Estadio Municipal Alfonso Murube · Avenida de Otero
AD Ceuta
Real ValladolidAD Ceuta 1 x 1 Real Valladolid: prediction analysis
The model predicted correctly 3 of 5 settled markets.
Correct predictions
- Over 2.5 goals: prediction Under 2.5; result 2 goals.
- Both Teams to Score: prediction Yes; result Yes.
- Over 9.5 corners: prediction Under 9.5; result 6 corners.
Incorrect predictions
- 1x2: prediction Real Valladolid; result Draw 1–1.
- Double Chance: prediction AD Ceuta or Real Valladolid; result Draw 1–1.
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 33.3% probability of a win for AD Ceuta, 28.3% for a draw, and 38.4% for a win for Real Valladolid.
Bookmakers' consensus: Real Valladolid is the favorite, with odds ranging from 2.4 to 2.8 (median 2.52). AD Ceuta is close behind, with odds ranging from 2.6 to 2.82 (median 2.75), while the draw is priced between 3.14 and 3.4 (median 3.27). The 11 bookmakers analyzed see the match as evenly balanced, with a slight preference for the visitors.
What the signals say: an objective reading of the data also points toward the visitors, but more strongly than the odds suggest. Seven of the ten indicators favor Valladolid, including recent form, PPG and the head-to-head record. The home/away context, however, shows a slight improvement in Ceuta’s xG differential, which explains why the gap is not even larger.
Convergence and divergence: the market and the signals agree on the favorite, but the data show a clearer away advantage than the odds indicate. The draw accounts for around 28.6% of the 1X2 consensus and remains a real possibility. Treat this difference as an interpretation, not as a guarantee of value. Uncertainty scenario: the home/away and head-to-head samples are small, and the margin between the teams is narrow—the match could easily be decided by one detail.
Market 1x2
Formula: 1 ÷ Estimated probability
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Formula: Probability − Implied probability (Book)
Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
Probability Models
| Model | AD Ceuta | Draw | Real Valladolid |
|---|---|---|---|
| Predora Market Median | 33.3% / 3.00 | 28.3% / 3.54 | 38.4% / 2.60 |
Market Double Chance
Formula: 1 ÷ Estimated probability
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Formula: Probability − Implied probability (Book)
Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
Probability Models
| Model | 1X | X2 | 12 |
|---|---|---|---|
| Predora Market Median | 61.6% / 1.62 | 66.7% / 1.50 | 71.7% / 1.39 |
AI Analysis
Market and data partially alignedTeam 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 | AD Ceuta | Real Valladolid |
|---|---|---|
| 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. | 0-0-2 | 2-0-0 |
| 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 % | 100 % |
Outcome Strength (Season)
Game context (Home/Away)
| Factor | AD Ceuta Home | Real Valladolid 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). | 0.33 pts/game | 0.5 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 % | 0 % |
| Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%. | 33.3 % | 50 % |
| 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 | -0.75 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. | 0.3 xG/game | 0.25 xG/game |
Season baseline (Overall) Temp
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game. | 0.17 pts/game | 0.83 pts/game |
| Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%. | 0 % | 16.7 % |
| Draw % (overall) Draw percentage this season (home + away). Example: 2 draws in 8 games = 25%. | 16.7 % | 33.3 % |
| Goal difference (overall) Average goal difference this season: (goals scored − goals conceded) / games. Example: 10−6 in 8 games → +0.5 per game. | -2.17 goals/game | -0.83 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. | -0.3 xG/game | 0.05 xG/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| 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. | 0-1-4 | 1-2-2 |
| PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game. | 0.2 pts/game | 1 pts/game |
Market Over 2.5 goals
Formula: 1 ÷ Estimated probability
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Formula: Probability − Implied probability (Book)
Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
Probability Models
| Model | Over | Under |
|---|---|---|
| Predora Market Median | 43.8% / 2.28 | 56.2% / 1.78 |
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. | 1.84 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 | 0 % |
| 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 | 100 % |
Season (baseline)
Game context (Home/Away)
| Factor | AD Ceuta Home | Real Valladolid 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. | 0.33 goals/game | 0.5 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. | 1.67 goals/game | 1.25 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. | 1.67 xG/game | 1.37 xG/game |
Overall (Season) Temp
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| 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 % | 17 % |
| Goals scored (overall) Average goals scored per game this season (home + away). Example: 20 goals in 13 games = 1.54 goals/game. | 0.5 goals/game | 0.5 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. | 2.83 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. | 2.67 goals/game | 1.33 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) | 2.47 goals/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| 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. | 0.4 goals/game | 0.6 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. | 2.2 goals/game | 1.2 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 | AD Ceuta | Real Valladolid |
|---|---|---|
| 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 | 100 % | 100 % |
Market Both Teams to Score
Formula: 1 ÷ Estimated probability
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Formula: Probability − Implied probability (Book)
Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
Probability Models
| Model | Yes | No |
|---|---|---|
| Predora Market Median | 50.0% / 2.00 | 50.0% / 2.00 |
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. | 17 % |
| 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 | 0 % |
Season baseline (Season)
Indicators associated with more BTTS outcomes
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%. | 50 % | 33 % |
| 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 % | 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%. | 100 % | 83 % |
Indicators associated with fewer BTTS outcomes
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| 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 % | 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%. | 50 % | 50 % |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| 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 % | 60 % |
| 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 % | 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%. | 0 % |
Market Over 9.5 corners
Formula: 1 ÷ Estimated probability
Formula: 1 ÷ Best market odds (highest-paying bookmaker)
Formula: Probability − Implied probability (Book)
Formula: (Estimated probability × Odds) − 1
Odds Comparison
Bookmakers
Probability Models
| Model | Over | Under |
|---|---|---|
| Predora Market Median | 38.6% / 2.59 | 61.4% / 1.63 |
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. | 11.67 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. | 84 % |
Season (baseline)
Game context (Home/Away)
| Factor | AD Ceuta Home | Real Valladolid 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. | 5.67 corners/game | 4.25 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. | 9.34 corners/game | 8 corners/game |
Overall (Season) Temp
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game. | 4.83 corners/game | 3.5 corners/game |
| Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game. | 7.66 corners/game | 7.17 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. | 8.62 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. | 6.33 cards/game |
Season baseline (Season) Temp
| Factor | AD Ceuta | Real Valladolid |
|---|---|---|
| Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game. | 2.5 cards/game | 3.5 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 % | 67 % |
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. | 4.97 cards/game |