Update app.py

app.py

@@ -42,174 +42,154 @@ def init_conn():
 
 gcservice_account, client, db = init_conn()
 
-NBA_Data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'
-
 percentages_format = {'PG': '{:.2%}', 'SG': '{:.2%}', 'SF': '{:.2%}', 'PF': '{:.2%}', 'C': '{:.2%}'}
 
 @st.cache_resource(ttl = 599)
 def init_baselines():
-    sh = gcservice_account.open_by_url(NBA_Data)
-    collection = db["gamelog"] 
+    collection = db["MLB_Hitters_DB"] 
     cursor = collection.find()  # Finds all documents in the collection
     
     raw_display = pd.DataFrame(list(cursor))
-    gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
-    gamelog_table = gamelog_table[['PLAYER_NAME', 'POS', 'GAME_ID', 'TEAM_NAME', 'OPP_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', 'FPPM']]
-    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['MIN'].replace("", 0, inplace=True)
-    gamelog_table['Fantasy'].replace("", 0, inplace=True)
-    gamelog_table['FD_Fantasy'].replace("", 0, inplace=True)
-    gamelog_table['FPPM'].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['MIN'] = gamelog_table['MIN'].astype(int)
-    gamelog_table['Fantasy'] = gamelog_table['Fantasy'].astype(float)
-    gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float)
-    gamelog_table['FPPM'] = gamelog_table['FPPM'].astype(float)
-    gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal']
-    gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
-    gamelog_table['Touch_per_min'] = gamelog_table['touches'] / gamelog_table['MIN']
-    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', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
-    gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
-    gamelog_table['team_score'] = gamelog_table.groupby(['TEAM_NAME', 'GAME_ID'], sort=False)['PTS'].transform('sum')
-    gamelog_table['opp_score'] = gamelog_table.groupby(['GAME_ID'], sort=False)['PTS'].transform('sum') - gamelog_table['team_score']
-    gamelog_table['spread'] = (gamelog_table['opp_score'] - gamelog_table['team_score']).abs()
-    gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
-    
-    spread_dict = dict(zip(gamelog_table['GAME_ID'], gamelog_table['spread']))
+    hitter_gamelog_table = raw_display[raw_display['NameASCII'] != ""]
+    hitter_gamelog_table = hitter_gamelog_table[['NameASCII', 'Team', 'Date', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                                                 'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%']]
     
-    gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'game_id', 'Team', 'Opp', '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', 'FPPM',
-                                            'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch', 'team_score', 'opp_score', 'spread'], axis=1)
+    data_cols = hitter_gamelog_table.columns.drop(['NameASCII', 'Team', 'Date'])
+    hitter_gamelog_table[data_cols] = hitter_gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
     
-    worksheet = sh.worksheet('Rotations')
-    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)
-    rot_table = raw_display[raw_display['Player'] != ""]
-    rot_table = rot_table[['Player', 'Team', 'PG', 'SG', 'SF', 'PF', 'C', 'Given Pos']]
-    data_cols = ['PG', 'SG', 'SF', 'PF', 'C']
-    rot_table[data_cols] = rot_table[data_cols].apply(pd.to_numeric, errors='coerce')
-    rot_table = rot_table[rot_table['Player'] != 0]
+    hitter_gamelog_table = hitter_gamelog_table.set_axis(['Player', 'Team', 'Date', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                                                          'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%'], axis=1)
     
     collection = db["rotations"] 
     cursor = collection.find()  # Finds all documents in the collection
     
     raw_display = pd.DataFrame(list(cursor))
-    game_rot = raw_display[raw_display['PLAYER_NAME'] != ""]
-    data_cols = game_rot.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_ABBREVIATION', 'OPP_ABBREVIATION', 'TEAM_NAME', 'OPP_NAME', 'GAME_DATE',
-                                       'MATCHUP', 'WL', 'backlog_lookup', 'Task', 'game_players'])
-    game_rot[data_cols] = game_rot[data_cols].apply(pd.to_numeric, errors='coerce')
-    game_rot['spread'] = game_rot['GAME_ID'].map(spread_dict)
-    game_rot['GAME_DATE'] = pd.to_datetime(game_rot['GAME_DATE']).dt.date
+    pitcher_gamelog_table = raw_display[raw_display['NameASCII'] != ""]
+    
+    pitcher_gamelog_table = pitcher_gamelog_table[['NameASCII', 'Team', 'Date', 'G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                                                   'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
+                                                   'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA (sc)', 'vFT (sc)', 'vFC (sc)', 'vFS (sc)', 'vFO (sc)', 'vSI (sc)',
+                                                   'vSL (sc)', 'vCU (sc)', 'vKC (sc)', 'vEP (sc)', 'vCH (sc)', 'vSC (sc)', 'vKN (sc)']]
+    
+    pitcher_gamelog_table.replace("", np.nan, inplace=True)
+    data_cols = pitcher_gamelog_table.columns.drop(['NameASCII', 'Team', 'Date'])
+    pitcher_gamelog_table[data_cols] = pitcher_gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
+    
+    pitcher_gamelog_table = pitcher_gamelog_table.set_axis(['Player', 'Team', 'Date', 'G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                                                            'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
+                                                            'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA', 'vFT', 'vFC', 'vFS', 'vFO', 'vSI',
+                                                            'vSL', 'vCU', 'vKC', 'vEP', 'vCH', 'vSC', 'vKN'], axis=1)
     
-    timestamp = gamelog_table['Date'].max()
+    timestamp = pitcher_gamelog_table['Date'].max()
     
-    return gamelog_table, rot_table, game_rot, timestamp
+    return hitter_gamelog_table, pitcher_gamelog_table, timestamp
 
