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Update app.py
Browse files
app.py
CHANGED
@@ -1,110 +1,96 @@
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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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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('
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raw_display = pd.DataFrame(worksheet.get_all_records())
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raw_display.replace('#DIV/0!', np.nan, inplace=True)
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return raw_display
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def player_stat_table():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Prop_Table')
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raw_display = pd.DataFrame(worksheet.get_all_records())
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raw_display.replace('', np.nan, inplace=True)
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@st.cache_data
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def timestamp_table():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('DK_ROO')
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raw_display = worksheet.acell('U2').value
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return raw_display
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def player_prop_table():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('prop_frame')
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raw_display = pd.DataFrame(worksheet.get_all_records())
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raw_display.replace('', np.nan, inplace=True)
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return
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timestamp = timestamp_table()
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prop_frame = player_prop_table()
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t_stamp = f"Last Update: " + str(timestamp) + f" CST"
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tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Simulations", "Stat Specific Simulations"])
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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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with tab1:
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st.info(t_stamp)
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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 =
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qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
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non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
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prop_frame = player_prop_table()
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t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
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line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
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team_frame = game_model
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if line_var1 == 'Percentage':
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team_frame = team_frame[['
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team_frame = team_frame.set_index('
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st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(
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if line_var1 == 'American':
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team_frame = team_frame[['
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team_frame = team_frame.set_index('
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st.dataframe(team_frame.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 Team Model",
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data=convert_df_to_csv(team_frame),
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file_name='
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mime='text/csv',
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key='team_export',
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)
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st.info(t_stamp)
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if st.button("Reset Data", key='reset2'):
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st.cache_data.clear()
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game_model =
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qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
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non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
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prop_frame = player_prop_table()
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t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
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split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
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if split_var1 == 'Specific Teams':
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team_var1 = st.multiselect('Which teams would you like to include in the tables?', options =
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elif split_var1 == 'All':
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team_var1 =
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st.dataframe(
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st.download_button(
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label="Export Prop Model",
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data=convert_df_to_csv(
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file_name='
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mime='text/csv',
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key='pitcher_prop_export',
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)
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with tab3:
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st.info(t_stamp)
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if st.button("Reset Data", key='reset3'):
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st.cache_data.clear()
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game_model = game_betting_model()
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overall_stats = player_stat_table()
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qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
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non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
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prop_frame = player_prop_table()
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t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
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split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
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if split_var2 == 'Specific Teams':
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team_var2 = st.multiselect('Which teams would you like to include in the tables?', options = non_qb_stats['Team'].unique(), key='team_var2')
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elif split_var2 == 'All':
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team_var2 = non_qb_stats.Team.values.tolist()
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non_qb_stats = non_qb_stats[non_qb_stats['Team'].isin(team_var2)]
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non_qb_stats_disp = non_qb_stats.set_index('Player')
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non_qb_stats_disp = non_qb_stats_disp.sort_values(by='PPR', ascending=False)
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st.dataframe(non_qb_stats_disp.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 Prop Model",
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data=convert_df_to_csv(non_qb_stats_disp),
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file_name='NFL_nonqb_stats_export.csv',
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mime='text/csv',
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key='hitter_prop_export',
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)
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with tab4:
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st.info(t_stamp)
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if st.button("Reset Data", key='reset4'):
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st.cache_data.clear()
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game_model =
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qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
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non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
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prop_frame = player_prop_table()
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t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
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col1, col2 = st.columns([1, 5])
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with col2:
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plot_hold_container = st.empty()
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with col1:
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player_check = st.selectbox('Select player to simulate props', options =
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prop_type_var = st.selectbox('Select type of prop to simulate', options = ['
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ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
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if prop_type_var == '
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value =
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elif prop_type_var == '
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
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elif prop_type_var == '
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 155.5, value = 25.5, step = .5)
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elif prop_type_var == 'Rush TDs':
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
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elif prop_type_var == '
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value =
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elif prop_type_var == '
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value =
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elif prop_type_var == '
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value =
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elif prop_type_var == '
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value =
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
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elif prop_type_var == 'PrizePicks':
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prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
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line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1000, max_value = 1000, value = -150, step = 1)
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line_var = line_var + 1
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if st.button('Simulate Prop'):
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with df_hold_container.container():
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df =
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total_sims = 5000
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player_var = df.loc[df['Player'] == player_check]
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player_var = player_var.reset_index()
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if prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == '
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df['Median'] = df['
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elif prop_type_var == 'PrizePicks':
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df['Median'] = df['Half_PPF']
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flex_file = df
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flex_file['Floor'] = flex_file['Median'] * .
