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| import pulp | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| from itertools import combinations | |
| 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" | |
| } | |
| credentials2 = { | |
| "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) | |
| gc2 = gspread.service_account_from_dict(credentials2) | |
| st.set_page_config(layout="wide") | |
| game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', | |
| 'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'} | |
| player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', | |
| '4x%': '{:.2%}','GPP%': '{:.2%}'} | |
| wrong_acro = ['AZ', 'CHW'] | |
| right_acro = ['ARI', 'CWS'] | |
| dk_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852' | |
| fd_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852' | |
| secondary_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852' | |
| secondary_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852' | |
| all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479' | |
| all_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479' | |
| final_Proj = 0 | |
| def load_time(): | |
| try: | |
| sh = gc.open_by_url(dk_player_projections) | |
| worksheet = sh.worksheet('Timestamp') | |
| raw_stamp = worksheet.acell('a1').value | |
| except: | |
| sh = gc2.open_by_url(dk_player_projections) | |
| worksheet = sh.worksheet('Timestamp') | |
| raw_stamp = worksheet.acell('a1').value | |
| t_stamp = f"Last update was at {raw_stamp}" | |
| return t_stamp | |
| def set_slate_teams(): | |
| try: | |
| sh = gc.open_by_url(all_dk_player_projections) | |
| worksheet = sh.worksheet('Site_Info') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| except: | |
| sh = gc2.open_by_url(all_dk_player_projections) | |
| worksheet = sh.worksheet('Site_Info') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['DK Main'] = raw_display['DK Main'].replace(wrong_acro, right_acro) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['DK Secondary'] = raw_display['DK Secondary'].replace(wrong_acro, right_acro) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['DK Overall'] = raw_display['DK Overall'].replace(wrong_acro, right_acro) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['FD Main'] = raw_display['FD Main'].replace(wrong_acro, right_acro) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['FD Secondary'] = raw_display['FD Secondary'].replace(wrong_acro, right_acro) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['FD Overall'] = raw_display['FD Overall'].replace(wrong_acro, right_acro) | |
| return raw_display | |
| def load_dk_player_projections(URL): | |
| try: | |
| sh = gc.open_by_url(URL) | |
| worksheet = sh.worksheet('DK_Projections') | |
| except: | |
| sh = gc2.open_by_url(URL) | |
| worksheet = sh.worksheet('DK_Projections') | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| load_display.replace('', np.nan, inplace=True) | |
| load_display = load_display.drop_duplicates(subset='Player') | |
| raw_display = load_display.dropna(subset=['Median']) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['Team'] = raw_display['Team'].replace(wrong_acro, right_acro) | |
| return raw_display | |
| def load_fd_player_projections(URL): | |
| try: | |
| sh = gc.open_by_url(URL) | |
| worksheet = sh.worksheet('FD_Projections') | |
| except: | |
| sh = gc2.open_by_url(URL) | |
| worksheet = sh.worksheet('FD_Projections') | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| load_display.replace('', np.nan, inplace=True) | |
| load_display = load_display.drop_duplicates(subset='Player') | |
| raw_display = load_display.dropna(subset=['Median']) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['Team'] = raw_display['Team'].replace(wrong_acro, right_acro) | |
| return raw_display | |
| def load_dk_player_roo(tab): | |
| try: | |
| sh = gc.open_by_url(all_dk_player_projections) | |
| worksheet = sh.worksheet(tab) | |
| except: | |
| sh = gc2.open_by_url(all_dk_player_projections) | |
| worksheet = sh.worksheet(tab) | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display = load_display | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['Team'] = raw_display['Team'].replace(wrong_acro, right_acro) | |
| return raw_display | |
| def load_fd_player_roo(tab): | |
| try: | |
| sh = gc.open_by_url(all_dk_player_projections) | |
| worksheet = sh.worksheet(tab) | |
| except: | |
| sh = gc2.open_by_url(all_dk_player_projections) | |
| worksheet = sh.worksheet(tab) | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display = load_display | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['Team'] = raw_display['Team'].replace(wrong_acro, right_acro) | |