 @st.cache_data(show_spinner=False)
-def seasonlong_build(data_sample):
-    season_long_table = data_sample[['Player', 'Pos', 'Team']]
-    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['Touch/Min'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int) /
-                                      data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('sum').astype(int))
-    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['FPPM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FPPM'].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))
+def hitter_seasonlong_build(data_sample):
+    season_long_table = data_sample[['Player', 'Team']]
+    season_long_table['G'] = data_sample.groupby(['Player', 'Team'], sort=False)['G'].transform('sum').astype(int)
+    season_long_table['AB'] = data_sample.groupby(['Player', 'Team'], sort=False)['AB'].transform('sum').astype(int)
+    season_long_table['PA'] = data_sample.groupby(['Player', 'Team'], sort=False)['PA'].transform('sum').astype(int)
+    season_long_table['H'] = data_sample.groupby(['Player', 'Team'], sort=False)['H'].transform('sum').astype(int)
+    season_long_table['1B'] = data_sample.groupby(['Player', 'Team'], sort=False)['1B'].transform('sum').astype(int)
+    season_long_table['2B'] = data_sample.groupby(['Player', 'Team'], sort=False)['2B'].transform('sum').astype(int)
+    season_long_table['3B'] = data_sample.groupby(['Player', 'Team'], sort=False)['3B'].transform('sum').astype(int)
+    season_long_table['HR'] = data_sample.groupby(['Player', 'Team'], sort=False)['HR'].transform('sum').astype(int)
+    season_long_table['R'] = data_sample.groupby(['Player', 'Team'], sort=False)['R'].transform('sum').astype(int)
+    season_long_table['RBI'] = data_sample.groupby(['Player', 'Team'], sort=False)['RBI'].transform('sum').astype(int)
+    season_long_table['BB'] = data_sample.groupby(['Player', 'Team'], sort=False)['BB'].transform('sum').astype(int)
+    season_long_table['IBB'] = data_sample.groupby(['Player', 'Team'], sort=False)['IBB'].transform('sum').astype(int)
+    season_long_table['SO'] = data_sample.groupby(['Player', 'Team'], sort=False)['SO'].transform('sum').astype(int)
+    season_long_table['HBP'] = data_sample.groupby(['Player', 'Team'], sort=False)['HBP'].transform('sum').astype(int)
+    season_long_table['SF'] = data_sample.groupby(['Player', 'Team'], sort=False)['SF'].transform('sum').astype(int)
+    season_long_table['SH'] = data_sample.groupby(['Player', 'Team'], sort=False)['SH'].transform('sum').astype(int)
+    season_long_table['GDP'] = data_sample.groupby(['Player', 'Team'], sort=False)['GDP'].transform('sum').astype(int)
+    season_long_table['SB'] = data_sample.groupby(['Player', 'Team'], sort=False)['SB'].transform('sum').astype(int)
+    season_long_table['CS'] = data_sample.groupby(['Player', 'Team'], sort=False)['CS'].transform('sum').astype(int)
+    season_long_table['Avg AVG'] = data_sample.groupby(['Player', 'Team'], sort=False)['AVG'].transform('mean').astype(int)
+    season_long_table['Avg SLG'] = data_sample.groupby(['Player', 'Team'], sort=False)['SLG'].transform('mean').astype(int)
+    season_long_table['Avg wRC+'] = data_sample.groupby(['Player', 'Team'], sort=False)['wRC+'].transform('mean').astype(int)
+    season_long_table['Avg LD%'] = data_sample.groupby(['Player', 'Team'], sort=False)['LD%'].transform('mean').astype(int)
+    season_long_table['Avg GB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['GB%'].transform('mean').astype(float)
+    season_long_table['Avg FB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['FB%'].transform('mean').astype(float)
+    season_long_table['Avg Hard%'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hard%'].transform('mean').astype(float)
+    season_long_table['Barrels'] = data_sample.groupby(['Player', 'Team'], sort=False)['Barrels'].transform('sum').astype(int)
+    season_long_table['Avg Barrel%'] = data_sample.groupby(['Player', 'Team'], sort=False)['Barrel%'].transform('mean').astype(float)
     season_long_table = season_long_table.drop_duplicates(subset='Player')
 
-    season_long_table = season_long_table.sort_values(by='Fantasy', ascending=False)
+    season_long_table = season_long_table.sort_values(by='Avg wRC+', ascending=False)
     
