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| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import plotly.figure_factory as ff | |
| from itertools import combinations | |
| 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) | |
| st.set_page_config(layout="wide") | |
| master_hold = 'https://docs.google.com/spreadsheets/d/15flX6E7lPxu_HC7IOHpB3VEg2Am1AmtxTo9c2y_I-Mw/edit?gid=676575006#gid=676575006' | |
| def init_baselines(): | |
| sh = gc.open_by_url(master_hold) | |
| worksheet = sh.worksheet('ADPs (model)') | |
| adp_hold = pd.DataFrame(worksheet.get_all_records()) | |
| adp_hold = adp_hold[['Player', 'Team', 'Bye', 'Position', 'Position Rank', 'Underdog', 'MFL10', 'RTSPORTS', 'AVG', 'Projection', 'Proj ADP', 'Diff']] | |
| adp_table = adp_hold.drop_duplicates(subset='Player') | |
| worksheet = sh.worksheet('Stacks (model)') | |
| stacks_hold = pd.DataFrame(worksheet.get_all_records()) | |
| stacks_table = stacks_hold.drop_duplicates(subset='Team') | |
| worksheet = sh.worksheet('Player Level Projections') | |
| proj_hold = pd.DataFrame(worksheet.get_all_records()) | |
| proj_table = proj_hold[['Player', 'Team', 'Pos', 'Pass Yards', 'PassTD', 'Rush Yards', 'RushTD', 'Receptions', 'Rec Yards', 'RecTD', 'Proj']] | |
| return adp_table, stacks_table, proj_table | |
| adp_table, stacks_table, proj_table = init_baselines() | |
| # tab1, tab2, tab3 = st.tabs(["ADPs and Ranks", "Team Projections", "Stack Tool", "Player Prop Simulations", "Stat Specific Simulations", "Bet Sheet"]) | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| col1, col2 = st.columns([1, 5]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset4'): | |
| st.cache_data.clear() | |
| adp_table, stacks_table, proj_table = init_baselines() | |
| site_var2 = st.radio("What site are you playing?", ('Underdog', 'MFL10'), key='site_var2') | |
| split_var2 = st.radio("Would you like to run stack analysis for the full slate or individual teams?", ('All Teams', 'Specific Teams'), key='split_var2') | |
| if split_var2 == 'Specific Teams': | |
| team_var2 = st.multiselect('Which teams would you like to include in the analysis?', options = adp_table['Team'].unique(), key='team_var2') | |
| elif split_var2 == 'All Teams': | |
| team_var2 = adp_table.Team.unique().tolist() | |
| pos_split2 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split2') | |
| if pos_split2 == 'Specific Positions': | |
| pos_var2 = st.multiselect('What Positions would you like to view?', options = ['QB', 'RB', 'WR', 'TE']) | |
| elif pos_split2 == 'All Positions': | |
| pos_var2 = 'All' | |
| if site_var2 == 'Underdog': | |
| adp_dict = dict(zip(adp_table.Player, adp_table.Underdog)) | |
| elif site_var2 == 'MFL10': | |
| adp_dict = dict(zip(adp_table.Player, adp_table.MFL10)) | |
| size_var2 = st.number_input('What size of stacks are you analyzing?', min_value = 3, max_value = 6, step=1) | |
| stack_size = size_var2 | |
| team_dict = dict(zip(adp_table.Player, adp_table.Team)) | |
| proj_dict = dict(zip(adp_table.Player, adp_table.Projection)) | |
| diff_dict = dict(zip(adp_table.Player, adp_table.Diff)) | |
| with col2: | |
| stack_hold_container = st.empty() | |
| if st.button('Run stack analysis'): | |
| comb_list = [] | |
| if pos_split2 == 'All Positions': | |
| slate_teams = adp_table['Team'].values.tolist() | |
| raw_baselines = adp_table.copy() | |
| elif pos_split2 != 'All Positions': | |
| slate_teams = adp_table['Team'].values.tolist() | |
| raw_baselines = adp_table[adp_table['Position'].str.contains('|'.join(pos_var2))] | |
| for cur_team in team_var2: | |
| working_baselines = raw_baselines | |
| working_baselines = working_baselines[working_baselines['Team'] == cur_team] | |
| order_list = working_baselines['Player'] | |
| comb = combinations(order_list, stack_size) | |
| for i in list(comb): | |
| comb_list.append(i) | |
| comb_DF = pd.DataFrame(comb_list) | |
| if stack_size == 3: | |
| comb_DF['Team'] = comb_DF[0].map(team_dict) | |
| comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict), | |
| comb_DF[1].map(proj_dict), | |
| comb_DF[2].map(proj_dict)]) | |
| comb_DF['ADPs'] = str(comb_DF[0].map(adp_dict)) + ', ' + str(comb_DF[1].map(adp_dict)) + ', ' + str(comb_DF[2].map(adp_dict)) | |
| comb_DF['Value'] = sum([comb_DF[0].map(diff_dict), | |
| comb_DF[1].map(diff_dict), | |
| comb_DF[2].map(diff_dict)]) * -1 | |
| elif stack_size == 4: | |
