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James McCool
Enhance lineup optimization in app.py: Modify optimize_lineup function to accept sidebar_site parameter, allowing for different salary cap constraints for DraftKings and FanDuel. Update UI to display optimal players by position, improving clarity and user experience.
15867d1
| 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 | |
| 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'] | |
| 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 | |
| def init_DK_lineups(): | |
| 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']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_FD_lineups(): | |
| 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']] | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def convert_df(array): | |
| array = pd.DataFrame(array, columns=column_names) | |
| return array.to_csv().encode('utf-8') | |
| 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 ... | |
| 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', | |
| ) |