-    season_long_table = season_long_table.set_axis(['Player', 'Pos', 'Team', 'Min', 'Touches', 'Touch/Min', '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',
-                                                    'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
+    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Date', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                                                    'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%'], 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['Fantasy']))
-        corr_frame[player] = corr_frame['DATE'].map(fantasy_map)
-    players_fantasy = corr_frame.drop('DATE', axis=1)
-    corrM = players_fantasy.corr()
-    
-    return corrM
+def pitcher_seasonlong_build(data_sample):
+    season_long_table = data_sample[['Player', 'Team']]
+    season_long_table['G'] = data_sample.groupby(['Player', 'Team'], sort=False)['G'].transform('sum').astype(int)
+    season_long_table['GS'] = data_sample.groupby(['Player', 'Team'], sort=False)['GS'].transform('sum').astype(int)
+    season_long_table['CG'] = data_sample.groupby(['Player', 'Team'], sort=False)['CG'].transform('sum').astype(int)
+    season_long_table['W'] = data_sample.groupby(['Player', 'Team'], sort=False)['W'].transform('sum').astype(int)
+    season_long_table['L'] = data_sample.groupby(['Player', 'Team'], sort=False)['L'].transform('sum').astype(int)
+    season_long_table['Avg ERA'] = data_sample.groupby(['Player', 'Team'], sort=False)['ERA'].transform('mean').astype(float)
+    season_long_table['ShO'] = data_sample.groupby(['Player', 'Team'], sort=False)['ShO'].transform('sum').astype(int)
+    season_long_table['SV'] = data_sample.groupby(['Player', 'Team'], sort=False)['SV'].transform('sum').astype(int)
+    season_long_table['HLD'] = data_sample.groupby(['Player', 'Team'], sort=False)['HLD'].transform('sum').astype(int)
+    season_long_table['BS'] = data_sample.groupby(['Player', 'Team'], sort=False)['BS'].transform('sum').astype(int)
+    season_long_table['IP'] = data_sample.groupby(['Player', 'Team'], sort=False)['IP'].transform('sum').astype(int)
+    season_long_table['TBF'] = data_sample.groupby(['Player', 'Team'], sort=False)['TBF'].transform('sum').astype(int)
+    season_long_table['H'] = data_sample.groupby(['Player', 'Team'], sort=False)['H'].transform('sum').astype(int)
+    season_long_table['R'] = data_sample.groupby(['Player', 'Team'], sort=False)['R'].transform('sum').astype(int)
+    season_long_table['ER'] = data_sample.groupby(['Player', 'Team'], sort=False)['ER'].transform('sum').astype(int)
+    season_long_table['HR'] = data_sample.groupby(['Player', 'Team'], sort=False)['HR'].transform('sum').astype(int)
+    season_long_table['BB'] = data_sample.groupby(['Player', 'Team'], sort=False)['BB'].transform('sum').astype(int)
+    season_long_table['IBB'] = data_sample.groupby(['Player', 'Team'], sort=False)['IBB'].transform('sum').astype(int)
+    season_long_table['HBP'] = data_sample.groupby(['Player', 'Team'], sort=False)['HBP'].transform('sum').astype(int)
+    season_long_table['WP'] = data_sample.groupby(['Player', 'Team'], sort=False)['WP'].transform('sum').astype(int)
+    season_long_table['BK'] = data_sample.groupby(['Player', 'Team'], sort=False)['BK'].transform('sum').astype(int)
+    season_long_table['SO'] = data_sample.groupby(['Player', 'Team'], sort=False)['SO'].transform('sum').astype(int)
+    season_long_table['Avg K/9'] = data_sample.groupby(['Player', 'Team'], sort=False)['K/9'].transform('mean').astype(float)
+    season_long_table['Avg BB/9'] = data_sample.groupby(['Player', 'Team'], sort=False)['BB/9'].transform('mean').astype(float)
+    season_long_table['Avg WHIP'] = data_sample.groupby(['Player', 'Team'], sort=False)['WHIP'].transform('mean').astype(float)
+    season_long_table['Avg BABIP'] = data_sample.groupby(['Player', 'Team'], sort=False)['BABIP'].transform('mean').astype(float)
+    season_long_table['Avg LOB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['LOB%'].transform('mean').astype(int)
+    season_long_table['Avg FIP'] = data_sample.groupby(['Player', 'Team'], sort=False)['FIP'].transform('mean').astype(float)
+    season_long_table['Avg xFIP'] = data_sample.groupby(['Player', 'Team'], sort=False)['xFIP'].transform('mean').astype(float)
+    season_long_table['Avg K%'] = data_sample.groupby(['Player', 'Team'], sort=False)['K%'].transform('mean').astype(float)
+    season_long_table['Avg BB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['BB%'].transform('mean').astype(float)
+    season_long_table['Avg SIERA'] = data_sample.groupby(['Player', 'Team'], sort=False)['SIERA'].transform('mean').astype(float)
+    season_long_table['Avg LD%'] = data_sample.groupby(['Player', 'Team'], sort=False)['LD%'].transform('mean').astype(float)
+    season_long_table['Avg GB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['GB%'].transform('mean').astype(float)
+    season_long_table['Avg FB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['FB%'].transform('mean').astype(float)
+    season_long_table['Avg HR/FB'] = data_sample.groupby(['Player', 'Team'], sort=False)['HR/FB'].transform('mean').astype(float)
+    season_long_table['Avg Hard%'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hard%'].transform('mean').astype(float)
+    season_long_table['Barrels'] = data_sample.groupby(['Player', 'Team'], sort=False)['Barrels'].transform('sum').astype(int)
+    season_long_table['Avg Barrel%'] = data_sample.groupby(['Player', 'Team'], sort=False)['Barrel%'].transform('mean').astype(float)
+    season_long_table['Avg xERA'] = data_sample.groupby(['Player', 'Team'], sort=False)['xERA'].transform('mean').astype(float)
+    season_long_table['Avg vFA'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFA'].transform('mean').astype(float)
+    season_long_table['Avg vFT'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFT'].transform('mean').astype(float)
+    season_long_table['Avg vFC'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFC'].transform('mean').astype(float)
+    season_long_table['Avg vFS'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFS'].transform('mean').astype(float)
+    season_long_table['Avg vFO'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFO'].transform('mean').astype(float)
+    season_long_table['Avg vSI'] = data_sample.groupby(['Player', 'Team'], sort=False)['vSI'].transform('mean').astype(float)
+    season_long_table['Avg vSL'] = data_sample.groupby(['Player', 'Team'], sort=False)['vSL'].transform('mean').astype(float)
+    season_long_table['Avg vCU'] = data_sample.groupby(['Player', 'Team'], sort=False)['vCU'].transform('mean').astype(float)
+    season_long_table['Avg vKC'] = data_sample.groupby(['Player', 'Team'], sort=False)['vKC'].transform('mean').astype(float)
+    season_long_table['Avg vEP'] = data_sample.groupby(['Player', 'Team'], sort=False)['vEP'].transform('mean').astype(float)
+    season_long_table['Avg vCH'] = data_sample.groupby(['Player', 'Team'], sort=False)['vCH'].transform('mean').astype(float)
+    season_long_table['Avg vSC'] = data_sample.groupby(['Player', 'Team'], sort=False)['vSC'].transform('mean').astype(float)
+    season_long_table['Avg vKN'] = data_sample.groupby(['Player', 'Team'], sort=False)['vKN'].transform('mean').astype(float)
+    season_long_table = season_long_table.drop_duplicates(subset='Player')
 
-@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['Min']))
-        corr_frame[player] = corr_frame['DATE'].map(fantasy_map)
-    players_fantasy = corr_frame.drop('DATE', axis=1)
-    corrM = players_fantasy.corr()
+    season_long_table = season_long_table.sort_values(by='SO', ascending=False)
     
-    return corrM
+    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Date', 'G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                                                    'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
+                                                    'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
+                                                    'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN'], axis=1)
+
+    return season_long_table
 
