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import streamlit as st
st.set_page_config(layout="wide")
import numpy as np
import pandas as pd
import time
from rapidfuzz import process, fuzz
import random
## import global functions
from global_func.clean_player_name import clean_player_name
from global_func.load_file import load_file
from global_func.load_ss_file import load_ss_file
from global_func.find_name_mismatches import find_name_mismatches
from global_func.predict_dupes import predict_dupes
from global_func.highlight_rows import highlight_changes, highlight_changes_winners, highlight_changes_losers
from global_func.load_csv import load_csv
from global_func.find_csv_mismatches import find_csv_mismatches
from global_func.trim_portfolio import trim_portfolio
from global_func.get_portfolio_names import get_portfolio_names
freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'}
player_wrong_names_mlb = ['Enrique Hernandez']
player_right_names_mlb = ['Kike Hernandez']
tab1, tab2 = st.tabs(["Data Load", "Manage Portfolio"])
with tab1:
if st.button('Clear data', key='reset1'):
st.session_state.clear()
# Add file uploaders to your app
col1, col2, col3 = st.columns(3)
with col1:
st.subheader("Draftkings/Fanduel CSV")
st.info("Upload the player pricing CSV from the site you are playing on. This is used in late swap exporting and/or with SaberSim portfolios, but is not necessary for the portfolio management functions.")
upload_csv_col, csv_template_col = st.columns([3, 1])
with upload_csv_col:
csv_file = st.file_uploader("Upload CSV File", type=['csv'])
if 'csv_file' in st.session_state:
del st.session_state['csv_file']
with csv_template_col:
csv_template_df = pd.DataFrame(columns=['Name', 'ID', 'Roster Position', 'Salary'])
st.download_button(
label="CSV Template",
data=csv_template_df.to_csv(index=False),
file_name="csv_template.csv",
mime="text/csv"
)
st.session_state['csv_file'] = load_csv(csv_file)
try:
st.session_state['csv_file']['Salary'] = st.session_state['csv_file']['Salary'].astype(str).str.replace(',', '').astype(int)
except:
pass
if csv_file:
st.session_state['csv_file'] = st.session_state['csv_file'].drop_duplicates(subset=['Name'])
st.success('Projections file loaded successfully!')
st.dataframe(st.session_state['csv_file'].head(10))
with col2:
st.subheader("Portfolio File")
st.info("Go ahead and upload a portfolio file here. Only include player columns and an optional 'Stack' column if you are playing MLB.")
saber_toggle = st.radio("Are you uploading from SaberSim?", options=['No', 'Yes'])
st.info("If you are uploading from SaberSim, you will need to upload a CSV file for the slate for name matching.")
if saber_toggle == 'Yes':
if csv_file is not None:
portfolio_file = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
if 'portfolio' in st.session_state:
del st.session_state['portfolio']
if 'export_portfolio' in st.session_state:
del st.session_state['export_portfolio']
else:
portfolio_file = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
if 'portfolio' in st.session_state:
del st.session_state['portfolio']
if 'export_portfolio' in st.session_state:
del st.session_state['export_portfolio']
if 'portfolio' not in st.session_state:
if portfolio_file:
if saber_toggle == 'Yes':
st.session_state['export_portfolio'], st.session_state['portfolio'] = load_ss_file(portfolio_file, st.session_state['csv_file'])
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all')
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True)
st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all')
st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True)
else:
st.session_state['export_portfolio'], st.session_state['portfolio'] = load_file(portfolio_file)
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all')
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True)
st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all')
st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True)
# Check if Stack column exists in the portfolio
if 'Stack' in st.session_state['portfolio'].columns:
# Create dictionary mapping index to Stack values
stack_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack']))
st.write(f"Found {len(stack_dict)} stack assignments")
st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['Stack'])
else:
stack_dict = None
st.info("No Stack column found in portfolio")
if st.session_state['portfolio'] is not None:
st.success('Portfolio file loaded successfully!')
st.session_state['portfolio'] = st.session_state['portfolio'].apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
st.dataframe(st.session_state['portfolio'].head(10))
with col3:
st.subheader("Projections File")
st.info("upload a projections file that has 'player_names', 'salary', 'median', 'ownership', and 'captain ownership' (Needed for Showdown) columns. Note that the salary for showdown needs to be the FLEX salary, not the captain salary.")
