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 pymongo from itertools import combinations @st.cache_resource def init_conn(): uri = st.secrets['mongo_uri'] client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) db = client["NFL_Database"] return db db = 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%}'} dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] @st.cache_resource(ttl=60) def player_stat_table(): collection = db["Player_Baselines"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['name', 'Team', 'Opp', 'Position', 'Salary', 'team_plays', 'team_pass', 'team_rush', 'team_tds', 'team_pass_tds', 'team_rush_tds', 'dropbacks', 'pass_yards', 'pass_tds', 'rush_att', 'rush_yards', 'rush_tds', 'targets', 'rec', 'rec_yards', 'rec_tds', 'PPR', 'Half_PPR', 'Own']] player_stats = raw_display[raw_display['Position'] != 'K'] collection = db["DK_NFL_ROO"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] load_display = raw_display[raw_display['Position'] != 'K'] dk_roo_raw = load_display.dropna(subset=['Median']) collection = db["FD_NFL_ROO"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] load_display = raw_display[raw_display['Position'] != 'K'] fd_roo_raw = load_display.dropna(subset=['Median']) collection = db["DK_DFS_Stacks"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']] dk_stacks_raw = raw_display.copy() collection = db["FD_DFS_Stacks"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']] fd_stacks_raw = raw_display.copy() return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw @st.cache_resource(ttl = 60) def init_DK_lineups(): collection = db['DK_NFL_name_map'] cursor = collection.find() raw_data = pd.DataFrame(list(cursor)) names_dict = dict(zip(raw_data['key'], raw_data['value'])) collection = db["DK_NFL_seed_frame"] cursor = collection.find().limit(10000) raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] for col in dict_columns: raw_display[col] = raw_display[col].map(names_dict) DK_seed = raw_display.to_numpy() return DK_seed @st.cache_resource(ttl = 60) def init_FD_lineups(): collection = db['FD_NFL_name_map'] cursor = collection.find() raw_data = pd.DataFrame(list(cursor)) names_dict = dict(zip(raw_data['key'], raw_data['value'])) collection = db["FD_NFL_seed_frame"] cursor = collection.find().limit(10000) raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] for col in dict_columns: raw_display[col] = raw_display[col].map(names_dict) FD_seed = raw_display.to_numpy() return FD_seed @st.cache_data def convert_df(array): array = pd.DataFrame(array, columns=column_names) return array.to_csv().encode('utf-8') @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table() try: dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() except: dk_lineups = pd.DataFrame(columns=dk_columns) fd_lineups = pd.DataFrame(columns=fd_columns) t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" tab1, tab2, tab3, tab4, tab5, tab6, tab7 = 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", "Optimals"]) # ... existing code ... @st.cache_data def optimize_lineup(player_data, sidebar_site): """ Creates optimal lineup based on median projections while respecting position and salary constraints """ # Create optimization problem prob = pulp.LpProblem("NFL_Lineup_Optimization", pulp.LpMaximize) # Create player dictionary with binary variables players = {} for idx, row in player_data.iterrows(): players[row['Player']] = pulp.LpVariable(f"player_{idx}", 0, 1, pulp.LpBinary) # Objective: Maximize total median points prob += pulp.lpSum([players[row['Player']] * row['Median'] for idx, row in player_data.iterrows()]) # Constraint: Salary cap if sidebar_site == 'Draftkings': prob += pulp.lpSum([players[row['Player']] * row['Salary'] for idx, row in player_data.iterrows()]) <= 50000 elif sidebar_site == 'Fanduel': prob += pulp.lpSum([players[row['Player']] * row['Salary'] for idx, row in player_data.iterrows()]) <= 60000 # Constraint: 9 players prob += pulp.lpSum([players[row['Player']] for idx, row in player_data.iterrows()]) == 9 # Position constraints qbs = player_data[player_data['Position'] == 'QB']['Player'].tolist() rbs = player_data[player_data['Position'] == 'RB']['Player'].tolist() wrs = player_data[player_data['Position'] == 'WR']['Player'].tolist() tes = player_data[player_data['Position'] == 'TE']['Player'].tolist() dsts = player_data[player_data['Position'] == 'DST']['Player'].tolist() # QB: exactly 1 prob += pulp.lpSum([players[p] for p in qbs]) == 1 # RB: 2-3 prob += pulp.lpSum([players[p] for p in rbs]) >= 2 prob += pulp.lpSum([players[p] for p in rbs]) <= 3 # WR: 3-4 prob += pulp.lpSum([players[p] for p in wrs]) >= 3 prob += pulp.lpSum([players[p] for p in wrs]) <= 4 # TE: 1-2 prob += pulp.lpSum([players[p] for p in tes]) >= 1 prob += pulp.lpSum([players[p] for p in tes]) <= 2 # DST: exactly 1 prob += pulp.lpSum([players[p] for p in dsts]) == 1 # Solve the problem prob.solve() # Get selected players selected_players = [] total_salary = 0 total_median = 0 for idx, row in player_data.iterrows(): if players[row['Player']].value() == 1: selected_players.append({ 'Player': row['Player'], 'Position': row['Position'], 'Salary': row['Salary'], 'Median': row['Median'] }) total_salary += row['Salary'] total_median += row['Median'] return selected_players, total_salary, total_median with st.sidebar: st.header("Quick Builder") sidebar_site = st.selectbox("What site are you running?", ('Draftkings', 'Fanduel'), key='sidebar_site') sidebar_slate = st.selectbox("What slate are you running?