diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -36,18 +36,7 @@ def init_conn(): gcservice_account = init_conn() -wrong_acro = ['WSH', 'AZ'] -right_acro = ['WAS', 'ARI'] - -game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', - 'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'} - -team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}', - '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'} - -expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'} - -all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348' +all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=943304327' @st.cache_resource(ttl = 600) def set_slate_teams(): @@ -58,95 +47,34 @@ def set_slate_teams(): return raw_display @st.cache_resource(ttl = 600) -def player_stat_table(): +def grab_baseline_stuff(): sh = gcservice_account.open_by_url(all_dk_player_projections) - worksheet = sh.worksheet('Player_Projections') + worksheet = sh.worksheet('Player_Data_Master') raw_display = pd.DataFrame(worksheet.get_all_records()) - - return raw_display - -@st.cache_resource(ttl = 600) -def load_dk_player_projections(): - sh = gcservice_account.open_by_url(all_dk_player_projections) - worksheet = sh.worksheet('DK_ROO') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) - - return raw_display - -@st.cache_resource(ttl = 600) -def load_fd_player_projections(): - sh = gcservice_account.open_by_url(all_dk_player_projections) - worksheet = sh.worksheet('FD_ROO') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) - - return raw_display - -@st.cache_resource(ttl = 600) -def load_dk_stacks(): - sh = gcservice_account.open_by_url(all_dk_player_projections) - worksheet = sh.worksheet('DK_Stacks') - load_display = pd.DataFrame(worksheet.get_all_records()) - raw_display = load_display - raw_display = raw_display.sort_values(by='Own', ascending=False) - - return raw_display - -@st.cache_resource(ttl = 600) -def load_fd_stacks(): - sh = gcservice_account.open_by_url(all_dk_player_projections) - worksheet = sh.worksheet('FD_Stacks') - load_display = pd.DataFrame(worksheet.get_all_records()) - raw_display = load_display - raw_display = raw_display.sort_values(by='Own', ascending=False) - - return raw_display - -@st.cache_resource(ttl = 3600) -def set_export_ids(): - sh = gcservice_account.open_by_url(all_dk_player_projections) - worksheet = sh.worksheet('DK_ROO') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) - dk_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) + dk_raw_proj = raw_display[['Clean Name', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'DK Salary', 'Final DK Projection', 'DK uploadID', 'DK_Own']] + dk_raw_proj = dk_raw_proj.set_axis(['Player', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'Salary', 'Median', 'player_id', 'Own'], axis=1) + fd_raw_proj = raw_display[['Clean Name', 'Team', 'Opp', 'Line', 'PP Unit', 'FD Position', 'FD Salary', 'Final FD Projection', 'FD uploadID', 'FD_Own']] + fd_raw_proj = fd_raw_proj.set_axis(['Player', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'Salary', 'Median', 'player_id', 'Own'], axis=1) + dk_ids = dict(zip(dk_raw_proj['Player'], dk_raw_proj['player_id'])) + fd_ids = dict(zip(fd_raw_proj['Player'], fd_raw_proj['player_id'])) - worksheet = sh.worksheet('FD_ROO') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) - fd_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) + worksheet = sh.worksheet('Timestamp') + timestamp = worksheet.acell('A1').value - return dk_ids, fd_ids + return dk_raw_proj, fd_raw_proj, dk_ids, fd_ids, timestamp @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') -player_stats = player_stat_table() -dk_stacks_raw = load_fd_stacks() -fd_stacks_raw = load_fd_stacks() -dk_roo_raw = load_dk_player_projections() -fd_roo_raw = load_fd_player_projections() -t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" -site_slates = set_slate_teams() -col1, col2 = st.columns([1, 5]) -dk_Max_Rank = dk_stacks_raw['Team'][0] -fd_Max_Rank = dk_stacks_raw['Team'][0] -dk_stacks_raw = dk_stacks_raw.sort_values(by='Median', ascending=False) -fd_stacks_raw = fd_stacks_raw.sort_values(by='Median', ascending=False) -dk_Max_Upside = dk_stacks_raw['Team'][0] -fd_Max_Upside = dk_stacks_raw['Team'][0] -opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp)) -dkid_dict, fdid_dict = set_export_ids() +dk_raw_proj, fd_raw_proj, dkid_dict, fdid_dict, timestamp = grab_baseline_stuff() +t_stamp = f"Last Update: " + str(timestamp) + f" CST" +opp_dict = dict(zip(dk_raw_proj.Team, dk_raw_proj.Opp)) tab1, tab2 = st.tabs(['Uploads and Info', 'Optimizer']) with tab1: - st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'.") + st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Line', 'PP Unit', 'Own', and 'player_id'. The player_id is the draftkings or fanduel ID associated with the player for upload.") col1, col2 = st.columns([1, 5]) with col1: @@ -167,67 +95,30 @@ with tab2: st.info(t_stamp) if st.button("Load/Reset Data", key='reset1'): st.cache_data.clear() - player_stats = player_stat_table() - dk_stacks_raw = load_fd_stacks() - fd_stacks_raw = load_fd_stacks() - dk_roo_raw = load_dk_player_projections() - fd_roo_raw = load_fd_player_projections() - t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" - site_slates = set_slate_teams() + dk_raw_proj, fd_raw_proj, dk_ids, fd_ids, timestamp = grab_baseline_stuff() + t_stamp = f"Last Update: " + str(timestamp) + f" CST" - slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games', 'User'), key='slate_var1') + slate_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='slate_var1') site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') if site_var1 == 'Draftkings': if slate_var1 == 'User': raw_baselines = proj_dataframe - Max_Rank = dk_Max_Rank - Max_Upside = dk_Max_Upside elif slate_var1 != 'User': - raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var1)] - raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] - Max_Rank = dk_Max_Rank - Max_Upside = dk_Max_Upside + raw_baselines = dk_raw_proj elif site_var1 == 