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import streamlit as st
import numpy as np
import pandas as pd
import time
import math
from difflib import SequenceMatcher
def calculate_weighted_ownership_vectorized(ownership_array):
"""
Vectorized version of calculate_weighted_ownership using NumPy operations.
Args:
ownership_array: 2D array of ownership values (rows x players)
Returns:
array: Calculated weighted ownership values for each row
"""
# Convert percentages to decimals and handle NaN values
ownership_array = np.where(np.isnan(ownership_array), 0, ownership_array) / 100
# Calculate row means
row_means = np.mean(ownership_array, axis=1, keepdims=True)
# Calculate average of each value with the overall mean
value_means = (ownership_array + row_means) / 2
# Take average of all those means
avg_of_means = np.mean(value_means, axis=1)
# Multiply by count of values
weighted = avg_of_means * ownership_array.shape[1]
# Subtract (max - min) for each row
row_max = np.max(ownership_array, axis=1)
row_min = np.min(ownership_array, axis=1)
weighted = weighted - (row_max - row_min)
# Convert back to percentage form
return weighted * 10000
def calculate_weighted_ownership_wrapper(row_ownerships):
"""
Wrapper function for the original calculate_weighted_ownership to work with Pandas .apply()
Args:
row_ownerships: Series containing ownership values in percentage form
Returns:
float: Calculated weighted ownership value
"""
# Convert Series to 2D array for vectorized function
ownership_array = row_ownerships.values.reshape(1, -1)
return calculate_weighted_ownership_vectorized(ownership_array)[0]
def calculate_player_similarity_score_vectorized(portfolio, player_columns):
"""
Vectorized version of calculate_player_similarity_score using NumPy operations.
"""
# Extract player data and convert to string array
player_data = portfolio[player_columns].astype(str).fillna('').values
# Get all unique players and create a mapping to numeric IDs
all_players = set()
for row in player_data:
for val in row:
if isinstance(val, str) and val.strip() != '':
all_players.add(val)
# Create player ID mapping
player_to_id = {player: idx for idx, player in enumerate(sorted(all_players))}
# Convert each row to a binary vector (1 if player is present, 0 if not)
n_players = len(all_players)
n_rows = len(portfolio)
binary_matrix = np.zeros((n_rows, n_players), dtype=np.int8)
# Vectorized binary matrix creation
for i, row in enumerate(player_data):
for val in row:
if isinstance(val, str) and str(val).strip() != '' and str(val) in player_to_id:
binary_matrix[i, player_to_id[str(val)]] = 1
# Vectorized Jaccard distance calculation
intersection_matrix = np.dot(binary_matrix, binary_matrix.T)
row_sums = np.sum(binary_matrix, axis=1)
union_matrix = row_sums[:, np.newaxis] + row_sums - intersection_matrix
# Calculate Jaccard distance: 1 - (intersection / union)
with np.errstate(divide='ignore', invalid='ignore'):
jaccard_similarity = np.divide(intersection_matrix, union_matrix,
out=np.zeros_like(intersection_matrix, dtype=float),
where=union_matrix != 0)
jaccard_distance = 1 - jaccard_similarity
# Exclude self-comparison and calculate average distance for each row
np.fill_diagonal(jaccard_distance, 0)
row_counts = n_rows - 1
similarity_scores = np.sum(jaccard_distance, axis=1) / row_counts
# Normalize to 0-1 scale
score_range = similarity_scores.max() - similarity_scores.min()
if score_range > 0:
similarity_scores = (similarity_scores - similarity_scores.min()) / score_range
return similarity_scores
def predict_dupes_vectorized(portfolio, maps_dict, site_var, type_var, Contest_Size, strength_var, sport_var):
"""
Vectorized version of predict_dupes using NumPy arrays for better performance.
