Adding Bet Sheet

src/streamlit_app.py

@@ -17,6 +17,7 @@ from database import db, prop_db
 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%}',
                'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
+bet_format = {'Edge': '{:.2%}'}
 sim_format = {'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}', 'Imp Over': '{:.2%}', 'Imp Under': '{:.2%}', 'Over%': '{:.2%}', 'Under%': '{:.2%}', 'Edge': '{:.2%}'}
 prop_table_options = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
 all_sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
@@ -140,6 +141,33 @@ def init_baselines():
     pick_frame = raw_display.drop_duplicates(subset=['Player', 'prop_type'], keep='first')
     pick_frame = pick_frame.reset_index(drop=True)
 
+    prop_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
+                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
+                                  'Trey Murphy III', 'Cam Thomas'], inplace=True)
+    pick_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
+                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
+                                  'Trey Murphy III', 'Cam Thomas'], inplace=True)
+
+    collection = db["Bet_Sheet"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display.replace('', np.nan, inplace=True)
+    raw_display = raw_display[['Name', 'OddsType', 'PropType', 'No Vig', 'Team', 'Projection', 'Edge']]
+    raw_display = raw_display.rename(columns={"Name": "Player", "OddsType": "book", "PropType": "prop_type"})
+    bet_sheet = raw_display.dropna(subset='Player')
+    bet_sheet = bet_sheet.reset_index(drop=True)
+    bet_sheet = bet_sheet.drop_duplicates(subset=['Player', 'prop_type'], keep='first')
+
+    collection = db["Pick6_Trends"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L5 Success', 'L10_Success', 'L20_success', 'L10 Avg', 'Projection',
+                               'Proj Diff', 'Matchup Boost', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
+    pick_frame = raw_display.drop_duplicates(subset=['Player', 'prop_type'], keep='first')
+    pick_frame = pick_frame.reset_index(drop=True)
+
     prop_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
                                  ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
                                   'Trey Murphy III', 'Cam Thomas'], inplace=True)
@@ -157,7 +185,7 @@ def init_baselines():
     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
+    return game_model, raw_baselines, player_stats, prop_frame, bet_sheet, pick_frame, market_props, timestamp
 
 def calculate_no_vig(row):
     def implied_probability(american_odds):
@@ -181,25 +209,41 @@ def calculate_no_vig(row):
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
+game_model, raw_baselines, player_stats, prop_frame, bet_sheet, pick_frame, market_props, timestamp = init_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 selected_tab = st.segmented_control(
     "Select Tab",
-    options=["Game Betting Model", 'Prop Market', "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"],
+    options=["Bet Sheet", "Game Betting Model", 'Prop Market', "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"],
     selection_mode='single',
-    default='Game Betting Model',
+    default='Bet Sheet',
     width='stretch',
     label_visibility='collapsed',
     key='tab_selector'
 )
 
+if selected_tab == 'Bet Sheet':
+    with st.expander("Info and Filters"):
+        st.info(t_stamp)
+        if st.button("Reset Data", key='reset1'):
+                st.cache_data.clear()
+                game_model, raw_baselines, player_stats, prop_frame, bet_sheet, pick_frame, market_props, timestamp = init_baselines()
+                t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+        st.dataframe(bet_sheet.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(bet_format, precision=2), use_container_width = True)
+        st.download_button(
+        label="Export Bet Sheet",
+        data=convert_df_to_csv(bet_sheet),
+        file_name='NBA_bet_sheet_export.csv',
+        mime='text/csv',
+        key='bet_sheet_export',
+    )
+
 if selected_tab == 'Game Betting Model':
     with st.expander("Info and Filters"):
         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, market_props, timestamp = init_baselines()
+                game_model, raw_baselines, player_stats, prop_frame, bet_sheet, 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
@@ -225,7 +269,7 @@ if selected_tab == 'Prop Market':
         st.info(t_stamp)
         if st.button("Reset Data", key='reset2'):
                 st.cache_data.clear()
-                game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
+                game_model, raw_baselines, player_stats, prop_frame, bet_sheet, 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()
@@ -261,7 +305,7 @@ if selected_tab == 'Player Projections':
         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, market_props, timestamp = init_baselines()
+                game_model, raw_baselines, player_stats, prop_frame, bet_sheet, 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':
@@ -284,7 +328,7 @@ if selected_tab == 'Prop Trend Table':
         st.info(t_stamp)
         if st.button("Reset Data", key='reset4'):
                 st.cache_data.clear()
-                game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
+                game_model, raw_baselines, player_stats, prop_frame, bet_sheet, 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':
@@ -313,7 +357,7 @@ if selected_tab == 'Player Prop Simulations':
     st.info(t_stamp)
     if st.button("Reset Data", key='reset5'):
               st.cache_data.clear()
-              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
+              game_model, raw_baselines, player_stats, prop_frame, bet_sheet, pick_frame, market_props, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])