'Fanduel': if slate_var1 == 'User': raw_baselines = proj_dataframe - Max_Rank = fd_Max_Rank - Max_Upside = fd_Max_Upside elif slate_var1 != 'User': - raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var1)] - raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] - Max_Rank = fd_Max_Rank - Max_Upside = fd_Max_Upside + raw_baselines = fd_raw_proj contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP', 'Round Robin'), key='contest_var1') - if contest_var1 == 'Small Field GPP': - st.info('The Pivot optimal uses backend functions to create a stack and lock in certain pieces, if you want control over QB pairing use the Manual model instead.') - opto_var1 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var1') - if opto_var1 == "Manual": - stack_var1 = st.selectbox('Which teams are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var1') - opp_var1 = opp_dict[stack_var1] - qbstack_var1 = st.selectbox('How many forced WR/TE stacked with QB?', options = [1, 2], key='qbstack_var1') - ministack_var1 = st.selectbox('How many forced bring backs?', options = [0, 1, 2], key='ministack_var1') - elif opto_var1 == "Pivot Optimal": - stack_var1 = Max_Rank - opp_var1 = opp_dict[stack_var1] - qbstack_var1 = 2 - ministack_var1 = 0 - elif contest_var1 == 'Large Field GPP': - st.info('The Pivot optimal uses backend functions to create a stack and lock in certain pieces, if you want control over QB pairing use the Manual model instead.') - opto_var1 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var1') - if opto_var1 == "Manual": - stack_var1 = st.selectbox('Which team are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var1') - opp_var1 = opp_dict[stack_var1] - qbstack_var1 = st.selectbox('How many forced WR/TE stacked with QB?', options = [1, 2], key='qbstack_var1') - ministack_var1 = st.selectbox('How many forced bring backs?', options = [0, 1, 2], key='ministack_var1') - elif opto_var1 == "Pivot Optimal": - stack_var1 = Max_Upside - opp_var1 = opp_dict[stack_var1] - qbstack_var1 = 2 - ministack_var1 = 1 - elif contest_var1 == 'Round Robin': - st.info('A Round Robin optimization will run a single optimal for all the teams on the slate based on your stacking inputs') - qbstack_var1 = st.selectbox('How many forced WR/TE stacked with QB?', options = [1, 2], key='qbstack_var1') - ministack_var1 = st.selectbox('How many forced bring backs?', options = [0, 1, 2], key='ministack_var1') + if contest_var1 != 'Cash': + stack_var1 = st.selectbox('Which team are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var1') + stack_size_var1 = st.selectbox('What size of stack?', options = [3, 4], key='stack_size_var1') + line_choice_var1 = st.selectbox('Which line for main?', options = [1, 2, 3, 4], key='line_choice_var1') + ministack_var1 = st.selectbox('Who should be the secondary stack?', options = raw_baselines['Team'].unique(), key='ministack_var1') + ministack_size_var1 = st.selectbox('What size of secondary stack?', options = [2, 3, 4], key='ministack_size_var1') + miniline_choice_var1 = st.selectbox('Which line for secondary?', options = [1, 2, 3, 4], key='miniline_choice_var1') + opp_var1 = opp_dict[stack_var1] 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 optimization?', options = raw_baselines['Team'].unique(), key='team_var1') @@ -235,10 +126,7 @@ with tab2: team_var1 = raw_baselines.Team.values.tolist() lock_var1 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1') avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = raw_baselines['Player'].unique(), key='avoid_var1') - if contest_var1 == 'Round Robin': - linenum_var1 = len(raw_baselines['Team'].unique()) - elif contest_var1 != 'Round Robin': - linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var1') + linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var1') if site_var1 == 'Draftkings': min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1') max_sal1 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal1') @@ -250,21 +138,21 @@ with tab2: raw_baselines = raw_baselines[~raw_baselines['Player'].isin(avoid_var1)] ownframe = raw_baselines.copy() if contest_var1 == 'Cash': - ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (10 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) - ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) + ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (10 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own']) + ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%']) - ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum()) + ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum()) if contest_var1 == 'Small Field GPP': - ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) - ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) + ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own']) + ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%']) - ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum()) + ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum()) if contest_var1 == 'Large Field GPP': - ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) - ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (1.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) + ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own']) + ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (1.