| return raw_display | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| t_stamp = load_time() | |
| site_slates = set_slate_teams() | |
| col1, col2, col3, col4, col5 = st.columns([2, 2, 2, 2, 2]) | |
| #st.info(t_stamp) | |
| if st.button("Load/Reset Data", key='reset30'): | |
| t_stamp = load_time() | |
| st.cache_data.clear() | |
| with col1: | |
| slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1') | |
| with col2: | |
| site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1') | |
| with col3: | |
| custom_var1 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var1') | |
| if custom_var1 == 'No': | |
| if slate_var1 == 'Main Slate': | |
| if site_var1 == 'Draftkings': | |
| slate_teams = site_slates['DK Main'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif site_var1 == 'Fanduel': | |
| slate_teams = site_slates['FD Main'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif slate_var1 == 'Secondary Slate': | |
| if site_var1 == 'Draftkings': | |
| slate_teams = site_slates['DK Secondary'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif site_var1 == 'Fanduel': | |
| slate_teams = site_slates['FD Secondary'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif slate_var1 == 'All Games': | |
| if site_var1 == 'Draftkings': | |
| slate_teams = site_slates['DK Overall'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif site_var1 == 'Fanduel': | |
| slate_teams = site_slates['FD Overall'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| if custom_var1 == 'Yes': | |
| contest_var1 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1') | |
| if slate_var1 == 'Main Slate': | |
| if site_var1 == 'Draftkings': | |
| slate_teams = site_slates['DK Main'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif site_var1 == 'Fanduel': | |
| slate_teams = site_slates['FD Main'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif slate_var1 == 'Secondary Slate': | |
| if site_var1 == 'Draftkings': | |
| slate_teams = site_slates['DK Secondary'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif site_var1 == 'Fanduel': | |
| slate_teams = site_slates['FD Secondary'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif slate_var1 == 'All Games': | |
| if site_var1 == 'Draftkings': | |
| slate_teams = site_slates['DK Overall'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| elif site_var1 == 'Fanduel': | |
| slate_teams = site_slates['FD Overall'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| with col4: | |
| split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1') | |
| if split_var1 == 'Specific Games': | |
| team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1') | |
| elif split_var1 == 'Full Slate Run': | |
| team_var1 = raw_baselines.Team.values.tolist() | |
| with col5: | |
| pos_split1 = st.radio("Are you viewing all positions, specific groups, 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 = ['SP', 'P', 'C', '1B', '2B', '3B', 'SS', 'OF']) | |
| elif pos_split1 == 'All Positions': | |
| pos_var1 = 'All' | |
| if custom_var1 == 'No': | |
| if slate_var1 == 'Main Slate': | |
| if site_var1 == 'Draftkings': | |
| final_Proj = load_dk_player_roo('Main_ROO') | |
| elif site_var1 == 'Fanduel': | |
| final_Proj = load_fd_player_roo('Main_FD_ROO') | |
| elif slate_var1 == 'Secondary Slate': | |
| if site_var1 == 'Draftkings': | |
| final_Proj = load_dk_player_roo('Secondary_ROO') | |
| elif site_var1 == 'Fanduel': | |
| final_Proj = load_fd_player_roo('Secondary_FD_ROO') | |
| elif slate_var1 == 'All Games': | |
| if site_var1 == 'Draftkings': | |
| final_Proj = load_dk_player_roo('Merged_ROO') | |
| elif site_var1 == 'Fanduel': | |
| final_Proj = load_fd_player_roo('Merged_FD_ROO') | |
| final_Proj = final_Proj[final_Proj['Team'].isin(team_var1)] | |
| if pos_var1 != 'All': | |
| final_Proj = final_Proj[final_Proj['Position'].str.contains('|'.join(pos_var1))] | |
| st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=1000, use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(final_Proj), | |
| file_name='Custom_MLB_export.csv', | |
| mime='text/csv', | |
| ) | |
| elif custom_var1 == 'Yes': | |
| hold_container = st.empty() | |
| if st.button('Create Range of Outcomes for Slate'): | |
| with hold_container: | |
| working_roo = raw_baselines | |
| working_roo = working_roo[working_roo['Team'].isin(team_var1)] | |
| own_dict = dict(zip(working_roo.Player, working_roo.Own)) | |
| team_dict = dict(zip(working_roo.Player, working_roo.Team)) | |
| total_sims = 1000 | |
| flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Ceiling_Var']] | |
| flex_file['Floor'] = flex_file['Median']*.25 | |
| flex_file['Ceiling'] = np.where(flex_file['Position'] == 'SP', (flex_file['Median'] + (flex_file['Floor'])) + ((flex_file['Ceiling_Var'] * 10) * 3), (flex_file['Median'] + (flex_file['Floor'])) + ((flex_file['Ceiling_Var'] * 10))) | |
