Sepsi Sf. Gheorghe 0-5 Univ. Craiova: result, our prediction
Sepsi Sf. GheorgheGheorgheLWLLD
Univ. CraiovaUniv. CraiovaWWWWWPre-match model estimates · MODELS TOP 3
Based on the Road2Sushi model. Not guaranteed outcomes.
Prediction snapshot · Model generated .
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.
Match data
| Sepsi Sf. GheorgheGheorghe | Statistic | Univ. CraiovaCraiova |
|---|---|---|
| 0 | Goals | 5 |
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: Univ. Craiova | Chance: 56.3% | Correct |
| 2 | Total goals 2.5 | Pick: Under 2.5 | Chance: 55.0% | Wrong5 goals |
| 3 | Total goals 1.5 | Pick: Over 1.5 | Chance: 71.4% | Correct5 goals |
| 4 | Total goals 3.5 | Pick: Under 3.5 | Chance: 76.2% | Wrong5 goals |
| 5 | Both teams to score | Pick: No | Chance: 54.0% | 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.
L
W
L
L
D
1 win · 1 draw · 3 losses
W
W
W
W
W
5 wins · 0 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, Goals and Corners: Bet365 prices via API-Football, observed 26 Oct 2026, 16:57 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
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 (APIF:Pinnacle:4) 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
Goals · 2.5
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 · Likely
Over 2.5 · Close call
BTTS · Yes · Close call
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 | 0-09.0%Fair odds 11.15 | 1-06.4%Fair odds 15.70 | 2-02.9%Fair odds 34.84 | 3-00.8%Fair odds 126.58 | 4-00.2%Fair odds 625.00 | 5-00.0%Fair odds 3,333.33 |
| Away goals 1 | 0-113.3%Fair odds 7.53 | 1-112.0%Fair odds 8.31 | 2-14.7%Fair odds 21.10 | 3-11.3%Fair odds 76.34 | 4-10.3%Fair odds 370.37 | 5-10.0%Fair odds 2,500.00 |
| Away goals 2 | 0-211.4%Fair odds 8.75 | 1-29.5%Fair odds 10.57 | 2-23.9%Fair odds 25.58 | 3-21.1%Fair odds 92.59 | 4-20.2%Fair odds 454.55 | 5-20.0%Fair odds 2,500.00 |
| Away goals 3 | 0-36.3%Fair odds 15.90 | 1-35.2%Fair odds 19.23 | 2-32.2%Fair odds 46.51 | 3-30.6%Fair odds 169.49 | 4-30.1%Fair odds 833.33 | 5-30.0%Fair odds 5,000.00 |
| Away goals 4 | 0-42.6%Fair odds 38.46 | 1-42.2%Fair odds 46.51 | 2-40.9%Fair odds 112.36 | 3-40.2%Fair odds 416.67 | 4-40.1%Fair odds 2,000.00 | 5-40.0%Fair odds 10,000.00 |
| Away goals 5 | 0-50.9%Fair odds 116.28 | 1-50.7%Fair odds 140.85 | 2-50.3%Fair odds 344.83 | 3-50.1%Fair odds 1,250.00 | 4-50.0%Fair odds 5,000.00 | 5-50.0%Fair odds · |
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.
Assigned per API-Football, seen 28 Oct 2026, 14:14 UTC. An assignment can change before kickoff.
