Hacken vs Goteborg prediction

Allsvenskan
HackenHackenDWWWW
Sun, 1 Nov 20261/1115:30UK
GoteborgGoteborgWWWDD
53.7%Home winFair odds 1.86
23.4%DrawFair odds 4.27
22.8%Away winFair odds 4.39
Most likely published score1-110.9%Fair odds 9.19

MODELS TOP 3

Based on the Road2Sushi model. Not guaranteed outcomes.

Over 1.5 goalsBoth teams combined81.5%
Hacken or draw77.1%
Under 3.5 goalsBoth teams combined63.4%

Early prediction · Model generated · Bookmaker odds pending.

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

Hacken
Match winner (1X2)53.7%
Fair odds1.86
Save model pickSaved to my bets

Top signals

Goals marketOver 1.5 goals
Model estimateModel81.5%
Fair oddsFair1.23
Double chance marketHacken or draw
Model estimateModel77.1%
Fair oddsFair1.30
Both teams to scoreBoth teams to score
Model estimateModel59.0%
Fair oddsFair1.69

Model read

Hacken lead the result estimates at 53.7%. The match still has meaningful room for a draw or an opponent win. Over 2.5 goals stands out at 59.0%. The Read full analysisShow less

Hacken lead the result estimates at 53.7%. The match still has meaningful room for a draw or an opponent win. Over 2.5 goals stands out at 59.0%. 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.

Over 2.5 goals

Model estimate
59.0%
Fair odds
1.69

Both teams to score

Model estimate
59.0%
Fair odds
1.69

Hacken win

Model estimate
53.7%
Fair odds
1.86

Context & limits

  • Hacken: 4 wins, 1 draws and 0 losses in the last 5 recorded matches. Recent form is context, not an extra win probability.
  • Goteborg: 3 wins, 2 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 3.4 goals. This is a historical sample, not a forecast for this fixture.
  • Recorded season averages: Hacken 1.9 and Goteborg 1.4 goals scored per game. These are team averages, not a forecast of this match total.
  • A complete matching price comparison is unavailable for this selection, so a price edge cannot be established.
Additional match context

The numbers leave little room for romance here: Hacken at 54% against Goteborg, Allsvenskan, 1 November 2026. Three things stand out in the data: 1) No strong lean on goals (59% Over 2.5). The interest, if any, lives elsewhere. 2) Recent form reads W4 D2 L0 for Hacken against W4 D2 L0 for Goteborg. 3) History adds colour: across the last 5 meetings it is 3 wins for Hacken, 1 draws and 1 for Goteborg at 3.4 goals per match. Discipline says sit it out: no bookmaker price published for this match yet.

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.

HackenHackenLast 5
D
Draw · 1-1
W
Win · 5-0
W
Win · 3-0
W
Win · 1-0
W
Win · 4-0

4 wins · 1 draw · 0 losses

GoteborgGoteborgLast 5
W
Win · 2-1
W
Win · 1-0
W
Win · 1-0
D
Draw · 2-2
D
Draw · 1-1

3 wins · 2 draws · 0 losses

W Win
D Draw
L Loss

Head-to-head

Last 5 meetings

3HackenHacken wins
1Draws
1GoteborgGoteborg wins
3.4Average goals
Date
Home team
Score
Away team
GoteborgGoteborg
0 – 2
HackenHacken
HackenHacken
1 – 2
GoteborgGoteborg
GoteborgGoteborg
2 – 3
HackenHacken
HackenHacken
3 – 3
GoteborgGoteborg
GoteborgGoteborg
0 – 1
HackenHacken

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 probabilities

Model estimates and recorded bookmaker probabilities.

1X2

No recorded bookmaker price for this market.

ModelProbability (%)
Outcome
MarketProbability (%)
Model probability: 53.7%
Hacken

Model minus market: —
Market probability: —
Model probability: 23.4%
Draw

Model minus market: —
Market probability: —
Model probability: 22.8%
Goteborg

Model minus market: —
Market probability: —

Goals

No recorded bookmaker price for this market.

ModelProbability (%)
Outcome
MarketProbability (%)
Model probability: 94.8%
Over 0.5 goals

Model minus market: —
Market probability: —
Model probability: 5.2%
Under 0.5 goals

Model minus market: —
Market probability: —
Model probability: 81.5%
Over 1.5 goals

Model minus market: —
Market probability: —
Model probability: 18.5%
Under 1.5 goals

Model minus market: —
Market probability: —
Model probability: 59.0%
Over 2.5 goals

Model minus market: —
Market probability: —
Model probability: 41.0%
Under 2.5 goals

Model minus market: —
Market probability: —
Model probability: 36.6%
Over 3.5 goals

Model minus market: —
Market probability: —
Model probability: 63.4%
Under 3.5 goals

Model minus market: —
Market probability: —
Model probability: 19.5%
Over 4.5 goals

Model minus market: —
Market probability: —
Model probability: 80.5%
Under 4.5 goals

Model minus market: —
Market probability: —
Model probability: 9.0%
Over 5.5 goals

Model minus market: —
Market probability: —
Model probability: 91.0%
Under 5.5 goals

Model minus market: —
Market probability: —
Model probability: 59.0%
Both teams to score · Yes

Model minus market: —
Market probability: —
Model probability: 41.0%
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.