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%']) - ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum()) - raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] + ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum()) + raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Line', 'PP Unit', 'Median', 'Own']] raw_baselines = raw_baselines.sort_values(by='Median', ascending=False) raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var1), 1, 0) st.dataframe(raw_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) @@ -290,940 +178,473 @@ with tab2: with st.spinner('Wait for it...'): with optimize_container: - if contest_var1 == 'Round Robin': + while x <= linenum_var1: + sorted_lineup = [] + p_used = [] + cvar = 0 + firvar = 0 + secvar = 0 + thirvar = 0 + + raw_proj_file = raw_baselines + raw_flex_file = raw_proj_file.dropna(how='all') + raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0] + flex_file = raw_flex_file + flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True) + flex_file['name_var'] = flex_file['Player'] + flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var1), 1, 0) + player_ids = flex_file.index + + overall_players = flex_file[['Player']] + overall_players['player_var_add'] = flex_file.index + overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str) + + player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger) + total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize) + player_match = dict(zip(overall_players['player_var'], overall_players['Player'])) + player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add'])) + + player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%'])) + player_team = dict(zip(flex_file['Player'], flex_file['Team'])) + player_pos = dict(zip(flex_file['Player'], flex_file['Position'])) + player_sal = dict(zip(flex_file['Player'], flex_file['Salary'])) + player_proj = dict(zip(flex_file['Player'], flex_file['Median'])) + player_line = dict(zip(flex_file['Player'], flex_file['Line'])) + player_ppunit = dict(zip(flex_file['Player'], flex_file['PP Unit'])) + + obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index} + total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1 + total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1 + + if site_var1 == 'Draftkings': - while x <= len(raw_baselines['Team'].unique()): - stack_var1 = raw_baselines['Team'].unique()[x-1] - opp_var1 = opp_dict[stack_var1] - st.write(stack_var1) - sorted_lineup = [] - p_used = [] - cvar = 0 - firvar = 0 - secvar = 0 - thirvar = 0 - - raw_proj_file = raw_baselines - raw_flex_file = raw_proj_file.dropna(how='all') - raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0] - flex_file = raw_flex_file - flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True) - flex_file['name_var'] = flex_file['Player'] - flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var1), 1, 0) - player_ids = flex_file.index - - overall_players = flex_file[['Player']] - overall_players['player_var_add'] = flex_file.index - overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str) - - player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger) - total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize) - player_match = dict(zip(overall_players['player_var'], overall_players['Player'])) - player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add'])) - - player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%'])) - player_team = dict(zip(flex_file['Player'], flex_file['Team'])) - player_pos = dict(zip(flex_file['Player'], flex_file['Position'])) - player_sal = dict(zip(flex_file['Player'], flex_file['Salary'])) - player_proj = dict(zip(flex_file['Player'], flex_file['Median'])) - - obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index} - total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1 - total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1 - - if site_var1 == 'Draftkings': - - if contest_var1 == 'Cash': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - elif contest_var1 == 'Small Field GPP': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - elif contest_var1 == 'Round Robin': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - elif contest_var1 == 'Large Field GPP': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - for flex in flex_file['lock'].unique(): - sub_idx = flex_file[flex_file['lock'] == 1].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1) - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] != "Var"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "QB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "RB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "RB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "WR"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "WR"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "TE"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "TE"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "DST"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - elif site_var1 == 'Fanduel': - - if contest_var1 == 'Cash': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - elif contest_var1 == 'Small Field GPP': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - elif contest_var1 == 'Round Robin': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - elif contest_var1 == 'Large Field GPP': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - for flex in flex_file['lock'].unique(): - sub_idx = flex_file[flex_file['lock'] == 1].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1) - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] != "Var"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "QB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "RB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "RB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "WR"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "WR"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "TE"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "TE"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "D"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - player_count = [] - player_trim = [] - lineup_list = [] - - if contest_var1 == 'Cash': - obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} - total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) - elif contest_var1 != 'Cash': - obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} - total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) - - - total_score.solve() - for v in total_score.variables(): - if v.varValue > 0: - lineup_list.append(v.name) - df = pd.DataFrame(lineup_list) - df['Names'] = df[0].map(player_match) - df['Cost'] = df['Names'].map(player_sal) - df['Proj'] = df['Names'].map(player_proj) - df['Own'] = df['Names'].map(player_own) - total_cost = sum(df['Cost']) - total_own = sum(df['Own']) - total_proj = sum(df['Proj']) - lineup_raw = pd.DataFrame(lineup_list) - lineup_raw['Names'] = lineup_raw[0].map(player_match) - lineup_raw['value'] = lineup_raw[0].map(player_index_match) - lineup_final = lineup_raw.sort_values(by=['value']) - del lineup_final[lineup_final.columns[0]] - del lineup_final[lineup_final.columns[1]] - lineup_final = lineup_final.reset_index(drop=True) - - if site_var1 == 'Draftkings': - line_hold = lineup_final[['Names']] - line_hold['pos'] = line_hold['Names'].map(player_pos) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] == 'QB': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - rbvar = 0 - for pname in range(0,len(line_hold)): - if rbvar == 2: - pname = len(line_hold) - elif rbvar < 2: - if line_hold.iat[pname,1] == 'RB': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - rbvar = rbvar + 1 - p_used.extend(sorted_lineup) - wrvar = 0 - for pname in range(0,len(line_hold)): - if wrvar == 3: - pname = len(line_hold) - elif wrvar < 3: - if line_hold.iat[pname,1] == 'WR': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - wrvar = wrvar + 1 - p_used.extend(sorted_lineup) - tevar = 0 - for pname in range(0,len(line_hold)): - if tevar == 1: - pname = len(line_hold) - elif tevar < 1: - if line_hold.iat[pname,1] == 'TE': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - tevar = tevar + 1 - p_used.extend(sorted_lineup) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] != 'DST': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] == 'DST': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - - lineup_final['sorted'] = sorted_lineup - lineup_final = lineup_final.drop(columns=['Names']) - lineup_final.rename(columns={"sorted": "Names"}, inplace = True) - - elif site_var1 == 'Fanduel': - line_hold = lineup_final[['Names']] - line_hold['pos'] = line_hold['Names'].map(player_pos) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] == 'QB': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - rbvar = 0 - for pname in range(0,len(line_hold)): - if rbvar == 2: - pname = len(line_hold) - elif rbvar < 2: - if line_hold.iat[pname,1] == 'RB': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - rbvar = rbvar + 1 - p_used.extend(sorted_lineup) - wrvar = 0 - for pname in range(0,len(line_hold)): - if wrvar == 3: - pname = len(line_hold) - elif wrvar < 3: - if line_hold.iat[pname,1] == 'WR': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - wrvar = wrvar + 1 - p_used.extend(sorted_lineup) - tevar = 0 - for pname in range(0,len(line_hold)): - if tevar == 1: - pname = len(line_hold) - elif tevar < 1: - if line_hold.iat[pname,1] == 'TE': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - tevar = tevar + 1 - p_used.extend(sorted_lineup) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] != 'DST': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] == 'DST': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - - lineup_final['sorted'] = sorted_lineup - lineup_final = lineup_final.drop(columns=['Names']) - lineup_final.rename(columns={"sorted": "Names"}, inplace = True) - - lineup_test = lineup_final - lineup_final = lineup_final.T - lineup_final['Cost'] = total_cost - lineup_final['Proj'] = total_proj - lineup_final['Own'] = total_own + if contest_var1 == 'Cash': + for flex in flex_file['Team'].unique(): + sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'G')].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 + elif contest_var1 != 'Cash': + for flex in flex_file['Team'].unique(): + sub_idx = flex_file[(flex_file['Team'] == stack_var1) & (flex_file['Position'] != 'G')].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == stack_size_var1 + for flex in flex_file['Team'].unique(): + sub_idx = flex_file[(flex_file['Team'] == ministack_var1) & (flex_file['Position'] != 'G')].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == ministack_size_var1 - lineup_test['Team'] = lineup_test['Names'].map(player_team) - lineup_test['Position'] = lineup_test['Names'].map(player_pos) - lineup_test['Salary'] = lineup_test['Names'].map(player_sal) - lineup_test['Proj'] = lineup_test['Names'].map(player_proj) - lineup_test['Own'] = lineup_test['Names'].map(player_own) - lineup_test = lineup_test.set_index('Names') - lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0) - - lineup_display.append(lineup_test) - - with col2: - with st.container(): - st.table(lineup_test) - - max_proj = total_proj - max_own = total_own - - check_list.append(total_proj) - - portfolio = pd.concat([portfolio, lineup_final], ignore_index = True) - - x += 1 + for flex in flex_file['lock'].unique(): + sub_idx = flex_file[flex_file['lock'] == 1].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1) + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] != "Var"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 10 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("SP")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "C"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "1B"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "2B"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "3B"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "SS"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "OF"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("C")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("1B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("2B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("3B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("SS")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "3B/SS")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 - elif contest_var1 != 'Round Robin': - while x <= linenum_var1: - sorted_lineup = [] - p_used = [] - cvar = 0 - firvar = 0 - secvar = 0 - thirvar = 0 - - raw_proj_file = raw_baselines - raw_flex_file = raw_proj_file.dropna(how='all') - raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0] - flex_file = raw_flex_file - flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True) - flex_file['name_var'] = flex_file['Player'] - flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var1), 1, 0) - player_ids = flex_file.index - - overall_players = flex_file[['Player']] - overall_players['player_var_add'] = flex_file.index - overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str) - - player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger) - total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize) - player_match = dict(zip(overall_players['player_var'], overall_players['Player'])) - player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add'])) - - player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%'])) - player_team = dict(zip(flex_file['Player'], flex_file['Team'])) - player_pos = dict(zip(flex_file['Player'], flex_file['Position'])) - player_sal = dict(zip(flex_file['Player'], flex_file['Salary'])) - player_proj = dict(zip(flex_file['Player'], flex_file['Median'])) - - obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index} - total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1 - total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1 - - if site_var1 == 'Draftkings': - - if contest_var1 == 'Cash': - qbfile = flex_file - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - elif contest_var1 == 'Small Field GPP': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - elif contest_var1 == 'Large Field GPP': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - for flex in flex_file['lock'].unique(): - sub_idx = flex_file[flex_file['lock'] == 1].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1) - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] != "Var"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "QB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "RB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "RB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "WR"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "WR"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "TE"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "TE"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "DST"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - elif site_var1 == 'Fanduel': - - if contest_var1 == 'Cash': - qbfile = flex_file - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - elif contest_var1 == 'Small Field GPP': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - elif contest_var1 == 'Large Field GPP': - qbfile = flex_file[flex_file['Team'] == stack_var1] - qbfile = qbfile[qbfile['Position'] == 'QB'] - qbfile = qbfile.reset_index() - qb_var = qbfile['Player'][0] - st.table(qbfile) - #st.write(stack_var1 + ' ' + qb_var) - for qbid in player_ids: - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-qbstack_var1*player_vars[qbid]]) >= 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == stack_var1 and - flex_file['Position'][i] in ('RB'))] + - [0*player_vars[qbid]]) == 0 - if flex_file['Position'][qbid] == 'QB': - total_score += pulp.lpSum([player_vars[i] for i in player_ids if - (flex_file['Team'][i] == opp_var1 and - flex_file['Position'][i] in ('WR', 'TE'))] + - [-ministack_var1*player_vars[qbid]]) >= 0 - for flex in flex_file['Player'].unique(): - sub_idx = flex_file[flex_file['Player'] == qb_var].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - for flex in flex_file['lock'].unique(): - sub_idx = flex_file[flex_file['lock'] == 1].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1) - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] != "Var"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "QB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "RB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "RB"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "WR"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "WR"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "TE"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "TE"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 - - for flex in flex_file['Position'].unique(): - sub_idx = flex_file[flex_file['Position'] == "D"].index - total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 - - player_count = [] - player_trim = [] - lineup_list = [] - - if contest_var1 == 'Cash': - obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} - total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) - total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001 - elif contest_var1 != 'Cash': - obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} - total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) - total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01 - - - total_score.solve() - for v in total_score.variables(): - if v.varValue > 0: - lineup_list.append(v.name) - df = pd.DataFrame(lineup_list) - df['Names'] = df[0].map(player_match) - df['Cost'] = df['Names'].map(player_sal) - df['Proj'] = df['Names'].map(player_proj) - df['Own'] = df['Names'].map(player_own) - total_cost = sum(df['Cost']) - total_own = sum(df['Own']) - total_proj = sum(df['Proj']) - lineup_raw = pd.DataFrame(lineup_list) - lineup_raw['Names'] = lineup_raw[0].map(player_match) - lineup_raw['value'] = lineup_raw[0].map(player_index_match) - lineup_final = lineup_raw.sort_values(by=['value']) - del lineup_final[lineup_final.columns[0]] - del lineup_final[lineup_final.columns[1]] - lineup_final = lineup_final.reset_index(drop=True) - - if site_var1 == 'Draftkings': - line_hold = lineup_final[['Names']] - line_hold['pos'] = line_hold['Names'].map(player_pos) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] == 'QB': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - rbvar = 0 - for pname in range(0,len(line_hold)): - if rbvar == 2: - pname = len(line_hold) - elif rbvar < 2: - if line_hold.iat[pname,1] == 'RB': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - rbvar = rbvar + 1 - p_used.extend(sorted_lineup) - wrvar = 0 - for pname in range(0,len(line_hold)): - if wrvar == 3: - pname = len(line_hold) - elif wrvar < 3: - if