import streamlit as st st.set_page_config(layout="wide") for name in dir(): if not name.startswith('_'): del globals()[name] import pulp import numpy as np import pandas as pd import streamlit as st import gspread from itertools import combinations @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 gc = init_conn() 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%}'} all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348' @st.cache_resource(ttl=3600) def set_slate_teams(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('Site_Info') raw_display = pd.DataFrame(worksheet.get_all_records()) return raw_display @st.cache_resource(ttl=600) def player_stat_table(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('Player_Projections') raw_display = pd.DataFrame(worksheet.get_all_records()) return raw_display @st.cache_resource(ttl=600) def load_dk_player_projections(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('DK_ROO') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) raw_display = load_display.dropna(subset=['Median']) return raw_display @st.cache_resource(ttl=600) def load_fd_player_projections(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('FD_ROO') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) raw_display = load_display.dropna(subset=['Median']) return raw_display @st.cache_resource(ttl=600) def load_dk_stacks(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('DK_Stacks') load_display = pd.DataFrame(worksheet.get_all_records()) raw_display = load_display return raw_display @st.cache_resource(ttl=600) def load_fd_stacks(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('FD_Stacks') load_display = pd.DataFrame(worksheet.get_all_records()) raw_display = load_display return raw_display @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') player_stats = player_stat_table() dk_stacks_raw = load_dk_stacks() fd_stacks_raw = load_fd_stacks() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Team Stacks Range of Outcomes", "Overall Range of Outcomes", "QB Range of Outcomes", "RB Range of Outcomes", "WR Range of Outcomes", "TE Range of Outcomes"]) with tab1: col1, col2 = st.columns([1, 5]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset1'): st.cache_data.clear() player_stats = player_stat_table() dk_stacks_raw = load_dk_stacks() fd_stacks_raw = load_fd_stacks() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var1') site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1') custom_var1 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var1') if custom_var1 == 'No': if site_var1 == 'Draftkings': raw_baselines = dk_stacks_raw[dk_stacks_raw['slate'] == str(slate_var1)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] raw_baselines = raw_baselines.iloc[:,:-2] elif site_var1 == 'Fanduel': raw_baselines = fd_stacks_raw[fd_stacks_raw['slate'] == str(slate_var1)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] raw_baselines = raw_baselines.iloc[:,:-2] split_var1 = st.radio("Would you like to view the whole slate or just specific 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() 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 site_var1 == 'Draftkings': raw_baselines = dk_stacks_raw[dk_stacks_raw['slate'] == str(slate_var1)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] elif site_var1 == 'Fanduel': raw_baselines = fd_stacks_raw[fd_stacks_raw['slate'] == str(slate_var1)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] 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 col2: if custom_var1 == 'No': final_stacks = raw_baselines[raw_baselines['Team'].isin(team_var1)] st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_stacks), file_name='NFL_stacks_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: if site_var1 == 'Draftkings': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) if site_var1 == 'Fanduel': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Team'].isin(team_var1)] total_sims = 1000 salary_dict = dict(zip(working_roo.name, working_roo.Salary)) own_dict = dict(zip(working_roo.name, working_roo.Own)) fantasy_dict = dict(zip(working_roo.name, working_roo.Fantasy)) QB_group = working_roo.loc[working_roo['Position'] == 'QB'] stacks_df = pd.DataFrame(columns=['Team','QB', 'WR1', 'WR2_TE']) for stack in range(0,len(QB_group)): team_var = QB_group.iat[stack,1] WR_group_1 = working_roo.loc[working_roo['Position'] == 'WR'] WR_group_2 = WR_group_1.loc[working_roo['Team'] == team_var] TE_group_1 = working_roo.loc[working_roo['Position'] == 'TE'] TE_group_2 = TE_group_1.loc[working_roo['Team'] == team_var] cur_list = [] qb_piece = QB_group.iat[stack,0] wr_piece = WR_group_2.iat[0,0] te_piece = TE_group_2.iat[0,0] cur_list.append(team_var) cur_list.append(qb_piece) cur_list.append(wr_piece) cur_list.append(te_piece) stacks_df.loc[len(stacks_df)] = cur_list cur_list = [] qb_piece = QB_group.iat[stack,0] wr_piece = WR_group_2.iat[1,0] te_piece = TE_group_2.iat[0,0] cur_list.append(team_var) cur_list.append(qb_piece) cur_list.append(wr_piece) cur_list.append(te_piece) stacks_df.loc[len(stacks_df)] = cur_list cur_list = [] qb_piece = QB_group.iat[stack,0] wr_piece = WR_group_2.iat[0,0] te_piece = WR_group_2.iat[1,0] cur_list.append(team_var) cur_list.append(qb_piece) cur_list.append(wr_piece) cur_list.append(te_piece) stacks_df.loc[len(stacks_df)] = cur_list stacks_df['Salary'] = sum([stacks_df['QB'].map(salary_dict), stacks_df['WR1'].map(salary_dict), stacks_df['WR2_TE'].map(salary_dict)]) stacks_df['Fantasy'] = sum([stacks_df['QB'].map(fantasy_dict), stacks_df['WR1'].map(fantasy_dict), stacks_df['WR2_TE'].map(fantasy_dict)]) stacks_df['Own'] = sum([stacks_df['QB'].map(own_dict), stacks_df['WR1'].map(own_dict), stacks_df['WR2_TE'].map(own_dict)]) stacks_df['team_combo'] = stacks_df['Team'] + " " + stacks_df['QB'] + " " + stacks_df['WR1'] + " " + stacks_df['WR2_TE'] own_dict = dict(zip(stacks_df.team_combo, stacks_df.Own)) qb_dict = dict(zip(stacks_df.team_combo, stacks_df.QB)) wr1_dict = dict(zip(stacks_df.team_combo, stacks_df.WR1)) wr2_dict = dict(zip(stacks_df.team_combo, stacks_df.WR2_TE)) team_dict = dict(zip(stacks_df.team_combo, stacks_df.Team)) flex_file = stacks_df[['team_combo', 'Salary', 'Fantasy']] flex_file.rename(columns={"Fantasy": "Median"}, inplace = True) flex_file['Floor'] = flex_file['Median']*.25 flex_file['Ceiling'] = flex_file['Median'] + flex_file['Floor'] flex_file['STD'] = flex_file['Median']/4 flex_file = flex_file[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] hold_file = flex_file overall_file = flex_file salary_file = flex_file overall_players = overall_file[['team_combo']] for x in range(0,total_sims): salary_file[x] = salary_file['Salary'] salary_file=salary_file.drop(['team_combo', '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(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) overall_file.astype('int').dtypes players_only = hold_file[['team_combo']] raw_lineups_file = players_only for x in range(0,total_sims): maps_dict = {'proj_map':dict(zip(hold_file.team_combo,hold_file[x]))} raw_lineups_file[x] = sum([raw_lineups_file['team_combo'].map(maps_dict['proj_map'])]) players_only[x] = raw_lineups_file[x].rank(ascending=False) players_only=players_only.drop(['team_combo'], 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)) 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['60+%'] = overall_file[overall_file >= 60].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['team_combo'] = hold_file[['team_combo']] final_outcomes = players_only[['team_combo', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']] final_stacks = pd.merge(hold_file, final_outcomes, on="team_combo") final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']] final_stacks['Own'] = final_stacks['team_combo'].map(own_dict) final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own']] final_stacks['Projection Rank'] = final_stacks.Median.rank(pct = True) final_stacks['Own Rank'] = final_stacks.Own.rank(pct = True) final_stacks['LevX'] = final_stacks['Projection Rank'] - final_stacks['Own Rank'] final_stacks['Team'] = final_stacks['team_combo'].map(team_dict) final_stacks['QB'] = final_stacks['team_combo'].map(qb_dict) final_stacks['WR1_TE'] = final_stacks['team_combo'].map(wr1_dict) final_stacks['WR2_TE'] = final_stacks['team_combo'].map(wr2_dict) final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']] final_stacks = final_stacks.sort_values(by='Median', ascending=False) with hold_container: hold_container = st.empty() final_stacks = final_stacks st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_stacks), file_name='Custom_NFL_stacks_export.csv', mime='text/csv', ) with tab2: col1, col2 = st.columns([1, 5]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset2'): st.cache_data.clear() player_stats = player_stat_table() dk_stacks_raw = load_dk_stacks() fd_stacks_raw = load_fd_stacks() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var2') site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') custom_var2 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var2') if custom_var2 == 'No': if site_var2 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var2)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] raw_baselines = raw_baselines.iloc[:,:-2] elif site_var2 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var2)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] raw_baselines = raw_baselines.iloc[:,:-2] split_var2 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var2') if split_var2 == 'Specific Games': team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2') elif split_var2 == 'Full Slate Run': team_var2 = raw_baselines.Team.values.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' sal_var2 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var2') if custom_var2 == 'Yes': contest_var2 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var2') if site_var2 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var2)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] elif site_var2 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var2)] raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2') if split_var2 == 'Specific Games': team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2') elif split_var2 == 'Full Slate Run': team_var2 = raw_baselines.Team.values.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' sal_var2 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var2') with col2: if custom_var2 == 'No': final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var2)] final_Proj = final_Proj[final_Proj['Salary'] >= sal_var2[0]] final_Proj = final_Proj[final_Proj['Salary'] <= sal_var2[1]] if pos_var2 != 'All': final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2))] final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='NFL_overall_export.csv', mime='text/csv', ) elif custom_var2 == 'Yes': hold_container = st.empty() if st.button('Create Range of Outcomes for Slate'): with hold_container: if site_var2 == 'Draftkings': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) if site_var2 == 'Fanduel': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Team'].isin(team_var2)] working_roo = working_roo[working_roo['Salary'] >= sal_var2[0]] working_roo = working_roo[working_roo['Salary'] <= sal_var2[1]] own_dict = dict(zip(working_roo.Player, working_roo.Own)) team_dict = dict(zip(working_roo.Player, working_roo.Team)) opp_dict = dict(zip(working_roo.Player, working_roo.Opp)) total_sims = 1000 flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']] flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True) flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions']) flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions'] flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] 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)) 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['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] 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%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']] final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = 0 final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['CPT_Own'] = final_Proj['Own'] / 4 final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] 