Spaces:
Sleeping
Sleeping
File size: 6,718 Bytes
2d7ef5b 3d75a25 2d7ef5b 3d75a25 2d7ef5b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
import streamlit as st
import numpy as np
import pandas as pd
from database import props_db, dfs_db
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%}'}
@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',
) |