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flex_file['Ceiling'] = flex_file['Median'] + (flex_file['
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flex_file['STD'] = flex_file['Median']
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flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
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hold_file = flex_file
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plot_hold_container = st.empty()
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st.plotly_chart(fig, use_container_width=True)
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with
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st.info(t_stamp)
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st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
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if st.button("Reset Data/Load Data", key='reset5'):
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st.cache_data.clear()
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game_model =
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qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
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non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
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prop_frame = player_prop_table()
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t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
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col1, col2 = st.columns([1, 5])
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with col2:
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export_container = st.empty()
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with col1:
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prop_type_var = st.selectbox('Select prop category', options = ['
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if st.button('Simulate Prop Category'):
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with col2:
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with df_hold_container.container():
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if prop_type_var == "
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prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
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prop_df = prop_df.loc[prop_df['prop_type'] == '
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prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
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prop_df = prop_df.loc[prop_df['Prop'] != 0]
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st.table(prop_df)
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prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
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prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
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df = pd.merge(
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elif prop_type_var == "
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prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
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prop_df = prop_df.loc[prop_df['prop_type'] == '
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prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
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prop_df = prop_df.loc[prop_df['Prop'] != 0]
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st.table(prop_df)
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prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
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prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
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df = pd.merge(
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elif prop_type_var == "
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prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
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prop_df = prop_df.loc[prop_df['prop_type'] == '
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prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
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prop_df = prop_df.loc[prop_df['Prop'] != 0]
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st.table(prop_df)
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prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
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prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
