Added a Props_DB database conn, added a new tab for prop market

app.py

@@ -51,15 +51,16 @@ def init_conn():
         uri = st.secrets['mongo_uri']
         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
         db = client["NBA_DFS"]
+        prop_db = client["Props_DB"]
 
         NBA_Data = st.secrets['NBA_Data']
 
         gc_con = gspread.service_account_from_dict(credentials)
         gc_con2 = gspread.service_account_from_dict(credentials2)
       
-        return gc_con, gc_con2, db, NBA_Data
+        return gc_con, gc_con2, db, prop_db, NBA_Data
 
-gcservice_account, gcservice_account2, db, NBA_Data = init_conn()
+gcservice_account, gcservice_account2, db, prop_db, NBA_Data = init_conn()
 
 game_format = {'Paydirt Win%': '{:.2%}', 'Vegas Win%': '{:.2%}'}
 prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
@@ -156,21 +157,50 @@ def init_baselines():
                                  ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
                                   'Trey Murphy III', 'Cam Thomas'], inplace=True)
     
-    return game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp
+    collection = prop_db["NBA_Props"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    market_props = raw_display[['Name', 'Position', 'Projection', 'PropType', 'OddsType', 'over_pay', 'under_pay']]
+    market_props['over_prop'] = market_props['Projection']
+    market_props['over_line'] = market_props['over_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
+    market_props['under_prop'] = market_props['Projection']
+    market_props['under_line'] = market_props['under_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
+    
+    return game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp
+
+def calculate_no_vig(row):
+    def implied_probability(american_odds):
+        if american_odds < 0:
+            return (-american_odds) / ((-american_odds) + 100)
+        else:
+            return 100 / (american_odds + 100)
+
+    over_line = row['over_line']
+    under_line = row['under_line']
+    over_prop = row['over_prop']
+    
+    over_prob = implied_probability(over_line)
+    under_prob = implied_probability(under_line)
+    
+    total_prob = over_prob + under_prob
+    no_vig_prob = (over_prob / total_prob + 0.5) * over_prop
+    
+    return no_vig_prob
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
+game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
-tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"])
+tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Game Betting Model", 'Prop Market', "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"])
 
 with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
+              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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
@@ -193,9 +223,44 @@ with tab1:
 
 with tab2:
     st.info(t_stamp)
-    if st.button("Reset Data", key='reset2'):
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    market_type = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options, key = 'market_type_key')
+    disp_market = market_props.copy()
+    disp_market = disp_market[disp_market['PropType'] == market_type]
+    disp_market['No_Vig_Prop'] = disp_market.apply(calculate_no_vig, axis=1)
+    fanduel_frame = disp_market[disp_market['OddsType'] == 'FANDUEL']
+    fanduel_dict = dict(zip(fanduel_frame['Name'], fanduel_frame['No_Vig_Prop']))
+    draftkings_frame = disp_market[disp_market['OddsType'] == 'DRAFTKINGS']
+    draftkings_dict = dict(zip(draftkings_frame['Name'], draftkings_frame['No_Vig_Prop']))
+    mgm_frame = disp_market[disp_market['OddsType'] == 'MGM']
+    mgm_dict = dict(zip(mgm_frame['Name'], mgm_frame['No_Vig_Prop']))
+    bet365_frame = disp_market[disp_market['OddsType'] == 'BET_365']
+    bet365_dict = dict(zip(bet365_frame['Name'], bet365_frame['No_Vig_Prop']))
+
+    disp_market['FANDUEL'] = disp_market['Name'].map(fanduel_dict)
+    disp_market['DRAFTKINGS'] = disp_market['Name'].map(draftkings_dict)
+    disp_market['MGM'] = disp_market['Name'].map(mgm_dict)
+    disp_market['BET365'] = disp_market['Name'].map(bet365_dict)
+
+    disp_market = disp_market[['Name', 'Position','FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365']]
+    disp_market = disp_market.drop_duplicates(subset=['Name'], keep='first', ignore_index=True)
+
+    st.dataframe(disp_market.style.background_gradient(axis=1, subset=['FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365'], cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True)
+    st.download_button(
+        label="Export Market Props",
+        data=convert_df_to_csv(disp_market),
+        file_name='NFL_market_props_export.csv',
+        mime='text/csv',
+    )
+
+with tab3:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
+              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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':
@@ -213,11 +278,11 @@ with tab2:
         mime='text/csv',
     )
     
-with tab3:
+with tab4:
     st.info(t_stamp)
-    if st.button("Reset Data", key='reset3'):
+    if st.button("Reset Data", key='reset4'):
               st.cache_data.clear()
-              game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
+              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     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':
@@ -242,11 +307,11 @@ with tab3:
         mime='text/csv',
     )
     
-with tab4:
+with tab5:
     st.info(t_stamp)
-    if st.button("Reset Data", key='reset4'):
+    if st.button("Reset Data", key='reset5'):
               st.cache_data.clear()
-              game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
+              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
@@ -389,12 +454,12 @@ with tab4:
                             plot_hold_container = st.empty()
                             st.plotly_chart(fig, use_container_width=True)
 
-with tab5:
+with tab6:
     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='reset6'):
               st.cache_data.clear()
-              game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
+              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])