import streamlit as st import numpy as np from numpy import where as np_where import pandas as pd import gspread import plotly.express as px import scipy.stats as stats from pymongo import MongoClient st.set_page_config(layout="wide") @st.cache_resource def init_conn(): uri = st.secrets['mongo_uri'] client = MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000) dfs_db = client["NCAAF_Database"] props_db = client["Props_DB"] return props_db, dfs_db props_db, dfs_db = init_conn() game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'} american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'} @st.cache_resource(ttl=600) def init_baselines(): 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', 'O/U']] game_model = game_model.replace('', np.nan) game_model = game_model.sort_values(by='O/U', ascending=False) game_model.loc[:, ~game_model.columns.isin(['Team', 'Opp'])] = game_model.loc[:, ~game_model.columns.isin(['Team', 'Opp'])].apply(pd.to_numeric) collection = props_db["NCAAF_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, 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_ATTEMPTS', 'NCAAF_GAME_PLAYER_PASSING_COMPLETIONS', 'NCAAF_GAME_PLAYER_PASSING_INTERCEPTIONS', 'NCAAF_GAME_PLAYER_PASSING_RUSHING_YARDS', 'NCAAF_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NCAAF_GAME_PLAYER_PASSING_YARDS', 'NCAAF_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NCAAF_GAME_PLAYER_RECEIVING_TOUCHDOWNS', 'NCAAF_GAME_PLAYER_RECEIVING_YARDS', 'NCAAF_GAME_PLAYER_RUSHING_ATTEMPTS', 'NCAAF_GAME_PLAYER_RUSHING_RECEIVING_YARDS', 'NCAAF_GAME_PLAYER_RUSHING_TOUCHDOWNS', 'NCAAF_GAME_PLAYER_RUSHING_YARDS', 'NCAAF_GAME_PLAYER_SCORE_TOUCHDOWN'] 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%}'} game_model, market_props = init_baselines() tab1, tab2 = st.tabs(["Game Model", "Prop Market"]) with tab1: if st.button("Reset Data", key='reset1'): st.cache_data.clear() 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': team_frame = team_frame[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']] team_frame = team_frame.set_index('Team') try: st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['PD Spread', 'Vegas Spread', 'Spread Diff']).format(game_format, precision=2), use_container_width = True) except: st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['PD Spread', 'Vegas Spread']).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') st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn_r').format(precision=2), height = 1000, use_container_width = True) st.download_button( label="Export Team Model", data=convert_df_to_csv(team_frame), 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', )