National Division Moldova Prediction Models
2026/2027Flat Betting Model
Odds Range Filter
Use minimum and maximum odds selection tool to identify the most profitable ranges for the Flat Betting football prediction model for National Division Moldova. After each change on the odds range, the system filters qualifying bets and recalculates profits and losses based on those selections.
Market ROI Summary
Flat $10 stake on every match from start to end of season. Profit/loss and ROI per market.
| Market | Bets | Wins | Win % | Staked | Return | P&L | ROI | Best Str. | Worst Str. | Trend |
|---|---|---|---|---|---|---|---|---|---|---|
| Home Bet | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| Draw Bet | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| Away Bet | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| Over 2.5 | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| Under 2.5 | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| BTTS Yes | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| Home Favorite | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| Away Underdog | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| Favorite | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 | |
| Underdog | 0 | 0 | 0% | $0 | $0 | $0.00 | 0% | 0 | 0 |
PPG Model
Odds Range Filter
Use minimum and maximum odds selection tool to identify the most profitable ranges for the PPG football prediction model for National Division Moldova. After each change on the odds range, the system filters qualifying bets and recalculates profits and losses based on those selections.
Recent Results — PPG Predictions vs Actual
How PPG predictions compared to actual match results
| Date | Match | PPG Pick | Prob | Fair Odds | Result | ✓ |
|---|---|---|---|---|---|---|
| 25 Oct | Zimbru vs CSF Bălți | Zimbru | 95% | 1.05 | 4 – 0 | ✓ |
| 24 Oct | Petrocub vs Milsami | Petrocub | 50.27% | 1.99 | 2 – 1 | ✓ |
| 24 Oct | Dacia-Buiucani vs Real Sireți | Dacia-Buiucani | 54.98% | 1.82 | 3 – 1 | ✓ |
| 24 Oct | Politehnica UTM vs Sheriff | Sheriff | 53.53% | 1.87 | 0 – 2 | ✓ |
| 18 Oct | Milsami vs Zimbru | Zimbru | 44.68% | 2.24 | 1 – 1 | ✗ |
Upcoming Fixtures — PPG Predictions
Win probabilities for upcoming matches based on current PPG ratings
| Date | Match | PPG Pick | Prob | Fair Odds |
|---|---|---|---|---|
| 31 Oct | CSF Bălți vs Petrocub | Petrocub | 89.29% | 1.12 |
| 31 Oct | Sheriff vs Zimbru | *** | 0% | 0 |
| 01 Nov | Real Sireți vs Politehnica UTM | 🔒 Login or register for free to see predictions data. | ||
| 01 Nov | Milsami vs Dacia-Buiucani | |||
PPG vs WPPG — Biggest Divergence
Teams where PPG and Weighted PPG disagree most — indicates recent form shift
Worked Example — Zimbru vs CSF Bălți
Poisson / Correct Score
Select Match
Dixon-Coles Score Matrix — CSF Bălți vs Petrocub
30 Oct · Each cell shows the probability of that exact scoreline. Dixon-Coles corrected (ρ = -0.13). · Upcoming
| H \ A | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|---|
| 0 | 3.9% | 9.4% | 14.9% | 14.9% | 11.2% | 6.7% | 3.4% |
| 1 | 0.9% | 5.0% | 6.6% | 6.6% | 4.9% | 3.0% | 1.5% |
| 2 | 0.3% | 1.0% | 1.4% | 1.4% | 1.1% | 0.7% | 0.3% |
| 3 | 0.1% | 0.1% | 0.2% | 0.2% | 0.2% | 0.1% | 0.1% |
| 4 | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| 5 | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| 6 | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
Derived Markets
Correct Score — Top 12
Dixon-Coles vs Plain Poisson
Where the low-score correction adds value
How Poisson Predictions Work
- Step 1 — Expected Goals:
- Home xG = Home Attack Strength × Away Defense Weakness × League Avg Home Goals. Each team's attack and defense ratings are calculated relative to the league average.
- Step 2 — Poisson Formula:
- P(k goals) = e⁻λ × λᵏ / k! — models the probability of scoring exactly k goals given the expected goals (λ).
- Step 3 — Score Matrix:
- Multiply home and away goal probabilities for every combination (0-0 through 6-6) to build a full probability grid for every possible scoreline.
- Step 4 — Dixon-Coles:
- Apply ρ correction to fix Poisson's underestimation of low-scoring draws (0-0, 1-1, 1-0, 0-1). This is the key improvement over basic Poisson.
- Step 5 — Derive Markets:
- Sum probabilities from the matrix to derive 1X2, Over/Under, BTTS, and correct score probabilities — then convert to fair odds for value identification.
Elo Rating
Current Elo Ratings
All teams ranked by Elo. Start rating: 1500. K-factor: 32. Home advantage: +65 Elo.
Elo → Win Probability Table
How Elo difference translates to win probability.
| Elo Diff | P(Higher wins) | Fair Odds | Typical Matchup |
|---|---|---|---|
| 0 | 50.0% | 2.00 | Evenly matched |
| 50 | 57.1% | 1.75 | Mid vs mid-lower |
| 100 | 64.0% | 1.56 | Top-8 vs bottom-8 |
| 150 | 70.3% | 1.42 | Top-4 vs relegation |
| 200 | 76.0% | 1.32 | Leader vs bottom half |
| 250 | 80.8% | 1.24 | Leader vs bottom 3 |
| 300 | 84.9% | 1.18 | Sheriff vs CSF Bălți |
Elo Rating History — Top 6
How the top 6 teams' Elo ratings evolved across the season.
How Elo Ratings Work
- Step 1 — Starting Rating:
- Every team starts at 1500. Higher Elo means a stronger team.
- Step 2 — Expected Score:
- E(A) = 1 / (1 + 10^(R_B − R_A) / 400). If Arsenal is rated 1600 and Wolves is 1450, Arsenal's expected score is 0.71.
- Step 3 — Update After Match:
- R_new = R_old + K × (S − E), where S is the actual result (1 = win, 0.5 = draw, 0 = loss) and K=32 controls sensitivity.
- Step 4 — Home Advantage:
- Home team gets +65 Elo boost when calculating expected scores, reflecting the statistical home advantage in football.
- Step 5 — Betting:
- Convert Elo difference into win/draw/loss probabilities, compare against bookmaker odds, and bet when the model spots value.
Value Scanner
No upcoming matches with odds available. Please check back later.