Viborg 1-2 Midtjylland: result, our prediction
Pre-match model estimates · MODELS TOP 3
Based on the Road2Sushi model. Not guaranteed outcomes.
Post-match analysis
Judged against the prediction this page published before kickoff. Right or wrong, it stays on the record.
A call is the side our published probability favoured. If we leaned towards under and three goals went in, that counts as a miss.
The half-by-half shots come from a different source than the match shot total, and we only use them when the shots before the break add up to the actual half-time score. That is why the two numbers can differ slightly.
Match data
| ViborgViborg | Statistic | MidtjyllandMidtjylland |
|---|---|---|
| 1 | Goals | 2 |
| 0 | Goals, 1st half | 0 |
| 1 | Goals, 2nd half | 2 |
| 19 | Shots | 12 |
| 11 | Shots, 1st half | 4 |
| 8 | Shots, 2nd half | 8 |
| 4 | Shots on target | 6 |
| 2 | On target, 1st half | 1 |
| 2 | On target, 2nd half | 5 |
| 3 | Corners | 3 |
| 3 | Yellow cards | 3 |
| 0 | Red cards | 0 |
| 16 | Fouls | 15 |
RefereeSandi Putros
The result, on the record
Recorded tally 5 of 5 published calls on this match were right.
Published calls · recorded grading
| # | Market | Our pick | Our chance | Recorded grade |
|---|---|---|---|---|
| 1 | Match result | Pick: Midtjylland | Chance: 46.8% | Correct |
| 2 | Total goals 2.5 | Pick: Over 2.5 | Chance: 57.9% | Correct3 goals |
| 3 | Total goals 1.5 | Pick: Over 1.5 | Chance: 80.8% | Correct3 goals |
| 4 | Total goals 3.5 | Pick: Under 3.5 | Chance: 64.5% | Correct3 goals |
| 5 | Both teams to score | Pick: Yes | Chance: 60.1% | Correct |
Our verdict
Road2Sushi's own call on this match: the likeliest result, how confident the model is, its strongest signals and the reasons behind them.
Every percentage is a model estimate and every fair price is 1 divided by that estimate. Neither is a bookmaker offer, and a model call is not a proven edge over the market.
Model pick
Save model pickSaved to my betsTop signals
Model read
Form & head-to-head
Recent form uses each club’s available results in their stored order. Select a W/D/L result for details.
Head-to-head figures use their own recorded sample. Where individual results are available, the figures and journal cover the same meetings. Missing results are never counted as draws.
W
L
D
W
W
3 wins · 1 draw · 1 loss
W
D
W
D
W
3 wins · 2 draws · 0 losses
Head-to-head
Last 5 meetings
Odds: model vs bookmaker
Every price for this match in one place: the model's probabilities next to the bookmaker's, the bookmaker's prices with our gap, and the model's own fair odds for each market.
Bookmaker probabilities are de-vigged (the margin is removed) and come from one bookmaker. A gap between the model and that bookmaker is a disagreement, not a proven edge. Fair odds are 1 divided by the model probability, not a bookmaker quote.
Model vs market
Pre-match estimates, recorded before the match.
1X2: Bet365 prices, observed 18 Oct 2026, 11:35 UTC, with the bookmaker margin taken out.
A market probability is the bookmaker's own price with its margin taken out, so the outcomes of each line add up to 100%. A gap from our model is a disagreement, not a proven edge: when we tested 1X2 and goal totals on matches the model had never seen, the bookmaker's closing price was more accurate. For corners, cards and shots we have no such test.
Every market price shown was recorded before kick-off.
1X2
Goals
No recorded bookmaker price for this market.
Cards
No estimates available for this market.
Corners
No estimates available for this market.
Shots
No estimates available for this market.
Bookmaker odds
Real bookmaker price (Bet365) next to our model fair price. Bookmaker prices include the bookmaker margin, so our margin-free fair price usually looks longer. We do not claim to beat the closing market.
Gap is our probability minus the probability the bookmaker price implies (1 divided by the price), in percentage points. Because the price includes the margin, most gaps are negative. Model highlights are outcomes where our probability is at least 15 points higher. A gap is a disagreement, not a proven edge.
After full time these are the stored pre-match prices, kept for the record. They are not current prices, so no outcome is highlighted.
Match result · 1X2
Model fair odds
Our published model probability as a decimal price (1 divided by probability). A fair model price, not a bookmaker quote. Only the supported 1X2 calibration pool is calibrated; other markets retain their model estimates.
