Sonderjyske 4-0 Silkeborg: result, our prediction

Danish Superliga
SonderjyskeSonderjyskeLLDWL
Full time4-0Sun, 18 Oct 202618/1013:00UK
SilkeborgSilkeborgDDDWD
38.2%Home winFair odds 2.62
24.7%DrawFair odds 4.05
37.1%Away winFair odds 2.70
Pre-match: Most likely published score1-111.2%Fair odds 8.94

Pre-match model estimates · MODELS TOP 3

Based on the Road2Sushi model. Not guaranteed outcomes.

Over 1.5 goalsBoth teams combined82.6%
Both teams to score63.3%
Sonderjyske or draw62.9%

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.

SonderjyskeSonderjyske
4-0FT
SilkeborgSilkeborg

Match data

Match data: Sonderjyske and Silkeborg
SonderjyskeSonderjyske Statistic SilkeborgSilkeborg
4 Goals 0

The result, on the record

Recorded tally 3 of 5 published calls on this match were right.

Published calls · recorded grading

# Market Our pick Our chance Recorded grade
1 Match result Pick: Sonderjyske Chance: 38.2% Correct
2 Total goals 2.5 Pick: Over 2.5 Chance: 60.8% Correct4 goals
3 Total goals 1.5 Pick: Over 1.5 Chance: 82.6% Correct4 goals
4 Total goals 3.5 Pick: Under 3.5 Chance: 61.6% Wrong4 goals
5 Both teams to score Pick: Yes Chance: 63.3% Wrong

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

Sonderjyske
Match winner (1X2)38.2%
Fair odds2.62
Save model pickSaved to my bets

Top signals

Goals marketOver 1.5 goals
Model estimateModel82.6%
Fair oddsFair1.21
Both teams to scoreBoth teams to score
Model estimateModel63.3%
Fair oddsFair1.58
Double chance marketSonderjyske or draw
Model estimateModel62.9%
Fair oddsFair1.59

Model read

This match has been played. This is a historical snapshot of the pre-match estimates, not a current price opportunity. An open result market: SonderjyRead full analysisShow less

This match has been played. This is a historical snapshot of the pre-match estimates, not a current price opportunity. An open result market: Sonderjyske lead at 38.2%, with draw 24.7% and Silkeborg at 37.1%. No result has a majority of the model probability. Both teams to score stands out at 63.3%. The estimate leans this way, with substantial uncertainty remaining. Without a complete matching price comparison, this is an event read rather than a verified price opportunity. The overall match assessment has not cleared the model's betting filters; this market read does not override that decision.

Both teams to score

Model estimate
63.3%
Fair odds
1.58

Over 2.5 goals

Model estimate
60.8%
Fair odds
1.65

Context & limits

  • Sonderjyske: 1 wins, 1 draws and 3 losses in the last 5 recorded matches. Recent form is context, not an extra win probability.
  • Silkeborg: 1 wins, 4 draws and 0 losses in the last 5 recorded matches. Recent form is context, not an extra win probability.
  • The last 5 recorded meetings averaged 2.6 goals. This is a historical sample, not a forecast for this fixture.
  • A complete matching price comparison is unavailable for this selection, so a price edge cannot be established.
Additional match context

Sonderjyske versus Silkeborg in the Danish Superliga on 18 October 2026 is finely balanced. Sonderjyske edge it at 38%, but the gap to the field is slim. Market and model are aligned here; the fair price says 39%. The goals profile is lively: Over 2.5 sits at 61% with both teams to score at 63%. The recent series reads 3 wins for Sonderjyske, 0 draws and 2 for Silkeborg over 5 games, 2.6 goals on average. The run in: Sonderjyske arrive W1 D1 L4, Silkeborg W1 D4 L1. Discipline says sit it out: value_gap +0.021 <= 0.03.

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.

SonderjyskeSonderjyskeLast 5
L
Loss · 1-3
L
Loss · 2-5
D
Draw · 1-1
W
Win · 4-2
L
Loss · 0-3

1 win · 1 draw · 3 losses

SilkeborgSilkeborgLast 5
D
Draw · 1-1
D
Draw · 2-2
D
Draw · 1-1
W
Win · 4-0
D
Draw · 3-3

1 win · 4 draws · 0 losses

W Win
D Draw
L Loss

Head-to-head

Last 5 meetings

3SonderjyskeSonderjyske wins
0Draws
2SilkeborgSilkeborg wins
2.6Average goals
Date
Home team
Score
Away team
SonderjyskeSonderjyske
2 – 1
SilkeborgSilkeborg
SilkeborgSilkeborg
0 – 2
SonderjyskeSonderjyske
SilkeborgSilkeborg
1 – 0
SonderjyskeSonderjyske
SonderjyskeSonderjyske
2 – 1
SilkeborgSilkeborg
SonderjyskeSonderjyske
1 – 3
SilkeborgSilkeborg

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

ModelProbability (%)
Outcome
MarketProbability (%)
Model probability: 38.2%
Sonderjyske

Model minus market: -0.7 pp
Market probability: 38.9%
Model probability: 24.7%
Draw

Model minus market: -1.4 pp
Market probability: 26.1%
Model probability: 37.1%
Silkeborg

Model minus market: +2.1 pp
Market probability: 35.0%

Goals

No recorded bookmaker price for this market.

