app.py

import streamlit as st
st.set_page_config(layout="wide")

for name in dir():
    if not name.startswith('_'):
        del globals()[name]

import numpy as np
import pandas as pd
import streamlit as st
import gspread
import plotly.express as px
import random
import gc

@st.cache_resource
def init_conn():
        scope = ['https://www.googleapis.com/auth/spreadsheets',
        "https://www.googleapis.com/auth/drive"]

        credentials = {
          "type": "service_account",
          "project_id": "model-sheets-connect",
          "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
          "client_email": "[email protected]",
          "client_id": "100369174533302798535",
          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
          "token_uri": "https://oauth2.googleapis.com/token",
          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
        }

        gc_con = gspread.service_account_from_dict(credentials)
      
        return gc_con

gcservice_account = init_conn()

master_hold = 'https://docs.google.com/spreadsheets/d/1D526UlXmrz-8qxVcUKrA-u7f6FftUiBufxDnzQv980k/edit#gid=791804525'


sim_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}'}

@st.cache_resource(ttl = 600)
def init_baselines():
    sh = gcservice_account.open_by_url(master_hold)
    worksheet = sh.worksheet('Pitcher_Proj')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", np.nan, inplace=True)
    pitcher_proj = raw_display.dropna()

    sh = gcservice_account.open_by_url(master_hold)
    worksheet = sh.worksheet('Hitter_Proj')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", np.nan, inplace=True)
    hitter_proj = raw_display.dropna()
    
    sh = gcservice_account.open_by_url(master_hold)
    worksheet = sh.worksheet('Display')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    wins_proj = raw_display.dropna()
    
    return pitcher_proj, hitter_proj, wins_proj

def convert_df_to_csv(df):
    return df.to_csv().encode('utf-8')

pitcher_proj, hitter_proj, wins_proj = init_baselines()
total_teams = pitcher_proj['Team'].values.tolist()

tab1, tab2, tab3, tab4, tab5 = st.tabs(["Team Win Projections", "Pitcher Projections", "Hitter Projections", "Pitcher Simulations", "Hitter Simulations"])

with tab1:
    if st.button("Reset Data", key='reset1'):
              st.cache_data.clear()
              pitcher_proj, hitter_proj, wins_proj = init_baselines()
              total_teams = pitcher_proj['Team'].values.tolist()
    raw_frame = wins_proj.copy()
    export_frame_team = raw_frame[['Team', '2B', 'HR', 'SB', 'P_SO', 'P_H', 'P_R', 'P_HR', 'P_BB', 'LY Added', 'Added', 'LY Adj Wins', 'Adj Wins', 'Vegas', 'Proj wins', 'Diff']]
    export_frame_team = export_frame_team.sort_values(by='Proj wins', ascending=False)
    disp_frame = raw_frame[['Team', '2B', 'HR', 'SB', 'P_SO', 'P_H', 'P_R', 'P_HR', 'P_BB', 'LY Added', 'Added', 'LY Adj Wins', 'Adj Wins', 'Vegas', 'Proj wins', 'Diff']]
    disp_frame = disp_frame.sort_values(by='Proj wins', ascending=False)

    st.dataframe(disp_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
    
    st.download_button(
        label="Export Team Win Projections",
        data=convert_df_to_csv(export_frame_team),
        file_name='MLB_team_win_export.csv',
        mime='text/csv',
        key='team_win_export',
    )

with tab2:
    if st.button("Reset Data", key='reset2'):
              st.cache_data.clear()
              pitcher_proj, hitter_proj, wins_proj = init_baselines()
              total_teams = pitcher_proj['Team'].values.tolist()
    raw_frame = pitcher_proj.copy()
    split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
    if split_var1 == 'Specific Teams':
        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='team_var1')
    elif split_var1 == 'All':
        team_var1 = total_teams
    
    working_data = raw_frame[raw_frame['Team'].isin(team_var1)]
    export_frame_sp = raw_frame[['Name', 'Team', 'TBF', 'Ceiling_var', 'True_AVG', 'Hits', 'Singles%', 'Singles', 'Doubles%', 'Doubles', 'xHR%', 'Homeruns', 'Strikeout%', 'Strikeouts',
                              'Walk%', 'Walks', 'Runs%', 'Runs', 'ERA', 'Wins', 'Quality_starts', 'ADP', 'UD_fpts', 'DK_fpts']]
    disp_frame_sp = working_data[['Name', 'Team', 'TBF', 'True_AVG', 'Hits', 'Singles', 'Doubles', 'Homeruns', 'Strikeouts',
                            'Walks', 'Runs', 'ERA', 'Wins', 'Quality_starts', 'ADP', 'UD_fpts', 'DK_fpts']]
    disp_frame_sp = disp_frame_sp.sort_values(by='UD_fpts', ascending=False)
    st.dataframe(disp_frame_sp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn_r').background_gradient(cmap='RdYlGn', subset=['TBF', 'Strikeouts', 'Wins', 'Quality_starts', 'UD_fpts', 'DK_fpts']).format(precision=2), height = 1000, use_container_width = True)
    
