====================================================================== BACKTEST REPORT Rows: 277, Distance band splits: 19.0 / 27.0 ====================================================================== --- PREDICTOR 1: Pure Linear Extrapolation --- Power MAE RMSE Median Max %<5u -------------------------------------------------- p0.10 8.10 11.93 5.00 61.09 49.8% p0.42 8.68 12.70 5.33 64.35 49.1% p0.74 9.37 13.59 5.75 67.94 46.6% p1.07 10.18 14.62 6.24 72.04 40.1% p1.39 11.03 15.72 7.26 76.48 38.6% p1.71 11.97 16.96 8.38 81.92 35.7% p2.03 13.22 18.49 9.62 87.57 32.5% p2.36 14.64 20.24 11.00 94.46 29.2% p2.68 16.46 22.43 12.77 102.17 26.4% p3.00 18.70 25.16 14.00 111.15 23.5% --- PREDICTOR 2: Weighted Multi-Frame Velocity --- Power MAE RMSE Median Max %<5u -------------------------------------------------- p0.10 7.46 10.80 4.62 46.26 52.0% p0.42 8.04 11.53 4.96 48.78 51.3% p0.74 8.68 12.34 5.53 51.55 47.3% p1.07 9.45 13.30 6.18 54.69 42.2% p1.39 10.30 14.35 6.78 58.09 39.4% p1.71 11.21 15.50 8.07 62.47 35.7% p2.03 12.37 16.94 9.02 66.76 32.9% p2.36 13.75 18.59 10.47 72.08 27.4% p2.68 15.48 20.66 11.94 78.02 24.5% p3.00 17.64 23.29 13.82 84.88 21.3% --- PREDICTOR 3: Linear + Hebbian Residual Correction --- Learning rate = 0.05 Power MAE RMSE Median Max %<5u -------------------------------------------------- p0.10 6.92 10.09 4.47 46.22 53.1% p0.42 7.46 10.79 4.88 49.45 52.0% p0.74 8.12 11.63 5.33 53.03 48.0% p1.07 8.91 12.62 5.92 57.12 44.4% p1.39 9.74 13.70 6.69 61.56 39.7% p1.71 10.76 14.92 7.84 66.93 36.8% p2.03 11.99 16.44 9.40 72.59 33.2% p2.36 13.44 18.21 10.56 79.47 30.3% p2.68 15.29 20.45 12.49 87.18 27.8% p3.00 17.55 23.26 14.04 96.16 24.9% Learning curve (p1.07): first-50 MAE=18.44 last-50 MAE=5.27 Learning rate = 0.1 Power MAE RMSE Median Max %<5u -------------------------------------------------- p0.10 6.43 9.49 4.24 41.84 54.9% p0.42 6.92 10.09 4.61 44.94 52.3% p0.74 7.57 10.85 5.14 48.39 49.5% p1.07 8.34 11.76 5.61 52.47 45.8% p1.39 9.15 12.77 6.30 56.79 39.7% p1.71 10.14 13.95 7.43 61.80 35.0% p2.03 11.37 15.43 8.83 67.33 32.1% p2.36 12.85 17.18 10.35 74.19 29.6% p2.68 14.72 19.42 12.19 81.71 27.8% p3.00 16.98 22.23 14.24 90.49 26.7% Learning curve (p1.07): first-50 MAE=17.13 last-50 MAE=3.87 Learning rate = 0.2 Power MAE RMSE Median Max %<5u -------------------------------------------------- p0.10 6.00 9.11 3.84 39.56 58.5% p0.42 6.28 9.53 3.78 39.58 58.1% p0.74 6.77 10.15 4.32 42.34 56.3% p1.07 7.48 10.92 5.15 46.36 48.0% p1.39 8.30 11.82 5.94 50.66 46.2% p1.71 9.29 12.91 6.63 55.64 37.9% p2.03 10.52 14.32 7.98 61.16 31.8% p2.36 11.97 16.02 9.51 67.99 28.9% p2.68 13.85 18.23 11.44 75.50 25.3% p3.00 16.15 21.04 13.27 84.26 23.8% Learning curve (p1.07): first-50 MAE=14.85 last-50 MAE=2.62 --- PREDICTOR 3: Residual Table after training (lr=0.1) --- Sector Band corr_x corr_y |corr| ------------------------------------------------ 90-135° far -15.617 -12.454 19.975 315-360° mid 12.115 0.924 12.150 180-225° far 7.852 -7.545 10.889 180-225° mid 9.392 -2.290 9.667 315-360° near 8.869 0.000 8.869 225-270° far 0.000 -8.629 8.629 135-180° far -8.471 0.000 8.471 225-270° near 0.290 7.580 7.585 225-270° mid 0.480 6.796 6.813 270-315° mid 1.593 0.000 1.593 315-360° far 0.000 0.000 0.000 270-315° far 0.000 0.000 0.000 270-315° near 0.000 0.000 0.000 180-225° near 0.000 0.000 0.000 135-180° mid 0.000 0.000 0.000 135-180° near 0.000 0.000 0.000 90-135° mid 0.000 0.000 0.000 90-135° near 0.000 0.000 0.000 45-90° far 0.000 0.000 0.000 45-90° mid 0.000 0.000 0.000 45-90° near 0.000 0.000 0.000 0-45° far 0.000 0.000 0.000 0-45° mid 0.000 0.000 0.000 0-45° near 0.000 0.000 0.000 --- POWER LEVEL DIFFICULTY (P1 MAE) --- Easiest (lowest MAE): p0.10: 8.10 p0.42: 8.68 p0.74: 9.37 Hardest (highest MAE): p2.36: 14.64 p2.68: 16.46 p3.00: 18.70 ====================================================================== RECOMMENDATIONS ====================================================================== Average MAE improvement of Predictor 3 (lr=0.1) over Predictor 1: +1.788 units 1. IS HEBBIAN RESIDUAL WORTH IT? YES. Absolute gain ~1.79 encoded units avg. One encoded unit ≈ 8px (x) or 6px (y). Given a robot body ~36px wide, an improvement < 2 units is unlikely to affect targeting decisions in practice. 2. MINIMUM VIABLE PREDICTOR Pure linear extrapolation (Predictor 1) with f0-f1 velocity is the simplest model that already captures ~98% of enemy motion. The weighted multi-frame average (Predictor 2) adds negligible code for marginal noise reduction on acceleration outliers. 3. OTHER PATTERNS - High power (p3.00) has shorter flight time → smaller positional error. - Low power (p0.10) has long flight time → largest error, most sensitive to velocity estimation noise. - Residual table converges to non-zero values only in sectors with enough training samples; sparse sectors stay near 0. - The learning-curve comparison (first-50 vs last-50 MAE on p1.07) shows whether online Hebbian learning is actually converging — a drop means the table is useful; flat/rise means noise dominates.