added connections and tabs

app.py

@@ -37,12 +37,10 @@ def init_conn():
           "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"
         }
-     
-        NFL_Data = st.secrets['NFL_Data']
 
         uri = st.secrets['mongo_uri']
         client = MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
-        dfs_db = client["NFL_Database"]
+        dfs_db = client["NCAAF_Database"]
         props_db = client["Props_DB"]
 
         gc = gspread.service_account_from_dict(credentials)
@@ -57,44 +55,12 @@ american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead
 
 @st.cache_resource(ttl=600)
 def init_baselines():
-    collection = dfs_db["Game_Betting_Model"] 
+    collection = dfs_db["NCAAF_GameModel"] 
     cursor = collection.find()
     raw_display = pd.DataFrame(list(cursor))
     game_model = raw_display[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
-    
-    collection = dfs_db["Player_Stats"] 
-    cursor = collection.find()
-    raw_display = pd.DataFrame(list(cursor))
-    overall_stats = raw_display[['Player', 'Position', 'Team', 'Opp', 'rush_att', 'rec', 'dropbacks', 'rush_yards', 'rush_tds', 'rec_yards', 'rec_tds', 'pass_att', 'pass_yards', 'pass_tds', 'PPR', 'Half_PPR']]
-    
-    collection = dfs_db["Prop_Trends"] 
-    cursor = collection.find()
-    raw_display = pd.DataFrame(list(cursor))
-    prop_trends = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
-                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
-    
-    collection = dfs_db["DK_NFL_ROO"] 
-    cursor = collection.find()
 
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
-                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
-    load_display = raw_display[raw_display['Position'] != 'K']
-    timestamp = load_display['timestamp'][0]
-    
-    collection = dfs_db["Prop_Trends"] 
-    cursor = collection.find()
-    raw_display = pd.DataFrame(list(cursor))
-    prop_frame = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
-                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
-    
-    collection = dfs_db['Pick6_Trends']
-    cursor = collection.find()
-    raw_display = pd.DataFrame(list(cursor))
-    pick_frame = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
-                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge', 'last_name', 'P6_name', 'Full_name']]
-
-    collection = props_db["NFL_Props"] 
+    collection = props_db["NCAAF_Props"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
@@ -104,22 +70,41 @@ 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, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props
+    return game_model, market_props
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
+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
+
+prop_table_options = ['NCAAF_GAME_PLAYER_PASSING_YARDS', 'NCAAF_GAME_PLAYER_RUSHING_YARDS', 'NCAAF_GAME_PLAYER_PASSING_ATTEMPTS', 'NCAAF_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NCAAF_GAME_PLAYER_PASSING_COMPLETIONS', 'NCAAF_GAME_PLAYER_RUSHING_ATTEMPTS',
+                      'NCAAF_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NCAAF_GAME_PLAYER_RECEIVING_YARDS', 'NCAAF_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
+prop_format = {'L3 Success': '{:.2%}', 'L6_Success': '{:.2%}', 'L10_success': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
+               'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
+
+tab1, tab2 = st.tabs(["Game Model", "Prop Market"])
+
 with tab1:
-    st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
-              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
-              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
-              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
-              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
-              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+              game_model, market_props = init_baselines()
     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':
@@ -137,7 +122,40 @@ with tab1:
     st.download_button(
         label="Export Team Model",
         data=convert_df_to_csv(team_frame),
-        file_name='NFL_team_betting_export.csv',
+        file_name='NCAAF_team_betting_export.csv',
         mime='text/csv',
         key='team_export',
+    )
+
+with tab2:
+    if st.button("Reset Data", key='reset4'):
+                st.cache_data.clear()
+                game_model, market_props = init_baselines()
+    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='NCAAF_market_props_export.csv',
+        mime='text/csv',
     )