    st.download_button(
        label="Export Pitcher Projections",
        data=convert_df_to_csv(export_frame_sp),
        file_name='MLB_pitcher_proj_export.csv',
        mime='text/csv',
        key='pitcher_proj_export',
    )

with tab3:
    if st.button("Reset Data", key='reset3'):
              st.cache_data.clear()
              pitcher_proj, hitter_proj, wins_proj = init_baselines()
              total_teams = pitcher_proj['Team'].values.tolist()
    raw_frame = hitter_proj.copy()
    split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
    if split_var2 == 'Specific Teams':
        team_var2 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='team_var2')
    elif split_var2 == 'All':
        team_var2 = total_teams
    
    working_data = raw_frame[raw_frame['Team'].isin(team_var2)]
    export_frame_h = raw_frame[['Name', 'Team', 'PA', 'Ceiling_var', 'Walk%', 'Walks', 'xHits', 'Singles%', 'Singles', 'Doubles%', 'Doubles',
                              'xHR%', 'Homeruns', 'Runs%', 'Runs', 'RBI%', 'RBI', 'Steal%', 'Stolen_bases', 'ADP', 'UD_fpts', 'DK_fpts']]
    disp_frame_h = working_data[['Name', 'Team', 'PA', 'Walks', 'xHits', 'Singles', 'Doubles',
                            'Homeruns', 'Runs', 'RBI', 'Stolen_bases', 'ADP', 'UD_fpts', 'DK_fpts']]
    disp_frame_h = disp_frame_h.sort_values(by='UD_fpts', ascending=False)
    st.dataframe(disp_frame_h.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['ADP']).format(precision=2), height = 1000, use_container_width = True)
    
    st.download_button(
        label="Export Hitter Projections",
        data=convert_df_to_csv(export_frame_h),
        file_name='MLB_hitter_proj_export.csv',
        mime='text/csv',
        key='hitter_proj_export',
    )
    
with tab4:
    if st.button("Reset Data", key='reset4'):
              st.cache_data.clear()
              pitcher_proj, hitter_proj, wins_proj = init_baselines()
              total_teams = pitcher_proj['Team'].values.tolist()
    col1, col2 = st.columns([1, 5])
    
    with col2:
        df_hold_container = st.empty()
    
    with col1:
        prop_type_var_sp = st.selectbox('Select type of prop to simulate', options = ['Strikeouts', 'Wins', 'Quality_starts'], key='prop_type_var_sp')

        if st.button('Simulate Stat', key='sim_sp'):
            with col2:
                   
                    with df_hold_container.container():

                        df = pitcher_proj.copy()
                        
                        total_sims = 5000

                        df.replace("", 0, inplace=True)

                        if prop_type_var_sp == 'Strikeouts':
                            df['Median'] = df['Strikeouts']
                            stat_cap = 300
                        elif prop_type_var_sp == 'Wins':
                            df['Median'] = df['Wins']
                            stat_cap = 25
                        elif prop_type_var_sp == 'Quality_starts':
                            df['Median'] = df['Quality_starts']
                            stat_cap = 30

                        flex_file = df.copy()
                        flex_file.rename(columns={"Name": "Player"}, inplace = True)
                        flex_file['Floor'] = (flex_file['Median'] * .25)
                        flex_file['Ceiling'] = np.where((flex_file['Median'] + (flex_file['Median'] * flex_file['Ceiling_var'])) > stat_cap, stat_cap + (flex_file['Median']/10), (flex_file['Median'] + (flex_file['Median'] * flex_file['Ceiling_var'])))
                        flex_file['STD'] = (flex_file['Median']/3)
                        flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]

                        hold_file = flex_file.copy()
                        hold_file = hold_file.sort_values(by='Median', ascending=False)
                        overall_file = flex_file.copy()
                        overall_file = overall_file.sort_values(by='Median', ascending=False)

                        overall_players = overall_file[['Player']]

                        for x in range(0,total_sims):
                            overall_file['g'] = np.random.gumbel(overall_file['Median'] * .75,overall_file['STD'])
                            overall_file[x] = np.where((overall_file['g']<=overall_file['Ceiling']),overall_file['g'],overall_file['Ceiling'])