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='sidebar_slate') if sidebar_site == 'Draftkings': roo_sample = dk_roo_raw[dk_roo_raw['slate'] == str(sidebar_slate)] roo_sample = roo_sample[roo_sample['version'] == 'overall'] #roo_sample = roo_sample.sort_values(by='Own', ascending=False) elif sidebar_site == 'Fanduel': roo_sample = fd_roo_raw[fd_roo_raw['slate'] == str(sidebar_slate)] roo_sample = roo_sample[roo_sample['version'] == 'overall'] #roo_sample = roo_sample.sort_values(by='Own', ascending=False) st.write("---") if st.button("Generate Optimal Lineup"): if sidebar_site == 'Draftkings': roo_data = dk_roo_raw[dk_roo_raw['slate'] == str(sidebar_slate)] roo_data = roo_data[roo_data['version'] == 'overall'] else: roo_data = fd_roo_raw[fd_roo_raw['slate'] == str(sidebar_slate)] roo_data = roo_data[roo_data['version'] == 'overall'] optimal_players, total_salary, total_median = optimize_lineup(roo_data, sidebar_site) st.write("Optimal Lineup:") # Sort players into position groups qb = [p for p in optimal_players if p['Position'] == 'QB'][0] rbs = [p for p in optimal_players if p['Position'] == 'RB'] wrs = [p for p in optimal_players if p['Position'] == 'WR'] tes = [p for p in optimal_players if p['Position'] == 'TE'] dst = [p for p in optimal_players if p['Position'] == 'DST'][0] # Display QB st.write(f"QB: {qb['Player']} (${qb['Salary']:,})") # Display RB1 and RB2 st.write(f"RB: {rbs[0]['Player']} (${rbs[0]['Salary']:,})") st.write(f"RB: {rbs[1]['Player']} (${rbs[1]['Salary']:,})") # Display WR1, WR2, WR3 st.write(f"WR: {wrs[0]['Player']} (${wrs[0]['Salary']:,})") st.write(f"WR: {wrs[1]['Player']} (${wrs[1]['Salary']:,})") st.write(f"WR: {wrs[2]['Player']} (${wrs[2]['Salary']:,})") # Display TE1 st.write(f"TE: {tes[0]['Player']} (${tes[0]['Salary']:,})") # Display FLEX (either RB3, WR4, or TE2) if len(rbs) > 2: st.write(f"FLEX (RB): {rbs[2]['Player']} (${rbs[2]['Salary']:,})") elif len(wrs) > 3: st.write(f"FLEX (WR): {wrs[3]['Player']} (${wrs[3]['Salary']:,})") elif len(tes) > 1: st.write(f"FLEX (TE): {tes[1]['Player']} (${tes[1]['Salary']:,})") # Display DST st.write(f"DST: {dst['Player']} (${dst['Salary']:,})") st.write(f"Total Salary: ${total_salary:,}") st.write(f"Projected Points: {total_median:.2f}") # Create empty lists to store selected players selected_qbs = [] selected_rb1 = [] selected_rb2 = [] selected_wr1 = [] selected_wr2 = [] selected_wr3 = [] selected_te = [] selected_flex = [] selected_dst = [] # Get unique players by position from dk_roo_raw qbs = roo_sample[roo_sample['Position'] == 'QB']['Player'].unique() rbs = roo_sample[roo_sample['Position'] == 'RB']['Player'].unique() wrs = roo_sample[roo_sample['Position'] == 'WR']['Player'].unique() tes = roo_sample[roo_sample['Position'] == 'TE']['Player'].unique() flex = roo_sample['Player'].unique() dst = roo_sample[roo_sample['Position'] == 'DST']['Player'].unique() # Create multiselect dropdowns for each position selected_qbs = st.multiselect('Select QB:', list(qbs), default=None, key='qb1') if selected_qbs: qb_team = roo_sample[roo_sample['Player'] == selected_qbs[0]]['Team'].values[0] qb_sample = roo_sample[roo_sample['Team'] == qb_team] bb_sample = roo_sample[roo_sample['Opp'] == qb_team] wr_suggest = qb_sample[qb_sample['Position'] == 'WR']['Player'].values[0] wr2_suggest = bb_sample[bb_sample['Position'] == 'WR']['Player'].values[0] te_suggest = qb_sample[qb_sample['Position'] == 'TE']['Player'].values[0] selected_rb1 = st.multiselect('Select RBs:', list(rbs), default=None, key='rb1') selected_rb2 = st.multiselect('Select RB2:', list(rbs), default=None, label_visibility='collapsed', key='rb2') if selected_qbs: selected_wr1 = st.multiselect('Select WRs:', list(wrs), default=None, placeholder=f'Suggestion: {wr_suggest}', key='wr1') else: selected_wr1 = st.multiselect('Select WRs:', list(wrs), default=None, key='wr1') if selected_qbs: selected_wr2 = st.multiselect('Select WR2:', list(wrs), default=None, placeholder=f'Suggestion: {wr2_suggest}', label_visibility='collapsed', key='wr2') else: selected_wr2 = st.multiselect('Select WR2:', list(wrs), default=None, label_visibility='collapsed', key='wr2') selected_wr3 = st.multiselect('Select WR3:', list(wrs), default=None, label_visibility='collapsed', key='wr3') if selected_qbs: selected_te = st.multiselect('Select