# Create two columns for the uploader and template button
upload_col, template_col = st.columns([3, 1])
with upload_col:
projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
if 'projections_df' in st.session_state:
del st.session_state['projections_df']
with template_col:
# Create empty DataFrame with required columns
template_df = pd.DataFrame(columns=['player_names', 'position', 'team', 'salary', 'median', 'ownership', 'captain ownership'])
# Add download button for template
st.download_button(
label="Template",
data=template_df.to_csv(index=False),
file_name="projections_template.csv",
mime="text/csv"
)
if projections_file:
export_projections, projections = load_file(projections_file)
if projections is not None:
st.success('Projections file loaded successfully!')
projections = projections.apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
st.dataframe(projections.head(10))
if portfolio_file and projections_file:
if st.session_state['portfolio'] is not None and projections is not None:
st.subheader("Name Matching Analysis")
# Initialize projections_df in session state if it doesn't exist
# Get unique names from portfolio
st.session_state['portfolio_names'] = get_portfolio_names(st.session_state['portfolio'])
# Get names from projections
projection_names = projections['player_names'].tolist()
# Create match dictionary for portfolio names to projection names
portfolio_match_dict = {}
unmatched_names = []
for portfolio_name in st.session_state['portfolio_names']:
match = process.extractOne(
portfolio_name,
projection_names,
score_cutoff=85
)
if match:
portfolio_match_dict[portfolio_name] = match[0]
st.write(f"{portfolio_name} matched to {match[0]}")
else:
portfolio_match_dict[portfolio_name] = portfolio_name
unmatched_names.append(portfolio_name)
# Update portfolio with matched names
portfolio = st.session_state['portfolio'].copy()
player_columns = [col for col in portfolio.columns
if col not in ['salary', 'median', 'Own']]
# For each player column, update names using the match dictionary
for col in player_columns:
portfolio[col] = portfolio[col].map(lambda x: portfolio_match_dict.get(x, x))
# Update the portfolio in session state
st.session_state['portfolio'] = portfolio
st.session_state['origin_portfolio'] = st.session_state['portfolio'].copy()
# Store the match dictionary for reference
st.session_state['portfolio_to_projection_matches'] = portfolio_match_dict
if 'projections_df' not in st.session_state:
st.session_state['projections_df'] = projections.copy()
st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
try:
name_id_map = dict(zip(
st.session_state['csv_file']['Name'],
st.session_state['csv_file']['Name + ID']
))
print("Using Name + ID mapping")
except:
name_id_map = dict(zip(
st.session_state['csv_file']['Nickname'],
st.session_state['csv_file']['Id']
))
print("Using Nickname + Id mapping")
# Get all names at once
names = projections['player_names'].tolist()
choices = list(name_id_map.keys())
# Create a dictionary to store matches
match_dict = {}
# Process each name individually but more efficiently
for name in names:
# Use extractOne with score_cutoff for efficiency
match = process.extractOne(
name,
choices,
score_cutoff=85
)
if match:
match_dict[name] = name_id_map[match[0]]
else:
match_dict[name] = name
# Apply the matches
projections['upload_match'] = projections['player_names'].map(match_dict)
st.session_state['export_dict'] = match_dict
if unmatched_names:
st.warning(f"Found {len(unmatched_names)} names in portfolio without matches in projections:")
for name in unmatched_names:
st.write(f"- {name}")
else:
st.success("All portfolio names were matched to projections!")
st.session_state['origin_portfolio'] = st.session_state['portfolio'].copy()
# with tab2:
# if st.button('Clear data', key='reset2'):
# st.session_state.clear()
# if 'portfolio' in st.session_state and 'projections_df' in st.session_state:
# optimized_df = None
# map_dict = {
# 'pos_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['position'])),
# 'salary_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['salary'])),
# 'proj_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['median'])),
# 'own_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['ownership'])),
# 'team_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['team']))
# }
# # Calculate new stats for optimized lineups
# st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
# )
# st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
# )
# st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
# )
# options_container = st.container()
# with options_container:
# col1, col2, col3, col4, col5, col6 = st.columns(6)
# with col1:
# curr_site_var = st.selectbox("Select your current site", options=['DraftKings', 'FanDuel'])
# with col2:
# curr_sport_var = st.selectbox("Select your current sport", options=['NBA', 'MLB', 'NFL', 'NHL', 'MMA'])
# with col3:
# swap_var = st.multiselect("Select late swap strategy", options=['Optimize', 'Increase volatility', 'Decrease volatility'])
# with col4:
# remove_teams_var = st.multiselect("What teams have already played?", options=st.session_state['projections_df']['team'].unique())
# with col5:
# winners_var = st.multiselect("Are there any players doing exceptionally well?", options=st.session_state['projections_df']['player_names'].unique(), max_selections=3)
# with col6:
# losers_var = st.multiselect("Are there any players doing exceptionally poorly?", options=st.session_state['projections_df']['player_names'].unique(), max_selections=3)
# if st.button('Clear Late Swap'):
# if 'optimized_df' in st.session_state:
# del st.session_state['optimized_df']
# map_dict = {
# 'pos_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['position'])),
# 'salary_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['salary'])),
# 'proj_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['median'])),
# 'own_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['ownership'])),
# 'team_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['team']))
# }
# # Calculate new stats for optimized lineups
# st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
# )
# st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
# )
# st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
# )
# if st.button('Run Late Swap'):
# st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['salary', 'median', 'Own'])
# if curr_sport_var == 'NBA':
# if curr_site_var == 'DraftKings':
# st.session_state['portfolio'] = st.session_state['portfolio'].set_axis(['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'], axis=1)
# else:
# st.session_state['portfolio'] = st.session_state['portfolio'].set_axis(['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C'], axis=1)
# # Define roster position rules
# if curr_site_var == 'DraftKings':
# position_rules = {
# 'PG': ['PG'],
# 'SG': ['SG'],
# 'SF': ['SF'],
# 'PF': ['PF'],
# 'C': ['C'],
# 'G': ['PG', 'SG'],
# 'F': ['SF', 'PF'],
# 'UTIL': ['PG', 'SG', 'SF', 'PF', 'C']
# }
# else:
# position_rules = {
# 'PG': ['PG'],
# 'SG': ['SG'],
# 'SF': ['SF'],
# 'PF': ['PF'],
# 'C': ['C'],
# }
# # Create position groups from projections data
# position_groups = {}
# for _, player in st.session_state['projections_df'].iterrows():
# positions = player['position'].split('/')
# for pos in positions:
# if pos not in position_groups:
# position_groups[pos] = []
# position_groups[pos].append({
# 'player_names': player['player_names'],
# 'salary': player['salary'],
# 'median': player['median'],
# 'ownership': player['ownership'],
# 'positions': positions # Store all eligible positions
# })
# def optimize_lineup(row):
# current_lineup = []
# total_salary = 0
# if curr_site_var == 'DraftKings':
# salary_cap = 50000
# else:
# salary_cap = 60000
# used_players = set()
# # Convert row to dictionary with roster positions
# roster = {}
# for col, player in zip(row.index, row):
# if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
# roster[col] = {
# 'name': player,
# 'position': map_dict['pos_map'].get(player, '').split('/'),
# 'team': map_dict['team_map'].get(player, ''),
# 'salary': map_dict['salary_map'].get(player, 0),
# 'median': map_dict['proj_map'].get(player, 0),
# 'ownership': map_dict['own_map'].get(player, 0)
# }
# total_salary += roster[col]['salary']
# used_players.add(player)
# # Optimize each roster position in random order
# roster_positions = list(roster.items())
# random.shuffle(roster_positions)
# for roster_pos, current in roster_positions:
# # Skip optimization for players from removed teams
# if current['team'] in remove_teams_var:
# continue
# valid_positions = position_rules[roster_pos]
# better_options = []
# # Find valid replacements for this roster position
# for pos in valid_positions:
# if pos in position_groups:
# pos_options = [
# p for p in position_groups[pos]
# if p['median'] > current['median']
# and (total_salary - current['salary'] + p['salary']) <= salary_cap
# and p['player_names'] not in used_players
# and any(valid_pos in p['positions'] for valid_pos in valid_positions)
# and map_dict['team_map'].get(p['player_names']) not in remove_teams_var # Check team restriction
# ]
# better_options.extend(pos_options)
# if better_options:
# # Remove duplicates
# better_options = {opt['player_names']: opt for opt in better_options}.values()
# # Sort by median projection and take the best one
# best_replacement = max(better_options, key=lambda x: x['median'])
# # Update the lineup and tracking variables
# used_players.remove(current['name'])
# used_players.add(best_replacement['player_names'])
# total_salary = total_salary - current['salary'] + best_replacement['salary']
# roster[roster_pos] = {
# 'name': best_replacement['player_names'],
# 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
# 