                        check_file = overall_file.copy()
                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD', 'g'], axis=1)
                        overall_file.astype('int').dtypes

                        players_only = hold_file[['Player']]
                        raw_lineups_file = players_only.copy()

                        for x in range(0,total_sims):
                            maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
                            raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                            players_only[x] = raw_lineups_file[x].rank(ascending=False)

                        players_only=players_only.drop(['Player'], axis=1)
                        players_only.astype('int').dtypes

                        players_only['Average_Rank'] = players_only.mean(axis=1)
                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
                        players_only['90%'] = overall_file.quantile(0.9, axis=1)

                        players_only['Player'] = hold_file[['Player']]

                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', '90%']]

                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                        final_Proj = final_Proj[['Player', '10%', 'Median', '90%', 'Top_finish', 'Top_5_finish', 'Top_10_finish']]
                        final_Proj.rename(columns={"Median": "Projection"}, inplace = True)
                        
                    with df_hold_container.container():     
                        df_hold_container = st.empty()
                        st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(sim_format, precision=2), use_container_width = True)
    
with tab5:
    if st.button("Reset Data", key='reset5'):
              st.cache_data.clear()
              pitcher_proj, hitter_proj, wins_proj = init_baselines()
              total_teams = pitcher_proj['Team'].values.tolist()
    col1, col2 = st.columns([1, 5])
    
    with col2:
        df_hold_container = st.empty()
    
    with col1:
        prop_type_var_h = st.selectbox('Select type of prop to simulate', options = ['Hits', 'Doubles', 'Home Runs', 'RBI', 'Stolen Bases'], key='prop_type_var_h')
        

        if st.button('Simulate Stat', key='sim_h'):
            with col2:
                   
                    with df_hold_container.container():

                        df = hitter_proj.copy()
                        
                        total_sims = 5000

                        df.replace("", 0, inplace=True)

                        if prop_type_var_h == 'Hits':
                            df['Median'] = df['xHits']
                            stat_cap = 250
                        elif prop_type_var_h == 'Doubles':
                            df['Median'] = df['Doubles']
                            stat_cap = 65
                        elif prop_type_var_h == 'Home Runs':
                            df['Median'] = df['Homeruns']
                            stat_cap = 75
                        elif prop_type_var_h == 'RBI':
                            df['Median'] = df['RBI']
                            stat_cap = 150
                        elif prop_type_var_h == 'Stolen Bases':
                            df['Median'] = df['Stolen_bases']
                            stat_cap = 80

                        flex_file = df.copy()
                        flex_file.rename(columns={"Name": "Player"}, inplace = True)
                        flex_file['Floor'] = (flex_file['Median'] * .15)
                        flex_file['Ceiling'] = np.where((flex_file['Median'] + (flex_file['Median'] * flex_file['Ceiling_var'])) > stat_cap, stat_cap + (flex_file['Median']/20), (flex_file['Median'] + (flex_file['Median'] * flex_file['Ceiling_var'])))
                        flex_file['STD'] = (flex_file['Median']/2)
                        flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]

                        hold_file = flex_file.copy()
                        hold_file = hold_file.sort_values(by='Median', ascending=False)
                        overall_file = flex_file.copy()
                        overall_file = overall_file.sort_values(by='Median', ascending=False)

                        overall_players = overall_file[['Player']]

                        for x in range(0,total_sims):
                            overall_file['g'] = np.random.gumbel(overall_file['Median'] * .5,overall_file['STD'])
                            overall_file[x] = np.where((overall_file['g']<=overall_file['Ceiling']),overall_file['g'],overall_file['Ceiling'])

                        check_file = overall_file.copy()
                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD', 'g'], axis=1)
                        overall_file.astype('int').dtypes

                        players_only = hold_file[['Player']]
                        raw_lineups_file = players_only.copy()

                        for x in range(0,total_sims):
                            maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
                            raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                            players_only[x] = raw_lineups_file[x].rank(ascending=False)

                        players_only=players_only.drop(['Player'], axis=1)
                        players_only.astype('int').dtypes

                        players_only['Average_Rank'] = players_only.mean(axis=1)
                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
                        players_only['90%'] = overall_file.quantile(0.9, axis=1)

                        players_only['Player'] = hold_file[['Player']]

                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', '90%']]

                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                        final_Proj = final_Proj[['Player', '10%', 'Median', '90%', 'Top_finish', 'Top_5_finish', 'Top_10_finish']]
                        final_Proj.rename(columns={"Median": "Projection"}, inplace = True)

                    with df_hold_container.container():     
                        df_hold_container = st.empty()
                        st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(sim_format, precision=2), use_container_width = True)