ModelProbability (%)
Outcome
MarketProbability (%)
Model probability: 95.2%
Over 0.5 goals

Model minus market: —
Market probability: —
Model probability: 4.9%
Under 0.5 goals

Model minus market: —
Market probability: —
Model probability: 82.6%
Over 1.5 goals

Model minus market: —
Market probability: —
Model probability: 17.4%
Under 1.5 goals

Model minus market: —
Market probability: —
Model probability: 60.8%
Over 2.5 goals

Model minus market: —
Market probability: —
Model probability: 39.2%
Under 2.5 goals

Model minus market: —
Market probability: —
Model probability: 38.5%
Over 3.5 goals

Model minus market: —
Market probability: —
Model probability: 61.6%
Under 3.5 goals

Model minus market: —
Market probability: —
Model probability: 20.9%
Over 4.5 goals

Model minus market: —
Market probability: —
Model probability: 79.1%
Under 4.5 goals

Model minus market: —
Market probability: —
Model probability: 9.9%
Over 5.5 goals

Model minus market: —
Market probability: —
Model probability: 90.1%
Under 5.5 goals

Model minus market: —
Market probability: —
Model probability: 63.3%
Both teams to score · Yes

Model minus market: —
Market probability: —
Model probability: 36.7%
Both teams to score · No

Model minus market: —
Market probability: —

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

Sonderjyske winBookmaker2.40Model fair2.62Model minus bookmaker probability, percentage points: -3.4 pp
DrawBookmaker3.50Model fair4.05Model minus bookmaker probability, percentage points: -3.8 pp
Silkeborg winBookmaker2.65Model fair2.70Model minus bookmaker probability, percentage points: -0.6 pp

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

Sonderjyske win2.62
Draw4.05
Silkeborg win2.70
Both teams to score1.58

Goals

Over 1.5 goals1.21
Over 2.5 goals1.65
Over 3.5 goals2.60
Over 4.5 goals4.78
Sonderjyske most2.62
Silkeborg most2.70

Totals line guide

Both teams combined.

Over 0.595.2%Over 1.582.6%Over 2.560.8%Over 3.538.5%Over 4.520.9%Over 5.59.9%
Our stored model focusOver 2.560.8%

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.

Low riskMedium riskHigh risk

Goals

Over 1.51.5 goals, Low risk
Model probability 82.6%
Fair odds Fair 1.21
Over 2.52.5 goals, Medium risk
Model probability 60.8%
Fair odds Fair 1.65
Over 3.53.5 goals, High risk
Model probability 38.5%
Fair odds Fair 2.60
Correct score probabilitiesModel probability / Fair odds
Goal axes Home goals
Away goals 0 1 2 3 4 5
Away goals 0 0-04.9%Fair odds 20.62 1-06.3%Fair odds 15.85 2-05.4%Fair odds 18.45 3-02.9%Fair odds 34.97 4-01.1%Fair odds 88.50 5-00.4%Fair odds 277.78
Away goals 1 0-16.2%Fair odds 16.13 1-111.2%Fair odds 8.94 2-18.5%Fair odds 11.83 3-14.5%Fair odds 22.42 4-11.8%Fair odds 56.50 5-10.6%Fair odds 178.57
Away goals 2 0-25.2%Fair odds 19.08 1-28.3%Fair odds 12.05 2-26.6%Fair odds 15.20 3-23.5%Fair odds 28.82 4-21.4%Fair odds 72.46 5-20.4%Fair odds 227.27
Away goals 3 0-32.7%Fair odds 36.76 1-34.3%Fair odds 23.20 2-33.4%Fair odds 29.33 3-31.8%Fair odds 55.56 4-30.7%Fair odds 140.85 5-30.2%Fair odds 434.78
Away goals 4 0-41.1%Fair odds 94.34 1-41.7%Fair odds 59.52 2-41.3%Fair odds 75.19 3-40.7%Fair odds 142.86 4-40.3%Fair odds 357.14 5-40.1%Fair odds 1,111.11
Away goals 5 0-50.3%Fair odds 303.03 1-50.5%Fair odds 192.31 2-50.4%Fair odds 243.90 3-50.2%Fair odds 454.55 4-50.1%Fair odds 1,111.11 5-50.0%Fair odds 3,333.33

Model’s 3 most likely score outcomes

Model probability · Fair odds

1-1
Model probability11.2%
Fair odds8.94
2-1
Model probability8.5%
Fair odds11.83
1-2
Model probability8.3%
Fair odds12.05

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.

