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NFL_Research_Sheets
File size: 8,897 Bytes 
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import pulp
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import time
import random
import scipy.stats

@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": "sheets-api-connect-378620",
            "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
            "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
            "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
            "client_id": "106625872877651920064",
            "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%40sheets-api-connect-378620.iam.gserviceaccount.com"
          }

          gc = gspread.service_account_from_dict(credentials)
          return gc

st.set_page_config(layout="wide")

gc = init_conn()

game_format = {'Dropback% Proj': '{:.2%}', 'DesRush%': '{:.2%}', 'Rush%': '{:.2%}'}

rb_util = {'Player Snaps%': '{:.2%}','Rush Att%': '{:.2%}', 'Routes%': '{:.2%}', 'Targets%': '{:.2%}', 'SDD Snaps%': '{:.2%}', 'i5 Rush%': '{:.2%}',
                   'LDD Snaps%': '{:.2%}','2-min%': '{:.2%}'}

wr_te_util = {'Routes%': '{:.2%}','Targets%': '{:.2%}', 'Air Yards%': '{:.2%}', 'Endzone Targets%': '{:.2%}', 'Third/Fourth%': '{:.2%}', 'Third/Fourth Targets%': '{:.2%}',
                   'Play Action Targets%': '{:.2%}','2-min%': '{:.2%}'}

all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=179416653'

@st.cache_resource(ttl = 300)
def rb_util_season():
    sh = gc.open_by_url(all_dk_player_projections)
    worksheet = sh.worksheet('RB_Util')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)      
    raw_display = raw_display[['player_name', 'position', 'week', 'team_season', 'player_snaps_per', 'rush_attempts_per', 'routes_per', 'targets_per',
                               'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
    raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%',
                               'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
    raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
    return raw_display

@st.cache_resource(ttl = 300)
def wr_te_util_season():
    sh = gc.open_by_url(all_dk_player_projections)
    worksheet = sh.worksheet('WR_TE_Util')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)      
    raw_display = raw_display[['player_name', 'position', 'week', 'team_season', 'routes_per', 'targets_per', 'tprr' , 'adot', 'air_yards_per',
                               'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
    raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%',
                               'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
    raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
    return raw_display

@st.cache_resource(ttl = 300)
def macro_pull():
    sh = gc.open_by_url(all_dk_player_projections)
    worksheet = sh.worksheet('FL_Macro')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.sort_values(by='Team Total', ascending=False)      

    return raw_display

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

rb_search = rb_util_season()
wr_search = wr_te_util_season()
macro_data = macro_pull()
pos_list = ['RB', 'WR', 'TE']

col1, col2 = st.columns([1, 8])

with col1:
    if st.button("Load/Reset Data", key='reset1'):
          st.cache_data.clear()
          rb_search = rb_util_season()
          wr_search = wr_te_util_season()
          macro_data = macro_pull()
    stat_type_var1 = st.radio("What table are you loading?", ('Macro Table', 'RB Usage', 'WR/TE Usage'), key='stat_type_var1')
    split_var1 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'), key='split_var1')
    pos_split1 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')      
    if pos_split1 == 'Specific Positions':
        pos_var1 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE'])
    elif pos_split1 == 'All Positions':
        pos_var1 = pos_list 
    if split_var1 == 'Specific Teams':
        team_var1 = st.multiselect('Which teams would you like to include in the Table?', options = rb_search['Team-Season'].unique(), key='team_var1')
    elif split_var1 == 'All Teams':
        team_var1 = rb_search['Team-Season'].unique().tolist()
    if stat_type_var1 == 'Macro Table':
        table_instance = macro_data
        table_instance = table_instance.set_index('team')  
    elif stat_type_var1 == 'RB Usage':
        table_instance = rb_search
        table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
        table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
    elif stat_type_var1 == 'WR/TE Usage':
        table_instance = wr_search    
        table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
        table_instance = table_instance[table_instance['Position'].isin(pos_var1)]

with col2:
    if stat_type_var1 == 'Macro Table':
        st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(game_format, precision=2), use_container_width = True)
    elif stat_type_var1 == 'RB Usage':
        st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(rb_util, precision=2), use_container_width = True)
    elif stat_type_var1 == 'WR/TE Usage':
        st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(wr_te_util, precision=2), use_container_width = True)

    st.download_button(
                    label="Export Tables",
                    data=convert_df_to_csv(table_instance),
                    file_name='MLB_Research_export.csv',
                    mime='text/csv',
    )