"""
# Set multipliers based on strength
if strength_var == 'Weak':
dupes_multiplier = 0.75
percentile_multiplier = 0.90
elif strength_var == 'Average':
dupes_multiplier = 1.00
percentile_multiplier = 1.00
elif strength_var == 'Sharp':
dupes_multiplier = 1.25
percentile_multiplier = 1.10
max_ownership = max(maps_dict['own_map'].values()) / 100
average_ownership = np.mean(list(maps_dict['own_map'].values())) / 100
# Convert portfolio to NumPy arrays for faster operations
portfolio_values = portfolio.values
n_rows = len(portfolio)
# Pre-allocate arrays for ownership data
if site_var == 'Fanduel':
if type_var == 'Showdown':
num_players = 5
salary_cap = 60000
player_cols = list(range(5)) # First 5 columns are players
elif type_var == 'Classic':
if sport_var == 'WNBA':
num_players = len([col for col in portfolio.columns if col not in ['salary', 'median', 'Own']])
salary_cap = 40000
player_cols = list(range(num_players))
else:
num_players = len([col for col in portfolio.columns if col not in ['salary', 'median', 'Own']])
salary_cap = 60000
player_cols = list(range(num_players))
elif site_var == 'Draftkings':
if type_var == 'Showdown':
num_players = 6
salary_cap = 50000
player_cols = list(range(6))
elif type_var == 'Classic':
if sport_var == 'CS2':
num_players = 6
salary_cap = 50000
player_cols = list(range(6))
else:
num_players = len([col for col in portfolio.columns if col not in ['salary', 'median', 'Own']])
salary_cap = 50000
player_cols = list(range(num_players))
# Pre-allocate ownership arrays
ownership_array = np.zeros((n_rows, num_players), dtype=np.float32)
ownership_rank_array = np.zeros((n_rows, num_players), dtype=np.float32)
# Vectorized ownership mapping
for i, col_idx in enumerate(player_cols):
if i == 0 and type_var == 'Showdown': # Captain
ownership_array[:, i] = np.vectorize(lambda x: maps_dict['cpt_own_map'].get(x, 0))(portfolio_values[:, col_idx]) / 100
ownership_rank_array[:, i] = np.vectorize(lambda x: maps_dict['cpt_own_map'].get(x, 0))(portfolio_values[:, col_idx])
else: # Flex players
ownership_array[:, i] = np.vectorize(lambda x: maps_dict['own_map'].get(x, 0))(portfolio_values[:, col_idx]) / 100
ownership_rank_array[:, i] = np.vectorize(lambda x: maps_dict['own_map'].get(x, 0))(portfolio_values[:, col_idx])
# Calculate ranks for flex players (excluding captain)
if type_var == 'Showdown':
flex_ownerships = ownership_rank_array[:, 1:].flatten()
flex_rank = pd.Series(flex_ownerships).rank(pct=True).values.reshape(n_rows, -1)
ownership_rank_array[:, 1:] = flex_rank
# Convert to percentile ranks
ownership_rank_array = ownership_rank_array / 100
# Vectorized calculations
own_product = np.prod(ownership_array, axis=1)
own_average = (portfolio_values[:, portfolio.columns.get_loc('Own')].max() * 0.33) / 100
own_sum = np.sum(ownership_array, axis=1)
avg_own_rank = np.mean(ownership_rank_array, axis=1)
# Calculate dupes formula vectorized
salary_col = portfolio.columns.get_loc('salary')
own_col = portfolio.columns.get_loc('Own')
dupes_calc = (own_product * avg_own_rank) * Contest_Size + \
((portfolio_values[:, salary_col] - (salary_cap - portfolio_values[:, own_col])) / 100) - \
((salary_cap - portfolio_values[:, salary_col]) / 100)
dupes_calc *= dupes_multiplier
# Round and handle negative values
dupes = np.where(np.round(dupes_calc, 0) <= 0, 0, np.round(dupes_calc, 0) - 1)
# Calculate own_ratio vectorized
max_own_mask = np.any(ownership_array == max_ownership, axis=1)
own_ratio = np.where(max_own_mask,
own_sum / own_average,
(own_sum - max_ownership) / own_average)
# Calculate Finish_percentile vectorized
percentile_cut_scalar = portfolio_values[:, portfolio.columns.get_loc('median')].max()
if type_var == 'Classic':
own_ratio_nerf = 2 if sport_var == 'CS2' or sport_var == 'LOL' else 1.5
elif type_var == 'Showdown':
own_ratio_nerf = 1.5
median_col = portfolio.columns.get_loc('median')