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df = pd.merge(
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prop_dict = dict(zip(df.Player, df.Prop))
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over_dict = dict(zip(df.Player, df.Over))
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under_dict = dict(zip(df.Player, df.Under))
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total_sims =
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df.replace("", 0, inplace=True)
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if prop_type_var ==
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df['Median'] = df['
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elif prop_type_var ==
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df['Median'] = df['
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elif prop_type_var ==
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df['Median'] = df['
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flex_file = df
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flex_file['Floor'] = flex_file['Median'] * .
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flex_file['Ceiling'] = flex_file['Median'] + (flex_file['
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flex_file['STD'] = flex_file['Median']
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flex_file['Prop'] = flex_file['Player'].map(prop_dict)
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flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
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st.download_button(
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label="Export Projections",
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data=convert_df_to_csv(final_outcomes),
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file_name='
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mime='text/csv',
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key='prop_proj',
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)
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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://www.googleapis.com/auth/spreadsheets',
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19 |
+
"https://www.googleapis.com/auth/drive"]
|
20 |
+
|
21 |
+
credentials = {
|
22 |
+
"type": "service_account",
|
23 |
+
"project_id": "model-sheets-connect",
|
24 |
+
"private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
|
25 |
+
"private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQDiu1v/e6KBKOcK\ncx0KQ23nZK3ZVvADYy8u/RUn/EDI82QKxTd/DizRLIV81JiNQxDJXSzgkbwKYEDm\n48E8zGvupU8+Nk76xNPakrQKy2Y8+VJlq5psBtGchJTuUSHcXU5Mg2JhQsB376PJ\nsCw552K6Pw8fpeMDJDZuxpKSkaJR6k9G5Dhf5q8HDXnC5Rh/PRFuKJ2GGRpX7n+2\nhT/sCax0J8jfdTy/MDGiDfJqfQrOPrMKELtsGHR9Iv6F4vKiDqXpKfqH+02E9ptz\nBk+MNcbZ3m90M8ShfRu28ebebsASfarNMzc3dk7tb3utHOGXKCf4tF8yYKo7x8BZ\noO9X4gSfAgMBAAECggEAU8ByyMpSKlTCF32TJhXnVJi/kS+IhC/Qn5JUDMuk4LXr\naAEWsWO6kV/ZRVXArjmuSzuUVrXumISapM9Ps5Ytbl95CJmGDiLDwRL815nvv6k3\nUyAS8EGKjz74RpoIoH6E7EWCAzxlnUgTn+5oP9Flije97epYk3H+e2f1f5e1Nn1d\nYNe8U+1HqJgILcxA1TAUsARBfoD7+K3z/8DVPHI8IpzAh6kTHqhqC23Rram4XoQ6\nzj/ZdVBjvnKuazETfsD+Vl3jGLQA8cKQVV70xdz3xwLcNeHsbPbpGBpZUoF73c65\nkAXOrjYl0JD5yAk+hmYhXr6H9c6z5AieuZGDrhmlFQKBgQDzV6LRXmjn4854DP/J\nI82oX2GcI4eioDZPRukhiQLzYerMQBmyqZIRC+/LTCAhYQSjNgMa+ZKyvLqv48M0\n/x398op/+n3xTs+8L49SPI48/iV+mnH7k0WI/ycd4OOKh8rrmhl/0EWb9iitwJYe\nMjTV/QxNEpPBEXfR1/mvrN/lVQKBgQDuhomOxUhWVRVH6x03slmyRBn0Oiw4MW+r\nrt1hlNgtVmTc5Mu+4G0USMZwYuOB7F8xG4Foc7rIlwS7Ic83jMJxemtqAelwOLdV\nXRLrLWJfX8+O1z/UE15l2q3SUEnQ4esPHbQnZowHLm0mdL14qSVMl1mu1XfsoZ3z\nJZTQb48CIwKBgEWbzQRtKD8lKDupJEYqSrseRbK/ax43DDITS77/DWwHl33D3FYC\nMblUm8ygwxQpR4VUfwDpYXBlklWcJovzamXpSnsfcYVkkQH47NuOXPXPkXQsw+w+\nDYcJzeu7F/vZqk9I7oBkWHUrrik9zPNoUzrfPvSRGtkAoTDSwibhoc5dAoGBAMHE\nK0T/ANeZQLNuzQps6S7G4eqjwz5W8qeeYxsdZkvWThOgDd/ewt3ijMnJm5X05hOn\ni4XF1euTuvUl7wbqYx76Wv3/1ZojiNNgy7ie4rYlyB/6vlBS97F4ZxJdxMlabbCW\n6b3EMWa4EVVXKoA1sCY7IVDE+yoQ1JYsZmq45YzPAoGBANWWHuVueFGZRDZlkNlK\nh5OmySmA0NdNug3G1upaTthyaTZ+CxGliwBqMHAwpkIRPwxUJpUwBTSEGztGTAxs\nWsUOVWlD2/1JaKSmHE8JbNg6sxLilcG6WEDzxjC5dLL1OrGOXj9WhC9KX3sq6qb6\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
|
26 |
+
"client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
|
27 |
+
"client_id": "100369174533302798535",
|
28 |
+
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
|
29 |
+
"token_uri": "https://oauth2.googleapis.com/token",
|
30 |
+
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
|
31 |
+
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
|
32 |
+