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), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='Custom_NFL_overall_export.csv', mime='text/csv', ) with tab3: col1, col2 = st.columns([1, 5]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset3'): st.cache_data.clear() player_stats = player_stat_table() dk_stacks_raw = load_dk_stacks() fd_stacks_raw = load_fd_stacks() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() slate_var3 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var3') site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3') custom_var3 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var3') if custom_var3 == 'No': if site_var3 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var3)] raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_qbs'] raw_baselines = raw_baselines.iloc[:,:-3] elif site_var3 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var3)] raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_qbs'] raw_baselines = raw_baselines.iloc[:,:-3] split_var3 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var3') if split_var3 == 'Specific Games': team_var3 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var3') elif split_var3 == 'Full Slate Run': team_var3 = raw_baselines.Team.values.tolist() pos_split3 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split3') if pos_split3 == 'Specific Positions': pos_var3 = st.multiselect('What Positions would you like to view?', options = ['QB'], key='pos_var3') elif pos_split3 == 'All Positions': pos_var3 = 'All' sal_var3 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var3') if custom_var3 == 'Yes': contest_var3 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var3') if site_var3 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var3)] raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_qbs'] raw_baselines = raw_baselines.iloc[:,:-3] elif site_var3 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var3)] raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_qbs'] raw_baselines = raw_baselines.iloc[:,:-3] split_var3 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var3') if split_var3 == 'Specific Games': team_var3 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var3') elif split_var3 == 'Full Slate Run': team_var3 = raw_baselines.Team.values.tolist() pos_split3 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split3') if pos_split3 == 'Specific Positions': pos_var3 = st.multiselect('What Positions would you like to view?', options = ['QB']) elif pos_split3 == 'All Positions': pos_var3 = 'All' sal_var3 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var3') with col2: if custom_var3 == 'No': final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var3)] final_Proj = final_Proj[final_Proj['Salary'] >= sal_var3[0]] final_Proj = final_Proj[final_Proj['Salary'] <= sal_var3[1]] if pos_var3 != 'All': final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var3))] final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='NFL_qb_export.csv', mime='text/csv', ) elif custom_var3 == 'Yes': hold_container = st.empty() if st.button('Create Range of Outcomes for Slate'): with hold_container: if site_var3 == 'Draftkings': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Position'] == 'QB'] if site_var3 == 'Fanduel': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Position'] == 'QB'] working_roo = working_roo[working_roo['Team'].isin(team_var3)] working_roo = working_roo[working_roo['Salary'] >= sal_var2[0]] working_roo = working_roo[working_roo['Salary'] <= sal_var2[1]] own_dict = dict(zip(working_roo.Player, working_roo.Own)) team_dict = dict(zip(working_roo.Player, working_roo.Team)) opp_dict = dict(zip(working_roo.Player, working_roo.Opp)) total_sims = 1000 flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']] flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True) flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions']) flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions'] flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] 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)) 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['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] 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%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']] final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = 0 final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['CPT_Own'] = final_Proj['Own'] / 4 final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] 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), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='Custom_NFL_qb_export.csv', mime='text/csv', ) with tab4: col1, col2 = st.columns([1, 5]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset4'): st.cache_data.clear() player_stats = player_stat_table() dk_stacks_raw = load_dk_stacks() fd_stacks_raw = load_fd_stacks() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() slate_var4 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var4') site_var4 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var4') custom_var4 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var4') if custom_var4 == 'No': if site_var4 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var4)] raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_rbs'] raw_baselines = raw_baselines.iloc[:,:-3] elif site_var4 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var4)] raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_rbs'] raw_baselines = raw_baselines.iloc[:,:-3] split_var4 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var4') if split_var4 == 'Specific Games': team_var4 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var4') elif split_var4 == 'Full Slate Run': team_var4 = raw_baselines.Team.values.tolist() pos_split4 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split4') if pos_split4 == 'Specific Positions': pos_var4 = st.multiselect('What Positions would you like to view?', options = ['RB'], key='pos_var4') elif pos_split4 == 'All Positions': pos_var4 = 'All' sal_var4 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var4') if