TE:', list(tes), default=None, placeholder=f'Suggestion: {te_suggest}', key='te') else: selected_te = st.multiselect('Select TE:', list(tes), default=None, key='te') selected_flex = st.multiselect('Select Flex:', list(flex), default=None, key='flex') selected_dst = st.multiselect('Select DST:', list(dst), default=None, key='dst') # Combine all selected players all_selected = selected_qbs + selected_rb1 + selected_rb2 + selected_wr1 + selected_wr2 + selected_wr3 + selected_te + selected_flex + selected_dst if all_selected: # Get stats for selected players selected_stats = roo_sample[roo_sample['Player'].isin(all_selected)] # Calculate sums salary_sum = selected_stats['Salary'].sum() median_sum = selected_stats['Median'].sum() own_sum = selected_stats['Own'].sum() levx_sum = selected_stats['LevX'].sum() # Display sums st.write('---') if sidebar_site == 'Draftkings': if salary_sum > 50000: st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $50,000') else: st.write(f'Total Salary: ${salary_sum:.2f}') elif sidebar_site == 'Fanduel': if salary_sum > 60000: st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $60,000') else: st.write(f'Total Salary: ${salary_sum:.2f}') st.write(f'Total Median: {median_sum:.2f}') st.write(f'Total Ownership: {own_sum:.2f}%') st.write(f'Total LevX: {levx_sum:.2f}') 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, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" for key in st.session_state.keys(): del st.session_state[key] slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var1') site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1') view_var1 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_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)] if view_var1 == 'Simple': final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']] elif view_var1 == 'Advanced': final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']] 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 if view_var1 == 'Simple': final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']] elif view_var1 == 'Advanced': final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']] 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, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" for key in st.session_state.keys(): del st.session_state[key] slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var2') site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') view_var2 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_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, 15000, (2000, 15000), 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, 15000, (2000, 15000), 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.sort_values(by='Median', ascending=False) if view_var2 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var2 == 'Advanced': 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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 if view_var2 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var2 == 'Advanced': 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']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" for key in st.session_state.keys(): del st.session_state[key] slate_var3 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var3') site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3') view_var3 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_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, 15000, (2000, 15000), 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, 15000, (2000, 15000), 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.sort_values(by='Median', ascending=False) if view_var3 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var3 == 'Advanced': 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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 if view_var3 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var3 == 'Advanced': 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']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" for key in st.session_state.keys(): del st.session_state[key] slate_var4 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var4') site_var4 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var4') view_var4 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_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, 15000, (2000, 15000), 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, 15000, (2000, 15000), 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) if view_var4 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var4 == 'Advanced': 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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 if view_var4 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var4 == 'Advanced': 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']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" for key in st.session_state.keys(): del