}
|
33 |
+
|
34 |
+
gc_con = gspread.service_account_from_dict(credentials)
|
35 |
+
|
36 |
+
return gc_con
|
37 |
+
|
38 |
+
gcservice_account = init_conn()
|
39 |
+
|
40 |
+
master_hold = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=853878325'
|
41 |
+
|
42 |
+
@st.cache_resource(ttl = 300)
|
43 |
+
def init_baselines():
|
44 |
sh = gc.open_by_url(master_hold)
|
45 |
+
worksheet = sh.worksheet('Betting Model Clean')
|
46 |
raw_display = pd.DataFrame(worksheet.get_all_records())
|
47 |
raw_display.replace('#DIV/0!', np.nan, inplace=True)
|
48 |
+
game_model = raw_display.dropna()
|
|
|
|
|
49 |
|
50 |
+
worksheet = sh.worksheet('DK_Build_Up')
|
|
|
|
|
|
|
51 |
raw_display = pd.DataFrame(worksheet.get_all_records())
|
52 |
raw_display.replace('', np.nan, inplace=True)
|
53 |
+
player_stats = raw_display.dropna()
|
54 |
+
|
55 |
+
worksheet = sh.worksheet('Timestamp')
|
56 |
+
timestamp = worksheet.acell('A1').value
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
57 |
|
58 |
+
worksheet = sh.worksheet('Prop_Frame')
|
|
|
|
|
|
|
59 |
raw_display = pd.DataFrame(worksheet.get_all_records())
|
60 |
raw_display.replace('', np.nan, inplace=True)
|
61 |
+
prop_frame = raw_display.dropna()
|
62 |
|
63 |
+
return game_model, player_stats, prop_frame, timestamp
|
64 |
|
65 |
+
def convert_df_to_csv(df):
|
66 |
+
return df.to_csv().encode('utf-8')
|
67 |
+
|
68 |
+
game_model, player_stats, prop_frame, timestamp = init_baselines()
|
|
|
|
|
69 |
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
|
70 |
|
71 |
tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Simulations", "Stat Specific Simulations"])
|
72 |
|
|
|
|
|
|
|
73 |
with tab1:
|
74 |
st.info(t_stamp)
|
75 |
if st.button("Reset Data", key='reset1'):
|
76 |
st.cache_data.clear()
|
77 |
+
game_model, player_stats, prop_frame, timestamp = init_baselines()
|
78 |
+
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
|
|
|
|
|
|
|
|
|
79 |
line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
|
80 |
team_frame = game_model
|
81 |
if line_var1 == 'Percentage':
|
82 |
+
team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Win%']]
|
83 |
+
team_frame = team_frame.set_index('Team')
|
84 |
+
st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
|
85 |
if line_var1 == 'American':
|
86 |
+
team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Odds Line']]
|
87 |
+
team_frame = team_frame.set_index('Team')
|
88 |
st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
|
89 |
|
90 |
st.download_button(
|
91 |
label="Export Team Model",
|
92 |
data=convert_df_to_csv(team_frame),
|
93 |
+
file_name='NBA_team_betting_export.csv',
|
94 |
mime='text/csv',
|
95 |
key='team_export',
|
96 |
)
|
|
|
99 |
st.info(t_stamp)
|
100 |
if st.button("Reset Data", key='reset2'):
|
101 |
st.cache_data.clear()
|
102 |
+
game_model, player_stats, prop_frame, timestamp = init_baselines()
|
103 |
+
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
|
|
|
|
|
|
|
|
|
104 |
split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
|
105 |
if split_var1 == 'Specific Teams':
|
106 |
+
team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var1')
|
107 |
elif split_var1 == 'All':
|
108 |
+
team_var1 = player_stats.Team.values.tolist()
|
109 |
+
player_stats = player_stats[player_stats['Team'].isin(team_var1)]
|
110 |
+
player_stats = player_stats.set_index('Player')
|
111 |
+
player_stats = player_stats.sort_values(by='Fantasy', ascending=False)
|
112 |
+
st.dataframe(player_stats.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
|
113 |
st.download_button(
|
114 |
label="Export Prop Model",
|
115 |
+
data=convert_df_to_csv(player_stats),
|
116 |
+
file_name='NBA_stats_export.csv',
|
117 |
mime='text/csv',
|
118 |
key='pitcher_prop_export',
|
119 |
)
|
120 |
+
|
121 |
with tab3:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
122 |
st.info(t_stamp)
|
123 |
if st.button("Reset Data", key='reset4'):
|
124 |
st.cache_data.clear()
|
125 |
+
game_model, player_stats, prop_frame, timestamp = init_baselines()
|
126 |
+
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
|
|
|
|
|
|
|
|
|
127 |
col1, col2 = st.columns([1, 5])
|
128 |
|
129 |
with col2:
|
|
|
132 |
plot_hold_container = st.empty()
|
133 |
|
134 |
with col1:
|
135 |
+
player_check = st.selectbox('Select player to simulate props', options = player_stats['Player'].unique())
|
136 |
+
prop_type_var = st.selectbox('Select type of prop to simulate', options = ['points', 'rebounds', 'assists', 'blocks', 'steals',
|
137 |
+
'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists'])
|
138 |
|