 @st.cache_data(show_spinner=False)
 def split_frame(input_df, rows):
@@ -219,31 +199,38 @@ def split_frame(input_df, rows):
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-gamelog_table, rot_table, game_rot, timestamp = init_baselines()
+hitter_gamelog_table, pitcher_gamelog_table, timestamp = init_baselines()
 t_stamp = f"Updated through: " + str(timestamp) + f" CST"
-basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
-basic_season_cols = ['Pos', 'Team', 'Min']
-data_cols = ['team_score', 'opp_score', 'spread', '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',
-             'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
-season_data_cols = ['Touches', 'Touch/Min', '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',
-                    'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
-game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
-                 'Fantasy', 'FD_Fantasy']
-indv_teams = gamelog_table.drop_duplicates(subset='Team')
+
+basic_cols = ['Player', 'Team']
+
+basic_season_cols = ['Team']
+
+hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                    'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%']
+
+season_hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                           'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%']
+
+pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                     'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
+                     'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA', 'vFT', 'vFC', 'vFS', 'vFO', 'vSI',
+                     'vSL', 'vCU', 'vKC', 'vEP', 'vCH', 'vSC', 'vKN']
+
+season_pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                            'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
+                            'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
+                            'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN']
+
+indv_teams = hitter_gamelog_table.drop_duplicates(subset='Team')
 total_teams = indv_teams.Team.values.tolist()
-indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-total_rot_teams = indv_rot_teams.Team.values.tolist()
-indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
-total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
-indv_players = gamelog_table.drop_duplicates(subset='Player')
-total_players = indv_players.Player.values.tolist()
-total_dates = gamelog_table.Date.values.tolist()
+indv_hitters = hitter_gamelog_table.drop_duplicates(subset='Player')
+total_hitters = indv_hitters.Player.values.tolist()
+indv_pitchers = hitter_gamelog_table.drop_duplicates(subset='Player')
+total_pitchers = indv_pitchers.Player.values.tolist()
+total_dates = hitter_gamelog_table.Date.values.tolist()
 
-tab1, tab2, tab3, tab4, tab5 = st.tabs(['Gamelogs', 'Correlation Matrix', 'Position vs. Opp', 'Positional Percentages', 'Game Rotations'])
+tab1, tab2 = st.tabs(['Hitter Gamelogs', 'Pitcher Gamelogs'])
 
 with tab1:
     st.info(t_stamp)
@@ -251,28 +238,36 @@ with tab1:
     with col1:
         if st.button("Reset Data", key='reset1'):
                   st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
-                  basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
-                  basic_season_cols = ['Pos', 'Team', 'Min']
-                  data_cols = ['team_score', 'opp_score', 'spread', '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',
-                               'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  season_data_cols = ['Touches', 'Touch/Min', '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',
-                                      'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
-                                   'Fantasy', 'FD_Fantasy']
-                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
+                  hitter_gamelog_table, pitcher_gamelog_table, timestamp = init_baselines()
+                  t_stamp = f"Updated through: " + str(timestamp) + f" CST"
+
+                  basic_cols = ['Player', 'Team']
+
+                  basic_season_cols = ['Team']
+
+                  hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                                      'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%']
+
+                  season_hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                                             'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%']
+
+                  pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                                       'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
+                                       'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA', 'vFT', 'vFC', 'vFS', 'vFO', 'vSI',
+                                       'vSL', 'vCU', 'vKC', 'vEP', 'vCH', 'vSC', 'vKN']
+
+                  season_pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                                              'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
+                                              'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
+                                              'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN']
+
+                  indv_teams = hitter_gamelog_table.drop_duplicates(subset='Team')
                   total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
-                  indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
-                  total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
-                  indv_players = gamelog_table.drop_duplicates(subset='Player')
-                  total_players = indv_players.Player.values.tolist()
-                  total_dates = gamelog_table.Date.values.tolist()
+                  indv_hitters = hitter_gamelog_table.drop_duplicates(subset='Player')
+                  total_hitters = indv_hitters.Player.values.tolist()
+                  indv_pitchers = hitter_gamelog_table.drop_duplicates(subset='Player')
+                  total_pitchers = indv_pitchers.Player.values.tolist()
+                  total_dates = hitter_gamelog_table.Date.values.tolist()
         
         split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1')
         split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
@@ -292,58 +287,46 @@ with tab1:
             if high_date is not None:
                 high_date = pd.to_datetime(high_date).date()
         elif split_var3 == 'All':
-            low_date = gamelog_table['Date'].min()
-            high_date = gamelog_table['Date'].max()
+            low_date = hitter_gamelog_table['Date'].min()
+            high_date = hitter_gamelog_table['Date'].max()
         
         split_var4 = st.radio("Would you like to view all players or specific ones?", ('All', 'Specific Players'), key='split_var4')
         
         if split_var4 == 'Specific Players':
-            player_var1 = st.multiselect('Which players would you like to include in the tables?', options = total_players, key='player_var1')
+            player_var1 = st.multiselect('Which players would you like to include in the tables?', options = total_hitters, key='player_var1')
         elif split_var4 == 'All':
-            player_var1 = total_players
-            
-        spread_var1 = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='spread_var1')
-        
-        min_var1 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var1')
+            player_var1 = total_hitters
     