finish_percentile = (own_ratio - own_ratio_nerf) / ((5 * (portfolio_values[:, median_col] / percentile_cut_scalar)) / 3)
finish_percentile = np.where(finish_percentile < 0.0005, 0.0005, finish_percentile / 2)
# Calculate other metrics vectorized
ref_proj = portfolio_values[:, median_col].max()
max_proj = ref_proj + 10
min_proj = ref_proj - 10
avg_ref = (max_proj + min_proj) / 2
win_percent = (((portfolio_values[:, median_col] / avg_ref) - (0.1 + ((ref_proj - portfolio_values[:, median_col])/100))) / (Contest_Size / 1000)) / 10
max_allowed_win = (1 / Contest_Size) * 5
win_percent = win_percent / win_percent.max() * max_allowed_win
finish_percentile = finish_percentile + 0.005 + (0.005 * (Contest_Size / 10000))
finish_percentile *= percentile_multiplier
win_percent *= (1 - finish_percentile)
# Calculate low ownership count vectorized
low_own_count = np.sum(ownership_array < 0.10, axis=1)
finish_percentile = np.where(low_own_count <= 0,
finish_percentile,
finish_percentile / low_own_count)
# Calculate Lineup Edge vectorized
lineup_edge = win_percent * ((0.5 - finish_percentile) * (Contest_Size / 2.5))
lineup_edge = np.where(dupes > 0, lineup_edge / (dupes + 1), lineup_edge)
lineup_edge = lineup_edge - lineup_edge.mean()
# Calculate Weighted Own vectorized
weighted_own = calculate_weighted_ownership_vectorized(ownership_array)
# Calculate Geomean vectorized
geomean = np.power(np.prod(ownership_array * 100, axis=1), 1 / num_players)
# Calculate Diversity vectorized
diversity = calculate_player_similarity_score_vectorized(portfolio, player_cols)
# Create result DataFrame with optimized data types
result_data = {
'Dupes': dupes.astype('uint16'),
'median': portfolio_values[:, portfolio.columns.get_loc('median')].astype('float32'),
'Own': portfolio_values[:, portfolio.columns.get_loc('Own')].astype('float32'),
'salary': portfolio_values[:, portfolio.columns.get_loc('salary')].astype('uint16'),
'Finish_percentile': finish_percentile.astype('float32'),
'Win%': win_percent.astype('float32'),
'Lineup Edge': lineup_edge.astype('float32'),
'Weighted Own': weighted_own.astype('float32'),
'Geomean': geomean.astype('float32'),
'Diversity': diversity.astype('float32')
}
# Add Size column if it exists
if 'Size' in portfolio.columns:
result_data['Size'] = portfolio_values[:, portfolio.columns.get_loc('Size')].astype('uint16')
# Add player columns back
for i, col_name in enumerate(portfolio.columns[:num_players]):
result_data[col_name] = portfolio_values[:, i]
return pd.DataFrame(result_data)
# Keep the original function for backward compatibility
def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, strength_var, sport_var):
if strength_var == 'Weak':
dupes_multiplier = .75
percentile_multiplier = .90
elif strength_var == 'Average':
dupes_multiplier = 1.00
percentile_multiplier = 1.00
elif strength_var == 'Sharp':
dupes_multiplier = 1.25
percentile_multiplier = 1.10
max_ownership = max(maps_dict['own_map'].values()) / 100
average_ownership = np.mean(list(maps_dict['own_map'].values())) / 100
if site_var == 'Fanduel':
if type_var == 'Showdown':
dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank']
own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own']
calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'own_ratio', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
# Get the original player columns (first 5 columns excluding salary, median, Own)
player_columns = [col for col in portfolio.columns[:5] if col not in ['salary', 'median', 'Own']]
flex_ownerships = pd.concat([
portfolio.iloc[:,1].map(maps_dict['own_map']),
portfolio.iloc[:,2].map(maps_dict['own_map']),
portfolio.iloc[:,3].map(maps_dict['own_map']),
portfolio.iloc[:,4].map(maps_dict['own_map'])
])
flex_rank = flex_ownerships.rank(pct=True)
# Assign ranks back to individual columns using the same rank scale
portfolio['CPT_Own_percent_rank'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).rank(pct=True)