| Per match | All matches | 2026/27 |
|---|---|---|
| Matches | 15 | 3 |
| Goals | 2.53 | Not available |
| Yellow cards | 3.87 | Not available |
| Corners | 9.9 | Not available |
| Fouls | 19.4 | Not available |
| Both teams scored | 60% | Not available |
| Over 2.5 goals | 40% | Not available |
| Home wins | 40% | Not available |
| Date | Match | Score | Goals | BTTS | Yellow | Red | Corners | Fouls |
|---|---|---|---|---|---|---|---|---|
| 8 Oct 2026 | CFR Cluj vs U. Cluj | 1-3 | 4 | Yes | 5 | Not available | 8 | 22 |
| 11 Sep 2026 | Farul Constanta vs UTA Arad | 4-2 | 6 | Yes | 2 | Not available | 6 | 12 |
| 29 Aug 2026 | Csikszereda M. Ciuc vs CFR Cluj | 2-3 | 5 | Yes | 3 | Not available | 6 | 29 |
| 22 May 2026 | CFR Cluj vs FC Arges | 1-1 | 2 | Yes | 6 | 1 | 7 | 21 |
| 19 Apr 2026 | Petrolul vs FC Hermannstadt | 1-1 | 2 | Yes | 6 | 1 | 17 | 14 |
| 5 Apr 2026 | Farul Constanta vs Unirea Slobozia | 0-1 | 1 | No | 4 | Not available | 13 | 21 |
| 14 Feb 2026 | Metaloglobus Bucharest vs Otelul | 0-1 | 1 | No | 3 | Not available | 7 | 13 |
| 3 Feb 2026 | Petrolul vs Unirea Slobozia | 1-0 | 1 | No | 2 | Not available | 17 | 13 |
| 12 Dec 2025 | CFR Cluj vs Csikszereda M. Ciuc | 3-1 | 4 | Yes | 5 | Not available | 7 | 21 |
| 3 Nov 2025 | Petrolul vs FC Botosani | 0-0 | 0 | No | 4 | Not available | 6 | 23 |
| Team | |||
|---|---|---|---|
| Sepsi Sf. Gheorghe | Not available | Not available | Not available |
| Univ. Craiova | 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.
Shooting
Scale: 0–20 shots per game. Each team uses the same scale independently.
Corners & discipline
0–10 corners per game; 0–20 fouls 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: 2.72 goals per match · Over 2.5 in 49.5% · BTTS in 48.5%. 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.
| Per game | 1H | 2H | Games |
|---|---|---|---|
| Goals scoredScored | 0.25 | 0.42 | 12 |
| Goals concededConceded | 0.75 | 0.33 | 12 |
| Shots | 4.8 | 6.7 | 12 |
| Shots facedOpp. shots | 7.8 | 8.7 | 12 |
| Shots on targetOn target | 1.8 | 1.8 | 12 |
| Shots on target facedOpp. on target | 2.7 | 2.3 | 12 |
| Corners wonCorners | 1.6 | 1.8 | 12 |
| Corners concededOpp. corners | 3.1 | 4.0 | 12 |
| Yellow cardsYellows | 0.7 | 1.2 | 9 |
| Opponent yellow cardsOpp. yellows | 0.6 | 1.2 | 9 |
| Per game | 1H | 2H | Games |
|---|---|---|---|
| Goals scoredScored | 0.58 | 1.67 | 12 |
| Goals concededConceded | 0.25 | 0.50 | 12 |
| Shots | 8.5 | 9.0 | 10 |
| Shots facedOpp. shots | 3.6 | 7.6 | 10 |
| Shots on targetOn target | 2.8 | 4.1 | 12 |
| Shots on target facedOpp. on target | 0.7 | 2.3 | 12 |
| Corners wonCorners | 2.3 | 2.4 | 12 |
| Corners concededOpp. corners | 1.3 | 2.0 | 12 |
| Yellow cardsYellows | 0.7 | 0.8 | 10 |
| Opponent yellow cardsOpp. yellows | 0.5 | 1.8 | 10 |
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.