custom_var4 == 'Yes': contest_var4 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var4') if site_var4 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var4)] raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_rbs'] elif site_var4 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var4)] raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_rbs'] split_var4 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var4') if split_var4 == 'Specific Games': team_var4 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var4') elif split_var4 == 'Full Slate Run': team_var4 = raw_baselines.Team.values.tolist() pos_split4 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split4') if pos_split4 == 'Specific Positions': pos_var4 = st.multiselect('What Positions would you like to view?', options = ['RB']) elif pos_split4 == 'All Positions': pos_var4 = 'All' sal_var4 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var4') with col2: if custom_var4 == 'No': final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var4)] final_Proj = final_Proj[final_Proj['Salary'] >= sal_var4[0]] final_Proj = final_Proj[final_Proj['Salary'] <= sal_var4[1]] if pos_var4 != 'All': final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var4))] final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='NFL_rb_export.csv', mime='text/csv', ) elif custom_var4 == 'Yes': hold_container = st.empty() if st.button('Create Range of Outcomes for Slate'): with hold_container: if site_var4 == 'Draftkings': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Position'] == 'RB'] if site_var4 == 'Fanduel': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Position'] == 'RB'] working_roo = working_roo[working_roo['Team'].isin(team_var4)] working_roo = working_roo[working_roo['Salary'] >= sal_var4[0]] working_roo = working_roo[working_roo['Salary'] <= sal_var4[1]] own_dict = dict(zip(working_roo.Player, working_roo.Own)) team_dict = dict(zip(working_roo.Player, working_roo.Team)) opp_dict = dict(zip(working_roo.Player, working_roo.Opp)) total_sims = 1000 flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']] flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True) flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions']) flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions'] flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] 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)) 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['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] 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%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']] final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = 0 final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['CPT_Own'] = final_Proj['Own'] / 4 final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] 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), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='Custom_NFL_rb_export.csv', mime='text/csv', ) with tab5: col1, col2 = st.columns([1, 5]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset5'): st.cache_data.clear() player_stats = player_stat_table() dk_stacks_raw = load_dk_stacks() fd_stacks_raw = load_fd_stacks() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() slate_var5 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var5') site_var5 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var5') custom_var5 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var5') if custom_var5 == 'No': if site_var5 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var5)] raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_wrs'] raw_baselines = raw_baselines.iloc[:,:-3] elif site_var5 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var5)] raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_wrs'] raw_baselines = raw_baselines.iloc[:,:-3] split_var5 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var5') if split_var5 == 'Specific Games': team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5') elif split_var5 == 'Full Slate Run': team_var5 = raw_baselines.Team.values.tolist() pos_split5 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split5') if pos_split5 == 'Specific Positions': pos_var5 = st.multiselect('What Positions would you like to view?', options = ['WR'], key='pos_var5') elif pos_split5 == 'All Positions': pos_var5 = 'All' sal_var5 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var5') if custom_var5 == 'Yes': contest_var5 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var5') if site_var5 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var5)] raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_wrs'] elif site_var5 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var5)] raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_wrs'] split_var5 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var5') if split_var5 == 'Specific Games': team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5') elif split_var5 == 'Full Slate Run': team_var5 = raw_baselines.Team.values.tolist() pos_split5 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split5') if pos_split5 == 'Specific Positions': pos_var5 = st.multiselect('What Positions would you like to view?', options = ['WR']) elif pos_split5 == 'All Positions': pos_var5 = 'All' sal_var5 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var5') with col2: if custom_var5 == 'No': final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var5)] final_Proj = final_Proj[final_Proj['Salary'] >= sal_var5[0]] final_Proj = final_Proj[final_Proj['Salary'] <= sal_var5[1]] if pos_var5 != 'All': final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var5))] final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='NFL_wr_export.csv', mime='text/csv', ) elif custom_var5 == 'Yes': hold_container = st.empty() if st.button('Create Range of Outcomes for Slate'): with hold_container: if site_var5 == 'Draftkings': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Position'] == 'WR'] if site_var5 == 'Fanduel': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Position'] == 