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| 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 | |
| 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' | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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', | |
| ) | |