Update app.py

app.py

@@ -34,101 +34,113 @@ def init_conn():
 
 gcservice_account = init_conn()
 
-NHL_Data = 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=1158895881'
+NBA_Data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'
 
 @st.cache_resource(ttl = 600)
 def init_baselines():
-    sh = gcservice_account.open_by_url(NHL_Data)
+    sh = gcservice_account.open_by_url(NBA_Data)
     
     worksheet = sh.worksheet('Gamelog')
     raw_display = pd.DataFrame(worksheet.get_values())
     raw_display.columns = raw_display.iloc[0]
     raw_display = raw_display[1:]
     raw_display = raw_display.reset_index(drop=True)
-    gamelog_table = raw_display[raw_display['Player'] != ""]
-    gamelog_table = gamelog_table[['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
-                                   'Shots', 'SH%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
-                                   'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
-                                   'Faceoffs Lost', 'Faceoffs %']]
-    data_cols = gamelog_table.columns.drop(['Player', 'Team', 'Position', 'Date'])
+    gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
+    gamelog_table = gamelog_table[['PLAYER_NAME', 'TEAM_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP', 'MIN', 'touches', 'PTS', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A',
+                                   'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB',
+                                   'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy']]
+    gamelog_table['assists'].replace("", 0, inplace=True)
+    gamelog_table['reboundChancesTotal'].replace("", 0, inplace=True)
+    gamelog_table['passes'].replace("", 0, inplace=True)
+    gamelog_table['touches'].replace("", 0, inplace=True)
+    gamelog_table['Fantasy'].replace("", 0, inplace=True)
+    gamelog_table['FD_Fantasy'].replace("", 0, inplace=True)
+    gamelog_table['REB'] = gamelog_table['REB'].astype(int)
+    gamelog_table['assists'] = gamelog_table['assists'].astype(int)
+    gamelog_table['reboundChancesTotal'] = gamelog_table['reboundChancesTotal'].astype(int)
+    gamelog_table['passes'] = gamelog_table['passes'].astype(int)
+    gamelog_table['touches'] = gamelog_table['touches'].astype(int)
+    gamelog_table['Fantasy'] = gamelog_table['Fantasy'].astype(float)
+    gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float)
+    gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal']
+    gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
+    gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
+    gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
+    data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'TEAM_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
     gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
-    gamelog_table['Date'] = pd.to_datetime(gamelog_table['Date']).dt.date
-    # gamelog_table['Shots'].replace("", 0, inplace=True)
-    # gamelog_table['Shots Blocked'].replace("", 0, inplace=True)
-    # gamelog_table['Goals'].replace("", 0, inplace=True)
-    # gamelog_table['Total Points'].replace("", 0, inplace=True)
-    # gamelog_table['Shots'] = gamelog_table['Shots'].astype(int)
-    # gamelog_table['Shots Blocked'] = gamelog_table['Shots Blocked'].astype(int)
-    # gamelog_table['Goals'] = gamelog_table['Goals'].astype(int)
-    # gamelog_table['Total Points'] = gamelog_table['Total Points'].astype(int)
-    gamelog_table['dk_shots_bonus'] = np.where((gamelog_table['Shots'] >= 5), 1, 0)
-    gamelog_table['dk_blocks_bonus'] = np.where((gamelog_table['Shots Blocked'] >= 3), 1, 0)
-    gamelog_table['dk_goals_bonus'] = np.where((gamelog_table['Goals'] >= 3), 1, 0)
-    gamelog_table['dk_points_bonus'] = np.where((gamelog_table['Total Points'] >= 3), 1, 0)
-    gamelog_table['dk_fantasy'] = sum([(gamelog_table['Goals'] * 8.5), (gamelog_table['Total Assists'] * 5), (gamelog_table['Shots'] * 1.5),
-                                       (gamelog_table['Shots Blocked'] * 1.3), (gamelog_table['dk_shots_bonus'] * 3), (gamelog_table['dk_blocks_bonus'] * 3),
-                                       (gamelog_table['dk_goals_bonus'] * 3), (gamelog_table['dk_points_bonus'] * 3)]).astype(float).round(2)
-    gamelog_table['fd_fantasy'] = sum([(gamelog_table['Goals'] * 12), (gamelog_table['Total Assists'] * 8), (gamelog_table['Shots'] * 1.6),
-                                       (gamelog_table['Shots Blocked'] * 1.6)]).astype(float).round(2)
+    gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
     