Market comparison awaits bookmaker odds

Comparable bookmaker odds are not available for these outcomes yet.

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

Hacken win1.86
Draw4.27
Goteborg win4.39
Both teams to score1.69

Goals

Over 0.5 goals1.05
Over 1.5 goals1.23
Over 2.5 goals1.69
Over 3.5 goals2.73
Hacken most1.86
Goteborg most4.38

Totals line guide

Both teams combined.

Over 0.594.8%Over 1.581.5%Over 2.559.0%Over 3.536.6%Over 4.519.5%Over 5.59.0%
Recommended focusOver 1.581.5%

Discipline: Our price. No bookmaker price published for this match yet.

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 81.5%
Fair odds Fair 1.23
Over 2.52.5 goals, Medium risk
Model probability 59.0%
Fair odds Fair 1.69
Over 3.53.5 goals, High risk
Model probability 36.6%
Fair odds Fair 2.73
Correct score probabilitiesModel probability / Fair odds
Goal axes Home goals
Away goals 0 1 2 3 4 5
Away goals 0 0-05.2%Fair odds 19.19 1-08.4%Fair odds 11.98 2-08.4%Fair odds 11.92 3-05.3%Fair odds 18.90 4-02.5%Fair odds 40.00 5-01.0%Fair odds 105.26
Away goals 1 0-15.0%Fair odds 20.16 1-110.9%Fair odds 9.19 2-19.8%Fair odds 10.20 3-16.2%Fair odds 16.18 4-12.9%Fair odds 34.25 5-11.1%Fair odds 90.09
Away goals 2 0-23.2%Fair odds 31.25 1-26.1%Fair odds 16.53 2-25.7%Fair odds 17.45 3-23.6%Fair odds 27.70 4-21.7%Fair odds 58.48 5-20.7%Fair odds 153.85
Away goals 3 0-31.3%Fair odds 80.00 1-32.4%Fair odds 42.37 2-32.2%Fair odds 44.84 3-31.4%Fair odds 70.92 4-30.7%Fair odds 149.25 5-30.3%Fair odds 400.00
Away goals 4 0-40.4%Fair odds 277.78 1-40.7%Fair odds 144.93 2-40.7%Fair odds 153.85 3-40.4%Fair odds 243.90 4-40.2%Fair odds 526.32 5-40.1%Fair odds 1,428.57
Away goals 5 0-50.1%Fair odds 1,111.11 1-50.2%Fair odds 625.00 2-50.2%Fair odds 666.67 3-50.1%Fair odds 1,000.00 4-50.1%Fair odds 2,000.00 5-50.0%Fair odds 5,000.00

Model’s 3 most likely score outcomes

Model probability · Fair odds

1-1
Model probability10.9%
Fair odds9.19
2-1
Model probability9.8%
Fair odds10.20
2-0
Model probability8.4%
Fair odds11.92

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 confirmed

No referee assignment is confirmed in our data yet. We never guess one from past matches.

Season stats

2026 season. Recorded results, not model estimates.

HackenHacken
GoteborgGoteborg

Overview

Statistic
Hacken
Goteborg
League position
#4 of 16
#9 of 16
Matches played
25
25
Record W-D-L
12-9-4
10-7-8
Recent form
DWWWW
Last 5 matches
WWWDD
Last 5 matches
Current streak
W4
D2
Points per game
1.80
1.48
Record, home vs away
6-5-2
4-4-5
PPG, home vs away
1.77
1.23

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

Show all season statsHide season stats

Goals

Statistic
Hacken
Goteborg
Goals scored per game
1.88
1.40
Goals conceded per game
1.20
1.76
Scored, home vs away
1.92
1.46
Conceded, home vs away
1.38
2.38
1st half goals per game
0.92
0.60
2nd half goals per game
0.96
0.80

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

Shooting

Statistic
Hacken
Goteborg
Shots per game
16.2
15.9
Shots faced per game
12.0
12.6
Shots on target per game
5.3
5.0
1st half shots per game
7.9
7.6
2nd half shots per game
8.4
8.2

Scale: 0–20 shots per game. Each team uses the same scale independently.