Match result
Goals
Goals
Both teams combined.
Read context
Both teams combined
Over 1.5 · Strongest read · Very likely
Over 2.5 · Close call
BTTS · Yes · Likely
Totals line guide
Both teams combined.
The stored model call was Fairly priced. The match has been played, so this is a historical snapshot: the graded result is below.
Read context
Both teams combined. Each bar uses the stored model estimate for that exact total-goals line. Missing estimates are unavailable; they are not zero.
Fair odds are the model’s own probability price, not a published bookmaker offer.
Market explorer
Model probabilities and fair odds.
Each bar is a specific Over selection for both teams combined. Its height is the model probability, on the same 0–100% scale.
Risk describes estimated likelihood, not profitability: low risk at least 62%, medium 48% to under 62%, high below 48%. Colors follow the actual estimate, even when a risk band is missing.
Up to four markets are selected from available model data, prioritising a spread of risk profiles. Fair odds are calculated from the model, not bookmaker prices: 1 divided by probability.
Scores are exact home-away outcomes from zero to five goals per team. Unavailable estimates stay blank; partial inputs are not expanded, inferred or normalised. Scores outside the matrix remain eligible for the top three.
Goals
| Goal axes | Home goals | |||||
|---|---|---|---|---|---|---|
| Away goals | 0 | 1 | 2 | 3 | 4 | 5 |
| Away goals 0 | 0-05.5%Fair odds 18.25 | 1-05.8%Fair odds 17.12 | 2-04.1%Fair odds 24.15 | 3-01.8%Fair odds 55.87 | 4-00.6%Fair odds 172.41 | 5-00.2%Fair odds 666.67 |
| Away goals 1 | 0-17.9%Fair odds 12.66 | 1-111.5%Fair odds 8.70 | 2-17.1%Fair odds 14.10 | 3-13.1%Fair odds 32.68 | 4-11.0%Fair odds 101.01 | 5-10.3%Fair odds 384.62 |
| Away goals 2 | 0-27.2%Fair odds 13.79 | 1-29.4%Fair odds 10.65 | 2-26.1%Fair odds 16.45 | 3-22.6%Fair odds 38.02 | 4-20.9%Fair odds 117.65 | 5-20.2%Fair odds 454.55 |
| Away goals 3 | 0-34.1%Fair odds 24.15 | 1-35.4%Fair odds 18.66 | 2-33.5%Fair odds 28.82 | 3-31.5%Fair odds 66.67 | 4-30.5%Fair odds 204.08 | 5-30.1%Fair odds 769.23 |
| Away goals 4 | 0-41.8%Fair odds 56.18 | 1-42.3%Fair odds 43.48 | 2-41.5%Fair odds 67.11 | 3-40.6%Fair odds 156.25 | 4-40.2%Fair odds 476.19 | 5-40.1%Fair odds 2,000.00 |
| Away goals 5 | 0-50.6%Fair odds 163.93 | 1-50.8%Fair odds 126.58 | 2-50.5%Fair odds 196.08 | 3-50.2%Fair odds 454.55 | 4-50.1%Fair odds 1,428.57 | 5-50.0%Fair odds 5,000.00 |
Model’s 3 most likely score outcomes
Model probability · Fair odds
Referee
One match referee is shown from an explicitly saved result or a fresh, identity-matched provider fixture. Previous referees are never used to guess an appointment. An API listing can change.
The referee’s totals count every completed match with this referee in our data: football-data.co.uk names the referee in English leagues, and API-Football names the referee of the same match (same teams, date and score) in every league from 2025/26. A rate needs at least 5 matches with that figure.
The team table shows each team’s published history with that same referee only. Matches, team cards per game and match cards per game are separate fields; their available samples can differ. Missing values are not zero. Cards include yellow and red cards in the current historical source.