Referee not recorded

No verified match referee is available in the saved data.

Season stats

2026/27 season. Recorded results, not model estimates.

SonderjyskeSonderjyske
SilkeborgSilkeborg

Overview

Statistic
Sonderjyske
Silkeborg
League position
#12 of 12
#7 of 12
Matches played
10
10
Record W-D-L
1-2-7
2-5-3
Recent form
LLDWL
Last 5 matches
DDDWD
Last 5 matches
Current streak
L1
D1
Points per game
0.50
1.10
Record, home vs away
1-1-3
1-2-2
PPG, home vs away
0.80
1.00

PPG scale: 0–3 points per game. Each team uses the same scale independently.

Show all season statsHide season stats

Goals

Statistic
Sonderjyske
Silkeborg
Goals scored per game
1.20
1.50
Goals conceded per game
2.40
1.50
Scored, home vs away
1.60
1.60
Conceded, home vs away
2.60
1.40

Scale: 0–3 goals or expected goals (xG) per game. Each team uses the same scale independently.

Goal rates

Statistic
Sonderjyske
Silkeborg
Over 2.5 rate
70.0%
50.0%
BTTS rate
60.0%
70.0%
Clean sheets
10.0%
20.0%

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 →

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.

Sonderjyske clean sheet 20.9%
Silkeborg clean sheet 20.4%
Sonderjyske win to nil 16.1%
Silkeborg win to nil 15.6%

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 38.2% draw 24.7% away 37.1%
double_chance
1X 62.9% 12 75.3% X2 61.8%
dnb
home 50.8% away 49.2%
btts
yes 63.3% no 36.7%
clean_sheet
home 21.1% away 20.5%
win_to_nil
home 16.2% away 15.7%
winning_margin
home_by_1 19.0% home_by_2 11.5% home_by_3plus 7.7%
away_by_1 18.7% away_by_2 11.1% away_by_3plus 7.2%
correct_score
0-0 4.9% 0-1 6.2% 0-2 5.2%
0-3 2.7% 0-4 1.1% 0-5 0.3%
1-0 6.3% 1-1 11.2% 1-2 8.3%
1-3 4.3% 1-4 1.7% 1-5 0.5%
2-0 5.4% 2-1 8.5% 2-2 6.6%
2-3 3.4% 2-4 1.3% 2-5 0.4%
3-0 2.9% 3-1 4.5% 3-2 3.5%
3-3 1.8% 3-4 0.7% 3-5 0.2%
4-0 1.1% 4-1 1.8% 4-2 1.4%
4-3 0.7% 4-4 0.3% 4-5 0.1%
5-0 0.4% 5-1 0.6% 5-2 0.4%
5-3 0.2% 5-4 0.1% 5-5 0.0%
other 1.1%
likeliest_score
h 100.0% a 100.0% p 11.2%
Goals: totals / handicap / exact calibrated
total
over_0.5 95.2% under_0.5 4.9% over_1.5 82.6%
under_1.5 17.4% over_2.5 60.8% under_2.5 39.2%
over_3.5 38.5% under_3.5 61.6% over_4.5 20.9%
under_4.5 79.1% over_5.5 9.9% under_5.5 90.1%
odd_even
odd 48.8% even 51.2%
team_home
over_0.5 79.5% under_0.5 20.5% over_1.5 47.0%
under_1.5 53.0% over_2.5 21.3% under_2.5 78.7%
team_away
over_0.5 78.9% under_0.5 21.1% over_1.5 46.1%
under_1.5 53.9% over_2.5 20.6% under_2.5 79.4%
most
home 38.2% draw 24.7% away 37.1%
handicap
home_-2.5 7.7% away_+2.5 92.3% home_-1.5 19.2%
away_+1.5 80.8% home_-0.5 38.2% away_+0.5 61.8%
home_+0.5 62.9% away_-0.5 37.1% home_+1.5 81.7%
away_-1.5 18.3% home_+2.5 92.8% away_-2.5 7.2%
total_exact
exact_0 4.9% exact_1 12.5% exact_2 21.9%
exact_3 22.3% exact_4 17.5% exact_5 11.0%
exact_6 5.8% exact_7plus 4.1%
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).

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