Corners & discipline

Statistic
Hacken
Goteborg
Corners won per game
5.6
6.8
Cards per game
—
1.8
Fouls per game
11.3
13.4
Red cards this season
—
1

0–10 corners per game; 0–5 cards per game; 0–20 fouls per game Each team uses the same scale independently.

Goal rates

Statistic
Hacken
Goteborg
Over 2.5 rate
56.0%
52.0%
BTTS rate
52.0%
60.0%
Clean sheets
44.0%
16.0%

Scale: 0–100% of recorded matches, not model estimates. Each team uses the same scale independently.

League averages this season: 2.92 goals per match · Over 2.5 in 53.5% · BTTS in 51.5%. 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.

HackenHacken
Hacken: season average per game by half
Per game 1H 2H Games
Goals scoredScored 0.92 0.96 25
Goals concededConceded 0.68 0.52 25
Shots 7.9 8.4 17
Shots facedOpp. shots 5.3 6.7 17
Shots on targetOn target 2.8 2.4 18
Shots on target facedOpp. on target 2.3 2.8 18
Corners wonCorners 2.7 2.8 18
Corners concededOpp. corners 2.7 2.7 18
Yellow cardsYellows 0.8 1.1 24
Opponent yellow cardsOpp. yellows 0.6 1.0 24
GoteborgGoteborg
Goteborg: season average per game by half
Per game 1H 2H Games
Goals scoredScored 0.60 0.80 25
Goals concededConceded 0.80 0.96 25
Shots 7.6 8.2 16
Shots facedOpp. shots 5.6 7.1 16
Shots on targetOn target 2.2 3.1 20
Shots on target facedOpp. on target 1.6 2.8 20
Corners wonCorners 3.4 3.6 21
Corners concededOpp. corners 2.5 2.8 21
Yellow cardsYellows 0.7 1.4 24
Opponent yellow cardsOpp. yellows 0.6 1.1 24
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.

Hacken clean sheet 30.7%
Goteborg clean sheet 15.1%
Hacken win to nil 25.5%
Goteborg win to nil 9.9%

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 53.7% draw 23.4% away 22.8%
double_chance
1X 77.2% 12 76.6% X2 46.3%
dnb
home 70.2% away 29.8%
btts
yes 59.0% no 41.0%
clean_sheet
home 31.1% away 15.1%
win_to_nil
home 25.9% away 9.9%
winning_margin
home_by_1 22.5% home_by_2 16.6% home_by_3plus 14.7%
away_by_1 13.7% away_by_2 6.3% away_by_3plus 2.8%
correct_score
0-0 5.2% 0-1 5.0% 0-2 3.2%
0-3 1.3% 0-4 0.4% 0-5 0.1%
1-0 8.4% 1-1 10.9% 1-2 6.1%
1-3 2.4% 1-4 0.7% 1-5 0.2%
2-0 8.4% 2-1 9.8% 2-2 5.7%
2-3 2.2% 2-4 0.7% 2-5 0.2%
3-0 5.3% 3-1 6.2% 3-2 3.6%
3-3 1.4% 3-4 0.4% 3-5 0.1%
4-0 2.5% 4-1 2.9% 4-2 1.7%
4-3 0.7% 4-4 0.2% 4-5 0.1%
5-0 1.0% 5-1 1.1% 5-2 0.7%
5-3 0.3% 5-4 0.1% 5-5 0.0%
other 1.4%
likeliest_score
h 100.0% a 100.0% p 10.9%
Goals: totals / handicap / exact calibrated
total
over_0.5 94.8% under_0.5 5.2% over_1.5 81.5%
under_1.5 18.5% over_2.5 59.0% under_2.5 41.0%
over_3.5 36.6% under_3.5 63.4% over_4.5 19.5%
under_4.5 80.5% over_5.5 9.0% under_5.5 91.0%
odd_even
odd 48.9% even 51.2%
team_home
over_0.5 84.9% under_0.5 15.1% over_1.5 56.4%
under_1.5 43.6% over_2.5 29.4% under_2.5 70.6%
team_away
over_0.5 68.9% under_0.5 31.1% over_1.5 32.6%
under_1.5 67.4% over_2.5 11.4% under_2.5 88.6%
most
home 53.7% draw 23.4% away 22.8%
handicap
home_-2.5 14.7% away_+2.5 85.3% home_-1.5 31.2%
away_+1.5 68.8% home_-0.5 53.7% away_+0.5 46.3%
home_+0.5 77.2% away_-0.5 22.8% home_+1.5 90.9%
away_-1.5 9.1% home_+2.5 97.2% away_-2.5 2.8%
total_exact
exact_0 5.2% exact_1 13.3% exact_2 22.5%
exact_3 22.4% exact_4 17.1% exact_5 10.5%
exact_6 5.4% exact_7plus 3.7%
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