Nottingham Forest4 - 1Burnley
Nottingham ForestWhy did AI enter 6 times at 25', 35', 47', 57', 63', 70' — and still finish +$327?
Match Overview
Nottingham Forest hosted Burnley in the Premier League, finishing 4-1. Goals: Z. Flemming (45'), M. Gibbs-White (62'), M. Gibbs-White (63'), M. Gibbs-White (69'), M. Gibbs-White (77'), Igor Jesus (90'). OddsFlow deployed 18 trades across 6 waves, achieving 56% precision with a net result of +$327.39 (ROI: +3.2%).
Pre-Match Odds
The Asian Handicap line moved from -1 to -1.25.
Trade Wave Analysis
### Wave 1 — Minute 25' (Score: 0-0) **Context:** Scoreless at 25'. 2 models (system4_MC_v10, system4_BLEND_v10) entered Asian Handicap and Moneyline markets, targeting Burnley -1 and Burnley -0.5 and Draw. Average expected value was 15.1%, with a combined stake of $3205. The sharpest edge was on Burnley -1 (Asian Handicap): OddsFlow calculated 79.5% probability versus the market's implied 58.0% — a 21.5 percentage point gap at odds of 1.725. **Result:** All 5 trades lost, netting $-3205.00. ### Wave 2 — Minute 35' (Score: 0-0) **Context:** Scoreless at 35'. 1 model (system4_BLEND_v10) entered Moneyline markets, targeting Draw. Average expected value was 4.7%, with a combined stake of $500. The sharpest edge was on Draw (Moneyline): OddsFlow calculated 32.9% probability versus the market's implied 26.7% — a 6.2 percentage point gap at odds of 3.75. **Result:** All 1 trades lost, netting $-500.00. ### Wave 3 — Minute 47' (Score: 0-1) **Context:** Burnley leading 0-1 at 47'. Z. Flemming scored moments ago. 2 models (system4_BLEND_v10, system4_MC_v10) entered Over/Under and Asian Handicap and Moneyline markets, targeting Over 1.5 and Nottingham Forest 0 and Burnley. Average expected value was 10.1%, with a combined stake of $3000. The sharpest edge was on Over 1.5 (Over/Under): OddsFlow calculated 70.0% probability versus the market's implied 55.6% — a 14.4 percentage point gap at odds of 1.8. **Result:** 4 of 6 trades won, netting +$991.50. ### Wave 4 — Minute 57' (Score: 0-1) **Context:** Burnley leading 0-1 at 57'. 2 models (system4_BLEND_v10, system4_MC_v10) entered Asian Handicap markets, targeting Nottingham Forest +0.5. Average expected value was 30.6%, with a combined stake of $1310. The sharpest edge was on Nottingham Forest +0.5 (Asian Handicap): OddsFlow calculated 86.1% probability versus the market's implied 58.8% — a 27.2 percentage point gap at odds of 1.7. **Result:** All 2 trades won, netting +$917.00. ### Wave 5 — Minute 63' (Score: 1-1) **Context:** Level at 1-1 after M. Gibbs-White's equalizer at 63'. 2 models (system4_BLEND_v10, system4_MC_v10) entered Over/Under markets, targeting Over 2.5. Average expected value was 29.4%, with a combined stake of $1000. The sharpest edge was on Over 2.5 (Over/Under): OddsFlow calculated 70.0% probability versus the market's implied 40.0% — a 30.0 percentage point gap at odds of 2.5. **Result:** All 2 trades won, netting +$1350.00. ### Wave 6 — Minute 70' (Score: 2-1) **Context:** Nottingham Forest leading 2-1 at 70'. M. Gibbs-White scored moments ago. 2 models (system4_MC_v10, system4_BLEND_v10) entered Moneyline markets, targeting Nottingham Forest. Average expected value was 24.9%, with a combined stake of $1162. The sharpest edge was on Nottingham Forest (Moneyline): OddsFlow calculated 80.3% probability versus the market's implied 60.0% — a 20.3 percentage point gap at odds of 1.666. **Result:** All 2 trades won, netting +$773.89.
Key Mispricing Moments
- **Minute 63'** — Over/Under Over 2.5: AI saw 70.0% vs market's 40.0% (30.0pp gap). correctly identified. - **Minute 57'** — Asian Handicap Nottingham Forest +0.5: AI saw 86.1% vs market's 58.8% (27.2pp gap). correctly identified. - **Minute 59'** — Asian Handicap Nottingham Forest +0.5: AI saw 85.6% vs market's 58.8% (26.8pp gap). correctly identified. - **Minute 63'** — Over/Under Over 2.5: AI saw 70.0% vs market's 45.5% (24.5pp gap). correctly identified. - **Minute 25'** — Asian Handicap Burnley -1: AI saw 79.5% vs market's 58.0% (21.5pp gap). did not convert, but edge was real.
Model Performance
- **system4_MC_v10**: 9 trades, 5W/4L, +$363.89 (ROI: +7.0%) - **system4_BLEND_v10**: 9 trades, 5W/4L, $-36.50 (ROI: -0.7%)
Match Statistics
Possession was 52%–48% in Nottingham Forest's favor. Nottingham Forest had 9 shots (3 on target) versus Burnley's 4 (3 on target). Expected goals: Nottingham Forest 1 — Burnley 0.36. Corners: 4-4.
Verdict
A positive result for OddsFlow with +3.2% ROI. The models found genuine edges though not all converted. Top performer: system4_MC_v10 at +$363.89.
What the closing market expected
The prices bookmakers settled on before kick-off, with the margin removed, next to what actually happened.
| Market | Line | Implied probability |
|---|---|---|
| 1X2 (home / draw / away) | Favourite: Home | 67.6% / 22.1% / 14.0% |
| Asian handicap | -1.25 | 47.9% |
| Over / Under | 2.75 | 51.3% |
Probabilities are de-vigged, so they describe the market's view rather than the price you would have been offered.
Bookmakers undervalued Nottingham Forest -1.25 Asian Handicap.
In the Premier League clash, Nottingham Forest secured a decisive 4-1 victory over Burnley. The 1x2 market closing probabilities were highly efficient, with Nottingham Forest's 67.57% implied win probability aligning perfectly with the actual outcome. Similarly, the Over/Under 2.75 goals market proved efficient; the 5 actual goals comfortably hit the Over, mirroring the 51.28% implied probability. However, the most significant market inefficiency emerged in the Asian Handicap market. Despite Nottingham Forest covering the -1.25 line with a dominant 3-goal margin, the market assigned only a 47.85% probability for them to cover. This presents a clear EV+ opportunity for bettors who backed Nottingham Forest -1.25, indicating a Closing Line Value (CLV) error where the bookmakers underestimated Forest's potential to cover the spread.
A positive result with +3.2% ROI. The models found genuine edges, finishing at +$327.39. Top performer: HDP Sniper at +$363.89.
See AI signals as they happen
This report shows settled results. Get real-time AI signals during live matches.