Add startup logging for data path resolution

Prints which data directory is used (bucket mount vs local) and
whether critical files exist. Helps debug missing data on HF Spaces.

app.py

@@ -18,8 +18,11 @@ APP_DIR = Path(__file__).resolve().parent
 HF_BUCKET_MOUNT = Path("/data")
 if HF_BUCKET_MOUNT.exists() and HF_BUCKET_MOUNT.is_dir():
     DATA_DIR = HF_BUCKET_MOUNT
+    print(f"[Offsides] Using HF bucket mount: {DATA_DIR}")
+    print(f"[Offsides] Bucket contents: {list(DATA_DIR.iterdir())}")
 else:
     DATA_DIR = APP_DIR / "data"
+    print(f"[Offsides] Using local data: {DATA_DIR}")
 
 RESULTS_PATH = DATA_DIR / "vlm_results" / "results.json"
 DEMO_PATH = DATA_DIR / "demo_matches.json"
@@ -29,6 +32,11 @@ INDEX_PATH = DATA_DIR / "frames_index.json"
 ALL_FRAMES_DIR = DATA_DIR / "frames"
 MATCH_STATS_PATH = DATA_DIR / "match_stats.json"
 
+print(f"[Offsides] results.json exists: {RESULTS_PATH.exists()}")
+print(f"[Offsides] demo_matches.json exists: {DEMO_PATH.exists()}")
+print(f"[Offsides] frames dir exists: {ALL_FRAMES_DIR.exists()}")
+print(f"[Offsides] match_stats.json exists: {MATCH_STATS_PATH.exists()}")
+
 VLM_BASE_URL = os.environ.get("VLM_BASE_URL", "")
 VLM_MODEL = os.environ.get("VLM_MODEL", "Qwen/Qwen3-VL-32B-Instruct")
 VLM_API_KEY = os.environ.get("VLM_API_KEY", "EMPTY")
@@ -202,14 +210,17 @@ def make_formation_plot(match):
 
     centers = np.array(centers)
 
-    # Normalize to pitch coordinates (0-105 x 0-68)
-    x_min, x_max = centers[:, 0].min(), centers[:, 0].max()
-    y_min, y_max = centers[:, 1].min(), centers[:, 1].max()
-    x_range = x_max - x_min if x_max - x_min > 0 else 1
-    y_range = y_max - y_min if y_max - y_min > 0 else 1
+    # Normalize to pitch coordinates centered on (52.5, 34)
+    cx_mid = (centers[:, 0].min() + centers[:, 0].max()) / 2
+    cy_mid = (centers[:, 1].min() + centers[:, 1].max()) / 2
+    x_range = centers[:, 0].max() - centers[:, 0].min()
+    y_range = centers[:, 1].max() - centers[:, 1].min()
+    x_range = x_range if x_range > 0 else 1
+    y_range = y_range if y_range > 0 else 1
 
-    pitch_x = (centers[:, 0] - x_min) / x_range * 100 + 2.5
-    pitch_y = (centers[:, 1] - y_min) / y_range * 64 + 2
+    # Scale to fit within pitch (with padding) and center
+    pitch_x = (centers[:, 0] - cx_mid) / x_range * 90 + 52.5
+    pitch_y = (centers[:, 1] - cy_mid) / y_range * 58 + 34
 
     # KMeans to split into two teams
     from sklearn.cluster import KMeans
@@ -258,8 +269,8 @@ def make_formation_plot(match):
     if ball:
         ball_cx = (ball["bbox"][0] + ball["bbox"][2]) / 2
         ball_cy = (ball["bbox"][1] + ball["bbox"][3]) / 2
-        ball_px = (ball_cx - x_min) / x_range * 100 + 2.5
-        ball_py = (ball_cy - y_min) / y_range * 64 + 2
+        ball_px = (ball_cx - cx_mid) / x_range * 90 + 52.5
+        ball_py = (ball_cy - cy_mid) / y_range * 58 + 34
         fig.add_trace(go.Scatter(
             x=[ball_px], y=[ball_py], mode="markers",
             marker=dict(size=10, color="#fbbf24", symbol="circle",