| Sepsi Sf. Gheorghe clean sheet | 19.2% |
|---|---|
| Univ. Craiova clean sheet | 43.4% |
| Sepsi Sf. Gheorghe win to nil | 10.2% |
| Univ. Craiova win to nil | 34.5% |
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 | 18.1% | draw | 25.6% | away | 56.4% |
|---|---|---|---|---|---|
double_chance
| 1X | 43.7% | 12 | 74.5% | X2 | 81.9% |
|---|---|---|---|---|---|
dnb
| home | 24.3% | away | 75.7% |
|---|
btts
| yes | 46.0% | no | 54.0% |
|---|
clean_sheet
| home | 19.2% | away | 43.7% |
|---|
win_to_nil
| home | 10.2% | away | 34.8% |
|---|
winning_margin
| home_by_1 | 12.3% | home_by_2 | 4.4% | home_by_3plus | 1.4% |
|---|---|---|---|---|---|
| away_by_1 | 25.2% | away_by_2 | 17.6% | away_by_3plus | 13.6% |
correct_score
| 0-0 | 9.0% | 0-1 | 13.3% | 0-2 | 11.4% |
|---|---|---|---|---|---|
| 0-3 | 6.3% | 0-4 | 2.6% | 0-5 | 0.9% |
| 1-0 | 6.4% | 1-1 | 12.0% | 1-2 | 9.5% |
| 1-3 | 5.2% | 1-4 | 2.2% | 1-5 | 0.7% |
| 2-0 | 2.9% | 2-1 | 4.7% | 2-2 | 3.9% |
| 2-3 | 2.2% | 2-4 | 0.9% | 2-5 | 0.3% |
| 3-0 | 0.8% | 3-1 | 1.3% | 3-2 | 1.1% |
| 3-3 | 0.6% | 3-4 | 0.2% | 3-5 | 0.1% |
| 4-0 | 0.2% | 4-1 | 0.3% | 4-2 | 0.2% |
| 4-3 | 0.1% | 4-4 | 0.1% | 4-5 | 0.0% |
| 5-0 | 0.0% | 5-1 | 0.0% | 5-2 | 0.0% |
| 5-3 | 0.0% | 5-4 | 0.0% | 5-5 | 0.0% |
| other | 0.7% |
likeliest_score
| h | 0.0% | a | 100.0% | p | 13.3% |
|---|---|---|---|---|---|
Goals: totals / handicap / exact calibrated
total
| over_0.5 | 91.0% | under_0.5 | 9.0% | over_1.5 | 71.4% |
|---|---|---|---|---|---|
| under_1.5 | 28.6% | over_2.5 | 45.0% | under_2.5 | 55.0% |
| over_3.5 | 23.8% | under_3.5 | 76.2% | over_4.5 | 10.6% |
| under_4.5 | 89.4% | over_5.5 | 4.1% | under_5.5 | 96.0% |
odd_even
| odd | 48.5% | even | 51.5% |
|---|
team_home
| over_0.5 | 56.3% | under_0.5 | 43.7% | over_1.5 | 20.1% |
|---|---|---|---|---|---|
| under_1.5 | 79.9% | over_2.5 | 5.1% | under_2.5 | 94.9% |
team_away
| over_0.5 | 80.8% | under_0.5 | 19.2% | over_1.5 | 49.1% |
|---|---|---|---|---|---|
| under_1.5 | 50.9% | over_2.5 | 23.0% | under_2.5 | 77.0% |
most
| home | 18.1% | draw | 25.6% | away | 56.4% |
|---|---|---|---|---|---|
handicap
| home_-2.5 | 1.4% | away_+2.5 | 98.7% | home_-1.5 | 5.8% |
|---|---|---|---|---|---|
| away_+1.5 | 94.2% | home_-0.5 | 18.1% | away_+0.5 | 81.9% |
| home_+0.5 | 43.7% | away_-0.5 | 56.4% | home_+1.5 | 68.8% |
| away_-1.5 | 31.2% | home_+2.5 | 86.4% | away_-2.5 | 13.6% |
total_exact
| exact_0 | 9.0% | exact_1 | 19.7% | exact_2 | 26.3% |
|---|---|---|---|---|---|
| exact_3 | 21.3% | exact_4 | 13.2% | exact_5 | 6.5% |
| exact_6 | 2.7% | exact_7plus | 1.4% |
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 Sepsi Sf. Gheorghe and Univ. Craiova
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.
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