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import streamlit as st |
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st.set_page_config(layout="wide") |
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for name in dir(): |
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if not name.startswith('_'): |
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del globals()[name] |
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import numpy as np |
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import pandas as pd |
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import streamlit as st |
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import gspread |
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import plotly.express as px |
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import random |
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import gc |
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@st.cache_resource |
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def init_conn(): |
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scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] |
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credentials = { |
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"type": "service_account", |
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"project_id": "dfsnew", |
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"private_key_id": "2432f6c3771f70a410c5c878d1359869fc9dddc8", |
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"private_key": "-----BEGIN PRIVATE KEY-----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\nbEyuT7sCPCLeOX/RLy/lCpA=\n-----END PRIVATE KEY-----\n", |
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"client_email": "[email protected]", |
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"client_id": "105107448378741046480", |
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"auth_uri": "https://accounts.google.com/o/oauth2/auth", |
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"token_uri": "https://oauth2.googleapis.com/token", |
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", |
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/dfsapps%40dfsnew.iam.gserviceaccount.com", |
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"universe_domain": "googleapis.com" |
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} |
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header= {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) ' |
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'AppleWebKit/537.11 (KHTML, like Gecko) ' |
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'Chrome/23.0.1271.64 Safari/537.11', |
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'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', |
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'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3', |
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'Accept-Encoding': 'none', |
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'Accept-Language': 'en-US,en;q=0.8', |
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'Connection': 'keep-alive'} |
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gc_con = gspread.service_account_from_dict(credentials, scope) |
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return gc_con |
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gcservice_account = init_conn() |
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NBAGetGameData = 'https://docs.google.com/spreadsheets/d/1tRQrF_I5rS7Q0g9vE8NrENDZ2P3_DvtbBZzKEakwOI0/edit#gid=1373653837' |
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NCAAMensBasketball = 'https://docs.google.com/spreadsheets/d/1flWwlx1T7Xu68AjOkeq7u4Y7nWzukZzKTEI17RJ6MfE/edit#gid=1975368022' |
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NBABettingModel = 'https://docs.google.com/spreadsheets/d/1WBnvOHQi_zVTGF63efejK5ho02AY00HiYrMHnMJXY1E/edit#gid=1157978351' |
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game_format = {'Injury and Rotation Adjusted Win %': '{:.2%}'} |
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percentages_format = {'Playoff Odds': '{:.2%}', 'Division Odds': '{:.2%}', 'Top 4 Seed Odds': '{:.2%}', '1 Seed Odds': '{:.2%}', 'Win 1st Round': '{:.2%}', |
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'Win 2nd Round': '{:.2%}', 'Win Conference': '{:.2%}', 'Win Title': '{:.2%}', '1': '{:.2%}', '2': '{:.2%}', '3': '{:.2%}', |
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'4': '{:.2%}', '5': '{:.2%}', '6': '{:.2%}', '7': '{:.2%}', '8': '{:.2%}', '9': '{:.2%}', '10': '{:.2%}', '11': '{:.2%}', |
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'12': '{:.2%}', '13': '{:.2%}', '14': '{:.2%}', '15': '{:.2%}'} |
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@st.cache_resource(ttl = 300) |
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def init_baselines(): |
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sh = gcservice_account.open_by_url(NCAAMensBasketball) |
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worksheet = sh.worksheet('ActiveBets') |
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raw_display = pd.DataFrame(worksheet.get_values()) |
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raw_display.columns = raw_display.iloc[0] |
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raw_display = raw_display[1:] |
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raw_display = raw_display.reset_index(drop=True) |
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raw_display.replace('', np.nan, inplace=True) |
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raw_display['Edge for Moneyline'] = np.where(raw_display['Home Win Edge'] > raw_display['Road Win Edge'], raw_display['Home Win Edge'], raw_display['Road Win Edge']) |
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game_model = raw_display[['date', 'gameid', 'HomeTeam', 'HomeDiv', 'HomeConf', 'AwayTeam', 'AwayDiv', 'AwayConf', |
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'total', 'mlHome', 'mlAway', 'spreadHome', 'spreadAway', 'booksid', 'inconf', 'Neutral', 'Home Team PM', |
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'Road Team PM', 'HCA', 'Home Team Margin', 'Home Team Win', 'Home ML Break Even %', |
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'Road ML Break Even %', 'Home Win Edge', 'Road Win Edge', 'Moneyline Bet Rec', 'Edge for Moneyline', 'Home Spread Edge', |
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'Road Team Edge', 'Home Spread HKelly', 'Away Spread HKelly', 'Spread Bet Rec', 'Edge for Spread']] |
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data_cols = ['gameid', 'total', 'mlHome', 'mlAway', 'spreadHome', 'spreadAway', 'Neutral', 'Home Team PM', |
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'Road Team PM', 'HCA', 'Home Team Margin', 'Home Team Win', 'Home ML Break Even %', |
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'Road ML Break Even %', 'Home Win Edge', 'Road Win Edge', 'Edge for Moneyline', 'Home Spread Edge', |
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'Road Team Edge', 'Home Spread HKelly', 'Away Spread HKelly', 'Edge for Spread'] |
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game_model[data_cols] = game_model[data_cols].apply(pd.to_numeric, errors='coerce') |
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game_model = game_model[game_model['date'] != ""] |
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just_win_probs = game_model[['date', 'HomeTeam', 'AwayTeam', 'booksid', 'total', 'mlHome', 'mlAway', 'spreadHome', 'spreadAway', |
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'Moneyline Bet Rec', 'Edge for Moneyline', 'Spread Bet Rec', 'Edge for Spread']] |
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timestamp = raw_display['Time'].head(1)[0] |
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return game_model, just_win_probs, timestamp |
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def convert_df_to_csv(df): |
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return df.to_csv().encode('utf-8') |
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game_model, just_win_probs, timestamp = init_baselines() |
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t_stamp = f"Last Update: " + str(timestamp) + f" EST" |
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st.info(t_stamp) |
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col1, col2 = st.columns([1, 9]) |
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with col1: |
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if st.button("Reset Data", key='reset1'): |
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st.cache_data.clear() |
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game_model, just_win_probs, timestamp = init_baselines() |
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t_stamp = f"Last Update: " + str(timestamp) + f" EST" |
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view_var1 = st.radio("Would you like to view math and stuff or just the win percentages and margins?", ('Just win probs', 'Gimme details'), key='view_var1') |
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view_var2 = st.radio("Would you like to view all books or specific ones?", ('All Books', 'Specific Books'), key='view_var2') |
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if view_var2 == 'All Books': |
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site_view = game_model.booksid.unique() |
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elif view_var2 == 'Specific Books': |
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site_view = st.multiselect("What books would you like included?", options = game_model.booksid.unique(), key='site_view') |
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with col2: |
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if view_var1 == 'Just win probs': |
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game_display = just_win_probs[just_win_probs['booksid'].isin(site_view)] |
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st.dataframe(game_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |
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st.download_button( |
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label="Export Game Model", |
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data=convert_df_to_csv(game_model), |
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file_name='AmericanNumbers_Game_Model_export.csv', |
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mime='text/csv', |
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) |
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elif view_var1 == 'Gimme details': |
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game_display = game_model[game_model['booksid'].isin(site_view)] |
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st.dataframe(game_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |
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st.download_button( |
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label="Export Game Model", |
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data=convert_df_to_csv(game_model), |
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file_name='AmericanNumbers_NCAA_BBall_export.csv', |
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mime='text/csv', |
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) |