portfolio['FLEX1_Own_percent_rank'] = portfolio.iloc[:,1].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX2_Own_percent_rank'] = portfolio.iloc[:,2].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX3_Own_percent_rank'] = portfolio.iloc[:,3].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX4_Own_percent_rank'] = portfolio.iloc[:,4].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX5_Own_percent_rank'] = portfolio.iloc[:,5].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['CPT_Own'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).astype('float32') / 100
portfolio['FLEX1_Own'] = portfolio.iloc[:,1].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX2_Own'] = portfolio.iloc[:,2].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX3_Own'] = portfolio.iloc[:,3].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX4_Own'] = portfolio.iloc[:,4].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX5_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']).astype('float32') / 100
portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
# Calculate dupes formula
portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (60000 - portfolio['Own'])) / 100) - ((60000 - portfolio['salary']) / 100)
portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
# Round and handle negative values
portfolio['Dupes'] = np.where(
np.round(portfolio['dupes_calc'], 0) <= 0,
0,
np.round(portfolio['dupes_calc'], 0) - 1
)
elif type_var == 'Classic':
num_players = len([col for col in portfolio.columns if col not in ['salary', 'median', 'Own']])
dup_count_columns = [f'player_{i}_percent_rank' for i in range(1, num_players + 1)]
own_columns = [f'player_{i}_own' for i in range(1, num_players + 1)]
calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'own_ratio', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
# Get the original player columns (first num_players columns excluding salary, median, Own)
player_columns = [col for col in portfolio.columns[:num_players] if col not in ['salary', 'median', 'Own']]
for i in range(1, num_players + 1):
portfolio[f'player_{i}_percent_rank'] = portfolio.iloc[:,i-1].map(maps_dict['own_percent_rank'])
portfolio[f'player_{i}_own'] = portfolio.iloc[:,i-1].map(maps_dict['own_map']).astype('float32') / 100
portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (60000 - portfolio['Own'])) / 100) - ((60000 - portfolio['salary']) / 100)
portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
# Round and handle negative values
portfolio['Dupes'] = np.where(
np.round(portfolio['dupes_calc'], 0) <= 0,
0,
np.round(portfolio['dupes_calc'], 0) - 1
)
elif site_var == 'Draftkings':
if type_var == 'Showdown':
if sport_var == 'GOLF':
dup_count_columns = ['FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank', 'FLEX6_Own_percent_rank']
own_columns = ['FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own', 'FLEX6_Own']
else:
dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
# Get the original player columns (first 6 columns excluding salary, median, Own)
player_columns = [col for col in portfolio.columns[:6] if col not in ['salary', 'median', 'Own']]
if sport_var == 'GOLF':
flex_ownerships = pd.concat([
portfolio.iloc[:,0].map(maps_dict['own_map']),
portfolio.iloc[:,1].map(maps_dict['own_map']),
portfolio.iloc[:,2].map(maps_dict['own_map']),
portfolio.iloc[:,3].map(maps_dict['own_map']),
portfolio.iloc[:,4].map(maps_dict['own_map']),
portfolio.iloc[:,5].map(maps_dict['own_map'])
])
else:
flex_ownerships = pd.concat([
portfolio.iloc[:,1].map(maps_dict['own_map']),
portfolio.iloc[:,2].map(maps_dict['own_map']),
portfolio.iloc[:,3].map(maps_dict['own_map']),
portfolio.iloc[:,4].map(maps_dict['own_map']),
portfolio.iloc[:,5].map(maps_dict['own_map'])
])
flex_rank = flex_ownerships.rank(pct=True)