-    
-    
-    gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
-                                            'Shots', 'Shots%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
-                                            'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
-                                            'Faceoffs Lost', 'Faceoffs%'], axis=1)
+    gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
+                                            'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+                                            'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
+                                            'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
     
     return gamelog_table
 
 @st.cache_data(show_spinner=False)
 def seasonlong_build(data_sample):
     season_long_table = data_sample[['Player', 'Team']]
-    season_long_table['TOI'] = data_sample.groupby(['Player', 'Team'], sort=False)['TOI'].transform('mean').astype(float)
-    season_long_table['Goals'] = data_sample.groupby(['Player', 'Team'], sort=False)['Goals'].transform('mean').astype(float)
-    season_long_table['Total Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Assists'].transform('mean').astype(float)
-    season_long_table['First Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['First Assists'].transform('mean').astype(float)
-    season_long_table['Second Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['Second Assists'].transform('mean').astype(float)
-    season_long_table['Total Points'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Points'].transform('mean').astype(float)
-    season_long_table['IPP'] = data_sample.groupby(['Player', 'Team'], sort=False)['IPP'].transform('mean').astype(float)
-    season_long_table['Shots'] = data_sample.groupby(['Player', 'Team'], sort=False)['Shots'].transform('mean').astype(float)
-    season_long_table['ixG'] = data_sample.groupby(['Player', 'Team'], sort=False)['ixG'].transform('mean').astype(float)
-    season_long_table['iCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iCF'].transform('mean').astype(float)
-    season_long_table['iFF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iFF'].transform('mean').astype(float)
-    season_long_table['iSCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iSCF'].transform('mean').astype(float)
-    season_long_table['iHDCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iHDCF'].transform('mean').astype(float)
-    season_long_table['Rush Attempts'] = data_sample.groupby(['Player', 'Team'], sort=False)['Rush Attempts'].transform('mean').astype(float)
-    season_long_table['Rebounds Created'] = data_sample.groupby(['Player', 'Team'], sort=False)['Rebounds Created'].transform('mean').astype(float)
-    season_long_table['PIM'] = data_sample.groupby(['Player', 'Team'], sort=False)['PIM'].transform('mean').astype(float)
-    season_long_table['Total Penalties'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Penalties'].transform('mean').astype(float)
-    season_long_table['Minor'] = data_sample.groupby(['Player', 'Team'], sort=False)['Minor'].transform('mean').astype(float)
-    season_long_table['Major'] = data_sample.groupby(['Player', 'Team'], sort=False)['Major'].transform('mean').astype(float)
-    season_long_table['Penalties Drawn'] = data_sample.groupby(['Player', 'Team'], sort=False)['Penalties Drawn'].transform('mean').astype(float)
-    season_long_table['Giveaways'] = data_sample.groupby(['Player', 'Team'], sort=False)['Giveaways'].transform('mean').astype(float)
-    season_long_table['Takeaways'] = data_sample.groupby(['Player', 'Team'], sort=False)['Takeaways'].transform('mean').astype(float)
-    season_long_table['Hits'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hits'].transform('mean').astype(float)
-    season_long_table['Hits Taken'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hits Taken'].transform('mean').astype(float)
-    season_long_table['Shots Blocked'] = data_sample.groupby(['Player', 'Team'], sort=False)['Shots Blocked'].transform('mean').astype(float)
-    season_long_table['Faceoffs Won'] = data_sample.groupby(['Player', 'Team'], sort=False)['Faceoffs Won'].transform('mean').astype(float)
-    season_long_table['Faceoffs Lost'] = data_sample.groupby(['Player', 'Team'], sort=False)['Faceoffs Lost'].transform('mean').astype(float)
+    season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
+    season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
+    season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float)
+    season_long_table['FGM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('mean').astype(float)
+    season_long_table['FGA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('mean').astype(float)
+    season_long_table['FG%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('sum').astype(int) /
+                                   data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('sum').astype(int))
+    season_long_table['FG3M'] = data_sample.groupby(['Player', 'Season'], sort=False)['FG3M'].transform('mean').astype(float)
+    season_long_table['FG3A'] = data_sample.groupby(['Player', 'Season'], sort=False)['FG3A'].transform('mean').astype(float)
+    season_long_table['FG3%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FG3M'].transform('sum').astype(int) /
+                                   data_sample.groupby(['Player', 'Season'], sort=False)['FG3A'].transform('sum').astype(int))
+    season_long_table['FTM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FTM'].transform('mean').astype(float)
+    season_long_table['FTA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FTA'].transform('mean').astype(float)
+    season_long_table['FT%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FTM'].transform('sum').astype(int) /
+                                   data_sample.groupby(['Player', 'Season'], sort=False)['FTA'].transform('sum').astype(int))
+    season_long_table['OREB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['OREB Chance'].transform('mean').astype(float)
+    season_long_table['OREB'] = data_sample.groupby(['Player', 'Season'], sort=False)['OREB'].transform('mean').astype(float)
+    season_long_table['DREB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['DREB Chance'].transform('mean').astype(float)
+    season_long_table['DREB'] = data_sample.groupby(['Player', 'Season'], sort=False)['DREB'].transform('mean').astype(float)
+    season_long_table['REB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('mean').astype(float)
+    season_long_table['REB'] = data_sample.groupby(['Player', 'Season'], sort=False)['REB'].transform('mean').astype(float)
+    season_long_table['Passes'] = data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('mean').astype(float)
+    season_long_table['Alt Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['Alt Assists'].transform('mean').astype(float)
+    season_long_table['FT Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['FT Assists'].transform('mean').astype(float)
+    season_long_table['Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('mean').astype(float)
+    season_long_table['Stl'] = data_sample.groupby(['Player', 'Season'], sort=False)['Stl'].transform('mean').astype(float)
+    season_long_table['Blk'] = data_sample.groupby(['Player', 'Season'], sort=False)['Blk'].transform('mean').astype(float)
+    season_long_table['Tov'] = data_sample.groupby(['Player', 'Season'], sort=False)['Tov'].transform('mean').astype(float)
+    season_long_table['PF'] = data_sample.groupby(['Player', 'Season'], sort=False)['PF'].transform('mean').astype(float)
+    season_long_table['DD'] = data_sample.groupby(['Player', 'Season'], sort=False)['DD'].transform('mean').astype(float)
+    season_long_table['TD'] = data_sample.groupby(['Player', 'Season'], sort=False)['TD'].transform('mean').astype(float)
+    season_long_table['Fantasy'] = data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('mean').astype(float)
+    season_long_table['FD_Fantasy'] = data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('mean').astype(float)
+    season_long_table['Rebound%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['REB'].transform('sum').astype(int) /
+                                     data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('sum').astype(int))
+    season_long_table['Assists/Pass'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('sum').astype(int) /
+                                             data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('sum').astype(int))
+    season_long_table['Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('sum').astype(int) /
+                                              data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int))
+    season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
+                                                 data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int))
     season_long_table = season_long_table.drop_duplicates(subset='Player')
     