     with col2:
-        working_data = gamelog_table
+        working_data = hitter_gamelog_table
         if split_var1 == 'Season Logs':
             choose_cols = st.container()
             with choose_cols:
-                choose_disp = st.multiselect('Which stats would you like to view?', options = season_data_cols, default = season_data_cols, key='col_display')
+                choose_disp = st.multiselect('Which stats would you like to view?', options = season_hitter_data_cols, default = season_hitter_data_cols, key='col_display')
             disp_stats = basic_season_cols + choose_disp
             display = st.container()
             working_data = working_data[working_data['Date'] >= low_date]
             working_data = working_data[working_data['Date'] <= high_date]
-            working_data = working_data[working_data['Min'] >= min_var1[0]]
-            working_data = working_data[working_data['Min'] <= min_var1[1]]
-            working_data = working_data[working_data['spread'] >= spread_var1[0]]
-            working_data = working_data[working_data['spread'] <= spread_var1[1]]
             working_data = working_data[working_data['Team'].isin(team_var1)]
             working_data = working_data[working_data['Player'].isin(player_var1)]
-            season_long_table = seasonlong_build(working_data)
+            season_long_table = hitter_seasonlong_build(working_data)
             season_long_table = season_long_table.set_index('Player')
             season_long_table_disp = season_long_table.reindex(disp_stats,axis="columns")
             display.dataframe(season_long_table_disp.style.format(precision=2), height=750, use_container_width = True)  
             st.download_button(
-                    label="Export seasonlogs Model",
+                    label="Export hitter seasonlogs Model",
                     data=convert_df_to_csv(season_long_table),
-                    file_name='Seasonlogs_NBA_View.csv',
+                    file_name='Seasonlogs_Hitter_View.csv',
                     mime='text/csv',
             )
             
         elif split_var1 == 'Gamelogs':
             choose_cols = st.container()
             with choose_cols:
-                choose_disp_gamelog = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='choose_disp_gamelog')
+                choose_disp_gamelog = st.multiselect('Which stats would you like to view?', options = hitter_data_cols, default = hitter_data_cols, key='choose_disp_gamelog')
             gamelog_disp_stats = basic_cols + choose_disp_gamelog
             working_data = working_data[working_data['Date'] >= low_date]
             working_data = working_data[working_data['Date'] <= high_date]
-            working_data = working_data[working_data['Min'] >= min_var1[0]]
-            working_data = working_data[working_data['Min'] <= min_var1[1]]
-            working_data = working_data[working_data['spread'] >= spread_var1[0]]
-            working_data = working_data[working_data['spread'] <= spread_var1[1]]
             working_data = working_data[working_data['Team'].isin(team_var1)]
             working_data = working_data[working_data['Player'].isin(player_var1)]
             working_data = working_data.reset_index(drop=True)
@@ -368,369 +351,133 @@ with tab1:
             # pages = pages.set_index('Player')
             display.dataframe(data=pages[current_page - 1].style.format(precision=2), height=500, use_container_width=True)
             st.download_button(
-                    label="Export gamelogs Model",
+                    label="Export hitter gamelogs model",
                     data=convert_df_to_csv(gamelog_data),
-                    file_name='Gamelogs_NBA_View.csv',
+                    file_name='Gamelogs_Hitter_View.csv',
                     mime='text/csv',
             )
-            
+
 with tab2:
     st.info(t_stamp)
     col1, col2 = st.columns([1, 9])
     with col1:
         if st.button("Reset Data", key='reset2'):
                   st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
-                  basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
-                  basic_season_cols = ['Pos', 'Team', 'Min']
-                  data_cols = ['team_score', 'opp_score', 'spread', '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',
-                               'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  season_data_cols = ['Touches', 'Touch/Min', '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',
-                                      'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
-                                   'Fantasy', 'FD_Fantasy']
-                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
+                  hitter_gamelog_table, pitcher_gamelog_table, timestamp = init_baselines()
+                  t_stamp = f"Updated through: " + str(timestamp) + f" CST"
+
+                  basic_cols = ['Player', 'Team']
+
+                  basic_season_cols = ['Team']
+
+                  hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                                      'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%']
+
+                  season_hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                                             'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%']
+
+                  pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                                       'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
+                                       'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA', 'vFT', 'vFC', 'vFS', 'vFO', 'vSI',
+                                       'vSL', 'vCU', 'vKC', 'vEP', 'vCH', 'vSC', 'vKN']
+
+                  season_pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                                              'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
+                                              'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
+                                              'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN']
+
+                  indv_teams = hitter_gamelog_table.drop_duplicates(subset='Team')
                   total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
-                  indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
-                  total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
-                  indv_players = gamelog_table.drop_duplicates(subset='Player')
-                  total_players = indv_players.Player.values.tolist()
-                  total_dates = gamelog_table.Date.values.tolist()
-        
-        corr_var = st.radio("Are you correlating fantasy or minutes?", ('Fantasy', 'Minutes'), key='corr_var')
+                  indv_hitters = hitter_gamelog_table.drop_duplicates(subset='Player')
+                  total_hitters = indv_hitters.Player.values.tolist()
+                  indv_pitchers = hitter_gamelog_table.drop_duplicates(subset='Player')
+                  total_pitchers = indv_pitchers.Player.values.tolist()
+                  total_dates = hitter_gamelog_table.Date.values.tolist()
         
-        split_var1_t2 = st.radio("Would you like to view specific teams or specific players?", ('Specific Teams', 'Specific Players'), key='split_var1_t2')
-        
-        if split_var1_t2 == 'Specific Teams':
-            corr_var1_t2 = st.multiselect('Which teams would you like to include in the correlation?', options = total_teams, key='corr_var1_t2')
-        elif split_var1_t2 == 'Specific Players':
-            corr_var1_t2 = st.multiselect('Which players would you like to include in the correlation?', options = total_players, key='corr_var1_t2')
-            
-        split_var2_t2 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3_t2')
+        split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='sp_split_var1')
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='sp_split_var2')
         
-        if split_var2_t2 == 'Specific Dates':
-            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="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()
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='sp_team_var1')
+        elif split_var2 == 'All':
+            team_var1 = total_teams
             
-        spread_var1_t2 = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='spread_var1_t2')
+        split_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='sp_split_var3')
         
