from numpy import nan as np_nan from numpy import where as np_where from numpy import random as np_random from numpy import zeros as np_zeros from numpy import array as np_array from pandas import concat as pd_concat from pandas import merge as pd_merge from pandas import DataFrame def DK_NFL_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribution_type): total_sims = 1000 projects_raw = projections_file.copy() projects_raw = projects_raw.replace("", np_nan) dk_df = projects_raw.sort_values(by='Median', ascending=False) basic_own_df = dk_df.copy() basic_own_df['name_team'] = basic_own_df['Player'] + basic_own_df['Position'] def calculate_ownership(df, position): # Filter the dataframe based on the position frame = df[df['Position'].str.contains(position)] # Calculate Small Field Own% frame['Small Field Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Small Field Own%'] = np_where( frame['Small Field Own%'] > 75, 75, frame['Small Field Own%'] ) # Calculate Large Field Own% frame['Large Field Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (2.5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Large Field Own%'] = np_where( frame['Large Field Own%'] > 75, 75, frame['Large Field Own%'] ) # Calculate Cash Own% frame['Cash Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (6 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Cash Own%'] = np_where( frame['Cash Own%'] > 75, 75, frame['Cash Own%'] ) return frame def calculate_ownership_overall(df): # Filter the dataframe based on the position frame = df # Calculate Small Field Own% frame['Small Field Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Small Field Own%'] = np_where( frame['Small Field Own%'] > 75, 75, frame['Small Field Own%'] ) # Calculate Large Field Own% frame['Large Field Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (2.5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Large Field Own%'] = np_where( frame['Large Field Own%'] > 75, 75, frame['Large Field Own%'] ) # Calculate Cash Own% frame['Cash Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (6 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Cash Own%'] = np_where( frame['Cash Own%'] > 75, 75, frame['Cash Own%'] ) return frame # Apply the function to each dataframe qb_frame = calculate_ownership(basic_own_df, 'QB') rb_frame = calculate_ownership(basic_own_df, 'RB') wr_frame = calculate_ownership(basic_own_df, 'WR') te_frame = calculate_ownership(basic_own_df, 'TE') dst_frame = calculate_ownership(basic_own_df, 'DST') qb_reg_norm_var = 100 / qb_frame['Own'].sum() qb_small_norm_var = 100 / qb_frame['Small Field Own%'].sum() qb_large_norm_var = 100 / qb_frame['Large Field Own%'].sum() qb_cash_norm_var = 100 / qb_frame['Cash Own%'].sum() qb_frame['Own'] = qb_frame['Own'] * qb_reg_norm_var qb_frame['Small Field Own%'] = qb_frame['Small Field Own%'] * qb_small_norm_var qb_frame['Large Field Own%'] = qb_frame['Large Field Own%'] * qb_large_norm_var qb_frame['Cash Own%'] = qb_frame['Cash Own%'] * qb_cash_norm_var rb_reg_norm_var = 235 / rb_frame['Own'].sum() rb_small_norm_var = 235 / rb_frame['Small Field Own%'].sum() rb_large_norm_var = 235 / rb_frame['Large Field Own%'].sum() rb_cash_norm_var = 235 / rb_frame['Cash Own%'].sum() rb_frame['Own'] = rb_frame['Own'] * rb_reg_norm_var rb_frame['Small Field Own%'] = rb_frame['Small Field Own%'] * rb_small_norm_var rb_frame['Large Field Own%'] = rb_frame['Large Field Own%'] * rb_large_norm_var rb_frame['Cash Own%'] = rb_frame['Cash Own%'] * rb_cash_norm_var wr_reg_norm_var = 355 / wr_frame['Own'].sum() wr_small_norm_var = 355 / wr_frame['Small Field Own%'].sum() wr_large_norm_var = 355 / wr_frame['Large Field Own%'].sum() wr_cash_norm_var = 355 / wr_frame['Cash Own%'].sum() wr_frame['Own'] = wr_frame['Own'] * wr_reg_norm_var wr_frame['Small Field Own%'] = wr_frame['Small Field Own%'] * wr_small_norm_var wr_frame['Large Field Own%'] = wr_frame['Large Field Own%'] * wr_large_norm_var wr_frame['Cash Own%'] = wr_frame['Cash Own%'] * wr_cash_norm_var te_reg_norm_var = 110 / te_frame['Own'].sum() te_small_norm_var = 110 / te_frame['Small Field Own%'].sum() te_large_norm_var = 110 / te_frame['Large Field Own%'].sum() te_cash_norm_var = 110 / te_frame['Cash Own%'].sum() te_frame['Own'] = te_frame['Own'] * te_reg_norm_var te_frame['Small Field Own%'] = te_frame['Small Field Own%'] * te_small_norm_var te_frame['Large Field Own%'] = te_frame['Large Field Own%'] * te_large_norm_var te_frame['Cash Own%'] = te_frame['Cash Own%'] * te_cash_norm_var dst_reg_norm_var = 100 / dst_frame['Own'].sum() dst_small_norm_var = 100 / dst_frame['Small Field Own%'].sum() dst_large_norm_var = 100 / dst_frame['Large Field Own%'].sum() dst_cash_norm_var = 100 / dst_frame['Cash Own%'].sum() dst_frame['Own'] = dst_frame['Own'] * dst_reg_norm_var dst_frame['Own'] = np_where(dst_frame['Own'] < 0, 1, dst_frame['Own']) dst_frame['Small Field Own%'] = dst_frame['Small Field Own%'] * dst_small_norm_var dst_frame['Small Field Own%'] = np_where(dst_frame['Small Field Own%'] < 0, 1, dst_frame['Small Field Own%']) dst_frame['Large Field Own%'] = dst_frame['Large Field Own%'] * dst_large_norm_var dst_frame['Large Field Own%'] = np_where(dst_frame['Large Field Own%'] < 0, 1, dst_frame['Large Field Own%']) dst_frame['Cash Own%'] = dst_frame['Cash Own%'] * dst_cash_norm_var dst_frame['Cash Own%'] = np_where(dst_frame['Cash Own%'] < 0, 1, dst_frame['Cash Own%']) basic_own_df = pd_concat([qb_frame, rb_frame, wr_frame, te_frame, dst_frame]) basic_own_df = calculate_ownership_overall(basic_own_df) own_norm_var_reg = 900 / basic_own_df['Own'].sum() own_norm_var_small = 900 / basic_own_df['Small Field Own%'].sum() own_norm_var_large = 900 / basic_own_df['Large Field Own%'].sum() own_norm_var_cash = 900 / basic_own_df['Cash Own%'].sum() basic_own_df['Own'] = basic_own_df['Own'] * own_norm_var_reg basic_own_df['Small_Own'] = basic_own_df['Small Field Own%'] * own_norm_var_small basic_own_df['Large_Own'] = basic_own_df['Large Field Own%'] * own_norm_var_large basic_own_df['Cash_Own'] = basic_own_df['Cash Own%'] * own_norm_var_cash basic_own_df['Own'] = np_where(basic_own_df['Own'] > 75, 75, basic_own_df['Own']) # Apply the function to each dataframe basic_own_df = calculate_ownership_overall(basic_own_df) own_norm_var_reg = 900 / basic_own_df['Own'].sum() own_norm_var_small = 900 / basic_own_df['Small Field Own%'].sum() own_norm_var_large = 900 / basic_own_df['Large Field Own%'].sum() own_norm_var_cash = 900 / basic_own_df['Cash Own%'].sum() basic_own_df['Own'] = basic_own_df['Own'] * own_norm_var_reg basic_own_df['Small_Own'] = basic_own_df['Small Field Own%'] * own_norm_var_small basic_own_df['Large_Own'] = basic_own_df['Large Field Own%'] * own_norm_var_large basic_own_df['Cash_Own'] = basic_own_df['Cash Own%'] * own_norm_var_cash own_dict = dict(zip(basic_own_df.Player, basic_own_df.Own)) small_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Small Field Own%'])) large_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Large Field Own%'])) cash_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Cash Own%'])) team_dict = dict(zip(basic_own_df.Player, basic_own_df.Team)) opp_dict = dict(zip(basic_own_df.Player, basic_own_df.Opp)) flex_file = projections_file[['Player', 'Position', 'Salary', 'Median', 'Rush Yards', 'Receptions']] flex_file['Floor'] = np_where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25) flex_file['Ceiling_raw'] = np_where(flex_file['Position'] == 'QB',(flex_file['Median'] * 1.75) + (flex_file['Rush Yards']*.01), (flex_file['Median'] * 1.75) + flex_file['Receptions']) flex_file['Ceiling'] = np_where(flex_file['Position'] == 'K', (flex_file['Median'] * 1.25), flex_file['Ceiling_raw']) flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions'] flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] flex_file = flex_file.reset_index(drop=True) hold_file = flex_file.copy() overall_file = flex_file.copy() salary_file = flex_file.copy() try: overall_floor_gpu = np_array(overall_file['Floor']) overall_ceiling_gpu = np_array(overall_file['Ceiling']) overall_median_gpu = np_array(overall_file['Median']) overall_std_gpu = np_array(overall_file['STD']) overall_salary_gpu = np_array(overall_file['Salary']) data_shape = (len(overall_file['Player']), total_sims) # Example: 1000 rows salary_array = np_zeros(data_shape) sim_array = np_zeros(data_shape) for x in range(0, total_sims): result_gpu = overall_salary_gpu salary_array[:, x] = result_gpu cupy_array = salary_array salary_file = salary_file.reset_index(drop=True) salary_cupy = DataFrame(cupy_array, columns=list(range(0, total_sims))) salary_check_file = pd_concat([salary_file, salary_cupy], axis=1) except: for x in range(0,total_sims): salary_file[x] = salary_file['Salary'] salary_check_file = salary_file.copy() salary_file=salary_check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) salary_file = salary_file.div(1000) try: for x in range(0, total_sims): if distribution_type == 'normal': # Normal distribution (existing logic) result_gpu = np_random.normal(overall_median_gpu, overall_std_gpu) elif distribution_type == 'poisson': # Poisson distribution - using median as lambda result_gpu = np_random.poisson(overall_median_gpu) elif distribution_type == 'bimodal': # Bimodal distribution - mixture of two normal distributions # First peak centered at 80% of median, second at 120% of median if np_random.random() < 0.5: result_gpu = np_random.normal(overall_floor_gpu, overall_std_gpu) else: result_gpu = np_random.normal(overall_ceiling_gpu, overall_std_gpu) else: raise ValueError("Invalid distribution type. Must be 'normal', 'poisson', or 'bimodal'") sim_array[:, x] = result_gpu add_array = sim_array overall_file = overall_file.reset_index(drop=True) df2 = DataFrame(add_array, columns=list(range(0, total_sims))) check_file = pd_concat([overall_file, df2], axis=1) except: for x in range(0,total_sims): if distribution_type == 'normal': overall_file[x] = np_random.normal(overall_file['Median'], overall_file['STD']) elif distribution_type == 'poisson': overall_file[x] = np_random.poisson(overall_file['Median']) elif distribution_type == 'bimodal': # Bimodal distribution fallback if np_random.random() < 0.5: overall_file[x] = np_random.normal(overall_file['Median'] * 0.8, overall_file['STD']) else: overall_file[x] = np_random.normal(overall_file['Median'] * 1.2, overall_file['STD']) check_file = overall_file.copy() overall_file=check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) players_only = hold_file[['Player']] raw_lineups_file = players_only for x in range(0,total_sims): maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) players_only[x] = raw_lineups_file[x].rank(ascending=False) players_only=players_only.drop(['Player'], axis=1) salary_2x_check = (overall_file - (salary_file*2)) salary_3x_check = (overall_file - (salary_file*3)) salary_4x_check = (overall_file - (salary_file*4)) players_only['Average_Rank'] = players_only.mean(axis=1) players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) players_only['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] final_Proj = pd_merge(hold_file, final_outcomes, on="Player") final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Small_Field_Own'] = final_Proj['Player'].map(small_own_dict) final_Proj['Large_Field_Own'] = final_Proj['Player'].map(large_own_dict) final_Proj['Cash_Field_Own'] = final_Proj['Player'].map(cash_own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own']] final_Proj = final_Proj.sort_values(by='Median', ascending=False) return final_Proj.copy() def FD_NFL_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribution_type): total_sims = 1000 projects_raw = projections_file.copy() fd_df = projects_raw.sort_values(by='Median', ascending=False) basic_own_df = fd_df.copy() basic_own_df['name_team'] = basic_own_df['Player'] + basic_own_df['Position'] def calculate_ownership(df, position): # Filter the dataframe based on the position frame = df[df['Position'].str.contains(position)] # Calculate Small Field Own% frame['Small Field Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Small Field Own%'] = np_where( frame['Small Field Own%'] > 75, 75, frame['Small Field