'team': map_dict['team_map'][best_replacement['player_names']],
# 'salary': best_replacement['salary'],
# 'median': best_replacement['median'],
# 'ownership': best_replacement['ownership']
# }
# # Return optimized lineup maintaining original column order
# return [roster[pos]['name'] for pos in row.index if pos in roster]
# def optimize_lineup_winners(row):
# current_lineup = []
# total_salary = 0
# if curr_site_var == 'DraftKings':
# salary_cap = 50000
# else:
# salary_cap = 60000
# used_players = set()
# # Check if any winners are in the lineup and count them
# winners_in_lineup = sum(1 for player in row if player in winners_var)
# changes_needed = min(winners_in_lineup, 3) if winners_in_lineup > 0 else 0
# changes_made = 0
# # Convert row to dictionary with roster positions
# roster = {}
# for col, player in zip(row.index, row):
# if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
# roster[col] = {
# 'name': player,
# 'position': map_dict['pos_map'].get(player, '').split('/'),
# 'team': map_dict['team_map'].get(player, ''),
# 'salary': map_dict['salary_map'].get(player, 0),
# 'median': map_dict['proj_map'].get(player, 0),
# 'ownership': map_dict['own_map'].get(player, 0)
# }
# total_salary += roster[col]['salary']
# used_players.add(player)
# # Only proceed with ownership-based optimization if we have winners in the lineup
# if changes_needed > 0:
# # Randomize the order of positions to optimize
# roster_positions = list(roster.items())
# random.shuffle(roster_positions)
# for roster_pos, current in roster_positions:
# # Stop if we've made enough changes
# if changes_made >= changes_needed:
# break
# # Skip optimization for players from removed teams or if the current player is a winner
# if current['team'] in remove_teams_var or current['name'] in winners_var:
# continue
# valid_positions = list(position_rules[roster_pos])
# random.shuffle(valid_positions)
# better_options = []
# # Find valid replacements with higher ownership
# for pos in valid_positions:
# if pos in position_groups:
# pos_options = [
# p for p in position_groups[pos]
# if p['ownership'] > current['ownership']
# and p['median'] >= current['median'] - 3
# and (total_salary - current['salary'] + p['salary']) <= salary_cap
# and (total_salary - current['salary'] + p['salary']) >= salary_cap - 1000
# and p['player_names'] not in used_players
# and any(valid_pos in p['positions'] for valid_pos in valid_positions)
# and map_dict['team_map'].get(p['player_names']) not in remove_teams_var
# ]
# better_options.extend(pos_options)
# if better_options:
# # Remove duplicates
# better_options = {opt['player_names']: opt for opt in better_options}.values()
# # Sort by ownership and take the highest owned option
# best_replacement = max(better_options, key=lambda x: x['ownership'])
# # Update the lineup and tracking variables
# used_players.remove(current['name'])
# used_players.add(best_replacement['player_names'])
# total_salary = total_salary - current['salary'] + best_replacement['salary']
# roster[roster_pos] = {
# 'name': best_replacement['player_names'],
# 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
# 'team': map_dict['team_map'][best_replacement['player_names']],
# 'salary': best_replacement['salary'],
# 'median': best_replacement['median'],
# 'ownership': best_replacement['ownership']
# }
# changes_made += 1
# # Return optimized lineup maintaining original column order
# return [roster[pos]['name'] for pos in row.index if pos in roster]
# def optimize_lineup_losers(row):
# current_lineup = []
# total_salary = 0
# if curr_site_var == 'DraftKings':
# salary_cap = 50000
# else:
# salary_cap = 60000
# used_players = set()
# # Check if any winners are in the lineup and count them
# losers_in_lineup = sum(1 for player in row if player in losers_var)
# changes_needed = min(losers_in_lineup, 3) if losers_in_lineup > 0 else 0
# changes_made = 0
# # Convert row to dictionary with roster positions
# roster = {}
# for col, player in zip(row.index, row):
# if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
# roster[col] = {
# 'name': player,
# 'position': map_dict['pos_map'].get(player, '').split('/'),
# 'team': map_dict['team_map'].get(player, ''),
# 'salary': map_dict['salary_map'].get(player, 0),
# 'median': map_dict['proj_map'].get(player, 0),
# 'ownership': map_dict['own_map'].get(player, 0)
# }
# total_salary += roster[col]['salary']
# used_players.add(player)
# # Only proceed with ownership-based optimization if we have winners in the lineup
# if changes_needed > 0:
# # Randomize the order of positions to optimize
# roster_positions = list(roster.items())
# random.shuffle(roster_positions)
# for roster_pos, current in roster_positions:
# # Stop if we've made enough changes
# if changes_made >= changes_needed:
# break
# # Skip optimization for players from removed teams or if the current player is a winner
# if current['team'] in remove_teams_var or current['name'] in losers_var:
