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import streamlit as st
st.set_page_config(layout="wide")
for name in dir():
if not name.startswith('_'):
del globals()[name]
import pulp
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import time
from itertools import combinations
@st.cache_resource
def init_conn():
scope = ['https://www.googleapis.com/auth/spreadsheets',
"https://www.googleapis.com/auth/drive"]
credentials = {
"type": "service_account",
"project_id": "sheets-api-connect-378620",
"private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
"private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
"client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
"client_id": "106625872877651920064",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
}
gc = gspread.service_account_from_dict(credentials)
return gc
gcservice_account = init_conn()
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():
sh = gcservice_account.open_by_url(all_dk_player_projections)
worksheet = sh.worksheet('Site_Info')
raw_display = pd.DataFrame(worksheet.get_all_records())
return raw_display
@st.cache_resource(ttl = 600)
def grab_baseline_stuff():
sh = gcservice_account.open_by_url(all_dk_player_projections)
worksheet = sh.worksheet('Player_Data_Master')
raw_display = pd.DataFrame(worksheet.get_all_records())
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('Timestamp')
timestamp = worksheet.acell('A1').value
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')
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', '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:
proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
if proj_file is not None:
try:
proj_dataframe = pd.read_csv(proj_file)
except:
proj_dataframe = pd.read_excel(proj_file)
with col2:
if proj_file is not None:
st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
with tab2:
col1, col2 = st.columns([1, 5])
with col1:
st.info(t_stamp)
if st.button("Load/Reset Data", key='reset1'):
st.cache_data.clear()
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?", ('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
elif slate_var1 != 'User':
raw_baselines = dk_raw_proj
elif site_var1 == 'Fanduel':
if slate_var1 == 'User':
raw_baselines = proj_dataframe
elif slate_var1 != 'User':
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 != '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')
elif split_var1 == 'Full Slate Run':
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')
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')
elif site_var1 == 'Fanduel':
min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 59900, value = 59000, step = 100, key='min_sal1')
max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 60000, value = 60000, step = 100, key='max_sal1')
with col2:
raw_baselines = raw_baselines[raw_baselines['Team'].isin(team_var1)]
raw_baselines = raw_baselines[~raw_baselines['Player'].isin(avoid_var1)]
ownframe = raw_baselines.copy()
if contest_var1 == 'Cash':
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%'] * (800 / ownframe['Own%'].sum())
if contest_var1 == 'Small Field GPP':
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%'] * (800 / ownframe['Own%'].sum())
if contest_var1 == 'Large Field GPP':
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%'] * (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)
st.download_button(
label="Export Projections",
data=convert_df_to_csv(raw_baselines),
file_name='NFL_proj_export.csv',
mime='text/csv',
)
if st.button('Optimize'):
max_proj = 1000
max_own = 1000
total_proj = 0
total_own = 0
optimize_container = st.empty()
download_container = st.empty()
freq_container = st.empty()
lineup_display = []
check_list = []
lineups = []
portfolio = pd.DataFrame()
x = 1
with st.spinner('Wait for it...'):
with optimize_container:
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':
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
elif site_var1 == 'Fanduel':
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':
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
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()
st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
with download_container:
download_container = st.empty()
st.download_button(
label="Export Optimals",
data=convert_df_to_csv(final_outcomes_export),
file_name='NFL_optimals_export.csv',
mime='text/csv',
)
with freq_container:
freq_container = st.empty()
st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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