Own%'] ) # Calculate Large Field Own% frame['Large Field Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (2.5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Large Field Own%'] = np_where( frame['Large Field Own%'] > 75, 75, frame['Large Field Own%'] ) # Calculate Cash Own% frame['Cash Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (6 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Cash Own%'] = np_where( frame['Cash Own%'] > 75, 75, frame['Cash Own%'] ) return frame def calculate_ownership_overall(df): # Filter the dataframe based on the position frame = df # Calculate Small Field Own% frame['Small Field Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Small Field Own%'] = np_where( frame['Small Field Own%'] > 75, 75, frame['Small Field Own%'] ) # Calculate Large Field Own% frame['Large Field Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (2.5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Large Field Own%'] = np_where( frame['Large Field Own%'] > 75, 75, frame['Large Field Own%'] ) # Calculate Cash Own% frame['Cash Own%'] = np_where( (frame['Own'] - frame['Own'].mean() >= 0), frame['Own'] * (6 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(), frame['Own'] ) frame['Cash Own%'] = np_where( frame['Cash Own%'] > 75, 75, frame['Cash Own%'] ) return frame # Apply the function to each dataframe qb_frame = calculate_ownership(basic_own_df, 'QB') rb_frame = calculate_ownership(basic_own_df, 'RB') wr_frame = calculate_ownership(basic_own_df, 'WR') te_frame = calculate_ownership(basic_own_df, 'TE') dst_frame = calculate_ownership(basic_own_df, 'D') qb_reg_norm_var = 100 / qb_frame['Own'].sum() qb_small_norm_var = 100 / qb_frame['Small Field Own%'].sum() qb_large_norm_var = 100 / qb_frame['Large Field Own%'].sum() qb_cash_norm_var = 100 / qb_frame['Cash Own%'].sum() qb_frame['Own'] = qb_frame['Own'] * qb_reg_norm_var qb_frame['Small Field Own%'] = qb_frame['Small Field Own%'] * qb_small_norm_var qb_frame['Large Field Own%'] = qb_frame['Large Field Own%'] * qb_large_norm_var qb_frame['Cash Own%'] = qb_frame['Cash Own%'] * qb_cash_norm_var rb_reg_norm_var = 235 / rb_frame['Own'].sum() rb_small_norm_var = 235 / rb_frame['Small Field Own%'].sum() rb_large_norm_var = 235 / rb_frame['Large Field Own%'].sum() rb_cash_norm_var = 235 / rb_frame['Cash Own%'].sum() rb_frame['Own'] = rb_frame['Own'] * rb_reg_norm_var rb_frame['Small Field Own%'] = rb_frame['Small Field Own%'] * rb_small_norm_var rb_frame['Large Field Own%'] = rb_frame['Large Field Own%'] * rb_large_norm_var rb_frame['Cash Own%'] = rb_frame['Cash Own%'] * rb_cash_norm_var wr_reg_norm_var = 355 / wr_frame['Own'].sum() wr_small_norm_var = 355 / wr_frame['Small Field Own%'].sum() wr_large_norm_var = 355 / wr_frame['Large Field Own%'].sum() wr_cash_norm_var = 355 / wr_frame['Cash Own%'].sum() wr_frame['Own'] = wr_frame['Own'] * wr_reg_norm_var wr_frame['Small Field Own%'] = wr_frame['Small Field Own%'] * wr_small_norm_var wr_frame['Large Field Own%'] = wr_frame['Large Field Own%'] * wr_large_norm_var wr_frame['Cash Own%'] = wr_frame['Cash Own%'] * wr_cash_norm_var te_reg_norm_var = 110 / te_frame['Own'].sum() te_small_norm_var = 110 / te_frame['Small Field Own%'].sum() te_large_norm_var = 110 / te_frame['Large Field Own%'].sum() te_cash_norm_var = 110 / te_frame['Cash Own%'].sum() te_frame['Own'] = te_frame['Own'] * te_reg_norm_var te_frame['Small Field Own%'] = te_frame['Small Field Own%'] * te_small_norm_var te_frame['Large Field Own%'] = te_frame['Large Field Own%'] * te_large_norm_var te_frame['Cash Own%'] = te_frame['Cash Own%'] * te_cash_norm_var dst_reg_norm_var = 100 / dst_frame['Own'].sum() dst_small_norm_var = 100 / dst_frame['Small Field Own%'].sum() dst_large_norm_var = 100 / dst_frame['Large Field Own%'].sum() dst_cash_norm_var = 100 / dst_frame['Cash Own%'].sum() dst_frame['Own'] = dst_frame['Own'] * dst_reg_norm_var dst_frame['Own'] = np_where(dst_frame['Own'] < 0, 1, dst_frame['Own']) dst_frame['Small Field Own%'] = dst_frame['Small Field Own%'] * dst_small_norm_var dst_frame['Small Field Own%'] = np_where(dst_frame['Small