| flex_file['STD'] = (flex_file['Median']/4) | |
| flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] | |
| if pos_split1 == 'All Positions': | |
| flex_file = flex_file | |
| elif pos_split1 != 'All Positions': | |
| if pos_var1 == 'Pitchers': | |
| flex_file = flex_file[flex_file['Position'] == 'SP'] | |
| elif pos_var1 == 'Hitters': | |
| flex_file = flex_file[flex_file['Position'] != 'SP'] | |
| elif pos_var1 not in ['Pitchers', 'Hitters']: | |
| flex_file = flex_file[flex_file['Position'].str.contains('|'.join(pos_var1))] | |
| hold_file = flex_file | |
| overall_file = flex_file | |
| salary_file = flex_file | |
| overall_players = overall_file[['Player']] | |
| for x in range(0,total_sims): | |
| salary_file[x] = salary_file['Salary'] | |
| salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| salary_file.astype('int').dtypes | |
| salary_file = salary_file.div(1000) | |
| for x in range(0,total_sims): | |
| overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD']) | |
| overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| overall_file.astype('int').dtypes | |
| players_only = hold_file[['Player']] | |
| raw_lineups_file = players_only | |
| for x in range(0,total_sims): | |
| maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_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 | |
| salary_2x_check = (overall_file - (salary_file*2)) | |
| salary_3x_check = (overall_file - (salary_file*3)) | |
| salary_4x_check = (overall_file - (salary_file*4)) | |
| gpp_check = (overall_file - ((salary_file*2)+10)) | |
| 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['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) | |
| players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['GPP%'] = gpp_check[gpp_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['Player'] = hold_file[['Player']] | |
| final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%']] | |
| final_Proj = pd.merge(hold_file, final_outcomes, on="Player") | |
| final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%']] | |
| final_Proj['Own'] = final_Proj['Player'].map(own_dict) | |
| final_Proj['Team'] = final_Proj['Player'].map(team_dict) | |
| final_Proj['Own'] = final_Proj['Own'].astype('float') | |
| if contest_var1 == 'Small Field GPP': | |
| if site_var1 == 'Draftkings': | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own']) | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (10 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%']) | |
| final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%']) | |
| elif site_var1 == 'Fanduel': | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own']) | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (10 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%']) | |
| final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%']) | |
| elif contest_var1 == 'Large Field GPP': | |
| if site_var1 == 'Draftkings': | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (2.5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own']) | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%']) | |
| final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%']) | |
| elif site_var1 == 'Fanduel': | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (2.5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own']) | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%']) | |
| final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%']) | |
| elif contest_var1 == 'Cash': | |
| if site_var1 == 'Draftkings': | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (6 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own']) | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (11 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%']) | |
| final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%']) | |
| elif site_var1 == 'Fanduel': | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (6 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own']) | |
| final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (11 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%']) | |
| final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%']) | |
| final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%', 'Own%']] | |
| final_Proj = final_Proj.set_index('Player') | |
| final_Proj = final_Proj.sort_values(by='Median', ascending=False) | |
| with hold_container: | |
| hold_container = st.empty() | |
| final_Proj = final_Proj | |
| st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=1000, use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(final_Proj), | |
| file_name='Custom_MLB_export.csv', | |
| mime='text/csv', | |
| ) |