Season Overview
The 2025/2026 season of the 2. Bundesliga saw a comprehensive analysis of 306 fixtures, with valid model signals identified in 245 of those matches. The overall signal hit rate for the season was 58.2%, indicating a relatively strong performance by the predictive models.
Signal Performance
The model's signals were broken down by home/draw/away outcomes and rated by their predictive strength (★★★, ★★, ★). Here’s the breakdown:
- Home signals:
- ★★★: 7 bets, 5 wins (71.4% hit rate), 1 draw
- ★★: 86 bets, 50 wins (58.1% hit rate), 16 draws
- ★: 72 bets, 41 wins (56.9% hit rate), 14 draws
- Overall: 96/165 (58.2% hit rate)
- Away signals:
- ★★: 47 bets, 23 wins (48.9% hit rate), 12 draws
- ★: 33 bets, 11 wins (33.3% hit rate), 11 draws
- Overall: 34/80 (42.5% hit rate)
The highest hit rates were observed in the ★★★ home signals, while ★★★ away signals were less successful. Overall, home signals performed better than away signals.
Match Winner P&L
Two approaches were tested for match winner bets: backing every signal with available odds and backing only value bets (edge > 0).
- Backing every signal:
- Bets: 130
- Wins: 61
- Win rate: 46.9%
- P&L: -7.23 units
- ROI: -5.56%
- Backing value bets only:
- Bets: 27
- Wins: 9
- Win rate: 33.3%
- P&L: -0.17 units
- ROI: -0.63%
- Max win streak: 4
- Max loss streak: 9
Filtering to value bets did not significantly improve the overall profitability, suggesting that backing every signal might be the more prudent approach.
BTTS by Probability Bracket
A detailed breakdown of the Both Teams to Score (BTTS) bets by probability bracket is as follows:
| Bracket | Fixtures | Hit% | Back All Bets | Back All P&L | Back All ROI | Value Bets | Value P&L | Value ROI |
|--------------|----------|------|---------------|--------------|--------------|------------|-----------|-----------|
| <45% | 6 | 66.7%| 6 | +0.81 | 13.5% | 0 | - | - |
| 45-50% | 38 | 68.4%| 38 | +4.13 | 10.9% | 0 | - | - |
| 50-55% | 61 | 62.3%| 61 | -1.29 | -2.1% | 5 | +2.20 | 44.0% |
| 55-60% | 43 | 58.1%| 43 | -4.66 | -10.8% | 8 | -3.08 | -38.5% |
| 60-65% | 5 | 60.0%| 5 | -0.59 | -11.8% | 1 | +0.57 | 57.0% |
| 65-70% | 0 | 0% | 0 | - | - | 0 | - | - |
| TOTALS | 153 | 58.8%| 153 | -1.60 | -1.0% | 14 | -0.31 | -2.2% |
The overall actual BTTS rate was 58.8%, slightly above the V12 model average of 57.6%.
Over 2.5 by Probability Bracket
Here’s the breakdown for Over 2.5 goals by probability bracket:
| Bracket | Fixtures | Hit% | Back All Bets | Back All P&L | Back All ROI | Value Bets | Value P&L | Value ROI |
|--------------|----------|------|---------------|--------------|--------------|------------|-----------|-----------|
| <45% | 4 | 75.0%| 4 | +1.17 | 29.3% | 0 | - | - |
| 45-50% | 23 | 60.9%| 23 | +1.76 | 7.7% | 1 | -1.00 | -100.0% |
| 50-55% | 48 | 58.3%| 48 | -2.70 | -5.6% | 3 | +0.91 | 30.3% |
| 55-60% | 40 | 65.0%| 40 | +1.95 | 4.9% | 8 | -1.04 | -13.0% |
| 60-65% | 32 | 40.6%| 32 | -11.86 | -37.1% | 11 | +0.24 | 2.2% |
| 65-70% | 6 | 50.0%| 6 | -1.45 | -24.2% | 3 | -1.25 | -41.7% |
| TOTALS | 153 | 57.2%| 153 | -11.13 | -7.3% | 26 | -2.14 | -8.2% |
The actual Over 2.5 rate was 57.2%, slightly above the V12 model average of 55.6%.
Team Analysis
Several teams over-performed or under-performed relative to their expected goals (xG):
- Over-performers:
- SV Elversberg: 64 goals (xG 46.8)
- Hannover 96: 60 goals (xG 46.3)
- SC Paderborn 07: 59 goals (xG 48.4)
- Arminia Bielefeld: 52 goals (xG 41.9)
- Dynamo Dresden: 54 goals (xG 44.5)
- Under-performers:
- Fortuna Düsseldorf: 32 goals (xG 36.7)
- Eintracht Braunschweig: 36 goals (xG 38.5)
- Preußen Münster: 38 goals (xG 37.6)
- High signal hit rates:
- Karlsruher SC: 100.0% (6/6)
- FC Schalke 04: 70.0% (14/20)
- SV Elversberg: 66.7% (14/21)
- Eintracht Braunschweig: 60.0% (3/5)
- SC Paderborn 07: 56.5% (13/23)
- 1. FC Kaiserslautern: 56.3% (9/16)
- Hertha BSC: 53.8% (7/13)
- 1. FC Nürnberg: 53.8% (7/13)
Players to Follow
Several standout performers in terms of goal scoring and assists:
- Top Scorers:
- Noel Futkeu (19 goals, 5 assists)
- Mateusz Żukowski (17 goals, 3 assists)
- Isac Lidberg (17 goals, 3 assists)
- Filip Bilbija (15 goals, 3 assists)
- Marvin Wanitzek (14 goals, 5 assists)
- Cédric Itten (14 goals, 3 assists)
- Benjamin Källman (14 goals, 1 assist)
- Kenan Karaman (14 goals, 5 assists)
- Top Assisters:
- Fabian Reese (13 assists)
- Barış Atik (11 assists)
- Marco Richter (9 assists)
- Branimir Hrgota (9 assists)
- Alexander Bernhardsson (9 assists)
- Tom Zimmerschied (9 assists)
- Players to Watch:
- Moussa Sylla (41/68 on target, 7 goals)
- Marvin Wanitzek (39/66 on target, 14 goals)
- Mateusz Żukowski (35/63 on target, 17 goals)
- Cédric Itten (35/65 on target, 14 goals)
- Fabian Reese (34/75 on target, 10 goals)
- Isac Lidberg (32/54 on target, 17 goals)
Key Takeaways
- Home signals outperformed away signals, with a 58.2% hit rate compared to 42.5%.
- Backing every signal was more profitable than filtering to value bets.
- The BTTS model slightly over-performed with a 58.8% actual rate versus 57.6% model average.
- Over 2.5 goals had an actual rate of 57.2%, slightly above the model’s 55.6% average.
- Several teams over-performed relative to their expected goals, providing potential value for next season.