-        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:
-        working_data = gamelog_table
-        if split_var1_t2 == 'Specific Teams':
-            display = st.container()
-            working_data = working_data.sort_values(by='Fantasy', ascending=False)
-            working_data = working_data[working_data['Date'] >= low_date_t2]
-            working_data = working_data[working_data['Date'] <= high_date_t2]
-            working_data = working_data[working_data['Min'] >= min_var1_t2[0]]
-            working_data = working_data[working_data['Min'] <= min_var1_t2[1]]
-            working_data = working_data[working_data['spread'] >= spread_var1_t2[0]]
-            working_data = working_data[working_data['spread'] <= spread_var1_t2[1]]
-            working_data = working_data[working_data['Team'].isin(corr_var1_t2)]
-            if corr_var == 'Fantasy':
-                corr_display = run_fantasy_corr(working_data)
-            elif corr_var == 'Minutes':
-                corr_display = run_min_corr(working_data)
-            display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)
-            
-        elif split_var1_t2 == 'Specific Players':
-            display = st.container()
-            working_data = working_data.sort_values(by='Fantasy', ascending=False)
-            working_data = working_data[working_data['Date'] >= low_date_t2]
-            working_data = working_data[working_data['Date'] <= high_date_t2]
-            working_data = working_data[working_data['Min'] >= min_var1_t2[0]]
-            working_data = working_data[working_data['Min'] <= min_var1_t2[1]]
-            working_data = working_data[working_data['spread'] >= spread_var1_t2[0]]
-            working_data = working_data[working_data['spread'] <= spread_var1_t2[1]]
-            working_data = working_data[working_data['Player'].isin(corr_var1_t2)]
-            if corr_var == 'Fantasy':
-                corr_display = run_fantasy_corr(working_data)
-            elif corr_var == 'Minutes':
-                corr_display = run_min_corr(working_data)
-            display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-        st.download_button(
-                label="Export Correlations Model",
-                data=convert_df_to_csv(corr_display),
-                file_name='Correlations_NBA_View.csv',
-                mime='text/csv',
-        )
-
-with tab3:
-    st.info(t_stamp)
-    col1, col2 = st.columns([1, 9])
-    with col1:
-        if st.button("Reset Data", key='reset3'):
-                  st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
-                  basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
-                  basic_season_cols = ['Pos', 'Team', 'Min']
-                  data_cols = ['team_score', 'opp_score', 'spread', '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',
-                               'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  season_data_cols = ['Touches', 'Touch/Min', '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',
-                                      'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
-                                   'Fantasy', 'FD_Fantasy']
-                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
-                  total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
-                  indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
-                  total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
-                  indv_players = gamelog_table.drop_duplicates(subset='Player')
-                  total_players = indv_players.Player.values.tolist()
-                  total_dates = gamelog_table.Date.values.tolist()
-        
-        team_var3 = st.selectbox('Which opponent would you like to view?', options = total_teams, key='team_var3')
-        pos_var3 = st.selectbox('Which position would you like to view?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var3')
-        disp_var3 = st.radio('Which view would you like to see?', options = ['Fantasy', 'Stats'], key='disp_var3')
-        date_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='date_var3')
-        
-        if date_var3 == 'Specific Dates':
-            low_date3 = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date3')
-            if low_date3 is not None:
-                low_date3 = pd.to_datetime(low_date3).date()
-            high_date3 = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date3')
-            if high_date3 is not None:
-                high_date3 = pd.to_datetime(high_date3).date()
-        elif date_var3 == 'All':
-            low_date3 = gamelog_table['Date'].min()
-            high_date3 = gamelog_table['Date'].max()
+        if split_var3 == 'Specific Dates':
+            low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='sp_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="YYYY-MM-DD", key='sp_high_date')
+            if high_date is not None:
+                high_date = pd.to_datetime(high_date).date()
+        elif split_var3 == 'All':
+            low_date = hitter_gamelog_table['Date'].min()
+            high_date = hitter_gamelog_table['Date'].max()
         
-        spread_var3 = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='spread_var3')
+        split_var4 = st.radio("Would you like to view all players or specific ones?", ('All', 'Specific Players'), key='sp_split_var4')
         
-        min_var3 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var3')
+        if split_var4 == 'Specific Players':
+            player_var1 = st.multiselect('Which players would you like to include in the tables?', options = total_hitters, key='sp_player_var1')
+        elif split_var4 == 'All':
+            player_var1 = total_hitters
     
     with col2:
-        if disp_var3 == 'Stats':
+        working_data = hitter_gamelog_table
+        if split_var1 == 'Season Logs':
             choose_cols = st.container()
             with choose_cols:
-                choose_disp_matchup = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='choose_disp_matchup')
-            matchup_disp_stats = basic_cols + choose_disp_matchup
-        working_data = gamelog_table
-        working_data = working_data[gamelog_table['Date'] >= low_date3]
-        working_data = working_data[gamelog_table['Date'] <= high_date3]
-        season_long_table = seasonlong_build(working_data)
-        fantasy_dict = dict(zip(season_long_table['Player'], season_long_table['Fantasy']))
-        fd_fantasy_dict = dict(zip(season_long_table['Player'], season_long_table['FD_Fantasy']))
+                choose_disp = st.multiselect('Which stats would you like to view?', options = season_hitter_data_cols, default = season_hitter_data_cols, key='sp_col_display')
+            disp_stats = basic_season_cols + choose_disp
+            display = st.container()
+            working_data = working_data[working_data['Date'] >= low_date]
+            working_data = working_data[working_data['Date'] <= high_date]
+            working_data = working_data[working_data['Team'].isin(team_var1)]
+            working_data = working_data[working_data['Player'].isin(player_var1)]
+            season_long_table = hitter_seasonlong_build(working_data)
+            season_long_table = season_long_table.set_index('Player')
+            season_long_table_disp = season_long_table.reindex(disp_stats,axis="columns")
+            display.dataframe(season_long_table_disp.style.format(precision=2), height=750, use_container_width = True)  
+            st.download_button(
+                    label="Export pitcher seasonlogs Model",
+                    data=convert_df_to_csv(season_long_table),
+                    file_name='Seasonlogs_Pitcher_View.csv',
+                    mime='text/csv',
+            )
             