line_hold.iat[pname,1] == 'WR': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - wrvar = wrvar + 1 - p_used.extend(sorted_lineup) - tevar = 0 - for pname in range(0,len(line_hold)): - if tevar == 1: - pname = len(line_hold) - elif tevar < 1: - if line_hold.iat[pname,1] == 'TE': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - tevar = tevar + 1 - p_used.extend(sorted_lineup) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] != 'DST': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] == 'DST': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - - lineup_final['sorted'] = sorted_lineup - lineup_final = lineup_final.drop(columns=['Names']) - lineup_final.rename(columns={"sorted": "Names"}, inplace = True) - - elif site_var1 == 'Fanduel': - line_hold = lineup_final[['Names']] - line_hold['pos'] = line_hold['Names'].map(player_pos) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] == 'QB': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - rbvar = 0 - for pname in range(0,len(line_hold)): - if rbvar == 2: - pname = len(line_hold) - elif rbvar < 2: - if line_hold.iat[pname,1] == 'RB': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - rbvar = rbvar + 1 - p_used.extend(sorted_lineup) - wrvar = 0 - for pname in range(0,len(line_hold)): - if wrvar == 3: - pname = len(line_hold) - elif wrvar < 3: - if line_hold.iat[pname,1] == 'WR': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - wrvar = wrvar + 1 - p_used.extend(sorted_lineup) - tevar = 0 - for pname in range(0,len(line_hold)): - if tevar == 1: - pname = len(line_hold) - elif tevar < 1: - if line_hold.iat[pname,1] == 'TE': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - tevar = tevar + 1 - p_used.extend(sorted_lineup) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] != 'DST': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - - for pname in range(0,len(line_hold)): - if line_hold.iat[pname,1] == 'DST': - if line_hold.iat[pname,0] not in p_used: - sorted_lineup.append(line_hold.iat[pname,0]) - p_used.extend(sorted_lineup) - - lineup_final['sorted'] = sorted_lineup - lineup_final = lineup_final.drop(columns=['Names']) - lineup_final.rename(columns={"sorted": "Names"}, inplace = True) - - lineup_test = lineup_final - lineup_final = lineup_final.T - lineup_final['Cost'] = total_cost - lineup_final['Proj'] = total_proj - lineup_final['Own'] = total_own + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "2B")| (flex_file['Position'] == "2B/SS")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 - lineup_test['Team'] = lineup_test['Names'].map(player_team) - lineup_test['Position'] = lineup_test['Names'].map(player_pos) - lineup_test['Salary'] = lineup_test['Names'].map(player_sal) - lineup_test['Proj'] = lineup_test['Names'].map(player_proj) - lineup_test['Own'] = lineup_test['Names'].map(player_own) - lineup_test = lineup_test.set_index('Names') - lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0) - - lineup_display.append(lineup_test) - - with col2: - with st.container(): - st.table(lineup_test) - - max_proj = total_proj - max_own = total_own - - check_list.append(total_proj) - - portfolio = pd.concat([portfolio, lineup_final], ignore_index = True) - - x += 1 - - if site_var1 == 'Draftkings': - portfolio.rename(columns={0: "QB", 1: "RB1", 2: "RB2", 3: "WR1", 4: "WR2", 5: "WR3", 6: "TE", 7: "UTIL", 8: "DST"}, inplace = True) + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "2B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "2B/3B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "1B/3B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "C")| (flex_file['Position'] == "1B/C")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "OF")| (flex_file['Position'] == "SS/OF")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 + elif site_var1 == 'Fanduel': - portfolio.rename(columns={0: "QB", 1: "RB1", 2: "RB2", 3: "WR1", 4: "WR2", 5: "WR3", 6: "TE", 7: "UTIL", 8: "DST"}, inplace = True) - portfolio = portfolio.dropna() - portfolio = portfolio.reset_index() - portfolio['Lineup_num'] = portfolio['index'] + 1 - portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True) - portfolio = portfolio.set_index('Lineup') - portfolio = portfolio.drop(columns=['index']) - - final_outcomes = portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'UTIL', 'DST', 'Cost', 'Proj', 'Own']] - final_outcomes = final_outcomes.set_axis(['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'UTIL', 'DST', 'Cost', 'Proj', 'Own'], axis=1) - final_outcomes_export = pd.DataFrame() - final_outcomes_export['QB'] = final_outcomes['QB'] - final_outcomes_export['RB1'] = final_outcomes['RB1'] - final_outcomes_export['RB2'] = final_outcomes['RB2'] - final_outcomes_export['WR1'] = final_outcomes['WR1'] - final_outcomes_export['WR2'] = final_outcomes['WR2'] - final_outcomes_export['WR3'] = final_outcomes['WR3'] - final_outcomes_export['TE'] = final_outcomes['TE'] - final_outcomes_export['UTIL'] = final_outcomes['UTIL'] - final_outcomes_export['DST'] = final_outcomes['DST'] - final_outcomes_export['Salary'] = final_outcomes['Cost'] - final_outcomes_export['Own'] = final_outcomes['Own'] - final_outcomes_export['Proj'] = final_outcomes['Proj'] + + if contest_var1 == 'Cash': + for flex in flex_file['Team'].unique(): + sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'G')].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 + elif contest_var1 != 'Cash': + for flex in flex_file['Team'].unique(): + sub_idx = flex_file[(flex_file['Team'] == stack_var1) & (flex_file['Position'] != 'G')].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == stack_size_var1 + for flex in flex_file['Team'].unique(): + sub_idx = flex_file[(flex_file['Team'] == ministack_var1) & (flex_file['Position'] != 'G')].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == ministack_size_var1 + + for flex in flex_file['lock'].unique(): + sub_idx = flex_file[flex_file['lock'] == 1].