-    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
-                                                    'IPP', 'Shots', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties',
-                                                    'Minor', 'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
-                                                    'Faceoffs Lost'], axis=1)
+    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
+                                                    'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+                                                    'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
+                                                    'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
 
     return season_long_table
 
 @st.cache_data(show_spinner=False)
 def run_fantasy_corr(data_sample):
     cor_testing = data_sample
+    cor_testing = cor_testing[cor_testing['Season'] == '22023']
     date_list = cor_testing['Date'].unique().tolist()
     player_list = cor_testing['Player'].unique().tolist()
     corr_frame = pd.DataFrame()
     corr_frame['DATE'] = date_list
     for player in player_list:
         player_testing = cor_testing[cor_testing['Player'] == player]
-        fantasy_map = dict(zip(player_testing['Date'], player_testing['dk_fantasy']))
+        fantasy_map = dict(zip(player_testing['Date'], player_testing['Fantasy']))
         corr_frame[player] = corr_frame['DATE'].map(fantasy_map)
     players_fantasy = corr_frame.drop('DATE', axis=1)
     corrM = players_fantasy.corr()
@@ -138,13 +150,14 @@ def run_fantasy_corr(data_sample):
 @st.cache_data(show_spinner=False)
 def run_min_corr(data_sample):
     cor_testing = data_sample
+    cor_testing = cor_testing[cor_testing['Season'] == '22023']
     date_list = cor_testing['Date'].unique().tolist()
     player_list = cor_testing['Player'].unique().tolist()
     corr_frame = pd.DataFrame()
     corr_frame['DATE'] = date_list
     for player in player_list:
         player_testing = cor_testing[cor_testing['Player'] == player]
-        fantasy_map = dict(zip(player_testing['Date'], player_testing['TOI']))
+        fantasy_map = dict(zip(player_testing['Date'], player_testing['Min']))
         corr_frame[player] = corr_frame['DATE'].map(fantasy_map)
     players_fantasy = corr_frame.drop('DATE', axis=1)
     corrM = players_fantasy.corr()
@@ -191,10 +204,10 @@ with tab1:
         split_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3')
         
         if split_var3 == 'Specific Dates':
-            low_date = st.date_input('Min Date:', value=None, format="MM/DD/YYYY", key='low_date')
+            low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date')
             if low_date is not None:
                 low_date = pd.to_datetime(low_date).date()
-            high_date = st.date_input('Max Date:', value=None, format="MM/DD/YYYY", key='high_date')
+            high_date = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date')
             if high_date is not None:
                 high_date = pd.to_datetime(high_date).date()
         elif split_var3 == 'All':
@@ -208,15 +221,15 @@ with tab1:
         elif split_var4 == 'All':
             player_var1 = total_players
         