# continue
# valid_positions = list(position_rules[roster_pos])
# random.shuffle(valid_positions)
# better_options = []
# # Find valid replacements with higher ownership
# for pos in valid_positions:
# if pos in position_groups:
# pos_options = [
# p for p in position_groups[pos]
# if p['ownership'] < current['ownership']
# and p['median'] >= current['median'] - 3
# and (total_salary - current['salary'] + p['salary']) <= salary_cap
# and (total_salary - current['salary'] + p['salary']) >= salary_cap - 1000
# and p['player_names'] not in used_players
# and any(valid_pos in p['positions'] for valid_pos in valid_positions)
# and map_dict['team_map'].get(p['player_names']) not in remove_teams_var
# ]
# better_options.extend(pos_options)
# if better_options:
# # Remove duplicates
# better_options = {opt['player_names']: opt for opt in better_options}.values()
# # Sort by ownership and take the highest owned option
# best_replacement = max(better_options, key=lambda x: x['ownership'])
# # Update the lineup and tracking variables
# used_players.remove(current['name'])
# used_players.add(best_replacement['player_names'])
# total_salary = total_salary - current['salary'] + best_replacement['salary']
# roster[roster_pos] = {
# 'name': best_replacement['player_names'],
# 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
# 'team': map_dict['team_map'][best_replacement['player_names']],
# 'salary': best_replacement['salary'],
# 'median': best_replacement['median'],
# 'ownership': best_replacement['ownership']
# }
# changes_made += 1
# # Return optimized lineup maintaining original column order
# return [roster[pos]['name'] for pos in row.index if pos in roster]
# # Create a progress bar
# progress_bar = st.progress(0)
# status_text = st.empty()
# # Process each lineup
# optimized_lineups = []
# total_lineups = len(st.session_state['portfolio'])
# for idx, row in st.session_state['portfolio'].iterrows():
# # First optimization pass
# first_pass = optimize_lineup(row)
# first_pass_series = pd.Series(first_pass, index=row.index)
# second_pass = optimize_lineup(first_pass_series)
# second_pass_series = pd.Series(second_pass, index=row.index)
# third_pass = optimize_lineup(second_pass_series)
# third_pass_series = pd.Series(third_pass, index=row.index)
# fourth_pass = optimize_lineup(third_pass_series)
# fourth_pass_series = pd.Series(fourth_pass, index=row.index)
# fifth_pass = optimize_lineup(fourth_pass_series)
# fifth_pass_series = pd.Series(fifth_pass, index=row.index)
# # Second optimization pass
# final_lineup = optimize_lineup(fifth_pass_series)
# optimized_lineups.append(final_lineup)
# if 'Optimize' in swap_var:
# progress = (idx + 1) / total_lineups
# progress_bar.progress(progress)
# status_text.text(f'Optimizing Lineups {idx + 1} of {total_lineups}')
# else:
# pass
# # Create new dataframe with optimized lineups
# if 'Optimize' in swap_var:
# st.session_state['optimized_df_medians'] = pd.DataFrame(optimized_lineups, columns=st.session_state['portfolio'].columns)
# else:
# st.session_state['optimized_df_medians'] = st.session_state['portfolio']
# # Create a progress bar
# progress_bar_winners = st.progress(0)
# status_text_winners = st.empty()
# # Process each lineup
# optimized_lineups_winners = []
# total_lineups = len(st.session_state['optimized_df_medians'])
# for idx, row in st.session_state['optimized_df_medians'].iterrows():
# final_lineup = optimize_lineup_winners(row)
# optimized_lineups_winners.append(final_lineup)
# if 'Decrease volatility' in swap_var:
# progress_winners = (idx + 1) / total_lineups
# progress_bar_winners.progress(progress_winners)
# status_text_winners.text(f'Lowering Volatility around Winners {idx + 1} of {total_lineups}')
# else:
# pass
# # Create new dataframe with optimized lineups
# if 'Decrease volatility' in swap_var:
# st.session_state['optimized_df_winners'] = pd.DataFrame(optimized_lineups_winners, columns=st.session_state['optimized_df_medians'].columns)
# else:
# st.session_state['optimized_df_winners'] = st.session_state['optimized_df_medians']
# # Create a progress bar
# progress_bar_losers = st.progress(0)
# status_text_losers = st.empty()
# # Process each lineup
# optimized_lineups_losers = []
# total_lineups = len(st.session_state['optimized_df_winners'])
# for idx, row in st.session_state['optimized_df_winners'].iterrows():
# final_lineup = optimize_lineup_losers(row)
# optimized_lineups_losers.append(final_lineup)
# if 'Increase volatility' in swap_var:
# progress_losers = (idx + 1) / total_lineups
# progress_bar_losers.progress(progress_losers)
# status_text_losers.text(f'Increasing Volatility around Losers {idx + 1} of {total_lineups}')
# else:
# pass
# # Create new dataframe with optimized lineups
# if 'Increase volatility' in swap_var:
# st.session_state['optimized_df'] = pd.DataFrame(optimized_lineups_losers, columns=st.session_state['optimized_df_winners'].columns)
