Update app.py

app.py

@@ -1,110 +1,96 @@
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+
 import numpy as np
 import pandas as pd
 import streamlit as st
 import gspread
 import plotly.express as px
-
-scope = ['https://www.googleapis.com/auth/spreadsheets',
-          "https://www.googleapis.com/auth/drive"]
-
-credentials = {
-  "type": "service_account",
-  "project_id": "sheets-api-connect-378620",
-  "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
-  "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQCtKa01beXwc88R\nnPZVQTNPVQuBnbwoOfc66gW3547ja/UEyIGAF112dt/VqHprRafkKGmlg55jqJNt\na4zceLKV+wTm7vBu7lDISTJfGzCf2TrxQYNqwMKE2LOjI69dBM8u4Dcb4k0wcp9v\ntW1ZzLVVuwTvmrg7JBHjiSaB+x5wxm/r3FOiJDXdlAgFlytzqgcyeZMJVKKBQHyJ\njEGg/1720A0numuOCt71w/2G0bDmijuj1e6tH32MwRWcvRNZ19K9ssyDz2S9p68s\nYDhIxX69OWxwScTIHLY6J2t8txf/XMivL/636fPlDADvBEVTdlT606n8CcKUVQeq\npUVdG+lfAgMBAAECggEAP38SUA7B69eTfRpo658ycOs3Amr0JW4H/bb1rNeAul0K\nZhwd/HnU4E07y81xQmey5kN5ZeNrD5EvqkZvSyMJHV0EEahZStwhjCfnDB/cxyix\nZ+kFhv4y9eK+kFpUAhBy5nX6T0O+2T6WvzAwbmbVsZ+X8kJyPuF9m8ldcPlD0sce\ntj8NwVq1ys52eosqs7zi2vjt+eMcaY393l4ls+vNq8Yf27cfyFw45W45CH/97/Nu\n5AmuzlCOAfFF+z4OC5g4rei4E/Qgpxa7/uom+BVfv9G0DIGW/tU6Sne0+37uoGKt\nW6DzhgtebUtoYkG7ZJ05BTXGp2lwgVcNRoPwnKJDxQKBgQDT5wYPUBDW+FHbvZSp\nd1m1UQuXyerqOTA9smFaM8sr/UraeH85DJPEIEk8qsntMBVMhvD3Pw8uIUeFNMYj\naLmZFObsL+WctepXrVo5NB6RtLB/jZYxiKMatMLUJIYtcKIp+2z/YtKiWcLnwotB\nWdCjVnPTxpkurmF2fWP/eewZ+wKBgQDRMtJg7etjvKyjYNQ5fARnCc+XsI3gkBe1\nX9oeXfhyfZFeBXWnZzN1ITgFHplDznmBdxAyYGiQdbbkdKQSghviUQ0igBvoDMYy\n1rWcy+a17Mj98uyNEfmb3X2cC6WpvOZaGHwg9+GY67BThwI3FqHIbyk6Ko09WlTX\nQpRQjMzU7QKBgAfi1iflu+q0LR+3a3vvFCiaToskmZiD7latd9AKk2ocsBd3Woy9\n+hXXecJHPOKV4oUJlJgvAZqe5HGBqEoTEK0wyPNLSQlO/9ypd+0fEnArwFHO7CMF\nycQprAKHJXM1eOOFFuZeQCaInqdPZy1UcV5Szla4UmUZWkk1m24blHzXAoGBAMcA\nyH4qdbxX9AYrC1dvsSRvgcnzytMvX05LU0uF6tzGtG0zVlub4ahvpEHCfNuy44UT\nxRWW/oFFaWjjyFxO5sWggpUqNuHEnRopg3QXx22SRRTGbN45li/+QAocTkgsiRh1\nqEcYZsO4mPCsQqAy6E2p6RcK+Xa+omxvSnVhq0x1AoGAKr8GdkCl4CF6rieLMAQ7\nLNBuuoYGaHoh8l5E2uOQpzwxVy/nMBcAv+2+KqHEzHryUv1owOi6pMLv7A9mTFoS\n18B0QRLuz5fSOsVnmldfC9fpUc6H8cH1SINZpzajqQA74bPwELJjnzrCnH79TnHG\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
-  "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
-  "client_id": "106625872877651920064",
-  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-  "token_uri": "https://oauth2.googleapis.com/token",
-  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
-}
-
-gc = gspread.service_account_from_dict(credentials)
-
-st.set_page_config(layout="wide")
-
-game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'}
-american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'}
-
-master_hold = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=694077504'
-
-@st.cache_data
-def game_betting_model():
+import random
+import gc
+
+@st.cache_resource
+def init_conn():
+        scope = ['https://www.googleapis.com/auth/spreadsheets',
+        "https://www.googleapis.com/auth/drive"]
+
+        credentials = {
+          "type": "service_account",
+          "project_id": "model-sheets-connect",
+          "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
+          "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
+          "client_id": "100369174533302798535",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
+        }
+
+        gc_con = gspread.service_account_from_dict(credentials)
+      
+        return gc_con
+
+gcservice_account = init_conn()
+
+master_hold = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=853878325'
+
+@st.cache_resource(ttl = 300)
+def init_baselines():
     sh = gc.open_by_url(master_hold)
-    worksheet = sh.worksheet('Game_Betting')
+    worksheet = sh.worksheet('Betting Model Clean')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('#DIV/0!', np.nan, inplace=True)
-    raw_display = raw_display.dropna()
-
-    return raw_display
+    game_model = raw_display.dropna()
 
