1–0.
  • Double Chance: prediction Wolverhampton Wanderers or draw; result Wolverhampton Wanderers 1–0.
  • Over 2.5 goals: prediction Under 2.5; result 1 goal.

Incorrect predictions

  • Both Teams to Score: prediction Yes; result No.
  • Over 9.5 corners: prediction Under 9.5; result 15 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 51.3% probability of a win for Wolverhampton Wanderers, 25.6% for a draw, and 23.1% for a win for West Bromwich Albion.

The market is clearly leaning toward Wolverhampton, with most bookmakers (11 in total) pricing the hosts in the 1.77 to 1.87 odds range, making the home side the favorite. The draw is priced between 3.50 and 3.79, while a West Brom win is priced between 4.00 and 4.46. These prices indicate that the market sees Wolverhampton as having about a 70% chance in head-to-head results (excluding the draw), while the deterministic data point to a more balanced outlook, with a slight edge for the visitors (about 50% to 50% in the direct comparison). This divergence may be explained by West Brom’s recent record in this matchup, which favors the visitors, and Wolverhampton’s good home form in terms of chance creation, reflected in its xG differential. However, there is considerable uncertainty: the draw, which is not an unlikely outcome, accounts for about 26% of the total probability, and the margin between the two sides in the signal model is very narrow, reinforcing that the game could easily defy the market’s logic.

Market 1x2

#72339860
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.95 / 51.2%
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)
1.90 / 52.6%
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)
-1.4 p.p.
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
-2.6%

Odds Comparison

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Probability Models

ModelWolverhampton WanderersDrawWest Bromwich Albion
Predora Market Median 51.2% / 1.95 25.6% / 3.90 23.1% / 4.32

Market Double Chance

#72339860
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 / 76.9%
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 / 81.8%
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)
-5.0 p.p.
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
-6.1%

Odds Comparison

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Probability Models

Model1XX212
Predora Market Median 76.9% / 1.30 48.8% / 2.05 74.4% / 1.34

AI Analysis

Market and data partially aligned

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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 Wolverhampton Wanderers West Bromwich Albion
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.1-1-33-1-1
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 %60 %

Outcome Strength (Season)

Game context (Home/Away)

Factor Wolverhampton Wanderers Home West Bromwich Albion 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).2.33 pts/game1.5 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%.66.7 %50 %
Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%.33.3 %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 = +0.5.1.33 goals/game-0.25 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.0.73 xG/game-0.9 xG/game

Season baseline (Overall) Temp

Factor Wolverhampton Wanderers West Bromwich Albion
PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game.2 pts/game1.75 pts/game
Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%.57.1 %50 %
Draw % (overall) Draw percentage this season (home + away). Example: 2 draws in 8 games = 25%.28.6 %25 %
Goal difference (overall) Average goal difference this season: (goals scored − goals conceded) / games. Example: 10−6 in 8 games → +0.5 per game.0.71 goals/game0.25 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.0.39 xG/game-0.15 xG/game

Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5

Factor Wolverhampton Wanderers West Bromwich Albion
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.2-1-22-2-1
PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game.1.4 pts/game1.6 pts/game

Market Over 2.5 goals

#72339860
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.98 / 50.5%
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)
1.88 / 53.2%
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.
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
-5.0%

Odds Comparison

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Probability Models

ModelOverUnder
Predora Market Median 49.5% / 2.02 50.5% / 1.98

Team Comparison

Predictions (AI) Prev

FactorGame
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.3.59 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 goals84 %
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 goals17 %

Season (baseline)

Game context (Home/Away)

Factor Wolverhampton Wanderers Home West Bromwich Albion 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.2.33 goals/game1 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/game1.25 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.1.81 xG/game0.95 xG/game

Overall (Season) Temp

Factor Wolverhampton Wanderers West Bromwich Albion
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 %71 %38 %
Goals scored (overall) Average goals scored per game this season (home + away). Example: 20 goals in 13 games = 1.54 goals/game.2.14 goals/game1.25 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.4.57 shots on target/game3.75 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.1.43 goals/game1 goals/game

League (baseline) League

FactorLeague
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)2.92 goals/game

Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5

Factor Wolverhampton Wanderers West Bromwich Albion
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 goals/game0.8 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/game0.6 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 Wolverhampton Wanderers West Bromwich Albion
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 2.540 %40 %

Market Both Teams to Score

#72339860
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.90 / 52.7%
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)
1.80 / 55.6%
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)
-2.8 p.p.
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
-5.1%

Odds Comparison

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Probability Models

ModelYesNo
Predora Market Median 52.7% / 1.90 47.3% / 2.12

Team Comparison

Predictions (AI) Prev

FactorGame
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.84 %
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 goals84 %

Season baseline (Season)

Indicators associated with more BTTS outcomes

Factor Wolverhampton Wanderers West Bromwich Albion
BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%.71 %63 %
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 %87 %
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%.71 %75 %

Indicators associated with fewer BTTS outcomes

Factor Wolverhampton Wanderers West Bromwich Albion
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%.29 %25 %
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 %13 %

Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5

Factor Wolverhampton Wanderers West Bromwich Albion
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 %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%.60 %60 %

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

FactorHead-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%.60 %

Market Over 9.5 corners

#72339860
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.92 / 52.0%
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)
1.80 / 55.6%
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)
-3.5 p.p.
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
-6.3%

Odds Comparison

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Probability Models

ModelOverUnder
Predora Market Median 48.0% / 2.08 52.0% / 1.92

Team Comparison

Predictions (AI) Prev

FactorGame
Potential: average corners Pregame estimate of total corners, based on historical data and AI. Example: 10.2 corners ≈ an estimate of 10 corners.9.67 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.75 %

Season (baseline)

Game context (Home/Away)

Factor Wolverhampton Wanderers Home West Bromwich Albion 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.6.67 corners/game4.5 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.11 corners/game13 corners/game

Overall (Season) Temp

Factor Wolverhampton Wanderers West Bromwich Albion
Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game.6.29 corners/game4.88 corners/game
Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game.10.86 corners/game11.01 corners/game

League (baseline) League

FactorLeague
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.10.61 corners/game

Cards

Team Comparison

Predictions (AI) Prev

FactorGame
Potential: average cards Pregame estimate of total cards, based on historical data and AI. Example: 4.8 cards ≈ an estimate of 5 cards.3.33 cards/game

Season baseline (Season) Temp

Factor Wolverhampton Wanderers West Bromwich Albion
Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game.1 cards/game2.13 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.28 %25 %

League (baseline) League

FactorLeague
League average cards Average cards per game in the league this season (competition baseline). Example: 4.3 cards/game ≈ 4–5 cards per game.3.89 cards/game