139 |
ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
|
140 |
+
if prop_type_var == 'points':
|
141 |
+
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 15.5, step = .5)
|
142 |
+
elif prop_type_var == 'rebounds':
|
143 |
+
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5)
|
144 |
+
elif prop_type_var == 'assists':
|
145 |
+
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5)
|
146 |
+
elif prop_type_var == 'blocks':
|
147 |
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
|
148 |
+
elif prop_type_var == 'steals':
|
|
|
|
|
149 |
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
|
150 |
+
elif prop_type_var == 'PRA':
|
151 |
+
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 65.5, value = 20.5, step = .5)
|
152 |
+
elif prop_type_var == 'points+rebounds':
|
153 |
+
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
|
154 |
+
elif prop_type_var == 'points+assists':
|
155 |
+
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
|
156 |
+
elif prop_type_var == 'rebounds+assists':
|
157 |
+
prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
|
158 |
+
line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1500, max_value = 1500, value = -150, step = 1)
|
|
|
|
|
|
|
|
|
159 |
line_var = line_var + 1
|
160 |
|
161 |
if st.button('Simulate Prop'):
|
|
|
163 |
|
164 |
with df_hold_container.container():
|
165 |
|
166 |
+
df = player_stats
|
167 |
|
168 |
total_sims = 5000
|
169 |
|
|
|
172 |
player_var = df.loc[df['Player'] == player_check]
|
173 |
player_var = player_var.reset_index()
|
174 |
|
175 |
+
if prop_type_var == 'points':
|
176 |
+
df['Median'] = df['Points']
|
177 |
+
elif prop_type_var == 'rebounds':
|
178 |
+
df['Median'] = df['Rebounds']
|
179 |
+
elif prop_type_var == 'assists':
|
180 |
+
df['Median'] = df['Assists']
|
181 |
+
elif prop_type_var == 'blocks':
|
182 |
+
df['Median'] = df['Blocks']
|
183 |
+
elif prop_type_var == 'steals':
|
184 |
+
df['Median'] = df['Steals']
|
185 |
+
elif prop_type_var == 'PRA':
|
186 |
+
df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
|
187 |
+
elif prop_type_var == 'points+rebounds':
|
188 |
+
df['Median'] = df['Points'] + df['Rebounds']
|
189 |
+
elif prop_type_var == 'points+assists':
|
190 |
+
df['Median'] = df['Points'] + df['Assists']
|
191 |
+
elif prop_type_var == 'rebounds+assists':
|
192 |
+
df['Median'] = df['Assists'] + df['Rebounds']
|
|
|
|
|
193 |
|
194 |
flex_file = df
|
195 |
+
flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
|
196 |
+
flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
|
197 |
+
flex_file['STD'] = (flex_file['Median']/4)
|
198 |
flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
|
199 |
|
200 |
hold_file = flex_file
|
|
|
258 |
plot_hold_container = st.empty()
|
259 |
st.plotly_chart(fig, use_container_width=True)
|
260 |
|
261 |
+
with tab4:
|
262 |
st.info(t_stamp)
|
263 |
st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
|
264 |
if st.button("Reset Data/Load Data", key='reset5'):
|
265 |
st.cache_data.clear()
|
266 |
+
game_model, player_stats, prop_frame, timestamp = init_baselines()
|
267 |
+
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
|
|
|
|
|
|
|
|
|
268 |
col1, col2 = st.columns([1, 5])
|
269 |
|
270 |
with col2:
|
|
|
274 |
export_container = st.empty()
|
275 |
|
276 |
with col1:
|
277 |
+
prop_type_var = st.selectbox('Select prop category', options = ['points', 'rebounds', 'assists', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists'])
|
278 |
|
279 |
if st.button('Simulate Prop Category'):
|
280 |
with col2:
|
281 |
|
282 |
with df_hold_container.container():
|
283 |
|
284 |
+
if prop_type_var == "points":
|
285 |
+
prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
|
286 |
+
prop_df = prop_df.loc[prop_df['prop_type'] == 'points']
|
287 |
+
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
288 |
+
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
289 |
+
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
290 |
+
st.table(prop_df)
|
291 |
+
prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
|
292 |
+
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
|
293 |
+
df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
294 |
+
elif prop_type_var == "rebounds":
|
295 |
+
prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
|
296 |
+
prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds']
|
297 |
+
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
298 |
+
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
299 |
+
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
300 |
+
st.table(prop_df)
|
301 |