-@st.cache_data
-def player_stat_table():
-    sh = gc.open_by_url(master_hold)
-    worksheet = sh.worksheet('Prop_Table')
+    worksheet = sh.worksheet('DK_Build_Up')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
-    raw_display = raw_display.dropna()
-
-    return raw_display
-
-@st.cache_data
-def timestamp_table():
-    sh = gc.open_by_url(master_hold)
-    worksheet = sh.worksheet('DK_ROO')
-    raw_display = worksheet.acell('U2').value
-
-    return raw_display
+    player_stats = raw_display.dropna()
+    
+    worksheet = sh.worksheet('Timestamp')
+    timestamp = worksheet.acell('A1').value
 
-@st.cache_data
-def player_prop_table():
-    sh = gc.open_by_url(master_hold)
-    worksheet = sh.worksheet('prop_frame')
+    worksheet = sh.worksheet('Prop_Frame')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
-    raw_display = raw_display.dropna()
+    prop_frame = raw_display.dropna()
 
-    return raw_display
+    return game_model, player_stats, prop_frame, timestamp
 
-game_model = game_betting_model()
-overall_stats = player_stat_table()
-qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-timestamp = timestamp_table()
-prop_frame = player_prop_table()
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+game_model, player_stats, prop_frame, timestamp = init_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Simulations", "Stat Specific Simulations"])
 
-def convert_df_to_csv(df):
-    return df.to_csv().encode('utf-8')
-
 with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
     team_frame = game_model
     if line_var1 == 'Percentage':
-        team_frame = team_frame[['team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
-        team_frame = team_frame.set_index('team')
-        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_format, precision=2), use_container_width = True)
+        team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Win%']]
+        team_frame = team_frame.set_index('Team')
+        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     if line_var1 == 'American':
-        team_frame = team_frame[['team', 'Opp', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
-        team_frame = team_frame.set_index('team')
+        team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Odds Line']]
+        team_frame = team_frame.set_index('Team')
         st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     
     st.download_button(
         label="Export Team Model",
         data=convert_df_to_csv(team_frame),
-        file_name='NFL_team_betting_export.csv',
+        file_name='NBA_team_betting_export.csv',
         mime='text/csv',
         key='team_export',
     )
@@ -113,66 +99,31 @@ with tab2:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
     if split_var1 == 'Specific Teams':
-        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = qb_stats['Team'].unique(), key='team_var1')
+        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var1')
     elif split_var1 == 'All':
-        team_var1 = qb_stats.Team.values.tolist()
-    qb_stats = qb_stats[qb_stats['Team'].isin(team_var1)]
-    qb_stats_disp = qb_stats.set_index('Player')
-    qb_stats_disp = qb_stats_disp.sort_values(by='PPR', ascending=False)
-    st.dataframe(qb_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        team_var1 = player_stats.Team.values.tolist()
+    player_stats = player_stats[player_stats['Team'].isin(team_var1)]
+    player_stats = player_stats.set_index('Player')
+    player_stats = player_stats.sort_values(by='Fantasy', ascending=False)
+    st.dataframe(player_stats.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     st.download_button(
         label="Export Prop Model",
-        data=convert_df_to_csv(qb_stats_disp),
-        file_name='NFL_qb_stats_export.csv',
+        data=convert_df_to_csv(player_stats),
+        file_name='NBA_stats_export.csv',
         mime='text/csv',
         key='pitcher_prop_export',
     )
-
+    
 with tab3:
-    st.info(t_stamp)
-    if st.button("Reset Data", key='reset3'):
-              st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
-    split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
-    if split_var2 == 'Specific Teams':
-        team_var2 = st.multiselect('Which teams would you like to include in the tables?', options = non_qb_stats['Team'].unique(), key='team_var2')
-    elif split_var2 == 'All':
-        team_var2 = non_qb_stats.Team.values.tolist()
-    non_qb_stats = non_qb_stats[non_qb_stats['Team'].isin(team_var2)]
-    non_qb_stats_disp = non_qb_stats.set_index('Player')
-    non_qb_stats_disp = non_qb_stats_disp.sort_values(by='PPR', ascending=False)
-    st.dataframe(non_qb_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-    st.download_button(
-        label="Export Prop Model",
-        data=convert_df_to_csv(non_qb_stats_disp),
-        file_name='NFL_nonqb_stats_export.csv',
-        mime='text/csv',
-        key='hitter_prop_export',
-    )
-
-with tab4:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset4'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
     with col2:
@@ -181,31 +132,30 @@ with tab4:
         plot_hold_container = st.empty()
     