# else:
# st.session_state['optimized_df'] = st.session_state['optimized_df_winners']
# # Calculate new stats for optimized lineups
# st.session_state['optimized_df']['salary'] = st.session_state['optimized_df'].apply(
# lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
# )
# st.session_state['optimized_df']['median'] = st.session_state['optimized_df'].apply(
# lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
# )
# st.session_state['optimized_df']['Own'] = st.session_state['optimized_df'].apply(
# lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
# )
# # Display results
# st.success('Optimization complete!')
# if 'optimized_df' in st.session_state:
# st.write("Increase in median highlighted in yellow, descrease in volatility highlighted in blue, increase in volatility highlighted in red:")
# st.dataframe(
# st.session_state['optimized_df'].style
# .apply(highlight_changes, axis=1)
# .apply(highlight_changes_winners, axis=1)
# .apply(highlight_changes_losers, axis=1)
# .background_gradient(axis=0)
# .background_gradient(cmap='RdYlGn')
# .format(precision=2),
# height=1000,
# use_container_width=True
# )
# # Option to download optimized lineups
# if st.button('Prepare Late Swap Export'):
# export_df = st.session_state['optimized_df'].copy()
# # Map player names to their export IDs for all player columns
# for col in export_df.columns:
# if col not in ['salary', 'median', 'Own']:
# export_df[col] = export_df[col].map(st.session_state['export_dict'])
# csv = export_df.to_csv(index=False)
# st.download_button(
# label="Download CSV",
# data=csv,
# file_name="optimized_lineups.csv",
# mime="text/csv"
# )
# else:
# st.write("Current Portfolio")
# st.dataframe(
# st.session_state['portfolio'].style
# .background_gradient(axis=0)
# .background_gradient(cmap='RdYlGn')
# .format(precision=2),
# height=1000,
# use_container_width=True
# )
with tab2:
if 'portfolio' in st.session_state and 'projections_df' in st.session_state:
excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Win%', 'Lineup Edge']
with st.container():
col1, col2, col3 = st.columns(3)
with col1:
site_var = st.selectbox("Select Site", ['Draftkings', 'Fanduel'])
if st.button('Reset Portfolio', key='reset_port'):
st.session_state['portfolio'] = st.session_state['origin_portfolio'].copy()
if st.button('Clear data', key='reset3'):
st.session_state.clear()
with col2:
sport_var = st.selectbox("Select Sport", ['NFL', 'MLB', 'NBA', 'NHL', 'MMA', 'CS2'])
type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
with col3:
Contest_Size = st.number_input("Enter Contest Size", value=25000, min_value=1, step=1)
strength_var = st.selectbox("Select field strength", ['Average', 'Sharp', 'Weak'])
if site_var == 'Draftkings':
if type_var == 'Classic':
if sport_var == 'CS2':
map_dict = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] * 1.5)),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
elif sport_var != 'CS2':
map_dict = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
elif type_var == 'Showdown':
if sport_var == 'NFL':
map_dict = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] * 1.5)),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
elif sport_var != 'NFL':
map_dict = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] / 1.5)),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
elif site_var == 'Fanduel':
map_dict = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
if type_var == 'Classic':
if sport_var == 'CS2':
# Calculate salary (CPT uses cpt_salary_map, others use salary_map)
st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) +
sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
# Calculate median (CPT uses cpt_proj_map, others use proj_map)
st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) +
sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
# Calculate ownership (CPT uses cpt_own_map, others use own_map)
st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) +
sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
elif sport_var != 'CS2':
st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
if stack_dict is not None:
st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].index.map(stack_dict)
elif type_var == 'Showdown':
# Calculate salary (CPT uses cpt_salary_map, others use salary_map)
st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) +
sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
# Calculate median (CPT uses cpt_proj_map, others use proj_map)
st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) +
sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
# Calculate ownership (CPT uses cpt_own_map, others use own_map)
st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) +
sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
col1, col2 = st.columns([2, 8])
with col1:
with st.expander('Macro Filter Options'):
with st.form(key='macro_filter_form'):
max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, step=1)
min_salary = st.number_input("Min acceptable salary?", value=1000, min_value=1000, step=100)
max_salary = st.number_input("Max acceptable salary?", value=60000, min_value=1000, step=100)
max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=.50, min_value=0.005, step=.001)
min_lineup_edge = st.number_input("Min acceptable Lineup Edge?", value=-.5, min_value=-1.00, step=.001)
if stack_dict is not None:
stack_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0)
stack_selections = st.multiselect("If Specific Stacks, Which to include?", options=sorted(list(set(stack_dict.values()))), default=[])
stack_remove = st.multiselect("If Specific Stacks, Which to remove?", options=sorted(list(set(stack_dict.values()))), default=[])
submitted = st.form_submit_button("Submit")
with st.expander('Micro Filter Options'):
with st.form(key='micro_filter_form'):
player_names = set()
for col in st.session_state['portfolio'].columns:
if col not in excluded_cols:
player_names.update(st.session_state['portfolio'][col].unique())
player_lock = st.multiselect("Lock players?", options=sorted(list(player_names)), default=[])
player_remove = st.multiselect("Remove players?", options=sorted(list(player_names)), default=[])
submitted = st.form_submit_button("Submit")
