SR-Bank Arena · Jåttåvågveien 11A, Jåttåvågen
Viking FK
Lillestroem SKViking FK 3 x 0 Lillestroem SK: prediction analysis
The model predicted correctly 4 of 5 settled markets.
Correct predictions
- 1x2: prediction Viking FK; result Viking FK 3–0.
- Double Chance: prediction Viking FK or draw; result Viking FK 3–0.
- Over 2.5 goals: prediction Over 2.5; result 3 goals.
- Over 9.5 corners: prediction Over 9.5; result 13 corners.
Incorrect predictions
- Both Teams to Score: prediction Yes; result No.
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 67.5% probability of a win for Viking FK, 18.4% for a draw, and 14.1% for a win for Lillestroem SK.
The consensus of 11 bookmakers makes Viking FK a clear favorite, with odds ranging from 1.33 to 1.41 (median 1.38). Lillestrøm is priced between 5.50 and 7.00, while the draw is between 5.00 and 5.75.
The deterministic signals point in the same direction as the market, but with less intensity : the data favor the home side with about 63% relative strength, while the market separates the draw and gives Viking approximately 82% of the two-sided matchup. In other words, the price reflects stronger favoritism than the isolated factors suggest.
The supporting factors for Viking come mainly from the home/away contrast (3.0 x 1.7 PPG), a perfect home performance figure, and recent dominance in H2H (80% wins in the last five). These factors align with the favoritism but do not fully explain the discrepancy between the relative strength of the signals and the pricing.
There is therefore a partial divergence : the market is more convinced than the data. This does not indicate that the bookmakers are wrong, only that they have different readings of the same match. The draw accounts for about 17.6% of the 1X2 consensus and should be considered a significant possibility given Viking's more modest recent advantage.
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 | Viking FK | Draw | Lillestroem SK |
|---|---|---|---|
| Predora Market Median | 67.5% / 1.48 | 18.4% / 5.43 | 14.1% / 7.09 |
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 | 85.9% / 1.16 | 32.5% / 3.07 | 81.6% / 1.23 |
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 | Viking FK | Lillestroem SK |
|---|---|---|
| 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. | 4-0-1 | 1-0-4 |
| 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%. | 80 % | 20 % |
Outcome Strength (Season)
Game context (Home/Away)
| Factor | Viking FK Home | Lillestroem SK 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). | 3 pts/game | 1.55 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%. | 100 % | 45.5 % |
| Draw % (home/away) Draw percentage in the home/away context. Example: 1 draw in 4 games = 25%. | 0 % | 18.2 % |
| 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. | 2.8 goals/game | -0.27 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. | 0.86 xG/game | -0.32 xG/game |
Season baseline (Overall) Temp
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| PPG (overall) Average points per game this season (home + away). Example: 12 points in 8 games = 1.5 points/game. | 2.38 pts/game | 1.52 pts/game |
| Win % (overall) Win percentage this season (home + away). Example: 4 wins in 8 games = 50%. | 76.2 % | 47.6 % |
| Draw % (overall) Draw percentage this season (home + away). Example: 2 draws in 8 games = 25%. | 9.5 % | 9.5 % |
| Goal difference (overall) Average goal difference this season: (goals scored − goals conceded) / games. Example: 10−6 in 8 games → +0.5 per game. | 1.62 goals/game | -0.05 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. | 0.5 xG/game | -0.21 xG/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| 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. | 3-1-1 | 3-0-2 |
| PPG (last 5) Average points from the last 5 games. Example: 7 points in 5 games = 1.4 points/game. | 2 pts/game | 1.8 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 | 73.1% / 1.37 | 26.9% / 3.71 |
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. | 3.6 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 | 65 % |
| 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 | Viking FK Home | Lillestroem SK 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. | 3.8 goals/game | 1 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/game | 1.27 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. | 2.13 xG/game | 1.45 xG/game |
Overall (Season) Temp
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| 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 % | 76 % | 52 % |
| Goals scored (overall) Average goals scored per game this season (home + away). Example: 20 goals in 13 games = 1.54 goals/game. | 2.57 goals/game | 1.33 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. | 6.1 shots on target/game | 4.05 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. | 0.95 goals/game | 1.38 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) | 3.16 goals/game |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| 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. | 3 goals/game | 1.2 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 goals/game | 1.8 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 | Viking FK | Lillestroem SK |
|---|---|---|
| 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 | 80 % | 80 % |
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 | 60.8% / 1.65 | 39.2% / 2.55 |
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. | 54 % |
| 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 | 65 % |
Season baseline (Season)
Indicators associated with more BTTS outcomes
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| BTTS (season) % Percentage of season games in which both teams scored. Example: 7 of 12 games = 58%. | 67 % | 43 % |
| 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%. | 95 % | 67 % |
| 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 % | 71 % |
Indicators associated with fewer BTTS outcomes
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| 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 % | 29 % |
| 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%. | 5 % | 33 % |
Recent form (Last 5) These statistics cover the team's last 5 games, regardless of league or season.Last 5
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| 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%. | 80 % | 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%. | 80 % |
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 | 64.7% / 1.55 | 35.3% / 2.83 |
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. | 12.37 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. | 95 % |
Season (baseline)
Game context (Home/Away)
| Factor | Viking FK Home | Lillestroem SK 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. | 7.6 corners/game | 4.82 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. | 13.1 corners/game | 11.46 corners/game |
Overall (Season) Temp
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| Corners for (overall) Average corners won per game this season (home + away). Example: 40 in 10 games = 4.0 per game. | 7 corners/game | 5.24 corners/game |
| Total corners (overall) Average total corners per game this season (home + away). Example: 105 in 10 games = 10.5 per game. | 12.43 corners/game | 10.91 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. | 10.88 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. | 3.68 cards/game |
Season baseline (Season) Temp
| Factor | Viking FK | Lillestroem SK |
|---|---|---|
| Total cards (average) Average cards per game this season (home + away). Example: 18 cards in 6 games = 3.0 cards/game. | 2.1 cards/game | 1.76 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 % | 33 % |
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. | 3.57 cards/game |