     with col1:
-        player_check = st.selectbox('Select player to simulate props', options = overall_stats['Player'].unique())
-        prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Pass Yards', 'Pass TDs', 'Rush Yards', 'Rush TDs', 'Receptions', 'Rec Yards', 'Rec TDs', 'Fantasy', 'FD Fantasy', 'PrizePicks'])
+        player_check = st.selectbox('Select player to simulate props', options = player_stats['Player'].unique())
+        prop_type_var = st.selectbox('Select type of prop to simulate', options = ['points', 'rebounds', 'assists', 'blocks', 'steals',
+                                                                                   'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists'])
 
         ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
-        if prop_type_var == 'Pass Yards':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 100.0, max_value = 400.5, value = 250.5, step = .5)
-        elif prop_type_var == 'Pass TDs':
+        if prop_type_var == 'points':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 15.5, step = .5)
+        elif prop_type_var == 'rebounds':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5)
+        elif prop_type_var == 'assists':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5)
+        elif prop_type_var == 'blocks':
             prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'Rush Yards':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 155.5, value = 25.5, step = .5)
-        elif prop_type_var == 'Rush TDs':
+        elif prop_type_var == 'steals':
             prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'Receptions':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 15.5, value = 5.5, step = .5)
-        elif prop_type_var == 'Rec Yards':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 155.5, value = 25.5, step = .5)
-        elif prop_type_var == 'Rec TDs':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'Fantasy':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
-        elif prop_type_var == 'FD Fantasy':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
-        elif prop_type_var == 'PrizePicks':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
-        line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1000, max_value = 1000, value = -150, step = 1)
+        elif prop_type_var == 'PRA':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 65.5, value = 20.5, step = .5)
+        elif prop_type_var == 'points+rebounds':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
+        elif prop_type_var == 'points+assists':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
+        elif prop_type_var == 'rebounds+assists':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
+        line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1500, max_value = 1500, value = -150, step = 1)
         line_var = line_var + 1
 
         if st.button('Simulate Prop'):
@@ -213,7 +163,7 @@ with tab4:
                    
                     with df_hold_container.container():
 
-                        df = overall_stats
+                        df = player_stats
 
                         total_sims = 5000
 
@@ -222,31 +172,29 @@ with tab4:
                         player_var = df.loc[df['Player'] == player_check]
                         player_var = player_var.reset_index()
 
-                        if prop_type_var == 'Pass Yards':
-                            df['Median'] = df['pass_yards']
-                        elif prop_type_var == 'Pass TDs':
-                            df['Median'] = df['pass_tds']
-                        elif prop_type_var == 'Rush Yards':
-                            df['Median'] = df['rush_yards']
-                        elif prop_type_var == 'Rush TDs':
-                            df['Median'] = df['rush_tds']
-                        elif prop_type_var == 'Receptions':
-                            df['Median'] = df['rec']
-                        elif prop_type_var == 'Rec Yards':
-                            df['Median'] = df['rec_yards']
-                        elif prop_type_var == 'Rec TDs':
-                            df['Median'] = df['rec_tds']
-                        elif prop_type_var == 'Fantasy':
-                            df['Median'] = df['PPR']
-                        elif prop_type_var == 'FD Fantasy':
-                            df['Median'] = df['Half_PPF']
-                        elif prop_type_var == 'PrizePicks':
-                            df['Median'] = df['Half_PPF']
+                        if prop_type_var == 'points':
+                            df['Median'] = df['Points']
+                        elif prop_type_var == 'rebounds':
+                            df['Median'] = df['Rebounds']
+                        elif prop_type_var == 'assists':
+                            df['Median'] = df['Assists']
+                        elif prop_type_var == 'blocks':
+                            df['Median'] = df['Blocks']
+                        elif prop_type_var == 'steals':
+                            df['Median'] = df['Steals']
+                        elif prop_type_var == 'PRA':
+                            df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
+                        elif prop_type_var == 'points+rebounds':
+                            df['Median'] = df['Points'] + df['Rebounds']
+                        elif prop_type_var == 'points+assists':
+                            df['Median'] = df['Points'] + df['Assists']
+                        elif prop_type_var == 'rebounds+assists':
+                            df['Median'] = df['Assists'] + df['Rebounds']
 