'WR'] working_roo = working_roo[working_roo['Team'].isin(team_var5)] working_roo = working_roo[working_roo['Salary'] >= sal_var5[0]] working_roo = working_roo[working_roo['Salary'] <= sal_var5[1]] own_dict = dict(zip(working_roo.Player, working_roo.Own)) team_dict = dict(zip(working_roo.Player, working_roo.Team)) opp_dict = dict(zip(working_roo.Player, working_roo.Opp)) total_sims = 1000 flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']] flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True) flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions']) flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions'] flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] 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)) 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['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] 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%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']] final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = 0 final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['CPT_Own'] = final_Proj['Own'] / 4 final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] 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), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='Custom_NFL_wr_export.csv', mime='text/csv', ) with tab6: col1, col2 = st.columns([1, 5]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset6'): st.cache_data.clear() player_stats = player_stat_table() dk_stacks_raw = load_dk_stacks() fd_stacks_raw = load_fd_stacks() dk_roo_raw = load_dk_player_projections() fd_roo_raw = load_fd_player_projections() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" site_slates = set_slate_teams() slate_var6 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var6') site_var6 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var6') custom_var6 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var6') if custom_var6 == 'No': if site_var6 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var6)] raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_tes'] raw_baselines = raw_baselines.iloc[:,:-3] elif site_var6 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var6)] raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_tes'] raw_baselines = raw_baselines.iloc[:,:-3] split_var6 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var6') if split_var6 == 'Specific Games': team_var6 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var6') elif split_var6 == 'Full Slate Run': team_var6 = raw_baselines.Team.values.tolist() pos_split6 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split6') if pos_split6 == 'Specific Positions': pos_var6 = st.multiselect('What Positions would you like to view?', options = ['TE'], key='pos_var6') elif pos_split5 == 'All Positions': pos_var6 = 'All' sal_var6 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var6') if custom_var6 == 'Yes': contest_var6 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var6') if site_var6 == 'Draftkings': raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var6)] raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_tes'] elif site_var6 == 'Fanduel': raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var6)] raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_tes'] split_var6 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var6') if split_var6 == 'Specific Games': team_var6 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var6') elif split_var6 == 'Full Slate Run': team_var6 = raw_baselines.Team.values.tolist() pos_split6 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split6') if pos_split6 == 'Specific Positions': pos_var6 = st.multiselect('What Positions would you like to view?', options = ['TE']) elif pos_split6 == 'All Positions': pos_var6 = 'All' sal_var6 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 10000), key='sal_var6') with col2: if custom_var6 == 'No': final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var6)] final_Proj = final_Proj[final_Proj['Salary'] >= sal_var6[0]] final_Proj = final_Proj[final_Proj['Salary'] <= sal_var6[1]] if pos_var6 != 'All': final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var6))] final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='NFL_te_export.csv', mime='text/csv', ) elif custom_var6 == 'Yes': hold_container = st.empty() if st.button('Create Range of Outcomes for Slate'): with hold_container: if site_var6 == 'Draftkings': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Position'] == 'TE'] if site_var6 == 'Fanduel': working_roo = player_stats working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True) working_roo.replace('', 0, inplace=True) working_roo = working_roo[working_roo['Position'] == 'TE'] working_roo = working_roo[working_roo['Team'].isin(team_var6)] working_roo = working_roo[working_roo['Salary'] >= sal_var6[0]] working_roo = working_roo[working_roo['Salary'] <= sal_var6[1]] own_dict = dict(zip(working_roo.Player, working_roo.Own)) team_dict = dict(zip(working_roo.Player, working_roo.Team)) opp_dict = dict(zip(working_roo.Player, working_roo.Opp)) total_sims = 1000 flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']] flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True) flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions']) flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions'] flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] 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)) 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['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] 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%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']] final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = 0 final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX']) final_Proj['CPT_Own'] = final_Proj['Own'] / 4 final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] 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), use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(final_Proj), file_name='Custom_NFL_te_export.csv', mime='text/csv', )