with st.expander('Trimming Options'):
st.info("Make sure you filter before trimming if you want to filter, trimming before a filter will reset your portfolio")
with st.form(key='trim_form'):
performance_type = st.selectbox("Select Sorting variable", ['median', 'Finish_percentile'])
own_type = st.selectbox("Select trimming variable", ['Own', 'Geomean', 'Weighted Own'])
submitted = st.form_submit_button("Trim")
if submitted:
st.write('initiated')
st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Dupes'] <= max_dupes]
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] >= min_salary]
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] <= max_salary]
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Finish_percentile'] <= max_finish_percentile]
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Lineup Edge'] >= min_lineup_edge]
if stack_dict is not None:
if stack_toggle == 'All Stacks':
st.session_state['portfolio'] = st.session_state['portfolio']
st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
else:
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Stack'].isin(stack_selections)]
st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
if player_remove:
# Create mask for lineups that contain any of the removed players
player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
remove_mask = st.session_state['portfolio'][player_columns].apply(
lambda row: not any(player in list(row) for player in player_remove), axis=1
)
st.session_state['portfolio'] = st.session_state['portfolio'][remove_mask]
if player_lock:
# Create mask for lineups that contain all locked players
player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
lock_mask = st.session_state['portfolio'][player_columns].apply(
lambda row: all(player in list(row) for player in player_lock), axis=1
)
st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
st.session_state['portfolio'] = trim_portfolio(st.session_state['portfolio'], performance_type, own_type)
st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
with col2:
st.write('initiated')
st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Dupes'] <= max_dupes]
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] >= min_salary]
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] <= max_salary]
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Finish_percentile'] <= max_finish_percentile]
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Lineup Edge'] >= min_lineup_edge]
if stack_dict is not None:
if stack_toggle == 'All Stacks':
st.session_state['portfolio'] = st.session_state['portfolio']
st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
else:
st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Stack'].isin(stack_selections)]
st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
if player_remove:
# Create mask for lineups that contain any of the removed players
player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
remove_mask = st.session_state['portfolio'][player_columns].apply(
lambda row: not any(player in list(row) for player in player_remove), axis=1
)
st.session_state['portfolio'] = st.session_state['portfolio'][remove_mask]
if player_lock:
# Create mask for lineups that contain all locked players
player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
lock_mask = st.session_state['portfolio'][player_columns].apply(
lambda row: all(player in list(row) for player in player_lock), axis=1
)
st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
with st.expander("Download options"):
if stack_dict is not None:
download_type = st.selectbox("Simple or Advanced Download?", options=['Simple', 'Advanced'], key='download_choice')
if download_type == 'Simple':
st.session_state['export_file'] = st.session_state['portfolio'].copy()
for col in st.session_state['export_file'].columns:
if col not in excluded_cols:
st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
else:
with st.form(key='stack_form'):
st.subheader("Stack Count Adjustments")
st.info("This allows you to fine tune the stacks that you wish to export. If you want to make sure you don't export any of a specific stack you can 0 it out.")
# Create a container for stack value inputs
sort_container = st.container()
with sort_container:
sort_var = st.selectbox("Sort export portfolio by:", options=['median', 'Lineup Edge', 'Own'])
# Get unique stack values
unique_stacks = sorted(list(set(stack_dict.values())))
# Create a dictionary to store stack multipliers
if 'stack_multipliers' not in st.session_state:
st.session_state.stack_multipliers = {stack: 0.0 for stack in unique_stacks}
# Create columns for the stack inputs
num_cols = 6 # Number of columns to display
for i in range(0, len(unique_stacks), num_cols):
cols = st.columns(num_cols)
for j, stack in enumerate(unique_stacks[i:i+num_cols]):
with cols[j]:
# Create a unique key for each number input
key = f"stack_count_{stack}"
# Get the current count of this stack in the portfolio
current_stack_count = len(st.session_state['portfolio'][st.session_state['portfolio']['Stack'] == stack])
# Create number input with current value and max value based on actual count
st.session_state.stack_multipliers[stack] = st.number_input(
f"{stack} count",
min_value=0.0,
max_value=float(current_stack_count),
value=0.0,
step=1.0,
key=key
)
portfolio_copy = st.session_state['portfolio'].copy()
submitted = st.form_submit_button("Submit")
if submitted:
# Create a list to store selected rows
selected_rows = []
# For each stack, select the top N rows based on the count value
for stack in unique_stacks:
if stack in st.session_state.stack_multipliers:
count = int(st.session_state.stack_multipliers[stack])
# Get rows for this stack
stack_rows = portfolio_copy[portfolio_copy['Stack'] == stack]
# Sort by median and take top N rows
top_rows = stack_rows.nlargest(count, sort_var)
selected_rows.append(top_rows)
# Combine all selected rows
portfolio_concat = pd.concat(selected_rows)
# Update export_file with filtered data
st.session_state['export_file'] = portfolio_concat.copy()
for col in st.session_state['export_file'].columns:
if col not in excluded_cols:
st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
st.write('Export portfolio updated!')
