Update app.py

app.py

@@ -107,33 +107,34 @@ selected_fielder = st.selectbox('',list(fielders_reversed.keys()))
 fielder_select = fielders_reversed[selected_fielder]
 
 # Define the URL
-url = f'https://baseballsavant.mlb.com/savant-player/{fielder_select}?stats=statcast-r-fielding-mlb'
+url = f"https://baseballsavant.mlb.com/player-services/range?playerId={player_id}&season=2024&playerType=fielder"
 
-# Make the GET request
-response = requests.get(url)
+# # Make the GET request
+# response = requests.get(url)
 
-# Check if the request was successful
-if response.status_code == 200:
-    print(response.status_code)
-else:
-    print(f"Failed to fetch data, status code: {response.status_code}")
+# # Check if the request was successful
+# if response.status_code == 200:
+#     print(response.status_code)
+# else:
+#     print(f"Failed to fetch data, status code: {response.status_code}")
 
-# Format the string as a JSON array
-text = response.text
-dict_catch = text.split('rangeLine')[1].split('data')[0].split('[')[1].split(']')[0]
-formatted_dict_catch = f'[{dict_catch}]'
+# # Format the string as a JSON array
+# text = response.text
+# dict_catch = text.split('rangeLine')[1].split('data')[0].split('[')[1].split(']')[0]
+# formatted_dict_catch = f'[{dict_catch}]'
 
-# Convert the formatted string to a list of dictionaries
-dict_catch_list = json.loads(formatted_dict_catch)
+# # Convert the formatted string to a list of dictionaries
+# dict_catch_list = json.loads(formatted_dict_catch)
 
-df_catch = pd.DataFrame(dict_catch_list)
+data = requests.get(url).json()
+df_catch = pd.DataFrame(data)
 df_catch['hang_time'] = df_catch['hang_time'].astype(float).round(1)
 df_catch['distance'] = df_catch['distance'].astype(float).round(1)
 
 df_merge = df.merge(df_catch, on='play_id', how='right', suffixes=('', '_fielder')).reset_index(drop=True)
 
 # Format the 'catch_rate' column as a percentage
-df_merge['catch_rate'] = df_merge['catch_rate'].astype(float).apply(lambda x: f"{x:.0%}")
+#df_merge['catch_rate'] = df_merge['catch_rate'].astype(float).apply(lambda x: f"{x:.0%}")
 
 df_merge['pos'] = df_merge['pos'].astype(int)
 df_merge['Position'] = df_merge['pos'].map(pos_dict)