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1) + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] != "Var"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 10 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("SP")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "C"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "1B"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "2B"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "3B"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "SS"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'] == "OF"].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("C")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("1B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("2B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("3B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("SS")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "3B/SS")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "2B")| (flex_file['Position'] == "2B/SS")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "2B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "2B/3B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "1B/3B")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "C")| (flex_file['Position'] == "1B/C")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 + + for flex in flex_file['Position'].unique(): + sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "OF")| (flex_file['Position'] == "SS/OF")].index + total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 + + player_count = [] + player_trim = [] + lineup_list = [] + + if contest_var1 == 'Cash': + obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} + total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) + total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001 + elif contest_var1 != 'Cash': + obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} + total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) + total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01 + + + total_score.solve() + for v in total_score.variables(): + if v.varValue > 0: + lineup_list.append(v.name) + df = pd.DataFrame(lineup_list) + df['Names'] = df[0].map(player_match) + df['Cost'] = df['Names'].map(player_sal) + df['Proj'] = df['Names'].map(player_proj) + df['Own'] = df['Names'].map(player_own) + total_cost = sum(df['Cost']) + total_own = sum(df['Own']) + total_proj = sum(df['Proj']) + lineup_raw = pd.DataFrame(lineup_list) + lineup_raw['Names'] = lineup_raw[0].map(player_match) + lineup_raw['value'] = lineup_raw[0].map(player_index_match) + lineup_final = lineup_raw.sort_values(by=['value']) + del lineup_final[lineup_final.columns[0]] + del lineup_final[lineup_final.columns[1]] + lineup_final = lineup_final.reset_index(drop=True) + if site_var1 == 'Draftkings': - final_outcomes_export['QB'].replace(dkid_dict, inplace=True) - final_outcomes_export['RB1'].replace(dkid_dict, inplace=True) - final_outcomes_export['RB2'].replace(dkid_dict, inplace=True) - final_outcomes_export['WR1'].replace(dkid_dict, inplace=True) - final_outcomes_export['WR2'].replace(dkid_dict, inplace=True) - final_outcomes_export['WR3'].replace(dkid_dict, inplace=True) - final_outcomes_export['TE'].replace(dkid_dict, inplace=True) - final_outcomes_export['UTIL'].replace(dkid_dict, inplace=True) - final_outcomes_export['DST'].replace(dkid_dict, inplace=True) + line_hold = lineup_final[['Names']] + line_hold['pos'] = line_hold['Names'].map(player_pos) + + for pname in range(0,len(line_hold)): + if line_hold.iat[pname,1] == 'QB': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + p_used.extend(sorted_lineup) + rbvar = 0 + for pname in range(0,len(line_hold)): + if rbvar == 2: + pname = len(line_hold) + elif rbvar < 2: + if line_hold.iat[pname,1] == 'RB': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + rbvar = rbvar + 1 + p_used.extend(sorted_lineup) + wrvar = 0 + for pname in range(0,len(line_hold)): + if wrvar == 3: + pname = len(line_hold) + elif wrvar < 3: + if line_hold.iat[pname,1] == 'WR': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + wrvar = wrvar + 1 + p_used.extend(sorted_lineup) + tevar = 0 + for pname in range(0,len(line_hold)): + if tevar == 1: + pname = len(line_hold) + elif tevar < 1: + if line_hold.iat[pname,1] == 'TE': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + tevar = tevar + 1 + p_used.extend(sorted_lineup) + + for pname in range(0,len(line_hold)): + if line_hold.iat[pname,1] != 'DST': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + p_used.extend(sorted_lineup) + + for pname in range(0,len(line_hold)): + if line_hold.iat[pname,1] == 'DST': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + p_used.extend(sorted_lineup) + + lineup_final['sorted'] = sorted_lineup + lineup_final = lineup_final.drop(columns=['Names']) + lineup_final.rename(columns={"sorted": "Names"}, inplace = True) + elif site_var1 == 'Fanduel': - final_outcomes_export['QB'].replace(fdid_dict, inplace=True) - final_outcomes_export['RB1'].replace(fdid_dict, inplace=True) - final_outcomes_export['RB2'].replace(fdid_dict, inplace=True) - final_outcomes_export['WR1'].replace(fdid_dict, inplace=True) - final_outcomes_export['WR2'].replace(fdid_dict, inplace=True) - final_outcomes_export['WR3'].replace(fdid_dict, inplace=True) - final_outcomes_export['TE'].replace(fdid_dict, inplace=True) - final_outcomes_export['UTIL'].replace(fdid_dict, inplace=True) - final_outcomes_export['DST'].replace(fdid_dict, inplace=True) - - player_freq = pd.DataFrame(np.column_stack(np.unique(portfolio.iloc[:,0:8].