SuperLiga

Stadionul Municipal · Calea Naţională, nr. 64

FC Botosani crestFC Botosani
5–0Finished
ACS Sepsi OSK Sfantu Gheorghe crestACS Sepsi OSK Sfantu Gheorghe

FC Botosani 5 x 0 ACS Sepsi OSK Sfantu Gheorghe: prediction analysis

The model predicted correctly 2 of 5 settled markets.

Correct predictions

  • 1x2: prediction FC Botosani; result FC Botosani 5–0.
  • Double Chance: prediction FC Botosani or draw; result FC Botosani 5–0.

Incorrect predictions

  • Over 2.5 goals: prediction Under 2.5; result 5 goals.
  • Both Teams to Score: prediction Yes; result No.
  • 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 44.2% probability of a win for FC Botosani, 28.3% for a draw, and 27.5% for a win for ACS Sepsi OSK Sfantu Gheorghe.

Bookmakers treat Botosani as the favorite, with odds ranging from 2.02 to 2.28—the market consensus puts the home side ahead of Sepsi in this assessment. The draw is priced in a range of 3.14–3.31; an away win is priced between 3.24 and 3.60. The prices indicate that the market clearly separates the two sides: the home side accounts for about 62% of the two-way comparison (after excluding the draw), while the away side accounts for 38%—figures that reflect the consensus, not an exact probability.

The deterministic signals point in the opposite direction: the data favor Sepsi, with relative strength of 53.9% against Botosani’s 46.1% in this comparison. This mismatch appears mainly in the head-to-head record—Sepsi won 80% of the last five matches between the teams—and in overall season performance, where the visitors have a better PPG and a higher reported performance figure.

Therefore, there is a clear divergence between what the market prices in and what the data suggest. The draw is a point to watch: it accounts for about 28.5% of the 1X2 consensus and is a significant source of uncertainty. Any assessment of value needs to account for this share of the outlook. The divergence should not be interpreted as the bookmakers being wrong, but as a difference in how the two sides are assessed.

Market 1x2

#72387980
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
2.26 / 44.2%
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)
2.28 / 43.9%
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)
+0.4 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
+0.8%

Odds Comparison

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Probability Models

ModelFC BotosaniDrawACS Sepsi OSK Sfantu Gheorghe
Predora Market Median 44.2% / 2.26 28.3% / 3.53 27.5% / 3.64

Market Double Chance

#72387980
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.38 / 72.5%
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)
1.31 / 76.3%
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)
-3.8 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
-5.0%

Odds Comparison

Bookmakers

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Probability Models

Model1XX212
Predora Market Median 72.5% / 1.38 55.8% / 1.79 71.7% / 1.40

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 FC Botosani ACS Sepsi OSK Sfantu Gheorghe
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.0-1-44-1-0
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%.0 %80 %

Outcome Strength (Season)

Game context (Home/Away)

Factor FC Botosani Home ACS Sepsi OSK Sfantu Gheorghe 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 pts/game1 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 %20 %
Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%.50 %40 %
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 = +0.5.1.5 goals/game-1 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.0.47 xG/game-0.82 xG/game

Season baseline (Overall) Temp

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game.1.25 pts/game1.25 pts/game
Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%.25 %25 %
Draw % (overall) Draw percentage this season (home + away). Example: 2 draws in 8 games = 25%.50 %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-0.5 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.-0.19 xG/game-0.51 xG/game

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

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
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.2-2-11-3-1
PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game.1.6 pts/game1.2 pts/game

Market Over 2.5 goals

#72387980
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.94 / 51.6%
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)
1.95 / 51.3%
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.
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
+0.7%

Odds Comparison

Bookmakers

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Probability Models

ModelOverUnder
Predora Market Median 48.4% / 2.07 51.6% / 1.94

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.1 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 goals0 %
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 goals100 %

Season (baseline)

Game context (Home/Away)

Factor FC Botosani Home ACS Sepsi OSK Sfantu Gheorghe 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.1.75 goals/game0.4 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.0.25 goals/game1.4 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.1.8 xG/game0.99 xG/game

Overall (Season) Temp

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
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 %50 %13 %
Goals scored (overall) Average goals scored per game this season (home + away). Example: 20 goals in 13 games = 1.54 goals/game.1.5 goals/game0.5 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.3.88 shots on target/game3.25 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 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)2.47 goals/game

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

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
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/game0.4 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.0.6 goals/game1.2 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 FC Botosani ACS Sepsi OSK Sfantu Gheorghe
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 2.560 %60 %

Market Both Teams to Score

#72387980
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.86 / 53.7%
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)
1.75 / 57.1%
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
-6.1%

Odds Comparison

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Probability Models

ModelYesNo
Predora Market Median 53.7% / 1.86 46.3% / 2.16

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.29 %
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 goals0 %

Season baseline (Season)

Indicators associated with more BTTS outcomes

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%.50 %25 %
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%.75 %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%.50 %50 %

Indicators associated with fewer BTTS outcomes

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
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%.50 %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%.25 %50 %

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

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
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%.60 %40 %
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%.20 %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

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%.40 %

Market Over 9.5 corners

#72387980
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.79 / 55.8%
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)
1.82 / 54.9%
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)
+0.9 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
+1.6%

Odds Comparison

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Probability Models

ModelOverUnder
Predora Market Median 55.8% / 1.79 44.2% / 2.26

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.11.08 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.46 %

Season (baseline)

Game context (Home/Away)

Factor FC Botosani Home ACS Sepsi OSK Sfantu Gheorghe 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.7.75 corners/game2.6 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.10.25 corners/game9 corners/game

Overall (Season) Temp

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game.5.5 corners/game3.75 corners/game
Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game.10.63 corners/game10.13 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.9.19 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.5.58 cards/game

Season baseline (Season) Temp

Factor FC Botosani ACS Sepsi OSK Sfantu Gheorghe
Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game.2.63 cards/game1.88 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.75 %38 %

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.4.83 cards/game