Serie A Week 27 AI Model Predictions & Accuracy Report
Gemma 3 12B and GLM-5 led with 3.13 avg points per match, followed by MiniMax M2.1 and DeepSeek V3.2 at 2.88. Models achieved 63.16% correct tendency overall. Torino's 2-0 win over Lazio was the biggest consensus miss.
Gemma 3 12B and GLM-5 led with 3.13 avg points per match, followed by MiniMax M2.1 and DeepSeek V3.2 at 2.88. Models achieved 63.16% correct tendency overall. Torino's 2-0 win over Lazio was the biggest consensus miss.
Serie A Regular Season - 27 featured 8 matches, including key fixtures such as AS Roma vs Juventus and high-stakes encounters affecting model consensus accuracy. AI prediction performance is critical for evaluating model reliability in competitive scenarios, transitioning to the detailed statistical audit below.
Top 10 Models
| # | Model | Matches | Total Points | Avg Pts/Match | Tendency % | Exact % |
|---|---|---|---|---|---|---|
| 1 | Gemma 3 12B (OpenRouter) | 8 | 25 | 3.13 | 75.0% | 37.5% |
| 2 | GLM-5 (OpenRouter) | 8 | 25 | 3.13 | 75.0% | 25.0% |
| 3 | MiniMax M2.1 (OpenRouter) | 8 | 23 | 2.88 | 62.5% | 37.5% |
| 4 | DeepSeek V3.2 (OpenRouter) | 8 | 23 | 2.88 | 62.5% | 37.5% |
| 5 | MiniMax M2.5 (OpenRouter) | 8 | 22 | 2.75 | 62.5% | 37.5% |
| 6 | Gemma 3 27B (OpenRouter) | 8 | 21 | 2.63 | 75.0% | 25.0% |
| 7 | Llama 4 Scout (OpenRouter) | 8 | 21 | 2.63 | 75.0% | 25.0% |
| 8 | Qwen3 30B A3B (OpenRouter) | 8 | 20 | 2.50 | 62.5% | 25.0% |
| 9 | Mistral Small 3.2 24B (OpenRouter) | 8 | 18 | 2.25 | 50.0% | 25.0% |
| 10 | DeepSeek R1-0528 (OpenRouter) | 8 | 18 | 2.25 | 75.0% | 0.0% |
Match-by-Match Audit
- AS Roma vs Juventus: Result 3-3. Correct tendency 68.4%, exact score hits 0.0%. Consensus: D (68.4%), correct.
- Torino vs Lazio: Result 2-0. Correct tendency 15.8%, exact score hits 0.0%. Consensus: D (73.7%), incorrect.
- Sassuolo vs Atalanta: Result 2-1. Correct tendency 21.1%, exact score hits 0.0%. Consensus: A (63.2%), incorrect.
- Cremonese vs AC Milan: Result 0-2. Correct tendency 78.9%, exact score hits 31.6%. Consensus: A (78.9%), correct.
- Inter vs Genoa: Result 2-0. Correct tendency 68.4%, exact score hits 21.1%. Consensus: H (68.4%), correct.
- Hellas Verona vs Napoli: Result 1-2. Correct tendency 84.2%, exact score hits 26.3%. Consensus: A (84.2%), correct.
- Como vs Lecce: Result 3-1. Correct tendency 73.7%, exact score hits 0.0%. Consensus: H (73.7%), correct.
- Parma vs Cagliari: Result 1-1. Correct tendency 94.7%, exact score hits 78.9%. Consensus: D (94.7%), correct.
Biggest Consensus Misses
- Torino vs Lazio (2-0): Consensus was D (73.7%) with H/D/A counts 3/14/2.
- Sassuolo vs Atalanta (2-1): Consensus was A (63.2%) with H/D/A counts 4/3/12.
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 - 27? A: Gemma 3 12B and GLM-5 tied for best with 3.13 average points per match.
Q: How accurate were AI predictions for Serie A this round? A: Models achieved 63.16% correct tendency and 19.74% exact score hit rate across 8 matches.
Q: What was the biggest upset in Serie A Regular Season - 27? A: Torino's 2-0 win over Lazio, where consensus predicted a draw (73.7%).
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: $0.0019
Tokens: 4,444 input + 1,644 output
Frequently Asked Questions
What is this article about?
Which AI model performed best in Serie A Regular Season - 27?**?
Q: Which AI model performed best in Serie A Regular Season - 27?
Q: How accurate were AI predictions for Serie A this round?
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