James McCool
Add geometric mean calculation in predict_dupes: introduce a new column 'Goemean' to compute the geometric mean of ownership percentages, enhancing portfolio analysis capabilities.
c7e2afa
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import time | |
| from fuzzywuzzy import process | |
| import math | |
| def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, strength_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'] | |
| 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['CPT_Own'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']) / 100 | |
| portfolio['FLEX1_Own'] = portfolio.iloc[:,1].map(maps_dict['own_map']) / 100 | |
| portfolio['FLEX2_Own'] = portfolio.iloc[:,2].map(maps_dict['own_map']) / 100 | |
| portfolio['FLEX3_Own'] = portfolio.iloc[:,3].map(maps_dict['own_map']) / 100 | |
| portfolio['FLEX4_Own'] = portfolio.iloc[:,4].map(maps_dict['own_map']) / 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 | |
| ) | |
| if 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'] | |
| 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']) / 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': | |
| 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'] | |
| 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']) / 100 | |
| portfolio['FLEX1_Own'] = portfolio.iloc[:,1].map(maps_dict['own_map']) / 100 | |
| portfolio['FLEX2_Own'] = portfolio.iloc[:,2].map(maps_dict['own_map']) / 100 | |
| portfolio['FLEX3_Own'] = portfolio.iloc[:,3].map(maps_dict['own_map']) / 100 | |
| portfolio['FLEX4_Own'] = portfolio.iloc[:,4].map(maps_dict['own_map']) / 100 | |
| portfolio['FLEX5_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']) / 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 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', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio'] | |
| 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']) / 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': | |
| own_ratio_nerf = 2 | |
| elif type_var == 'Showdown': | |
| own_ratio_nerf = 1.5 | |
| portfolio['Finish_percentile'] = portfolio.apply( | |
| lambda row: .0005 if (row['own_ratio'] - own_ratio_nerf) / ((10 * (row['median'] / percentile_cut_scalar)) / 2) < .0005 | |
| else (row['own_ratio'] - own_ratio_nerf) / ((10 * (row['median'] / percentile_cut_scalar)) / 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'] * ((portfolio[own_columns].max(axis=1) - portfolio[own_columns].min(axis=1)) / 100)) * 200 | |
| portfolio['Goemean'] = math.pow((portfolio[own_columns] * 100).product(axis=1), 1 / len(own_columns)) | |
| portfolio = portfolio.drop(columns=dup_count_columns) | |
| portfolio = portfolio.drop(columns=own_columns) | |
| portfolio = portfolio.drop(columns=calc_columns) | |
| return portfolio |