# Assign ranks back to individual columns using the same rank scale
if sport_var == 'GOLF':
portfolio['FLEX1_Own_percent_rank'] = portfolio.iloc[:,0].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX2_Own_percent_rank'] = portfolio.iloc[:,1].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX3_Own_percent_rank'] = portfolio.iloc[:,2].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX4_Own_percent_rank'] = portfolio.iloc[:,3].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX5_Own_percent_rank'] = portfolio.iloc[:,4].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX6_Own_percent_rank'] = portfolio.iloc[:,5].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX1_Own'] = portfolio.iloc[:,0].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX2_Own'] = portfolio.iloc[:,1].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX3_Own'] = portfolio.iloc[:,2].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX4_Own'] = portfolio.iloc[:,3].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX5_Own'] = portfolio.iloc[:,4].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX6_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']).astype('float32') / 100
else:
portfolio['CPT_Own_percent_rank'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).rank(pct=True)
portfolio['FLEX1_Own_percent_rank'] = portfolio.iloc[:,1].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX2_Own_percent_rank'] = portfolio.iloc[:,2].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX3_Own_percent_rank'] = portfolio.iloc[:,3].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX4_Own_percent_rank'] = portfolio.iloc[:,4].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX5_Own_percent_rank'] = portfolio.iloc[:,5].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['CPT_Own'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).astype('float32') / 100
portfolio['FLEX1_Own'] = portfolio.iloc[:,1].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX2_Own'] = portfolio.iloc[:,2].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX3_Own'] = portfolio.iloc[:,3].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX4_Own'] = portfolio.iloc[:,4].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX5_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']).astype('float32') / 100
portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
# Calculate dupes formula
portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
# Round and handle negative values
portfolio['Dupes'] = np.where(
np.round(portfolio['dupes_calc'], 0) <= 0,
0,
np.round(portfolio['dupes_calc'], 0) - 1
)
elif type_var == 'Classic':
if sport_var == 'CS2':
dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
# Get the original player columns (first 6 columns excluding salary, median, Own)
player_columns = [col for col in portfolio.columns[:6] if col not in ['salary', 'median', 'Own']]
flex_ownerships = pd.concat([
portfolio.iloc[:,1].map(maps_dict['own_map']),
portfolio.iloc[:,2].map(maps_dict['own_map']),
portfolio.iloc[:,3].map(maps_dict['own_map']),
portfolio.iloc[:,4].map(maps_dict['own_map']),
portfolio.iloc[:,5].map(maps_dict['own_map'])
])
flex_rank = flex_ownerships.rank(pct=True)
# Assign ranks back to individual columns using the same rank scale
portfolio['CPT_Own_percent_rank'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).rank(pct=True)
portfolio['FLEX1_Own_percent_rank'] = portfolio.iloc[:,1].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX2_Own_percent_rank'] = portfolio.iloc[:,2].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX3_Own_percent_rank'] = portfolio.iloc[:,3].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX4_Own_percent_rank'] = portfolio.iloc[:,4].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['FLEX5_Own_percent_rank'] = portfolio.iloc[:,5].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['CPT_Own'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).astype('float32') / 100
