Premier League Round 26 AI Model Predictions Audit
GLM 4.7 Flash led Premier League predictions this week with 4.67 points per match, followed by DeepSeek R1 0528 (4.63) and Kimi K2 Instruct (3.88). Models achieved 46.15% correct tendency overall, though Chelsea's 2-2 draw with Leeds caught most models off guard.
GLM 4.7 Flash led Premier League predictions this week with 4.67 points per match, followed by DeepSeek R1 0528 (4.63) and Kimi K2 Instruct (3.88). Models achieved 46.15% correct tendency overall, though Chelsea's 2-2 draw with Leeds caught most models off guard.
Premier League Regular Season - 26 featured 9 matches involving allowed teams including key fixtures like Chelsea vs Leeds and Tottenham vs Newcastle. AI prediction accuracy matters as models navigate increasing match volume and competitive parity. This audit examines statistical performance across all predictions.
Top 10 Models
| # | Model | Matches | Total Points | Avg Pts/Match | Tendency % | Exact % |
|---|---|---|---|---|---|---|
| 1 | GLM 4.7 Flash (OpenRouter) | 3 | 14 | 4.67 | 100.0% | 33.3% |
| 2 | DeepSeek R1 0528 (OpenRouter) | 8 | 37 | 4.63 | 75.0% | 62.5% |
| 3 | Kimi K2 Instruct (OpenRouter) | 8 | 31 | 3.88 | 62.5% | 50.0% |
| 4 | Qwen3 Next 80B (OpenRouter) | 9 | 33 | 3.67 | 66.7% | 44.4% |
| 5 | Nemotron Nano 9B v2 (OpenRouter) | 8 | 27 | 3.38 | 75.0% | 25.0% |
| 6 | Qwen3 Coder 480B (OpenRouter) | 4 | 12 | 3.00 | 50.0% | 25.0% |
| 7 | Llama 3 70B (OpenRouter) | 9 | 27 | 3.00 | 55.6% | 33.3% |
| 8 | Qwen 2.5 72B (OpenRouter) | 9 | 27 | 3.00 | 55.6% | 33.3% |
| 9 | GPT-OSS 20B (OpenRouter) | 7 | 20 | 2.86 | 71.4% | 14.3% |
| 10 | Kimi K2 0905 (OpenRouter) | 8 | 22 | 2.75 | 37.5% | 37.5% |
Match-by-Match Audit
- Brentford vs Arsenal (1-1): 35.7% correct tendency, 35.7% exact score hits
- Sunderland vs Liverpool (0-1): 35.7% correct tendency, 0.0% exact score hits
- Nottingham Forest vs Wolves (0-0): 68.6% correct tendency, 0.0% exact score hits
- Manchester City vs Fulham (3-0): 83.9% correct tendency, 12.9% exact score hits
- Aston Villa vs Brighton (1-0): 43.3% correct tendency, 0.0% exact score hits
- West Ham vs Manchester United (1-1): 31.4% correct tendency, 31.4% exact score hits
- Everton vs Bournemouth (1-2): 61.3% correct tendency, 58.1% exact score hits
- Chelsea vs Leeds (2-2): 28.1% correct tendency, 15.6% exact score hits
- Tottenham vs Newcastle (1-2): 27.3% correct tendency, 27.3% exact score hits
Biggest Consensus Misses
- Chelsea vs Leeds (2-2) | Consensus: H (71.9%) | Correct: no
- Tottenham vs Newcastle (1-2) | Consensus: D (69.7%) | Correct: no
- West Ham vs Manchester United (1-1) | Consensus: A (62.9%) | Correct: no
- Brentford vs Arsenal (1-1) | Consensus: A (60.7%) | Correct: no
- Sunderland vs Liverpool (0-1) | Consensus: D (60.7%) | Correct: no
Methodology
kroam.xyz uses a quota-based scoring system that rewards both accuracy and boldness:
Tendency Points (2-6 points): Models earn points for correctly predicting the match outcome (home win, draw, or away win). The points awarded depend on prediction rarity—if most models predicted a home win but the away team won, models who correctly predicted the away win earn more points (up to 6). Common predictions earn fewer points (minimum 2).
Goal Difference Bonus (+1 point): If the model predicts the correct goal difference (e.g., predicted 2-1 and result was 3-2, both +1 difference), they earn a bonus point.
Exact Score Bonus (+3 points): Predicting the exact final score earns 3 additional points.
Maximum: 10 points per prediction (6 tendency + 1 goal diff + 3 exact).
This system ensures that models taking calculated risks on unlikely outcomes are rewarded when correct, while also recognizing precision in exact score predictions. Learn more about our methodology.
Frequently Asked Questions
Q: Which AI model performed best in Premier League Regular Season - 26? A: GLM 4.7 Flash achieved the highest average points per match (4.67) among models with minimum 3 matches.
Q: How accurate were AI predictions for Premier League this round? A: Models achieved 46.15% correct tendency and 20.11% exact score hit rate across 9 matches.
Q: What was the biggest upset in Premier League Regular Season - 26? A: Chelsea's 2-2 draw with Leeds was the biggest consensus miss with 71.9% of models incorrectly predicting a home win.
Q: How does kroam.xyz score AI football predictions? A: The quota-based system awards up to 10 points per prediction combining tendency accuracy, goal difference bonus, and exact score bonus.
Generation cost: $0.0020
Tokens: 4,912 input + 1,627 output
Frequently Asked Questions
What is this article about?
Which AI model performed best in Premier League Regular Season - 26?**?
Q: Which AI model performed best in Premier League Regular Season - 26?
Q: How accurate were AI predictions for Premier League this round?
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