| comb_DF['Team'] = comb_DF[0].map(team_dict) | |
| comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict), | |
| comb_DF[1].map(proj_dict), | |
| comb_DF[2].map(proj_dict), | |
| comb_DF[3].map(proj_dict)]) | |
| comb_DF['ADPs'] = str(comb_DF[0].map(adp_dict)) + ', ' + str(comb_DF[1].map(adp_dict)) + ', ' + str(comb_DF[2].map(adp_dict)) + ', ' + str(comb_DF[3].map(adp_dict)) | |
| comb_DF['Value'] = sum([comb_DF[0].map(diff_dict), | |
| comb_DF[1].map(diff_dict), | |
| comb_DF[2].map(diff_dict), | |
| comb_DF[3].map(diff_dict)]) * -1 | |
| elif stack_size == 5: | |
| comb_DF['Team'] = comb_DF[0].map(team_dict) | |
| comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict), | |
| comb_DF[1].map(proj_dict), | |
| comb_DF[2].map(proj_dict), | |
| comb_DF[3].map(proj_dict), | |
| comb_DF[4].map(proj_dict)]) | |
| comb_DF['ADPs'] = str(comb_DF[0].map(adp_dict)) + ', ' + str(comb_DF[1].map(adp_dict)) + ', ' + str(comb_DF[2].map(adp_dict)) + ', ' + str(comb_DF[3].map(adp_dict)) + ', ' + str(comb_DF[4].map(adp_dict)) | |
| comb_DF['Value'] = sum([comb_DF[0].map(diff_dict), | |
| comb_DF[1].map(diff_dict), | |
| comb_DF[2].map(diff_dict), | |
| comb_DF[3].map(diff_dict), | |
| comb_DF[4].map(diff_dict)]) * -1 | |
| elif stack_size == 6: | |
| comb_DF['Team'] = comb_DF[0].map(team_dict) | |
| comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict), | |
| comb_DF[1].map(proj_dict), | |
| comb_DF[2].map(proj_dict), | |
| comb_DF[3].map(proj_dict), | |
| comb_DF[4].map(proj_dict), | |
| comb_DF[5].map(proj_dict)]) | |
| comb_DF['ADPs'] = str(comb_DF[0].map(adp_dict)) + ', ' + str(comb_DF[1].map(adp_dict)) + ', ' + str(comb_DF[2].map(adp_dict)) + ', ' + str(comb_DF[3].map(adp_dict)) + ', ' + str(comb_DF[4].map(adp_dict)) + ', ' + str(comb_DF[5].map(adp_dict)) | |
| comb_DF['Value'] = sum([comb_DF[0].map(diff_dict), | |
| comb_DF[1].map(diff_dict), | |
| comb_DF[2].map(diff_dict), | |
| comb_DF[3].map(diff_dict), | |
| comb_DF[4].map(diff_dict), | |
| comb_DF[5].map(diff_dict)]) * -1 | |
| comb_DF = comb_DF.sort_values(by='Proj', ascending=False) | |
| # cut_var = 0 | |
| # if stack_size == 3: | |
| # while cut_var <= int(len(comb_DF)): | |
| # try: | |
| # if int(cut_var) == 0: | |
| # cur_proj = float(comb_DF.iat[cut_var,4]) | |
| # cur_own = float(comb_DF.iat[cut_var,6]) | |
| # elif int(cut_var) >= 1: | |
| # check_own = float(comb_DF.iat[cut_var,6]) | |
| # if check_own > cur_own: | |
| # comb_DF = comb_DF.drop([cut_var]) | |
| # cur_own = cur_own | |
| # cut_var = cut_var - 1 | |
| # comb_DF = comb_DF.reset_index() | |
| # comb_DF = comb_DF.drop(['index'], axis=1) | |
| # elif check_own <= cur_own: | |
| # cur_own = float(comb_DF.iat[cut_var,6]) | |
| # cut_var = cut_var | |
| # cut_var += 1 | |
| # except: | |
| # cut_var += 1 | |
| # elif stack_size == 4: | |
| # while cut_var <= int(len(comb_DF)): | |
| # try: | |
| # if int(cut_var) == 0: | |
| # cur_proj = float(comb_DF.iat[cut_var,5]) | |
| # cur_own = float(comb_DF.iat[cut_var,7]) | |
| # elif int(cut_var) >= 1: | |
| # check_own = float(comb_DF.iat[cut_var,7]) | |
| # if check_own > cur_own: | |
| # comb_DF = comb_DF.drop([cut_var]) | |
| # cur_own = cur_own | |
| # cut_var = cut_var - 1 | |
| # comb_DF = comb_DF.reset_index() | |
| # comb_DF = comb_DF.drop(['index'], axis=1) | |
| # elif check_own <= cur_own: | |
| # cur_own = float(comb_DF.iat[cut_var,7]) | |
| # cut_var = cut_var | |
| # cut_var += 1 | |
| # except: | |
| # cut_var += 1 | |
| # elif stack_size == 5: | |
| # while cut_var <= int(len(comb_DF)): | |
| # try: | |
| # if int(cut_var) == 0: | |
| # cur_proj = float(comb_DF.iat[cut_var,6]) | |
| # cur_own = float(comb_DF.iat[cut_var,8]) | |
| # elif int(cut_var) >= 1: | |
| # check_own = float(comb_DF.iat[cut_var,8]) | |
| # if check_own > cur_own: | |
| # comb_DF = comb_DF.drop([cut_var]) | |
| # cur_own = cur_own | |
| # cut_var = cut_var - 1 | |
| # comb_DF = comb_DF.reset_index() | |
| # comb_DF = comb_DF.drop(['index'], axis=1) | |
| # elif check_own <= cur_own: | |
| # cur_own = float(comb_DF.iat[cut_var,8]) | |
| # cut_var = cut_var | |
| # cut_var += 1 | |
| # except: | |
| # cut_var += 1 | |
| with stack_hold_container: | |
| stack_hold_container = st.empty() | |
| st.dataframe(comb_DF.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(comb_DF), | |
| file_name='NFL_Stack_Options_export.csv', | |
| mime='text/csv', | |
| ) |