portfolio['FLEX1_Own'] = portfolio.iloc[:,1].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX2_Own'] = portfolio.iloc[:,2].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX3_Own'] = portfolio.iloc[:,3].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX4_Own'] = portfolio.iloc[:,4].map(maps_dict['own_map']).astype('float32') / 100
portfolio['FLEX5_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']).astype('float32') / 100
portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
# Calculate dupes formula
portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
# Round and handle negative values
portfolio['Dupes'] = np.where(
np.round(portfolio['dupes_calc'], 0) <= 0,
0,
np.round(portfolio['dupes_calc'], 0) - 1
)
if sport_var == 'LOL':
dup_count_columns = ['CPT_Own_percent_rank', 'TOP_Own_percent_rank', 'JNG_Own_percent_rank', 'MID_Own_percent_rank', 'ADC_Own_percent_rank', 'SUP_Own_percent_rank', 'Team_Own_percent_rank']
own_columns = ['CPT_Own', 'TOP_Own', 'JNG_Own', 'MID_Own', 'ADC_Own', 'SUP_Own', 'Team_Own']
calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
# Get the original player columns (first 6 columns excluding salary, median, Own)
player_columns = [col for col in portfolio.columns[:6] if col not in ['salary', 'median', 'Own']]
flex_ownerships = pd.concat([
portfolio.iloc[:,1].map(maps_dict['own_map']),
portfolio.iloc[:,2].map(maps_dict['own_map']),
portfolio.iloc[:,3].map(maps_dict['own_map']),
portfolio.iloc[:,4].map(maps_dict['own_map']),
portfolio.iloc[:,5].map(maps_dict['own_map']),
portfolio.iloc[:,6].map(maps_dict['own_map'])
])
flex_rank = flex_ownerships.rank(pct=True)
# Assign ranks back to individual columns using the same rank scale
portfolio['CPT_Own_percent_rank'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).rank(pct=True)
portfolio['TOP_Own_percent_rank'] = portfolio.iloc[:,1].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['JNG_Own_percent_rank'] = portfolio.iloc[:,2].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['MID_Own_percent_rank'] = portfolio.iloc[:,3].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['ADC_Own_percent_rank'] = portfolio.iloc[:,4].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['SUP_Own_percent_rank'] = portfolio.iloc[:,5].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['Team_Own_percent_rank'] = portfolio.iloc[:,6].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
portfolio['CPT_Own'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).astype('float32') / 100
portfolio['TOP_Own'] = portfolio.iloc[:,1].map(maps_dict['own_map']).astype('float32') / 100
portfolio['JNG_Own'] = portfolio.iloc[:,2].map(maps_dict['own_map']).astype('float32') / 100
portfolio['MID_Own'] = portfolio.iloc[:,3].map(maps_dict['own_map']).astype('float32') / 100
portfolio['ADC_Own'] = portfolio.iloc[:,4].map(maps_dict['own_map']).astype('float32') / 100
portfolio['SUP_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']).astype('float32') / 100
portfolio['Team_Own'] = portfolio.iloc[:,6].map(maps_dict['own_map']).astype('float32') / 100
portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
# Calculate dupes formula
portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
# Round and handle negative values
portfolio['Dupes'] = np.where(
np.round(portfolio['dupes_calc'], 0) <= 0,
0,
np.round(portfolio['dupes_calc'], 0) - 1
)
elif sport_var != 'CS2' and sport_var != 'LOL':
num_players = len([col for col in portfolio.columns if col not in ['salary', 'median', 'Own']])
dup_count_columns = [f'player_{i}_percent_rank' for i in range(1, num_players + 1)]
own_columns = [f'player_{i}_own' for i in range(1, num_players + 1)]
calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
# Get the original player columns (first num_players columns excluding salary, median, Own)
player_columns = [col for col in portfolio.columns[:num_players] if col not in ['salary', 'median', 'Own']]
