Serie A Week 26 AI Predictions: DeepSeek Leads, 37.5% Tendency Accuracy
DeepSeek R1-0528 topped Serie A predictions with 2.38 avg points/match, followed by MiniMax M2.5 and GPT-OSS 20B at 1.88. Models achieved 37.50% correct tendency overall. The biggest upset was AC Milan's 0-1 home loss to Parma, missed by 89.5% of models.
DeepSeek R1-0528 topped Serie A predictions with 2.38 avg points/match, followed by MiniMax M2.5 and GPT-OSS 20B at 1.88. Models achieved 37.50% correct tendency overall. The biggest upset was AC Milan's 0-1 home loss to Parma, missed by 89.5% of models. Serie A Regular Season - 26 featured 8 matches with several unexpected results impacting prediction accuracy. AI model performance is critical for assessing reliability in high-stakes league fixtures. Below is the detailed statistical audit.
Top 10 Models
| # | Model | Matches | Total Points | Avg Pts/Match | Tendency % | Exact % |
|---|---|---|---|---|---|---|
| 1 | DeepSeek R1-0528 (OpenRouter) | 8 | 19 | 2.38 | 50.0% | 12.5% |
| 2 | MiniMax M2.5 (OpenRouter) | 8 | 15 | 1.88 | 50.0% | 12.5% |
| 3 | GPT-OSS 20B (OpenRouter) | 8 | 15 | 1.88 | 50.0% | 12.5% |
| 4 | MiniMax M2.1 (OpenRouter) | 8 | 14 | 1.75 | 37.5% | 12.5% |
| 5 | GLM-5 (OpenRouter) | 8 | 13 | 1.63 | 37.5% | 12.5% |
| 6 | DeepSeek V3.2 (OpenRouter) | 8 | 12 | 1.50 | 37.5% | 12.5% |
| 7 | Qwen3 30B A3B (OpenRouter) | 8 | 12 | 1.50 | 37.5% | 12.5% |
| 8 | GPT-OSS 120B (OpenRouter) | 8 | 12 | 1.50 | 37.5% | 0.0% |
| 9 | Llama 4 Scout (OpenRouter) | 8 | 11 | 1.38 | 37.5% | 0.0% |
| 10 | Devstral 2 (OpenRouter) | 8 | 11 | 1.38 | 50.0% | 0.0% |
Match-by-Match Audit
- AS Roma vs Cremonese (3-0): 57.9% correct tendency, 0.0% exact score hits. Consensus: H (57.9%), correct.
- AC Milan vs Parma (0-1): 10.5% correct tendency, 0.0% exact score hits. Consensus: H (73.7%), incorrect.
- Atalanta vs Napoli (2-1): 15.8% correct tendency, 10.5% exact score hits. Consensus: D (57.9%), incorrect.
- Genoa vs Torino (3-0): 5.3% correct tendency, 0.0% exact score hits. Consensus: D (89.5%), incorrect.
- Cagliari vs Lazio (0-0): 47.4% correct tendency, 0.0% exact score hits. Consensus: D (47.4%), correct.
- Lecce vs Inter (0-2): 78.9% correct tendency, 31.6% exact score hits. Consensus: A (78.9%), correct.
- Juventus vs Como (0-2): 5.3% correct tendency, 0.0% exact score hits. Consensus: D (63.2%), incorrect.
- Sassuolo vs Verona (3-0): 78.9% correct tendency, 0.0% exact score hits. Consensus: H (78.9%), correct.
Biggest Consensus Misses
- Genoa vs Torino (3-0): Consensus D (89.5%) incorrect, counts H/D/A: 1/17/1
- AC Milan vs Parma (0-1): Consensus H (73.7%) incorrect, counts H/D/A: 14/3/2
- Juventus vs Como (0-2): Consensus D (63.2%) incorrect, counts H/D/A: 6/12/1
- Atalanta vs Napoli (2-1): Consensus D (57.9%) incorrect, counts H/D/A: 3/11/5
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 Serie A Regular Season - 26? A: DeepSeek R1-0528 (OpenRouter) performed best with 2.38 average points per match.
Q: How accurate were AI predictions for Serie A this round? A: Models achieved 37.50% correct tendency accuracy and 5.26% exact score hit rate.
Q: What was the biggest upset in Serie A Regular Season - 26? A: AC Milan's 0-1 loss to Parma, where 73.7% consensus predicted a home win.
Q: How does kroam.xyz score AI football predictions? A: Using a quota-based system awarding up to 10 points per match for tendency, goal difference, and exact score accuracy.
Generation cost: $0.0020
Tokens: 4,492 input + 1,732 output
Frequently Asked Questions
What is this article about?
Which AI model performed best in Serie A Regular Season - 26?**?
Q: Which AI model performed best in Serie A Regular Season - 26?
Q: How accurate were AI predictions for Serie A this round?
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