Update app.py

app.py

@@ -30,49 +30,31 @@ american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead
 master_hold = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=694077504'
 
 @st.cache_data
-def game_betting_model():
+def init_baselines():
     sh = gc.open_by_url(master_hold)
     worksheet = sh.worksheet('Game_Betting')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('#DIV/0!', np.nan, inplace=True)
-    raw_display = raw_display.dropna()
-
-    return raw_display
-
-@st.cache_data
-def player_stat_table():
-    sh = gc.open_by_url(master_hold)
+    game_model = raw_display.dropna()
+    
     worksheet = sh.worksheet('Prop_Table')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
-    raw_display = raw_display.dropna()
-
-    return raw_display
-
-@st.cache_data
-def timestamp_table():
-    sh = gc.open_by_url(master_hold)
+    overall_stats = raw_display.dropna()
+    
     worksheet = sh.worksheet('DK_ROO')
-    raw_display = worksheet.acell('U2').value
-
-    return raw_display
-
-@st.cache_data
-def player_prop_table():
-    sh = gc.open_by_url(master_hold)
+    timestamp = worksheet.acell('U2').value
+    
     worksheet = sh.worksheet('prop_frame')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
-    raw_display = raw_display.dropna()
+    prop_frame = raw_display.dropna()
 
-    return raw_display
+    return game_model, overall_stats, timestamp, prop_frame
 
-game_model = game_betting_model()
-overall_stats = player_stat_table()
+game_model, overall_stats, timestamp, prop_frame = init_baselines()
 qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
 non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-timestamp = timestamp_table()
-prop_frame = player_prop_table()
 team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
@@ -89,12 +71,11 @@ with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
+              game_model, overall_stats, timestamp, prop_frame = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
     team_frame = game_model
     if line_var1 == 'Percentage':
@@ -118,12 +99,11 @@ with tab2:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
+              game_model, overall_stats, timestamp, prop_frame = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
     if split_var1 == 'Specific Teams':
         team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = qb_stats['Team'].unique(), key='team_var1')
@@ -145,12 +125,11 @@ with tab3:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
+              game_model, overall_stats, timestamp, prop_frame = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
     if split_var2 == 'Specific Teams':
         team_var2 = st.multiselect('Which teams would you like to include in the tables?', options = non_qb_stats['Team'].unique(), key='team_var2')
@@ -172,12 +151,11 @@ with tab4:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset4'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
+              game_model, overall_stats, timestamp, prop_frame = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
     with col2:
@@ -320,12 +298,11 @@ with tab5:
     st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
     if st.button("Reset Data/Load Data", key='reset5'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
+              game_model, overall_stats, timestamp, prop_frame = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
     with col2: