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Update app.py
Browse files
app.py
CHANGED
@@ -1,17 +1,6 @@
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import pulp
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import numpy as np
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import pandas as pd
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import random
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import sys
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import openpyxl
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import re
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import time
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import streamlit as st
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import matplotlib
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from matplotlib.colors import LinearSegmentedColormap
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from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode
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import json
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import requests
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import gspread
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import plotly.figure_factory as ff
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@@ -41,91 +30,42 @@ american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead
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master_hold = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=340831852'
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@st.
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def
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Pitcher_Stats')
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props_frame_hold = pd.DataFrame(worksheet.get_all_records())
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props_frame_hold.rename(columns={"Names": "Player"}, inplace = True)
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props_frame_hold = props_frame_hold[['Player', 'Team', 'BB', 'Hits', 'HRs', 'ERs', 'Ks', 'Outs', 'Fantasy', 'FD_Fantasy', 'PrizePicks']]
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return props_frame_hold
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@st.cache_data
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def load_time():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Timestamp')
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raw_stamp = worksheet.acell('a1').value
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t_stamp = f"Last update was at {raw_stamp}"
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return t_stamp
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@st.cache_data
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def load_hitter_props():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Hitter_Stats')
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props_frame_hold = pd.DataFrame(worksheet.get_all_records())
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props_frame_hold.rename(columns={"Names": "Player"}, inplace = True)
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props_frame_hold = props_frame_hold[['Player', 'Team', 'Walks', 'Steals', 'Hits', 'Singles', 'Doubles', 'HRs', 'RBIs', 'Runs', 'Fantasy', 'FD_Fantasy', 'PrizePicks']]
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props_frame_hold['Total Bases'] = props_frame_hold['Singles'] + (props_frame_hold['Doubles'] * 2) + (props_frame_hold['HRs'] * 4)
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props_frame_hold['Hits + Runs + RBIs'] = props_frame_hold['Hits'] + props_frame_hold['Runs'] + props_frame_hold['RBIs']
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return props_frame_hold
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@st.cache_data
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def load_team_table():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Game_Betting_Model')
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team_frame = pd.DataFrame(worksheet.get_all_records())
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team_frame = team_frame.drop_duplicates(subset='Names')
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team_frame['Win Percentage'] = team_frame['Win Percentage'].str.replace('%', '').astype('float')/100
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team_frame['Cover Spread Percentage'] = team_frame['Cover Spread Percentage'].str.replace('%', '').astype('float')/100
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Strikeout_Props')
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prop_type_frame = pd.DataFrame(worksheet.get_all_records())
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prop_type_frame = prop_type_frame.drop_duplicates(subset='Player')
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return prop_type_frame
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@st.cache_data
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def load_total_outs_props():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Total_Outs_Props')
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prop_type_frame = pd.DataFrame(worksheet.get_all_records())
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prop_type_frame = prop_type_frame.drop_duplicates(subset='Player')
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return prop_type_frame
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@st.cache_data
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def load_total_bases_props():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Total_Base_Props')
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prop_type_frame = pd.DataFrame(worksheet.get_all_records())
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prop_type_frame = prop_type_frame.drop_duplicates(subset='Player')
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return prop_type_frame
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@st.cache_data
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def load_stolen_bases_props():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('SB_Props')
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prop_type_frame = pd.DataFrame(worksheet.get_all_records())
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prop_type_frame = prop_type_frame.drop_duplicates(subset='Player')
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return
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hitter_frame_hold = load_hitter_props()
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team_frame_hold = load_team_table()
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t_stamp = load_time()
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tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "Pitcher Prop Projections", "Hitter Prop Projections", "Player Prop Simulations", "Stat Specific Simulations"])
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@@ -136,12 +76,8 @@ 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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hitter_frame_hold = load_hitter_props()
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team_frame_hold = load_team_table()
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t_stamp = load_time()
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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 = team_frame_hold
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if line_var1 == 'Percentage':
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team_frame = team_frame[['Names', 'Game', 'Win Percentage', 'Spread', 'Cover Spread Percentage', 'Avg Score', 'Game Total', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage']]
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team_frame = team_frame.set_index('Names')
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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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hitter_frame_hold = load_hitter_props()
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team_frame_hold = load_team_table()
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t_stamp = load_time()
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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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pitcher_frame =
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pitcher_frame = pitcher_frame.sort_values(by='Ks', ascending=False)
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st.dataframe(pitcher_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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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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hitter_frame_hold = load_hitter_props()
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team_frame_hold = load_team_table()
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t_stamp = load_time()
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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 =
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elif split_var2 == 'All':
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team_var2 =
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hitter_frame =
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hitter_frame = hitter_frame.sort_values(by='Hits + Runs + RBIs', ascending=False)
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st.dataframe(hitter_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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@@ -214,10 +144,7 @@ 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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hitter_frame_hold = load_hitter_props()
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team_frame_hold = load_team_table()
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t_stamp = load_time()
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col1, col2 = st.columns([1, 5])
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with col2:
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with col1:
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prop_group_var = st.selectbox('What kind of props are you simulating?', options = ['Pitchers', 'Hitters'])
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if prop_group_var == 'Pitchers':
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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 = ['Strikeouts', 'Walks', 'Hits', 'Homeruns', 'Earned Runs', 'Outs', 'Fantasy', 'FD_Fantasy', 'PrizePicks'])
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elif prop_group_var == 'Hitters':
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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 = ['Total Bases', 'Walks', 'Steals', 'Hits', 'Singles', 'Doubles', 'Homeruns', 'RBIs', 'Runs', 'Hits + Runs + RBIs', 'Fantasy', 'FD_Fantasy', 'PrizePicks'])
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ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
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with df_hold_container.container():
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if prop_group_var == 'Pitchers':
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df =
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elif prop_group_var == 'Hitters':
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df =
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total_sims = 1000
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# Clear values from *all* all in-memory and on-disk data caches:
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# i.e. clear values from both square and cube
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st.cache_data.clear()
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t_stamp =
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col1, col2 = st.columns([1, 5])
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with col2:
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with df_hold_container.container():
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if prop_type_var == "Strikeouts (Pitchers)":
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player_df =
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prop_df =
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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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prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
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df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
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elif prop_type_var == "Total Outs (Pitchers)":
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player_df =
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prop_df =
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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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prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
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df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
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elif prop_type_var == "Total Bases (Hitters)":
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player_df =
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prop_df =
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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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prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
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df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
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elif prop_type_var == "Stolen Bases (Hitters)":
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player_df =
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prop_df =
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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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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.figure_factory as ff
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master_hold = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=340831852'
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@st.cache_resource(ttl = 299)
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def init_baselines():
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sh = gc.open_by_url(master_hold)
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worksheet = sh.worksheet('Pitcher_Stats')
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props_frame_hold = pd.DataFrame(worksheet.get_all_records())
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props_frame_hold.rename(columns={"Names": "Player"}, inplace = True)
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props_frame_hold = props_frame_hold[['Player', 'Team', 'BB', 'Hits', 'HRs', 'ERs', 'Ks', 'Outs', 'Fantasy', 'FD_Fantasy', 'PrizePicks']]
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pitcher_stats = props_frame_hold.drop_duplicates(subset='Player')
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worksheet = sh.worksheet('Timestamp')
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raw_stamp = worksheet.acell('a1').value
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t_stamp = f"Last update was at {raw_stamp}"
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worksheet = sh.worksheet('Hitter_Stats')
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props_frame_hold = pd.DataFrame(worksheet.get_all_records())
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props_frame_hold.rename(columns={"Names": "Player"}, inplace = True)
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props_frame_hold = props_frame_hold[['Player', 'Team', 'Walks', 'Steals', 'Hits', 'Singles', 'Doubles', 'HRs', 'RBIs', 'Runs', 'Fantasy', 'FD_Fantasy', 'PrizePicks']]
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props_frame_hold['Total Bases'] = props_frame_hold['Singles'] + (props_frame_hold['Doubles'] * 2) + (props_frame_hold['HRs'] * 4)
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props_frame_hold['Hits + Runs + RBIs'] = props_frame_hold['Hits'] + props_frame_hold['Runs'] + props_frame_hold['RBIs']
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hitter_stats = props_frame_hold.drop_duplicates(subset='Player')
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worksheet = sh.worksheet('Game_Betting_Model')
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team_frame = pd.DataFrame(worksheet.get_all_records())
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team_frame = team_frame.drop_duplicates(subset='Names')
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team_frame['Win Percentage'] = team_frame['Win Percentage'].str.replace('%', '').astype('float')/100
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team_frame['Cover Spread Percentage'] = team_frame['Cover Spread Percentage'].str.replace('%', '').astype('float')/100
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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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prop_frame = raw_display.dropna(subset='Team')
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return pitcher_stats, hitter_stats, team_frame, prop_frame, t_stamp
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pitcher_stats, hitter_stats, team_frame, prop_frame, t_stamp = init_baselines()
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tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "Pitcher Prop Projections", "Hitter Prop Projections", "Player Prop Simulations", "Stat Specific Simulations"])
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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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pitcher_stats, hitter_stats, team_frame, prop_frame, t_stamp = init_baselines()
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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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if line_var1 == 'Percentage':
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team_frame = team_frame[['Names', 'Game', 'Win Percentage', 'Spread', 'Cover Spread Percentage', 'Avg Score', 'Game Total', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage']]
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team_frame = team_frame.set_index('Names')
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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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pitcher_stats, hitter_stats, team_frame, prop_frame, t_stamp = init_baselines()
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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 = pitcher_stats['Team'].unique(), key='team_var1')
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elif split_var1 == 'All':
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team_var1 = pitcher_stats.Team.values.tolist()
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pitcher_stats = pitcher_stats[pitcher_stats['Team'].isin(team_var1)]
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pitcher_frame = pitcher_stats.set_index('Player')
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pitcher_frame = pitcher_frame.sort_values(by='Ks', ascending=False)
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st.dataframe(pitcher_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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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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pitcher_stats, hitter_stats, team_frame, prop_frame, t_stamp = init_baselines()
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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 = hitter_stats['Team'].unique(), key='team_var2')
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elif split_var2 == 'All':
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team_var2 = hitter_stats.Team.values.tolist()
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hitter_stats = hitter_stats[hitter_stats['Team'].isin(team_var2)]
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hitter_frame = hitter_stats.set_index('Player')
|
133 |
hitter_frame = hitter_frame.sort_values(by='Hits + Runs + RBIs', ascending=False)
|
134 |
st.dataframe(hitter_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
|
135 |
st.download_button(
|
|
|
144 |
st.info(t_stamp)
|
145 |
if st.button("Reset Data", key='reset4'):
|
146 |
st.cache_data.clear()
|
147 |
+
pitcher_stats, hitter_stats, team_frame, prop_frame, t_stamp = init_baselines()
|
|
|
|
|
|
|
148 |
col1, col2 = st.columns([1, 5])
|
149 |
|
150 |
with col2:
|
|
|
155 |
with col1:
|
156 |
prop_group_var = st.selectbox('What kind of props are you simulating?', options = ['Pitchers', 'Hitters'])
|
157 |
if prop_group_var == 'Pitchers':
|
158 |
+
player_check = st.selectbox('Select player to simulate props', options = pitcher_stats['Player'].unique())
|
159 |
prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Strikeouts', 'Walks', 'Hits', 'Homeruns', 'Earned Runs', 'Outs', 'Fantasy', 'FD_Fantasy', 'PrizePicks'])
|
160 |
elif prop_group_var == 'Hitters':
|
161 |
+
player_check = st.selectbox('Select player to simulate props', options = hitter_stats['Player'].unique())
|
162 |
prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Total Bases', 'Walks', 'Steals', 'Hits', 'Singles', 'Doubles', 'Homeruns', 'RBIs', 'Runs', 'Hits + Runs + RBIs', 'Fantasy', 'FD_Fantasy', 'PrizePicks'])
|
163 |
|
164 |
ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
|
|
|
172 |
with df_hold_container.container():
|
173 |
|
174 |
if prop_group_var == 'Pitchers':
|
175 |
+
df = pitcher_stats
|
176 |
elif prop_group_var == 'Hitters':
|
177 |
+
df = hitter_stats
|
178 |
|
179 |
total_sims = 1000
|
180 |
|
|
|
311 |
# Clear values from *all* all in-memory and on-disk data caches:
|
312 |
# i.e. clear values from both square and cube
|
313 |
st.cache_data.clear()
|
314 |
+
pitcher_stats, hitter_stats, team_frame, prop_frame, t_stamp = init_baselines()
|
315 |
col1, col2 = st.columns([1, 5])
|
316 |
|
317 |
with col2:
|
|
|
329 |
with df_hold_container.container():
|
330 |
|
331 |
if prop_type_var == "Strikeouts (Pitchers)":
|
332 |
+
player_df = pitcher_stats
|
333 |
+
prop_df = pitcher_stats[pitcher_stats['prop_type'] == 'pitcher_strikeouts']
|
334 |
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
335 |
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
336 |
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
|
|
338 |
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
|
339 |
df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
340 |
elif prop_type_var == "Total Outs (Pitchers)":
|
341 |
+
player_df = pitcher_stats
|
342 |
+
prop_df = pitcher_stats[pitcher_stats['prop_type'] == 'pitcher_strikeouts']
|
343 |
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
344 |
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
345 |
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
|
|
347 |
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
|
348 |
df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
349 |
elif prop_type_var == "Total Bases (Hitters)":
|
350 |
+
player_df = hitter_stats
|
351 |
+
prop_df = hitter_stats[hitter_stats['prop_type'] == 'batter_total_bases']
|
352 |
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
353 |
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
354 |
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|
|
|
356 |
prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
|
357 |
df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
|
358 |
elif prop_type_var == "Stolen Bases (Hitters)":
|
359 |
+
player_df = hitter_stats
|
360 |
+
prop_df = hitter_stats[hitter_stats['prop_type'] == 'batter_stolen_bases']
|
361 |
prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
|
362 |
prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
|
363 |
prop_df = prop_df.loc[prop_df['Prop'] != 0]
|