st.session_state[key] slate_var5 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var5') site_var5 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var5') view_var5 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_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, 15000, (2000, 15000), 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, 15000, (2000, 15000), 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) if view_var5 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var5 == 'Advanced': 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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 if view_var5 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var5 == 'Advanced': 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']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" for key in st.session_state.keys(): del st.session_state[key] slate_var6 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var6') site_var6 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var6') view_var6 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_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, 15000, (2000, 15000), 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, 15000, (2000, 15000), 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) if view_var6 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var6 == 'Advanced': 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', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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 if view_var6 == 'Simple': final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']] disp_proj = final_Proj.set_index('Player') elif view_var6 == 'Advanced': 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']] disp_proj = final_Proj.set_index('Player') st.dataframe(disp_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', ) with tab7: col1, col2 = st.columns([1, 7]) with col1: if st.button("Load/Reset Data", key='reset7'): st.cache_data.clear() player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" for key in st.session_state.keys(): del st.session_state[key] slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate')) site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1) if site_var1 == 'Draftkings': ROO_slice = dk_roo_raw[dk_roo_raw['site'] == 'Draftkings'] id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_id)) column_names = dk_columns player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') if player_var1 == 'Specific Players': player_var2 = st.multiselect('Which players do you want?', options = dk_roo_raw['Player'].unique()) elif player_var1 == 'Full Slate': player_var2 = dk_roo_raw.Player.values.tolist() elif site_var1 == 'Fanduel': ROO_slice = fd_roo_raw[fd_roo_raw['site'] == 'Fanduel'] id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_id)) column_names = fd_columns player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') if player_var1 == 'Specific Players': player_var2 = st.multiselect('Which players do you want?', options = fd_roo_raw['Player'].unique()) elif player_var1 == 'Full Slate': player_var2 = fd_roo_raw.Player.values.tolist() if st.button("Prepare data export", key='data_export'): data_export = st.session_state.working_seed.copy() if site_var1 == 'Draftkings': for col_idx in range(9): data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) elif site_var1 == 'Fanduel': for col_idx in range(9): data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) st.download_button( label="Export optimals set", data=convert_df(data_export), file_name='NFL_optimals_export.csv', mime='text/csv', ) with col2: if site_var1 == 'Draftkings': if 'working_seed' in st.session_state: st.session_state.working_seed = st.session_state.working_seed if player_var1 == 'Specific Players': st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] elif player_var1 == 'Full Slate': st.session_state.working_seed = dk_lineups.copy() st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) elif 'working_seed' not in st.session_state: st.session_state.working_seed = dk_lineups.copy() st.session_state.working_seed = st.session_state.working_seed if player_var1 == 'Specific Players': st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] elif player_var1 == 'Full Slate': st.session_state.working_seed = dk_lineups.copy() st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) elif site_var1 == 'Fanduel': if 'working_seed' in st.session_state: st.session_state.working_seed = st.session_state.working_seed if player_var1 == 'Specific Players': st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] elif player_var1 == 'Full Slate': st.session_state.working_seed = fd_lineups.copy() st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) elif 'working_seed' not in st.session_state: st.session_state.working_seed = fd_lineups.copy() st.session_state.working_seed = st.session_state.working_seed