Update stuff_model/calculate_arm_angles.py

stuff_model/calculate_arm_angles.py

@@ -3,43 +3,58 @@ import numpy as np
 import requests
 from io import StringIO
 
-def calculate_arm_angles(df: pl.DataFrame,pitcher_id:int) -> pl.DataFrame:
-
-    url = f"https://baseballsavant.mlb.com/leaderboard/pitcher-arm-angles?batSide=&dateStart={df['game_date'][0]}&dateEnd={df['game_date'][-1]}&gameType=R&groupBy=&min=1&minGroupPitches=1&perspective=back&pitchHand=&pitchType=&season={int(df['game_date'][0][0:4])}&size=small&sort=ascending&team=&csv=true"
-    
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
-    }
-    
-    response = requests.get(url, headers=headers)
+def calculate_arm_angles(df: pl.DataFrame, pitcher_id: int) -> pl.DataFrame:
+    def fetch_arm_angle_data(url: str):
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
+        }
+        response = requests.get(url, headers=headers)
+        if not response.ok or "<html" in response.text.lower():
+            return None
+        return pl.read_csv(StringIO(response.text), truncate_ragged_lines=True)
+
+    date_start = df['game_date'][0]
+    date_end = df['game_date'][-1]
+    season = int(date_start[:4])
+    daily_check = date_start == date_end
+
+    # Try fetching current-season arm angle data
+    url = (
+        f"https://baseballsavant.mlb.com/leaderboard/pitcher-arm-angles"
+        f"?batSide=&dateStart={date_start}&dateEnd={date_end}&gameType=R&groupBy=&min=1"
+        f"&minGroupPitches=1&perspective=back&pitchHand=&pitchType=&season={season}"
+        f"&size=small&sort=ascending&team=&csv=true"
+    )
+    df_arm_angle = fetch_arm_angle_data(url)
 
     old_data = False
-    daily_check = df['game_date'][0] == df['game_date'][-1]
-    
-    # Assuming response.text contains the CSV formatted string
-    csv_data = response.text
-    
-    # Use StringIO to convert the string into a file-like object
-    data = StringIO(csv_data)
-    
-    # Read the CSV data into a DataFrame
-    df_arm_angle = pl.read_csv(data)
-    
-    if pitcher_id not in df_arm_angle["pitcher"]:
+    if df_arm_angle is None or pitcher_id not in df_arm_angle["pitcher"]:
         old_data = True
-        df_arm_angle = pl.read_csv('stuff_model/pitcher_arm_angles_2024.csv')
 
-        
-    #pitcher_id = 489446
+        # Fallback to saved CSVs if 2025 data isn't fetched or pitcher not found
+        try:
+            df_arm_angle_2025 = pl.read_csv("stuff_model/pitcher_arm_angles_2025.csv", truncate_ragged_lines=True)
+        except Exception as e:
+            raise RuntimeError("Failed to load fallback 2025 arm angle CSV.") from e
+
+        try:
+            df_arm_angle_2024 = pl.read_csv("stuff_model/pitcher_arm_angles_2024.csv", truncate_ragged_lines=True)
+            df_arm_angle_2024 = df_arm_angle_2024.cast(df_arm_angle_2025.schema)
+        except Exception as e:
+            raise RuntimeError("Failed to load or cast 2024 arm angle CSV.") from e
+
+        df_arm_angle = pl.concat([df_arm_angle_2025, df_arm_angle_2024]).unique(subset=["pitcher"], keep="first")
+
+    # Filter your tracking data
     df_filter = df.filter(pl.col("pitcher_id") == pitcher_id).drop_nulls(subset=["release_pos_x", "release_pos_z"])
-    # data = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}').json()
 
