SuperLiga Sérvia

Sportsko-Rekreativni Centar Mladost · Makedonska

FK Zeleznicar Pancevo crestFK Zeleznicar Pancevo
2–0Finished
FK Macva Sabac crestFK Macva Sabac

FK Zeleznicar Pancevo 2 x 0 FK Macva Sabac: prediction analysis

The model predicted correctly 4 of 5 settled markets.

Correct predictions

  • 1x2: prediction FK Zeleznicar Pancevo; result FK Zeleznicar Pancevo 2–0.
  • Double Chance: prediction FK Zeleznicar Pancevo or draw; result FK Zeleznicar Pancevo 2–0.
  • Both Teams to Score: prediction No; result No.
  • Over 9.5 corners: prediction Under 9.5; result 9 corners.

Incorrect predictions

  • Over 2.5 goals: prediction Over 2.5; result 2 goals.

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 64.1% probability of a win for FK Zeleznicar Pancevo, 21.7% for a draw, and 14.2% for a win for FK Macva Sabac.

The 1X2 market is clearly tilted toward the home team. Bookmakers price Zeleznicar Pancevo in a range of 1.40 to 1.50, with a median of 1.46, reflecting consistent favoritism. The draw is priced at around 4.20, while a Macva Sabac win is priced in a much higher range, between 5.80 and 7.02, with a median of 6.20.

The deterministic signals also point to the home side, but less strongly than the market. While the bookmakers' consensus gives Zeleznicar around 80.9% relative strength (excluding the draw), the data indicate an advantage of approximately 68.7% for the home team. In other words, there is partial alignment: both sides see Zeleznicar as the favorite, but the market appears more confident than the numbers suggest.

This difference may be explained by Zeleznicar's strong home performance and Macva's weakness away from home, factors the market likely weights more heavily. On the other hand, the visitor's recent form, with a better PPG over the last five games, is a point of concern that the data capture and the odds may not fully reflect.

The uncertainty lies with the draw, which accounts for around 22.2% of the 1X2 consensus, and the possibility that Macva, buoyed by its form, could pull off a surprise. The market is confident, but there are no guarantees.

Market 1x2

#72083040
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.56 / 64.1%
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)
1.47 / 68.0%
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)
-3.9 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
-5.7%

Odds Comparison

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Probability Models

ModelFK Zeleznicar PancevoDrawFK Macva Sabac
Predora Market Median 64.1% / 1.56 21.7% / 4.61 14.2% / 7.04

Market Double Chance

#72083040
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.17 / 85.8%
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)
1.11 / 90.0%
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.
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
-4.7%

Odds Comparison

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Probability Models

Model1XX212
Predora Market Median 85.8% / 1.17 35.9% / 2.79 78.3% / 1.28

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 FK Zeleznicar Pancevo FK Macva Sabac
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.2-1-11-1-2
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%.50 %25 %

Outcome Strength (Season)

Game context (Home/Away)

Factor FK Zeleznicar Pancevo Home FK Macva Sabac 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/game0.25 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 %0 %
Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%.33.3 %25 %
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 = +0.5.1.67 goals/game-2 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.0.95 xG/game-1.55 xG/game

Season baseline (Overall) Temp

Factor FK Zeleznicar Pancevo FK Macva Sabac
PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game.1.33 pts/game1 pts/game
Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%.33.3 %25 %
Draw % (overall) Draw percentage this season (home + away). Example: 2 draws in 8 games = 25%.33.3 %25 %
Goal difference (overall) Average goal difference this season: (goals scored − goals conceded) / games. Example: 10−6 in 8 games → +0.5 per game.0.33 goals/game-1 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.0.18 xG/game-1.19 xG/game

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

Factor FK Zeleznicar Pancevo FK Macva Sabac
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.1-2-22-2-1
PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game.1 pts/game1.6 pts/game

Market Over 2.5 goals

#72083040
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 / 50.4%
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)
-2.2 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
-4.2%

Odds Comparison

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Probability Models

ModelOverUnder
Predora Market Median 50.4% / 1.98 49.6% / 2.02

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 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 goals59 %
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 goals42 %

Season (baseline)

Game context (Home/Away)

Factor FK Zeleznicar Pancevo Home FK Macva Sabac 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/game0.5 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.0.67 goals/game2.5 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.1.96 xG/game0.84 xG/game

Overall (Season) Temp

Factor FK Zeleznicar Pancevo FK Macva Sabac
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 %38 %
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/game0.63 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.4.5 shots on target/game2 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.1.17 goals/game1.63 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)2.44 goals/game

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

Factor FK Zeleznicar Pancevo FK Macva Sabac
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.1.4 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.1.2 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 FK Zeleznicar Pancevo FK Macva Sabac
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 2.525 %25 %

Market Both Teams to Score

#72083040
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.82 / 54.9%
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)
1.75 / 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)
-2.3 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
-4.0%

Odds Comparison

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Probability Models

ModelYesNo
Predora Market Median 45.1% / 2.22 54.9% / 1.82

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.59 %
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 goals59 %

Season baseline (Season)

Indicators associated with more BTTS outcomes

Factor FK Zeleznicar Pancevo FK Macva Sabac
BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%.50 %38 %
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%.50 %62 %
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%.83 %62 %

Indicators associated with fewer BTTS outcomes

Factor FK Zeleznicar Pancevo FK Macva Sabac
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%.17 %38 %
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%.50 %38 %

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

Factor FK Zeleznicar Pancevo FK Macva Sabac
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%.40 %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 %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%.50 %

Market Over 9.5 corners

#72083040
Odds (Active Model) Fair odds according to the active model The odds calculated from the active model's estimated probability.
Formula: 1 ÷ Estimated probability
1.50 / 66.8%
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)
1.44 / 69.4%
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
-3.8%

Odds Comparison

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Probability Models

ModelOverUnder
Predora Market Median 33.2% / 3.01 66.8% / 1.50

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.7.5 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.29 %

Season (baseline)

Game context (Home/Away)

Factor FK Zeleznicar Pancevo Home FK Macva Sabac 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/game1.5 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.8.67 corners/game8.25 corners/game

Overall (Season) Temp

Factor FK Zeleznicar Pancevo FK Macva Sabac
Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game.5.17 corners/game2.5 corners/game
Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game.8 corners/game8.25 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.8.92 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.4 cards/game

Season baseline (Season) Temp

Factor FK Zeleznicar Pancevo FK Macva Sabac
Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game.2.5 cards/game3.38 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.67 %50 %

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.5.67 cards/game