Comprehensive ablation study results
| Component removed | Tactical accuracy | Latency (ms) | Sample efficiency | Football | Basketball | Rugby | Statistical significance |
|---|---|---|---|---|---|---|---|
| Complete Framework | 87.8% ± 1.2% | 30.9 ± 2.1 | 500 matches | 87.8% | 85.3% | 80.7% | Baseline |
| Hierarchical Decomposition | 69.5% ± 2.1% | 187.2 ± 15.3 | 2,100 matches | 68.1% | 71.2% | 69.1% | p < 0.001 |
| Dynamic Graph Construction | 73.1% ± 1.8% | 119.8 ± 8.7 | 1,350 matches | 72.4% | 74.1% | 72.8% | p < 0.001 |
| Cross-Attention Bridge | 75.4% ± 1.6% | 65.1 ± 4.2 | 980 matches | 74.8% | 76.3% | 75.1% | p < 0.001 |
| Adaptive Computation | 84.6% ± 1.4% | 284.7 ± 21.8 | 520 matches | 84.1% | 85.2% | 84.4% | p < 0.001 |
| Opponent Modeling (IRL) | 71.0% ± 2.3% | 42.3 ± 3.1 | 1,680 matches | 70.2% | 72.1% | 70.7% | p < 0.001 |
| Uncertainty Estimation | 82.3% ± 1.7% | 29.1 ± 1.8 | 580 matches | 81.8% | 83.1% | 82.0% | p < 0.01 |
| Multi-scale Reward Structure | 76.2% ± 2.0% | 45.7 ± 3.8 | 1,150 matches | 75.6% | 77.1% | 76.0% | p < 0.001 |
| Component removed | Tactical accuracy | Latency (ms) | Sample efficiency | Football | Basketball | Rugby | Statistical significance |
|---|---|---|---|---|---|---|---|
| Complete Framework | 87.8% ± 1.2% | 30.9 ± 2.1 | 500 matches | 87.8% | 85.3% | 80.7% | Baseline |
| Hierarchical Decomposition | 69.5% ± 2.1% | 187.2 ± 15.3 | 2,100 matches | 68.1% | 71.2% | 69.1% | |
| Dynamic Graph Construction | 73.1% ± 1.8% | 119.8 ± 8.7 | 1,350 matches | 72.4% | 74.1% | 72.8% | |
| Cross-Attention Bridge | 75.4% ± 1.6% | 65.1 ± 4.2 | 980 matches | 74.8% | 76.3% | 75.1% | |
| Adaptive Computation | 84.6% ± 1.4% | 284.7 ± 21.8 | 520 matches | 84.1% | 85.2% | 84.4% | |
| Opponent Modeling (IRL) | 71.0% ± 2.3% | 42.3 ± 3.1 | 1,680 matches | 70.2% | 72.1% | 70.7% | |
| Uncertainty Estimation | 82.3% ± 1.7% | 29.1 ± 1.8 | 580 matches | 81.8% | 83.1% | 82.0% | |
| Multi-scale Reward Structure | 76.2% ± 2.0% | 45.7 ± 3.8 | 1,150 matches | 75.6% | 77.1% | 76.0% |
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