Yankee Stadium · One East 161st Street, The Bronx
New York City FC
New York Red BullsNew York City FC 0 x 1 New York Red Bulls: prediction analysis
The model predicted correctly 1 of 5 settled markets.
Correct predictions
- Double Chance: prediction New York City FC or New York Red Bulls; result New York Red Bulls 0–1.
Incorrect predictions
- 1x2: prediction New York City FC; result New York Red Bulls 0–1.
- Over 2.5 goals: prediction Over 2.5; result 1 goal.
- Both Teams to Score: prediction Yes; result No.
- Over 9.5 corners: prediction Under 9.5; result 13 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 51.6% probability of a win for New York City FC, 23.9% for a draw, and 24.5% for a win for New York Red Bulls.
The 1X2 market shows a clear consensus: New York City FC is the favorite, with bookmakers clustering the home side’s odds in the 1.75 to 1.85 range. The draw is priced around 4.06, while a New York Red Bulls win is listed between 3.67 and 4.10.
The deterministic signals point in the same direction as the market, both favoring the home side, but with different levels of intensity. While the market gives NYC FC a relative advantage of approximately 68% over the visitors (excluding the draw), the signals suggest a smaller advantage, around 57%. This difference indicates that the price on the home side may be slightly inflated relative to the reading from the numbers.
The strongest data in NYC FC’s favor come from goal difference and its recent record against its rival. However, the xG analysis shows a closer balance, with Red Bulls even slightly ahead this season. The draw, with about 23% of the consensus, remains a significant risk, especially in a derby with a history of closely contested matches.
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 | New York City FC | Draw | New York Red Bulls |
|---|---|---|---|
| Predora Market Median | 51.6% / 1.94 | 23.9% / 4.19 | 24.5% / 4.08 |
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 | 75.5% / 1.32 | 48.4% / 2.07 | 76.1% / 1.31 |
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 | New York City FC | New York Red Bulls |
|---|---|---|
| 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. | 3-1-1 | 1-1-3 |
| 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%. | 60 % | 20 % |
Outcome Strength (Season)
Game context (Home/Away)
| Factor | New York City FC Home | New York Red Bulls 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). | 1.23 pts/game | 1.15 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%. | 30.8 % | 30.8 % |
| Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%. | 30.8 % | 23.1 % |
| 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. | 0.46 goals/game | -0.77 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. | 0.16 xG/game | -0.22 xG/game |
Season baseline (Overall) Temp
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game. | 1.28 pts/game | 1.2 pts/game |
| Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%. | 32 % | 32 % |
| Draw % (overall) Draw percentage this season (home + away). Example: 2 draws in 8 games = 25%. | 32 % | 24 % |
| Goal difference (overall) Average goal difference this season: (goals scored − goals conceded) / games. Example: 10−6 in 8 games → +0.5 per game. | 0.24 goals/game | -0.6 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. | -0.11 xG/game | -0.02 xG/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| 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. | 1-3-1 | 1-2-2 |
| PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game. | 1.2 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 | 61.1% / 1.64 | 38.9% / 2.57 |
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. | 3.31 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 | 66 % |
| 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 | 35 % |
Season (baseline)
Game context (Home/Away)
| Factor | New York City FC Home | New York Red Bulls 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 goals/game | 1.15 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. | 1.54 goals/game | 1.92 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. | 1.53 xG/game | 1.49 xG/game |
Overall (Season) Temp
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| 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 % | 52 % | 56 % |
| Goals scored (overall) Average goals scored per game this season (home + away). Example: 20 goals in 13 games = 1.54 goals/game. | 1.56 goals/game | 1.32 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. | 4.68 shots on target/game | 4.68 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. | 1.32 goals/game | 1.92 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) | 3.2 goals/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| 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 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. | 0.8 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 | New York City FC | New York Red Bulls |
|---|---|---|
| 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 | 40 % | 40 % |
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 | 61.3% / 1.63 | 38.8% / 2.58 |
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. | 69 % |
| 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 | 66 % |
Season baseline (Season)
Indicators associated with more BTTS outcomes
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%. | 68 % | 64 % |
| 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%. | 84 % | 72 % |
| 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 % | 88 % |
Indicators associated with fewer BTTS outcomes
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| 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 % | 12 % |
| 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%. | 16 % | 28 % |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| 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 % | 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%. | 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
| 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%. | 60 % |
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 | 46.4% / 2.16 | 53.6% / 1.87 |
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. | 10.54 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. | 54 % |
Season (baseline)
Game context (Home/Away)
| Factor | New York City FC Home | New York Red Bulls 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. | 4.77 corners/game | 5.77 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. | 8.46 corners/game | 11.77 corners/game |
Overall (Season) Temp
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game. | 3.76 corners/game | 5.68 corners/game |
| Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game. | 8.56 corners/game | 10.88 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. | 9.98 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. | 3.46 cards/game |
Season baseline (Season) Temp
| Factor | New York City FC | New York Red Bulls |
|---|---|---|
| Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game. | 1.76 cards/game | 2.16 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. | 52 % | 28 % |
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. | 4.32 cards/game |