                         flex_file = df
-                        flex_file['Floor'] = flex_file['Median'] * .20
-                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
-                        flex_file['STD'] = flex_file['Median'] / 4
+                        flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+                        flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+                        flex_file['STD'] = (flex_file['Median']/4)
                         flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
 
                         hold_file = flex_file
@@ -310,17 +258,13 @@ with tab4:
                             plot_hold_container = st.empty()
                             st.plotly_chart(fig, use_container_width=True)
 
-with tab5:
+with tab4:
     st.info(t_stamp)
     st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
     if st.button("Reset Data/Load Data", key='reset5'):
               st.cache_data.clear()
-              game_model = game_betting_model()
-              overall_stats = player_stat_table()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
-              prop_frame = player_prop_table()
-              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
     with col2:
@@ -330,63 +274,111 @@ with tab5:
         export_container = st.empty()
     
     with col1:
-        prop_type_var = st.selectbox('Select prop category', options = ['Pass Yards', 'Rush Yards', 'Receiving Yards'])
+        prop_type_var = st.selectbox('Select prop category', options = ['points', 'rebounds', 'assists', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists'])
 
         if st.button('Simulate Prop Category'):
             with col2:
                    
                     with df_hold_container.container():
 
-                        if prop_type_var == "Pass Yards":
+                        if prop_type_var == "points":
+                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'points']
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            st.table(prop_df)
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "rebounds":
+                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds']
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            st.table(prop_df)
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "assists":
+                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'assists']
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            st.table(prop_df)
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "PRA":
                             prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_yards']
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'PRA']
                             prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                             prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                             prop_df = prop_df.loc[prop_df['Prop'] != 0]
                             st.table(prop_df)
                             prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
                             prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "Rush Yards":
+                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "points+rebounds":
                             prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rush_yards']
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'points+rebounds']
                             prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                             prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                             prop_df = prop_df.loc[prop_df['Prop'] != 0]
                             st.table(prop_df)
                             prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
                             prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "Receiving Yards":
+                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "points+assists":
                             prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rec_yards']
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'points+assists']
                             prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                             prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                             prop_df = prop_df.loc[prop_df['Prop'] != 0]
                             st.table(prop_df)
                             prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
                             prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "rebounds+assists":
+                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds+assists']
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            st.table(prop_df)
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                         
                         prop_dict = dict(zip(df.Player, df.Prop))
                         over_dict = dict(zip(df.Player, df.Over))
                         under_dict = dict(zip(df.Player, df.Under))
                         
-                        total_sims = 1000
+                        total_sims = 5000
 
                         df.replace("", 0, inplace=True)
 
-                        if prop_type_var == "Pass Yards":
-                            df['Median'] = df['pass_yards']
-                        elif prop_type_var == "Rush Yards":
-                            df['Median'] = df['rush_yards']
-                        elif prop_type_var == "Receiving Yards":
-                            df['Median'] = df['rec_yards']
+                        if prop_type_var == 'points':
+                            df['Median'] = df['Points']
+                        elif prop_type_var == 'rebounds':
+                            df['Median'] = df['Rebounds']
+                        elif prop_type_var == 'assists':
+                            df['Median'] = df['Assists']
+                        elif prop_type_var == 'PRA':
+                            df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
+                        elif prop_type_var == 'points+rebounds':
+                            df['Median'] = df['Points'] + df['Rebounds']
+                        elif prop_type_var == 'points+assists':
+                            df['Median'] = df['Points'] + df['Assists']
+                        elif prop_type_var == 'rebounds+assists':
+                            df['Median'] = df['Assists'] + df['Rebounds']
 
                         flex_file = df
-                        flex_file['Floor'] = flex_file['Median'] * .20
-                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
-                        flex_file['STD'] = flex_file['Median'] / 4
+                        flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+                        flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+                        flex_file['STD'] = (flex_file['Median']/4)
                         flex_file['Prop'] = flex_file['Player'].map(prop_dict)
                         flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
 
@@ -448,7 +440,7 @@ with tab5:
                             st.download_button(
                                 label="Export Projections",
                                 data=convert_df_to_csv(final_outcomes),
-                                file_name='NFL_prop_proj.csv',
+                                file_name='Nba_prop_proj.csv',
                                 mime='text/csv',
                                 key='prop_proj',
                             )