+
prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
|
302 |
+
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
|
303 |
+
df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
304 |
+
elif prop_type_var == "assists":
|
305 |
+
prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
|
306 |
+
prop_df = prop_df.loc[prop_df['prop_type'] == 'assists']
|
307 |
+
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
308 |
+
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
309 |
+
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
310 |
+
st.table(prop_df)
|
311 |
+
prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
|
312 |
+
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
|
313 |
+
df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
314 |
+
elif prop_type_var == "PRA":
|
315 |
prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
|
316 |
+
prop_df = prop_df.loc[prop_df['prop_type'] == 'PRA']
|
317 |
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
318 |
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
319 |
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
320 |
st.table(prop_df)
|
321 |
prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
|
322 |
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
|
323 |
+
df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
324 |
+
elif prop_type_var == "points+rebounds":
|
325 |
prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
|
326 |
+
prop_df = prop_df.loc[prop_df['prop_type'] == 'points+rebounds']
|
327 |
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
328 |
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
329 |
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
330 |
st.table(prop_df)
|
331 |
prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
|
332 |
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
|
333 |
+
df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
334 |
+
elif prop_type_var == "points+assists":
|
335 |
prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
|
336 |
+
prop_df = prop_df.loc[prop_df['prop_type'] == 'points+assists']
|
337 |
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
338 |
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
339 |
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
340 |
st.table(prop_df)
|
341 |
prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
|
342 |
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
|
343 |
+
df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
344 |
+
elif prop_type_var == "rebounds+assists":
|
345 |
+
prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
|
346 |
+
prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds+assists']
|
347 |
+
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
348 |
+
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
349 |
+
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
350 |
+
st.table(prop_df)
|
351 |
+
prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
|
352 |
+
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
|
353 |
+
df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
354 |
|
355 |
prop_dict = dict(zip(df.Player, df.Prop))
|
356 |
over_dict = dict(zip(df.Player, df.Over))
|
357 |
under_dict = dict(zip(df.Player, df.Under))
|
358 |
|
359 |
+
total_sims = 5000
|
360 |
|
361 |
df.replace("", 0, inplace=True)
|
362 |
|
363 |
+
if prop_type_var == 'points':
|
364 |
+
df['Median'] = df['Points']
|
365 |
+
elif prop_type_var == 'rebounds':
|
366 |
+
df['Median'] = df['Rebounds']
|
367 |
+
elif prop_type_var == 'assists':
|
368 |
+
df['Median'] = df['Assists']
|
369 |
+
elif prop_type_var == 'PRA':
|
370 |
+
df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
|
371 |
+
elif prop_type_var == 'points+rebounds':
|
372 |
+
df['Median'] = df['Points'] + df['Rebounds']
|
373 |
+
elif prop_type_var == 'points+assists':
|
374 |
+
df['Median'] = df['Points'] + df['Assists']
|
375 |
+
elif prop_type_var == 'rebounds+assists':
|
376 |
+
df['Median'] = df['Assists'] + df['Rebounds']
|
377 |
|
378 |
flex_file = df
|
379 |
+
flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
|
380 |
+
flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
|
381 |
+
flex_file['STD'] = (flex_file['Median']/4)
|
382 |
flex_file['Prop'] = flex_file['Player'].map(prop_dict)
|
383 |
flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
|
384 |
|
|
|
440 |
st.download_button(
|
441 |
label="Export Projections",
|
442 |
data=convert_df_to_csv(final_outcomes),
|
443 |
+
file_name='Nba_prop_proj.csv',
|
444 |
mime='text/csv',
|
445 |
key='prop_proj',
|
446 |
)
|