-        working_data = working_data[working_data['Pos'] == pos_var3]
-        working_data = working_data[working_data['Min'] >= min_var3[0]]
-        working_data = working_data[working_data['Min'] <= min_var3[1]]
-        working_data = working_data[working_data['spread'] >= spread_var3[0]]
-        working_data = working_data[working_data['spread'] <= spread_var3[1]]
-        working_data = working_data[working_data['Opp'] == team_var3]
-        working_data = working_data.reset_index(drop=True)
-        if disp_var3 == 'Fantasy':
-            gamelog_display = working_data[['Player', 'Pos', 'Team', 'Opp', 'Date', 'Min', 'Fantasy', 'FD_Fantasy']]
-        elif disp_var3 == 'Stats':
-            gamelog_data = working_data.reindex(matchup_disp_stats,axis="columns")
-            gamelog_display = gamelog_data
-        gamelog_display['Avg_Fantasy'] = gamelog_display['Player'].map(fantasy_dict)
-        gamelog_display['Avg_FD_Fantasy'] = gamelog_display['Player'].map(fd_fantasy_dict)
-        display = st.container()
-
-        # pages = pages.set_index('Player')
-        display.dataframe(gamelog_display.style.format(precision=2), height=500, use_container_width=True)
-        st.download_button(
-                label="Export Matchups Model",
-                data=convert_df_to_csv(gamelog_display),
-                file_name='Matchups_NBA_View.csv',
-                mime='text/csv',
-        )
-
-with tab4:
-    st.info(t_stamp)
-    col1, col2 = st.columns([1, 9])
-    with col1:
-        if st.button("Reset Data", key='reset4'):
-                  st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
-                  basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
-                  basic_season_cols = ['Pos', 'Team', 'Min']
-                  data_cols = ['team_score', 'opp_score', 'spread', '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',
-                               'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  season_data_cols = ['Touches', 'Touch/Min', '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',
-                                      'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
-                                   'Fantasy', 'FD_Fantasy']
-                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
-                  total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
-                  indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
-                  total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
-                  indv_players = gamelog_table.drop_duplicates(subset='Player')
-                  total_players = indv_players.Player.values.tolist()
-                  total_dates = gamelog_table.Date.values.tolist()
-        
-        split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
-        
-        if split_var5 == 'Specific Teams':
-            team_var4 = st.multiselect('Which teams would you like to view?', options = total_rot_teams, key='team_var4')
-        elif split_var5 == 'All':
-            team_var4 = total_rot_teams
-        
-    
-    with col2:
-        working_data = rot_table
-        rot_display = working_data[working_data['Team'].isin(team_var4)]
-        display = st.container()
-
-        # rot_display = rot_display.set_index('Player')
-        display.dataframe(rot_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), height=500, use_container_width=True)
-        st.download_button(
-                label="Export Rotations Model",
-                data=convert_df_to_csv(rot_display),
-                file_name='Rotations_NBA_View.csv',
-                mime='text/csv',
-        )
-
-with tab5:
-    st.info(t_stamp)
-    col1, col2 = st.columns([1, 9])
-    with col1:
-        if st.button("Reset Data", key='reset5'):
-                  st.cache_data.clear()
-                  gamelog_table, rot_table, game_rot, timestamp = init_baselines()
-                  basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
-                  basic_season_cols = ['Pos', 'Team', 'Min']
-                  data_cols = ['team_score', 'opp_score', 'spread', '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',
-                               'FPPM', 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  season_data_cols = ['Touches', 'Touch/Min', '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',
-                                      'FPPM', 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch']
-                  game_rot_cols = ['PLAYER_NAME', 'backlog_lookup', 'spread', 'MIN', 'PTS', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FTM', 'FTA', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF',
-                                   'Fantasy', 'FD_Fantasy']
-                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
-                  total_teams = indv_teams.Team.values.tolist()
-                  indv_rot_teams = rot_table.drop_duplicates(subset='Team')
-                  total_rot_teams = indv_rot_teams.Team.values.tolist()
-                  indv_game_rot_teams = game_rot.drop_duplicates(subset='TEAM_ABBREVIATION')
-                  total_game_rot_teams = indv_game_rot_teams.TEAM_ABBREVIATION.values.tolist()
-                  indv_players = gamelog_table.drop_duplicates(subset='Player')
-                  total_players = indv_players.Player.values.tolist()
-                  total_dates = gamelog_table.Date.values.tolist()
-        
-        game_rot_view = st.radio("What set would you like to view?", ('Team Rotations', 'Player Rotations'), key='game_rot_view')
+        elif split_var1 == 'Gamelogs':
+            choose_cols = st.container()
+            with choose_cols:
+                choose_disp_gamelog = st.multiselect('Which stats would you like to view?', options = hitter_data_cols, default = hitter_data_cols, key='sp_choose_disp_gamelog')
+            gamelog_disp_stats = basic_cols + choose_disp_gamelog
+            working_data = working_data[working_data['Date'] >= low_date]
+            working_data = working_data[working_data['Date'] <= high_date]
+            working_data = working_data[working_data['Team'].isin(team_var1)]
+            working_data = working_data[working_data['Player'].isin(player_var1)]
+            working_data = working_data.reset_index(drop=True)
+            gamelog_data = working_data.reindex(gamelog_disp_stats,axis="columns")
+            display = st.container()
         