if player_var1 == 'Specific Players': st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] elif player_var1 == 'Full Slate': st.session_state.working_seed = fd_lineups.copy() st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) export_file = st.session_state.data_export_display.copy() if site_var1 == 'Draftkings': for col_idx in range(9): export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) elif site_var1 == 'Fanduel': for col_idx in range(9): export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) with st.container(): if st.button("Reset Optimals", key='reset9'): for key in st.session_state.keys(): del st.session_state[key] if site_var1 == 'Draftkings': st.session_state.working_seed = dk_lineups.copy() elif site_var1 == 'Fanduel': st.session_state.working_seed = fd_lineups.copy() if 'data_export_display' in st.session_state: st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True) st.download_button( label="Export display optimals", data=convert_df(export_file), file_name='NFL_display_optimals.csv', mime='text/csv', ) with st.container(): if 'working_seed' in st.session_state: # Create a new dataframe with summary statistics if site_var1 == 'Draftkings': summary_df = pd.DataFrame({ 'Metric': ['Min', 'Average', 'Max', 'STDdev'], 'Salary': [ np.min(st.session_state.working_seed[:,9]), np.mean(st.session_state.working_seed[:,9]), np.max(st.session_state.working_seed[:,9]), np.std(st.session_state.working_seed[:,9]) ], 'Proj': [ np.min(st.session_state.working_seed[:,10]), np.mean(st.session_state.working_seed[:,10]), np.max(st.session_state.working_seed[:,10]), np.std(st.session_state.working_seed[:,10]) ], 'Own': [ np.min(st.session_state.working_seed[:,15]), np.mean(st.session_state.working_seed[:,15]), np.max(st.session_state.working_seed[:,15]), np.std(st.session_state.working_seed[:,15]) ] }) elif site_var1 == 'Fanduel': summary_df = pd.DataFrame({ 'Metric': ['Min', 'Average', 'Max', 'STDdev'], 'Salary': [ np.min(st.session_state.working_seed[:,9]), np.mean(st.session_state.working_seed[:,9]), np.max(st.session_state.working_seed[:,9]), np.std(st.session_state.working_seed[:,9]) ], 'Proj': [ np.min(st.session_state.working_seed[:,10]), np.mean(st.session_state.working_seed[:,10]), np.max(st.session_state.working_seed[:,10]), np.std(st.session_state.working_seed[:,10]) ], 'Own': [ np.min(st.session_state.working_seed[:,15]), np.mean(st.session_state.working_seed[:,15]), np.max(st.session_state.working_seed[:,15]), np.std(st.session_state.working_seed[:,15]) ] }) # Set the index of the summary dataframe as the "Metric" column summary_df = summary_df.set_index('Metric') # Display the summary dataframe st.subheader("Optimal Statistics") st.dataframe(summary_df.style.format({ 'Salary': '{:.2f}', 'Proj': '{:.2f}', 'Own': '{:.2f}' }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True) with st.container(): tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"]) with tab1: if 'data_export_display' in st.session_state: if site_var1 == 'Draftkings': player_columns = st.session_state.data_export_display.iloc[:, :9] elif site_var1 == 'Fanduel': player_columns = st.session_state.data_export_display.iloc[:, :9] # Flatten the DataFrame and count unique values value_counts = player_columns.values.flatten().tolist() value_counts = pd.Series(value_counts).value_counts() percentages = (value_counts / lineup_num_var * 100).round(2) # Create a DataFrame with the results summary_df = pd.DataFrame({ 'Player': value_counts.index, 'Frequency': value_counts.values, 'Percentage': percentages.values }) # Sort by frequency in descending order summary_df = summary_df.sort_values('Frequency', ascending=False) # Display the table st.write("Player Frequency Table:") st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True) st.download_button( label="Export player frequency", data=convert_df_to_csv(summary_df), file_name='NFL_player_frequency.csv', mime='text/csv', ) with tab2: if 'working_seed' in st.session_state: if site_var1 == 'Draftkings': player_columns = st.session_state.working_seed[:, :9] elif site_var1 == 'Fanduel': player_columns = st.session_state.working_seed[:, :9] # Flatten the DataFrame and count unique values value_counts = player_columns.flatten().tolist() value_counts = pd.Series(value_counts).value_counts() percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2) # Create a DataFrame with the results summary_df = pd.DataFrame({ 'Player': value_counts.index, 'Frequency': value_counts.values, 'Percentage': percentages.values }) # Sort by frequency in descending order summary_df = summary_df.sort_values('Frequency', ascending=False) # Display the table st.write("Seed Frame Frequency Table:") st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True) st.download_button( label="Export seed frame frequency", data=convert_df_to_csv(summary_df), file_name='NFL_seed_frame_frequency.csv', mime='text/csv', )