     if pitcher_id not in df_arm_angle["pitcher"]:
-
         data = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}').json()
         height_in = data['people'][0]['height']
         height = int(height_in.split("'")[0]) * 12 + int(height_in.split("'")[1].split('"')[0])
-        df_filter = (df_filter.with_columns(
+        
+        df_filter = (
+            df_filter.with_columns(
                 (pl.col("release_pos_x") * 12).alias("release_pos_x"),
                 (pl.col("release_pos_z") * 12).alias("release_pos_z"),
                 (pl.lit(height * 0.70)).alias("shoulder_pos"),
@@ -50,72 +65,44 @@ def calculate_arm_angles(df: pl.DataFrame,pitcher_id:int) -> pl.DataFrame:
             )
             .with_columns(
                 pl.struct(["Opp", "Adj"]).map_elements(lambda x: np.arctan2(x["Opp"], x["Adj"])).alias("arm_angle_rad")
-            ))
-
-        df_filter = (df_filter.with_columns(    
-            
+            )
+            .with_columns(
                 pl.col("arm_angle_rad").degrees().alias("arm_angle")
-            
-            #.drop(["Opp", "arm_angle_rad"])
-            ))
-            
-    else:
-        shoulder_x = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["relative_shoulder_x"][0]
-        shoulder_z = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["shoulder_z"][0]
-        rel_x = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["relative_release_ball_x"][0]
-        rel_z = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["release_ball_z"][0]
-
-
-        ball_angle = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["ball_angle"][0]
+            )
+        )
 
+    else:
+        row = df_arm_angle.filter(pl.col("pitcher") == pitcher_id).select([
+            "relative_shoulder_x", "shoulder_z", "relative_release_ball_x", "release_ball_z", "ball_angle"
+        ]).row(0)
+        shoulder_x, shoulder_z, rel_x, rel_z, ball_angle = row
         hyp = np.sqrt((rel_x - shoulder_x)**2 + (rel_z - shoulder_z)**2)
 
-        print(shoulder_x, shoulder_z)
-
-        df_filter = (df_filter.with_columns(
-
-            )
-            .with_columns(
+        df_filter = (
+            df_filter.with_columns(
                 (pl.col("release_pos_z") - shoulder_z).alias("Opp"),
-                (pl.lit(hyp)).alias("Hyp"),
+                pl.lit(hyp).alias("Hyp"),
             )
             .with_columns(
-                pl.struct(["Opp","Hyp"]).map_elements(lambda x: np.arcsin(x["Opp"] / x["Hyp"])).alias("arm_angle_rad")
+                pl.struct(["Opp", "Hyp"]).map_elements(lambda x: np.arcsin(x["Opp"] / x["Hyp"])).alias("arm_angle_rad")
             )
             .with_columns(
                 pl.col("arm_angle_rad").degrees().alias("arm_angle")
             )
-            #.drop(["Opp", "arm_angle_rad"])
-            )
-        
+        )
+
+        # Adjust based on data source freshness
         if old_data:
-            df_filter = df_filter.with_columns(
-                    ((pl.col("arm_angle") * 0.5) + (ball_angle * 0.5)).alias("arm_angle")
-                )
-            
+            df_filter = df_filter.with_columns(((pl.col("arm_angle") * 0.5) + (ball_angle * 0.5)).alias("arm_angle"))
         elif daily_check:
-            df_filter = df_filter.with_columns(
-                    ((pl.col("arm_angle") * 0.25) + (ball_angle * 0.75)).alias("arm_angle")
-                )
-            
+            df_filter = df_filter.with_columns(((pl.col("arm_angle") * 0.25) + (ball_angle * 0.75)).alias("arm_angle"))
         else:
-            df_filter = df_filter.with_columns(
-                    ((pl.col("arm_angle") * 0.0) + (ball_angle * 1)).alias("arm_angle")
-                )        
+            df_filter = df_filter.with_columns(((pl.col("arm_angle") * 0.0) + (ball_angle * 1)).alias("arm_angle"))
 
+        # Fill missing arm_angle values with mean
         valid_mean = df_filter["arm_angle"].fill_nan(None).drop_nulls().mean()
-
-        
         df_filter = df_filter.with_columns(
-            df_filter["arm_angle"]
-            .fill_nan(None)  # Convert NaN to null
-            .fill_null(valid_mean)  # Fill nulls with mean
+            df_filter["arm_angle"].fill_nan(None).fill_null(valid_mean)
         )
-   
-
-        #print([x for x in df_filter["arm_angle"]])
-                
-
 
-    
-    return df_filter
+    return df_filter