-        if game_rot_view == 'Team Rotations':
-            game_rot_team = st.selectbox("What team would you like to work with?", options = total_game_rot_teams, key='game_rot_team')
-            
-            game_rot_spread = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='game_rot_spread')
-            
-            game_rot_min = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='game_rot_min')
-            
-            game_rot_dates = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='game_rot_dates')
-            
-            if game_rot_dates == 'Specific Dates':
-                game_rot_low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='game_rot_low_date')
-                if game_rot_low_date is not None:
-                    game_rot_low_date = pd.to_datetime(low_date).date()
-                game_rot_high_date = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='game_rot_high_date')
-                if game_rot_high_date is not None:
-                    game_rot_high_date = pd.to_datetime(high_date).date()
-            elif game_rot_dates == 'All':
-                game_rot_low_date = gamelog_table['Date'].min()
-                game_rot_high_date = gamelog_table['Date'].max()
-        elif game_rot_view == 'Player Rotations':
-            game_rot_team = st.multiselect("What players would you like to work with?", options = total_players, key='game_rot_team')
-            
-            game_rot_spread = st.slider("Is there a certain spread range you want to view?", 0, 100, (0, 100), key='game_rot_spread')
-            
-            game_rot_min = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='game_rot_min')
-            
-            game_rot_dates = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='game_rot_dates')
-            
-            if game_rot_dates == 'Specific Dates':
-                game_rot_low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='game_rot_low_date')
-                if game_rot_low_date is not None:
-                    game_rot_low_date = pd.to_datetime(low_date).date()
-                game_rot_high_date = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='game_rot_high_date')
-                if game_rot_high_date is not None:
-                    game_rot_high_date = pd.to_datetime(high_date).date()
-            elif game_rot_dates == 'All':
-                game_rot_low_date = gamelog_table['Date'].min()
-                game_rot_high_date = gamelog_table['Date'].max()
-                
+            bottom_menu = st.columns((4, 1, 1))
+            with bottom_menu[2]:
+                batch_size = st.selectbox("Page Size", options=[25, 50, 100])
+            with bottom_menu[1]:
+                total_pages = (
+                    int(len(gamelog_data) / batch_size) if int(len(gamelog_data) / batch_size) > 0 else 1
+                )
+                current_page = st.number_input(
+                    "Page", min_value=1, max_value=total_pages, step=1
+                )
+            with bottom_menu[0]:
+                st.markdown(f"Page **{current_page}** of **{total_pages}** ")
             
-    with col2:
-        if game_rot_view == 'Player Rotations':
-            team_backlog = game_rot[game_rot['PLAYER_NAME'].isin(game_rot_team)]
-            team_backlog = team_backlog[team_backlog['GAME_DATE'] >= game_rot_low_date]
-            team_backlog = team_backlog[team_backlog['GAME_DATE'] <= game_rot_high_date]
-            team_backlog = team_backlog[team_backlog['MIN'] >= game_rot_min[0]]
-            team_backlog = team_backlog[team_backlog['MIN'] <= game_rot_min[1]]
-            team_backlog = team_backlog[team_backlog['spread'] >= game_rot_spread[0]]
-            team_backlog = team_backlog[team_backlog['spread'] <= game_rot_spread[1]]
-            working_data = game_rot
-            display = st.container()
-            stats_disp = st.container()
-            check_rotation = team_backlog.sort_values(by='GAME_DATE', ascending=False)
-            game_rot_stats = check_rotation.reindex(game_rot_cols,axis="columns")
-            game_rot_stats = game_rot_stats.drop_duplicates(subset='backlog_lookup')
-
-            fig = px.timeline(check_rotation, x_start="Start", x_end="Finish", y="Task", range_x=[0,check_rotation["Finish"].max()], text='minutes')
-            fig.update_yaxes(autorange="reversed") 
-
-            fig.layout.xaxis.type = 'linear'
-            fig.data[0].x = check_rotation.delta.tolist()
-            fig.add_vline(x=12, line_width=3, line_dash="dash", line_color="green")
-            fig.add_vline(x=24, line_width=3, line_dash="dash", line_color="green")
-            fig.add_vline(x=36, line_width=3, line_dash="dash", line_color="green")
-            # pages = pages.set_index('Player')
-            display.plotly_chart(fig, use_container_width=True)
-            stats_disp.dataframe(game_rot_stats.style.format(precision=2), hide_index=True, use_container_width = True)
             
-        elif game_rot_view == 'Team Rotations':
-            team_backlog = game_rot[game_rot['TEAM_ABBREVIATION'] == game_rot_team]
-            team_backlog = team_backlog[team_backlog['GAME_DATE'] >= game_rot_low_date]
-            team_backlog = team_backlog[team_backlog['GAME_DATE'] <= game_rot_high_date]
-            team_backlog = team_backlog[team_backlog['MIN'] >= game_rot_min[0]]
-            team_backlog = team_backlog[team_backlog['MIN'] <= game_rot_min[1]]
-            team_backlog = team_backlog[team_backlog['spread'] >= game_rot_spread[0]]
-            team_backlog = team_backlog[team_backlog['spread'] <= game_rot_spread[1]]
-            game_id_var = st.selectbox("What game would you like to view?", options = team_backlog['backlog_lookup'].unique(), key='game_id_var')
-            working_data = game_rot
-            display = st.container()
-            stats_disp = st.container()
-            check_rotation = working_data[working_data['backlog_lookup'] == game_id_var]
-            check_rotation = check_rotation.sort_values(by='Start', ascending=True)
-            game_rot_stats = check_rotation.reindex(game_rot_cols,axis="columns")
-            game_rot_stats = game_rot_stats.drop_duplicates(subset='PLAYER_NAME')
-
-            fig = px.timeline(check_rotation, x_start="Start", x_end="Finish", y="Task", range_x=[0,check_rotation["Finish"].max()], text='minutes')
-            fig.update_yaxes(autorange="reversed") 
-
-            fig.layout.xaxis.type = 'linear'
-            fig.data[0].x = check_rotation.delta.tolist()
-            fig.add_vline(x=12, line_width=3, line_dash="dash", line_color="green")
-            fig.add_vline(x=24, line_width=3, line_dash="dash", line_color="green")
-            fig.add_vline(x=36, line_width=3, line_dash="dash", line_color="green")
+            pages = split_frame(gamelog_data, batch_size)
             # pages = pages.set_index('Player')
-            display.plotly_chart(fig, use_container_width=True)
-            stats_disp.dataframe(game_rot_stats.style.format(precision=2), hide_index=True, use_container_width = True)
+            display.dataframe(data=pages[current_page - 1].style.format(precision=2), height=500, use_container_width=True)
+            st.download_button(
+                    label="Export pitcher gamelogs model",
+                    data=convert_df_to_csv(gamelog_data),
+                    file_name='Gamelogs_Hitter_View.csv',
+                    mime='text/csv',
+            )