Recorded match referee.
| Per match | All matches | 2026/27 |
|---|---|---|
| Matches | 22 | 6 |
| Goals | 2.73 | 2.33 |
| Yellow cards | 3.86 | 4.67 |
| Corners | 8.9 | 7.8 |
| Fouls | 24.4 | 24.7 |
| Both teams scored | 50% | 33% |
| Over 2.5 goals | 50% | 50% |
| Home wins | 41% | 67% |
| Date | Match | Score | Goals | BTTS | Yellow | Red | Corners | Fouls |
|---|---|---|---|---|---|---|---|---|
| 19 Oct 2026 | Viborg vs Midtjylland | 1-2 | 3 | Yes | 6 | 0 | 6 | 31 |
| 18 Sep 2026 | Lyngby vs Silkeborg | 0-4 | 4 | No | 6 | 1 | 8 | 23 |
| 3 Sep 2026 | FC Copenhagen vs Nordsjaelland | 2-0 | 2 | No | 5 | 0 | 2 | 24 |
| 30 Aug 2026 | Randers FC vs Aarhus | 2-1 | 3 | Yes | 5 | 1 | 6 | 29 |
| 16 Aug 2026 | Nordsjaelland vs Silkeborg | 1-0 | 1 | No | 4 | Not available | 9 | 19 |
| 3 Aug 2026 | Odense vs Sonderjyske | 1-0 | 1 | No | 2 | Not available | 16 | 22 |
| 17 May 2026 | Aarhus vs Viborg | 6-2 | 8 | Yes | 4 | Not available | 4 | 29 |
| 26 Apr 2026 | Fredericia vs Odense | 0-2 | 2 | No | 3 | Not available | 7 | 25 |
| 20 Apr 2026 | Midtjylland vs Aarhus | 2-1 | 3 | Yes | 4 | Not available | 8 | 34 |
| 6 Apr 2026 | Vejle vs Randers FC | 1-1 | 2 | Yes | 4 | Not available | 6 | 25 |
| Team | |||
|---|---|---|---|
| Viborg | Not available | Not available | Not available |
| Midtjylland | Not available | Not available | Not available |
No matching history for this referee is available for one or both teams.
Season stats
2026/27 season. Recorded results, not model estimates.
Overview
PPG scale: 0–3 points per game. Each team uses the same scale independently.
Show all season statsHide season stats
Goals
Scale: 0–3 goals or expected goals (xG) per game. Each team uses the same scale independently.
Goal rates
Scale: 0–100% of recorded matches, not model estimates. Each team uses the same scale independently.
League averages this season: 3.02 goals per match · Over 2.5 in 55.0% · BTTS in 58.3%. Full league tables & stats →
First half / second half
Averages per game in this season’s league matches, split at half-time. Games is the number of each team’s matches with verified half data for that row; below five matches the row stays blank, and a missing value is never a zero.
Corners, shots on target and yellow cards per half come from the data provider’s half-by-half match statistics and are only used when the two halves add up to the full-time count. Cards are split by the minute they were shown, and first-half stoppage time counts as the first half. These are recorded results, not predictions.
In the Premier League, La Liga, Bundesliga, Serie A and Ligue 1 the shots per half come from a different source than the match shot totals, so the two can differ slightly.
This match
| Viborg | Midtjylland | |||
|---|---|---|---|---|
| Statistic | 1H | 2H | 1H | 2H |
| Goals | 0 | 1 | 0 | 2 |
| Shots | 11 | 8 | 4 | 8 |
| Shots on target | 2 | 2 | 1 | 5 |
| Corners | 1 | 2 | 1 | 2 |
Not enough verified half data for this team yet.
Not enough verified half data for this team yet.
All model outcomes · full market spectrum & player markets
Shots
Shots on target
Cards
Clean sheet & win to nil
Worked out from the scoreline grid above: a clean sheet is every scoreline where the other side fails to score. It is the model’s own percentage, scored against the result afterwards, and it is not a tip.
| Viborg clean sheet | 18.0% |
|---|---|
| Midtjylland clean sheet | 27.2% |
| Viborg win to nil | 12.5% |
| Midtjylland win to nil | 21.7% |
Full market spectrum · model probabilities
The model’s own percentages, with no odds attached. The smaller markets are here so you can see everything we hold a number on, not because we claim an advantage in them. The colour says how close our percentages have landed to reality on matches the model had never seen. This is the raw model spectrum. Its match-result marginal can differ from the published 1X2 above; it does not replace the saved prediction.