-        min_var1 = st.slider("Is there a certain TOI range you want to view?", 0, 50, (0, 50), key='min_var1')
+        min_var1 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var1')
     
     with col2:
         if split_var1 == 'Season Logs':
             display = st.container()
             gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date]
             gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date]
-            gamelog_table = gamelog_table[gamelog_table['TOI'] >= min_var1[0]]
-            gamelog_table = gamelog_table[gamelog_table['TOI'] <= min_var1[1]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var1[0]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var1[1]]
             gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
             gamelog_table = gamelog_table[gamelog_table['Player'].isin(player_var1)]
             season_long_table = seasonlong_build(gamelog_table)
@@ -226,8 +239,8 @@ with tab1:
         elif split_var1 == 'Gamelogs':
             gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date]
             gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date]
-            gamelog_table = gamelog_table[gamelog_table['TOI'] >= min_var1[0]]
-            gamelog_table = gamelog_table[gamelog_table['TOI'] <= min_var1[1]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var1[0]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var1[1]]
             gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
             gamelog_table = gamelog_table[gamelog_table['Player'].isin(player_var1)]
             gamelog_table = gamelog_table.reset_index(drop=True)
@@ -263,7 +276,7 @@ with tab2:
                   total_players = indv_players.Player.values.tolist()
                   total_dates = gamelog_table.Date.values.tolist()
         
-        corr_var = st.radio("Are you correlating fantasy or TOI?", ('Fantasy', 'TOI'), key='corr_var')
+        corr_var = st.radio("Are you correlating fantasy or minutes?", ('Fantasy', 'Minutes'), key='corr_var')
         
         split_var1_t2 = st.radio("Would you like to view specific teams or specific players?", ('Specific Teams', 'Specific Players'), key='split_var1_t2')
         
@@ -275,17 +288,17 @@ with tab2:
         split_var2_t2 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3_t2')
         
         if split_var2_t2 == 'Specific Dates':
-            low_date_t2 = st.date_input('Min Date:', value=None, format="MM/DD/YYYY", key='low_date_t2')
+            low_date_t2 = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date_t2')
             if low_date_t2 is not None:
                 low_date_t2 = pd.to_datetime(low_date_t2).date()
-            high_date_t2 = st.date_input('Max Date:', value=None, format="MM/DD/YYYY", key='high_date_t2')
+            high_date_t2 = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date_t2')
             if high_date_t2 is not None:
                 high_date_t2 = pd.to_datetime(high_date_t2).date()
         elif split_var2_t2 == 'All':
             low_date_t2 = gamelog_table['Date'].min()
             high_date_t2 = gamelog_table['Date'].max()
         
-        min_var1_t2 = st.slider("Is there a certain minutes range you want to view?", 0, 50, (0, 50), key='min_var1_t2')
+        min_var1_t2 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var1_t2')
     
     with col2:
         if split_var1_t2 == 'Specific Teams':
@@ -293,12 +306,12 @@ with tab2:
             gamelog_table = gamelog_table.sort_values(by='Fantasy', ascending=False)
             gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date_t2]
             gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date_t2]
-            gamelog_table = gamelog_table[gamelog_table['TOI'] >= min_var1_t2[0]]
-            gamelog_table = gamelog_table[gamelog_table['TOI'] <= min_var1_t2[1]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var1_t2[0]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var1_t2[1]]
             gamelog_table = gamelog_table[gamelog_table['Team'].isin(corr_var1_t2)]
             if corr_var == 'Fantasy':
                 corr_display = run_fantasy_corr(gamelog_table)
-            elif corr_var == 'TOI':
+            elif corr_var == 'Minutes':
                 corr_display = run_min_corr(gamelog_table)
             display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)
             
@@ -307,11 +320,11 @@ with tab2:
             gamelog_table = gamelog_table.sort_values(by='Fantasy', ascending=False)
             gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date_t2]
             gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date_t2]
-            gamelog_table = gamelog_table[gamelog_table['TOI'] >= min_var1_t2[0]]
-            gamelog_table = gamelog_table[gamelog_table['TOI'] <= min_var1_t2[1]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var1_t2[0]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var1_t2[1]]
             gamelog_table = gamelog_table[gamelog_table['Player'].isin(corr_var1_t2)]
             if corr_var == 'Fantasy':
                 corr_display = run_fantasy_corr(gamelog_table)
-            elif corr_var == 'TOI':
+            elif corr_var == 'Minutes':
                 corr_display = run_min_corr(gamelog_table)
             display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)