else:
st.session_state['export_file'] = st.session_state['portfolio'].copy()
for col in st.session_state['export_file'].columns:
if col not in excluded_cols:
st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
if 'export_file' in st.session_state:
st.download_button(label="Download Portfolio", data=st.session_state['export_file'].to_csv(index=False), file_name="portfolio.csv", mime="text/csv")
else:
st.error("No portfolio to download")
# Add pagination controls below the dataframe
total_rows = len(st.session_state['portfolio'])
rows_per_page = 500
total_pages = (total_rows + rows_per_page - 1) // rows_per_page # Ceiling division
# Initialize page number in session state if not exists
if 'current_page' not in st.session_state:
st.session_state.current_page = 1
# Display current page range info and pagination control in a single line
st.write(
f"Showing rows {(st.session_state.current_page - 1) * rows_per_page + 1} "
f"to {min(st.session_state.current_page * rows_per_page, total_rows)} of {total_rows}"
)
# Add page number input
st.session_state.current_page = st.number_input(
f"Page (1-{total_pages})",
min_value=1,
max_value=total_pages,
value=st.session_state.current_page
)
# Calculate start and end indices for current page
start_idx = (st.session_state.current_page - 1) * rows_per_page
end_idx = min(start_idx + rows_per_page, total_rows)
# Get the subset of data for the current page
current_page_data = st.session_state['portfolio'].iloc[start_idx:end_idx]
# Display the paginated dataframe first
st.dataframe(
current_page_data.style
.background_gradient(axis=0)
.background_gradient(cmap='RdYlGn')
.background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])
.format(freq_format, precision=2),
height=1000,
use_container_width=True
)
# Create player summary dataframe
player_stats = []
player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
if type_var == 'Showdown':
# Handle Captain positions
for player in player_names:
# Create mask for lineups where this player is Captain (first column)
cpt_mask = st.session_state['portfolio'][player_columns[0]] == player
if cpt_mask.any():
player_stats.append({
'Player': f"{player} (CPT)",
'Lineup Count': cpt_mask.sum(),
'Avg Median': st.session_state['portfolio'][cpt_mask]['median'].mean(),
'Avg Own': st.session_state['portfolio'][cpt_mask]['Own'].mean(),
'Avg Dupes': st.session_state['portfolio'][cpt_mask]['Dupes'].mean(),
'Avg Finish %': st.session_state['portfolio'][cpt_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': st.session_state['portfolio'][cpt_mask]['Lineup Edge'].mean(),
})
# Create mask for lineups where this player is FLEX (other columns)
flex_mask = st.session_state['portfolio'][player_columns[1:]].apply(
lambda row: player in list(row), axis=1
)
if flex_mask.any():
player_stats.append({
'Player': f"{player} (FLEX)",
'Lineup Count': flex_mask.sum(),
'Avg Median': st.session_state['portfolio'][flex_mask]['median'].mean(),
'Avg Own': st.session_state['portfolio'][flex_mask]['Own'].mean(),
'Avg Dupes': st.session_state['portfolio'][flex_mask]['Dupes'].mean(),
'Avg Finish %': st.session_state['portfolio'][flex_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': st.session_state['portfolio'][flex_mask]['Lineup Edge'].mean(),
})
else:
if sport_var == 'CS2':
# Handle Captain positions
for player in player_names:
# Create mask for lineups where this player is Captain (first column)
cpt_mask = st.session_state['portfolio'][player_columns[0]] == player
if cpt_mask.any():
player_stats.append({
'Player': f"{player} (CPT)",
'Lineup Count': cpt_mask.sum(),
'Avg Median': st.session_state['portfolio'][cpt_mask]['median'].mean(),
'Avg Own': st.session_state['portfolio'][cpt_mask]['Own'].mean(),
'Avg Dupes': st.session_state['portfolio'][cpt_mask]['Dupes'].mean(),
'Avg Finish %': st.session_state['portfolio'][cpt_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': st.session_state['portfolio'][cpt_mask]['Lineup Edge'].mean(),
})
# Create mask for lineups where this player is FLEX (other columns)
flex_mask = st.session_state['portfolio'][player_columns[1:]].apply(
lambda row: player in list(row), axis=1
)
if flex_mask.any():
player_stats.append({
'Player': f"{player} (FLEX)",
'Lineup Count': flex_mask.sum(),
'Avg Median': st.session_state['portfolio'][flex_mask]['median'].mean(),
'Avg Own': st.session_state['portfolio'][flex_mask]['Own'].mean(),
'Avg Dupes': st.session_state['portfolio'][flex_mask]['Dupes'].mean(),
'Avg Finish %': st.session_state['portfolio'][flex_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': st.session_state['portfolio'][flex_mask]['Lineup Edge'].mean(),
})
elif sport_var != 'CS2':
# Original Classic format processing
for player in player_names:
player_mask = st.session_state['portfolio'][player_columns].apply(
lambda row: player in list(row), axis=1
)
if player_mask.any():
player_stats.append({
'Player': player,
'Lineup Count': player_mask.sum(),
'Avg Median': st.session_state['portfolio'][player_mask]['median'].mean(),
'Avg Own': st.session_state['portfolio'][player_mask]['Own'].mean(),
'Avg Dupes': st.session_state['portfolio'][player_mask]['Dupes'].mean(),
'Avg Finish %': st.session_state['portfolio'][player_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': st.session_state['portfolio'][player_mask]['Lineup Edge'].mean(),
})
player_summary = pd.DataFrame(player_stats)
player_summary = player_summary.sort_values('Lineup Count', ascending=False)
st.subheader("Player Summary")
st.dataframe(
player_summary.style
.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
.format({
'Avg Median': '{:.2f}',
'Avg Own': '{:.2f}',
'Avg Dupes': '{:.2f}',
'Avg Finish %': '{:.2%}',
'Avg Lineup Edge': '{:.2%}'
}),
height=400,
use_container_width=True
) |