Match result & goals calibrated
1×2
| home | 28.5% | draw | 24.8% | away | 46.8% |
|---|---|---|---|---|---|
double_chance
| 1X | 53.3% | 12 | 75.2% | X2 | 71.5% |
|---|---|---|---|---|---|
dnb
| home | 37.9% | away | 62.2% |
|---|
btts
| yes | 60.1% | no | 39.9% |
|---|
clean_sheet
| home | 18.0% | away | 27.4% |
|---|
win_to_nil
| home | 12.5% | away | 21.9% |
|---|
winning_margin
| home_by_1 | 16.1% | home_by_2 | 8.2% | home_by_3plus | 4.2% |
|---|---|---|---|---|---|
| away_by_1 | 21.5% | away_by_2 | 14.3% | away_by_3plus | 10.9% |
correct_score
| 0-0 | 5.5% | 0-1 | 7.9% | 0-2 | 7.2% |
|---|---|---|---|---|---|
| 0-3 | 4.1% | 0-4 | 1.8% | 0-5 | 0.6% |
| 1-0 | 5.8% | 1-1 | 11.5% | 1-2 | 9.4% |
| 1-3 | 5.4% | 1-4 | 2.3% | 1-5 | 0.8% |
| 2-0 | 4.1% | 2-1 | 7.1% | 2-2 | 6.1% |
| 2-3 | 3.5% | 2-4 | 1.5% | 2-5 | 0.5% |
| 3-0 | 1.8% | 3-1 | 3.1% | 3-2 | 2.6% |
| 3-3 | 1.5% | 3-4 | 0.6% | 3-5 | 0.2% |
| 4-0 | 0.6% | 4-1 | 1.0% | 4-2 | 0.9% |
| 4-3 | 0.5% | 4-4 | 0.2% | 4-5 | 0.1% |
| 5-0 | 0.2% | 5-1 | 0.3% | 5-2 | 0.2% |
| 5-3 | 0.1% | 5-4 | 0.1% | 5-5 | 0.0% |
| other | 1.1% |
likeliest_score
| h | 100.0% | a | 100.0% | p | 11.5% |
|---|---|---|---|---|---|
Goals: totals / handicap / exact calibrated
total
| over_0.5 | 94.5% | under_0.5 | 5.5% | over_1.5 | 80.8% |
|---|---|---|---|---|---|
| under_1.5 | 19.2% | over_2.5 | 57.9% | under_2.5 | 42.1% |
| over_3.5 | 35.5% | under_3.5 | 64.5% | over_4.5 | 18.6% |
| under_4.5 | 81.4% | over_5.5 | 8.5% | under_5.5 | 91.5% |
odd_even
| odd | 48.8% | even | 51.2% |
|---|
team_home
| over_0.5 | 72.6% | under_0.5 | 27.4% | over_1.5 | 37.2% |
|---|---|---|---|---|---|
| under_1.5 | 62.8% | over_2.5 | 14.2% | under_2.5 | 85.8% |
team_away
| over_0.5 | 82.0% | under_0.5 | 18.0% | over_1.5 | 51.1% |
|---|---|---|---|---|---|
| under_1.5 | 48.9% | over_2.5 | 24.7% | under_2.5 | 75.3% |
most
| home | 28.5% | draw | 24.8% | away | 46.8% |
|---|---|---|---|---|---|
handicap
| home_-2.5 | 4.2% | away_+2.5 | 95.8% | home_-1.5 | 12.4% |
|---|---|---|---|---|---|
| away_+1.5 | 87.6% | home_-0.5 | 28.5% | away_+0.5 | 71.5% |
| home_+0.5 | 53.3% | away_-0.5 | 46.8% | home_+1.5 | 74.7% |
| away_-1.5 | 25.3% | home_+2.5 | 89.1% | away_-2.5 | 10.9% |
total_exact
| exact_0 | 5.5% | exact_1 | 13.7% | exact_2 | 22.9% |
|---|---|---|---|---|---|
| exact_3 | 22.4% | exact_4 | 16.9% | exact_5 | 10.2% |
| exact_6 | 5.1% | exact_7plus | 3.4% |
Method, limits & performance
How this works (honestly)
We are transparent about our method, our limits and how we measure performance.
What we use
Results, odds & closing prices, xG, shots, lineups, referee and schedule load.
Our method
Dixon-Coles + an xG strength model + champion/challenger selection + probability calibration + a trust gate.
What we don’t do
We do not beat the sharp market. The value is honest probabilities, No-bet calls and CLV.
Performance snapshot
Top-pick hit rate 47.7% (reliability, not edge). Calibration error 0.0106 (56038 matches).
Next fixtures for Viborg and Midtjylland
Not financial advice. No model beats the sharp closing market on public data. The value here is honest probabilities, No-bet calls, and CLV history.
Explore this match
Markets
All matches by market