for i in range(1, num_players + 1):
portfolio[f'player_{i}_percent_rank'] = portfolio.iloc[:,i-1].map(maps_dict['own_percent_rank'])
portfolio[f'player_{i}_own'] = portfolio.iloc[:,i-1].map(maps_dict['own_map']).astype('float32') / 100
portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
# Round and handle negative values
portfolio['Dupes'] = np.where(
np.round(portfolio['dupes_calc'], 0) <= 0,
0,
np.round(portfolio['dupes_calc'], 0) - 1
)
portfolio['Dupes'] = np.round(portfolio['Dupes'], 0)
portfolio['own_ratio'] = np.where(
portfolio[own_columns].isin([max_ownership]).any(axis=1),
portfolio['own_sum'] / portfolio['own_average'],
(portfolio['own_sum'] - max_ownership) / portfolio['own_average']
)
percentile_cut_scalar = portfolio['median'].max() # Get scalar value
if type_var == 'Classic':
if sport_var == 'CS2' or sport_var == 'LOL':
own_ratio_nerf = 2
elif sport_var != 'CS2' and sport_var != 'LOL':
own_ratio_nerf = 1.5
elif type_var == 'Showdown':
own_ratio_nerf = 1.5
portfolio['Finish_percentile'] = portfolio.apply(
lambda row: .0005 if (row['own_ratio'] - own_ratio_nerf) / ((5 * (row['median'] / percentile_cut_scalar)) / 3) < .0005
else ((row['own_ratio'] - own_ratio_nerf) / ((5 * (row['median'] / percentile_cut_scalar)) / 3)) / 2,
axis=1
)
portfolio['Ref_Proj'] = portfolio['median'].max()
portfolio['Max_Proj'] = portfolio['Ref_Proj'] + 10
portfolio['Min_Proj'] = portfolio['Ref_Proj'] - 10
portfolio['Avg_Ref'] = (portfolio['Max_Proj'] + portfolio['Min_Proj']) / 2
portfolio['Win%'] = (((portfolio['median'] / portfolio['Avg_Ref']) - (0.1 + ((portfolio['Ref_Proj'] - portfolio['median'])/100))) / (Contest_Size / 1000)) / 10
max_allowed_win = (1 / Contest_Size) * 5
portfolio['Win%'] = portfolio['Win%'] / portfolio['Win%'].max() * max_allowed_win
portfolio['Finish_percentile'] = portfolio['Finish_percentile'] + .005 + (.005 * (Contest_Size / 10000))
portfolio['Finish_percentile'] = portfolio['Finish_percentile'] * percentile_multiplier
portfolio['Win%'] = portfolio['Win%'] * (1 - portfolio['Finish_percentile'])
portfolio['low_own_count'] = portfolio[own_columns].apply(lambda row: (row < 0.10).sum(), axis=1)
portfolio['Finish_percentile'] = portfolio.apply(lambda row: row['Finish_percentile'] if row['low_own_count'] <= 0 else row['Finish_percentile'] / row['low_own_count'], axis=1)
portfolio['Lineup Edge'] = portfolio['Win%'] * ((.5 - portfolio['Finish_percentile']) * (Contest_Size / 2.5))
portfolio['Lineup Edge'] = portfolio.apply(lambda row: row['Lineup Edge'] / (row['Dupes'] + 1) if row['Dupes'] > 0 else row['Lineup Edge'], axis=1)
portfolio['Lineup Edge'] = portfolio['Lineup Edge'] - portfolio['Lineup Edge'].mean()
portfolio['Weighted Own'] = portfolio[own_columns].apply(calculate_weighted_ownership_wrapper, axis=1)
portfolio['Geomean'] = np.power((portfolio[own_columns] * 100).product(axis=1), 1 / len(own_columns))
# Calculate similarity score based on actual player selection
portfolio['Diversity'] = calculate_player_similarity_score_vectorized(portfolio, player_columns)
portfolio = portfolio.drop(columns=dup_count_columns)
portfolio = portfolio.drop(columns=own_columns)
portfolio = portfolio.drop(columns=calc_columns)
int16_columns_stacks = ['Dupes', 'Size', 'salary']
int16_columns_nstacks = ['Dupes', 'salary']
float32_columns = ['median', 'Own', 'Finish_percentile', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
print(portfolio.columns)
print(portfolio.head(10))
try:
portfolio[int16_columns_stacks] = portfolio[int16_columns_stacks].astype('uint16')
except:
pass
try:
portfolio[int16_columns_nstacks] = portfolio[int16_columns_nstacks].astype('uint16')
except:
pass
portfolio[float32_columns] = portfolio[float32_columns].astype('float32')
return portfolio
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