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - player_freq['Freq'] = player_freq['Freq'].astype(int) - player_freq['Position'] = player_freq['Player'].map(player_pos) - player_freq['Salary'] = player_freq['Player'].map(player_sal) - player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100 - player_freq['Exposure'] = player_freq['Freq']/(linenum_var1) - player_freq['Team'] = player_freq['Player'].map(player_team) - - player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']] - player_freq = player_freq.set_index('Player') + line_hold = lineup_final[['Names']] + line_hold['pos'] = line_hold['Names'].map(player_pos) + + for pname in range(0,len(line_hold)): + if line_hold.iat[pname,1] == 'QB': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + p_used.extend(sorted_lineup) + rbvar = 0 + for pname in range(0,len(line_hold)): + if rbvar == 2: + pname = len(line_hold) + elif rbvar < 2: + if line_hold.iat[pname,1] == 'RB': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + rbvar = rbvar + 1 + p_used.extend(sorted_lineup) + wrvar = 0 + for pname in range(0,len(line_hold)): + if wrvar == 3: + pname = len(line_hold) + elif wrvar < 3: + if line_hold.iat[pname,1] == 'WR': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + wrvar = wrvar + 1 + p_used.extend(sorted_lineup) + tevar = 0 + for pname in range(0,len(line_hold)): + if tevar == 1: + pname = len(line_hold) + elif tevar < 1: + if line_hold.iat[pname,1] == 'TE': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + tevar = tevar + 1 + p_used.extend(sorted_lineup) + + for pname in range(0,len(line_hold)): + if line_hold.iat[pname,1] != 'DST': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + p_used.extend(sorted_lineup) + + for pname in range(0,len(line_hold)): + if line_hold.iat[pname,1] == 'DST': + if line_hold.iat[pname,0] not in p_used: + sorted_lineup.append(line_hold.iat[pname,0]) + p_used.extend(sorted_lineup) + + lineup_final['sorted'] = sorted_lineup + lineup_final = lineup_final.drop(columns=['Names']) + lineup_final.rename(columns={"sorted": "Names"}, inplace = True) + + lineup_test = lineup_final + lineup_final = lineup_final.T + lineup_final['Cost'] = total_cost + lineup_final['Proj'] = total_proj + lineup_final['Own'] = total_own + + lineup_test['Team'] = lineup_test['Names'].map(player_team) + lineup_test['Position'] = lineup_test['Names'].map(player_pos) + lineup_test['Salary'] = lineup_test['Names'].map(player_sal) + lineup_test['Proj'] = lineup_test['Names'].map(player_proj) + lineup_test['Own'] = lineup_test['Names'].map(player_own) + lineup_test = lineup_test.set_index('Names') + lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0) + + lineup_display.append(lineup_test) + + with col2: + with st.container(): + st.table(lineup_test) + + max_proj = total_proj + max_own = total_own + + check_list.append(total_proj) + + portfolio = pd.concat([portfolio, lineup_final], ignore_index = True) + + x += 1 + + if site_var1 == 'Draftkings': + portfolio.rename(columns={0: "QB", 1: "RB1", 2: "RB2", 3: "WR1", 4: "WR2", 5: "WR3", 6: "TE", 7: "UTIL", 8: "DST"}, inplace = True) + elif site_var1 == 'Fanduel': + portfolio.rename(columns={0: "QB", 1: "RB1", 2: "RB2", 3: "WR1", 4: "WR2", 5: "WR3", 6: "TE", 7: "UTIL", 8: "DST"}, inplace = True) + portfolio = portfolio.dropna() + portfolio = portfolio.reset_index() + portfolio['Lineup_num'] = portfolio['index'] + 1 + portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True) + portfolio = portfolio.set_index('Lineup') + portfolio = portfolio.drop(columns=['index']) + + final_outcomes = portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'UTIL', 'DST', 'Cost', 'Proj', 'Own']] + final_outcomes = final_outcomes.set_axis(['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'UTIL', 'DST', 'Cost', 'Proj', 'Own'], axis=1) + final_outcomes_export = pd.DataFrame() + final_outcomes_export['QB'] = final_outcomes['QB'] + final_outcomes_export['RB1'] = final_outcomes['RB1'] + final_outcomes_export['RB2'] = final_outcomes['RB2'] + final_outcomes_export['WR1'] = final_outcomes['WR1'] + final_outcomes_export['WR2'] = final_outcomes['WR2'] + final_outcomes_export['WR3'] = final_outcomes['WR3'] + final_outcomes_export['TE'] = final_outcomes['TE'] + final_outcomes_export['UTIL'] = final_outcomes['UTIL'] + final_outcomes_export['DST'] = final_outcomes['DST'] + final_outcomes_export['Salary'] = final_outcomes['Cost'] + final_outcomes_export['Own'] = final_outcomes['Own'] + final_outcomes_export['Proj'] = final_outcomes['Proj'] + if site_var1 == 'Draftkings': + final_outcomes_export['QB'].replace(dkid_dict, inplace=True) + final_outcomes_export['RB1'].replace(dkid_dict, inplace=True) + final_outcomes_export['RB2'].replace(dkid_dict, inplace=True) + final_outcomes_export['WR1'].replace(dkid_dict, inplace=True) + final_outcomes_export['WR2'].replace(dkid_dict, inplace=True) + final_outcomes_export['WR3'].replace(dkid_dict, inplace=True) + final_outcomes_export['TE'].replace(dkid_dict, inplace=True) + final_outcomes_export['UTIL'].replace(dkid_dict, inplace=True) + final_outcomes_export['DST'].replace(dkid_dict, inplace=True) + elif site_var1 == 'Fanduel': + final_outcomes_export['QB'].replace(fdid_dict, inplace=True) + final_outcomes_export['RB1'].replace(fdid_dict, inplace=True) + final_outcomes_export['RB2'].replace(fdid_dict, inplace=True) + final_outcomes_export['WR1'].replace(fdid_dict, inplace=True) + final_outcomes_export['WR2'].replace(fdid_dict, inplace=True) + final_outcomes_export['WR3'].replace(fdid_dict, inplace=True) + final_outcomes_export['TE'].replace(fdid_dict, inplace=True) + final_outcomes_export['UTIL'].replace(fdid_dict, inplace=True) + final_outcomes_export['DST'].replace(fdid_dict, inplace=True) + + player_freq = pd.DataFrame(np.column_stack(np.unique(portfolio.iloc[:,0:8].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + player_freq['Freq'] = player_freq['Freq'].astype(int) + player_freq['Position'] = player_freq['Player'].map(player_pos) + player_freq['Salary'] = player_freq['Player'].map(player_sal) + player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100 + player_freq['Exposure'] = player_freq['Freq']/(linenum_var1) + player_freq['Team'] = player_freq['Player'].map(player_team) + + player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']] + player_freq = player_freq.set_index('Player') with optimize_container: optimize_container = st.empty() @@ -1238,4 +659,4 @@ with tab2: ) with freq_container: freq_container = st.empty() - st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True) + st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)