Field Own%'] < 0, 1, dst_frame['Small Field Own%']) dst_frame['Large Field Own%'] = dst_frame['Large Field Own%'] * dst_large_norm_var dst_frame['Large Field Own%'] = np_where(dst_frame['Large Field Own%'] < 0, 1, dst_frame['Large Field Own%']) dst_frame['Cash Own%'] = dst_frame['Cash Own%'] * dst_cash_norm_var dst_frame['Cash Own%'] = np_where(dst_frame['Cash Own%'] < 0, 1, dst_frame['Cash Own%']) basic_own_df = pd_concat([qb_frame, rb_frame, wr_frame, te_frame, dst_frame]) basic_own_df = calculate_ownership_overall(basic_own_df) own_norm_var_reg = 900 / basic_own_df['Own'].sum() own_norm_var_small = 900 / basic_own_df['Small Field Own%'].sum() own_norm_var_large = 900 / basic_own_df['Large Field Own%'].sum() own_norm_var_cash = 900 / basic_own_df['Cash Own%'].sum() basic_own_df['Own'] = basic_own_df['Own'] * own_norm_var_reg basic_own_df['Small_Own'] = basic_own_df['Small Field Own%'] * own_norm_var_small basic_own_df['Large_Own'] = basic_own_df['Large Field Own%'] * own_norm_var_large basic_own_df['Cash_Own'] = basic_own_df['Cash Own%'] * own_norm_var_cash basic_own_df['Own'] = np_where(basic_own_df['Own'] > 75, 75, basic_own_df['Own']) # Apply the function to each dataframe basic_own_df = calculate_ownership_overall(basic_own_df) own_norm_var_reg = 900 / basic_own_df['Own'].sum() own_norm_var_small = 900 / basic_own_df['Small Field Own%'].sum() own_norm_var_large = 900 / basic_own_df['Large Field Own%'].sum() own_norm_var_cash = 900 / basic_own_df['Cash Own%'].sum() basic_own_df['Own'] = basic_own_df['Own'] * own_norm_var_reg basic_own_df['Small_Own'] = basic_own_df['Small Field Own%'] * own_norm_var_small basic_own_df['Large_Own'] = basic_own_df['Large Field Own%'] * own_norm_var_large basic_own_df['Cash_Own'] = basic_own_df['Cash Own%'] * own_norm_var_cash own_dict = dict(zip(basic_own_df.Player, basic_own_df.Own)) small_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Small Field Own%'])) large_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Large Field Own%'])) cash_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Cash Own%'])) team_dict = dict(zip(basic_own_df.Player, basic_own_df.Team)) opp_dict = dict(zip(basic_own_df.Player, basic_own_df.Opp)) flex_file = projections_file[['Player', 'Position', 'Salary', 'Median', 'Rush Yards', 'Receptions']] flex_file['Floor'] = np_where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25) flex_file['Ceiling_raw'] = np_where(flex_file['Position'] == 'QB',(flex_file['Median'] * 1.75) + (flex_file['Rush Yards']*.01), (flex_file['Median'] * 1.75) + flex_file['Receptions']) flex_file['Ceiling'] = np_where(flex_file['Position'] == 'K', (flex_file['Median'] * 1.25), flex_file['Ceiling_raw']) flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions'] flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] flex_file = flex_file.reset_index(drop=True) hold_file = flex_file.copy() overall_file = flex_file.copy() salary_file = flex_file.copy() try: overall_floor_gpu = np_array(overall_file['Floor']) overall_ceiling_gpu = np_array(overall_file['Ceiling']) overall_median_gpu = np_array(overall_file['Median']) overall_std_gpu = np_array(overall_file['STD']) overall_salary_gpu = np_array(overall_file['Salary']) data_shape = (len(overall_file['Player']), total_sims) # Example: 1000 rows salary_array = np_zeros(data_shape) sim_array = np_zeros(data_shape) for x in range(0, total_sims): result_gpu = overall_salary_gpu salary_array[:, x] = result_gpu cupy_array = salary_array salary_file = salary_file.reset_index(drop=True) salary_cupy = DataFrame(cupy_array, columns=list(range(0, total_sims))) salary_check_file = pd_concat([salary_file, salary_cupy], axis=1) except: for x in range(0,total_sims): salary_file[x] = salary_file['Salary'] salary_check_file = salary_file.copy() salary_file=salary_check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) salary_file = salary_file.div(1000) try: for x in range(0, total_sims): if distribution_type == 'normal': # Normal distribution (existing logic) result_gpu = np_random.normal(overall_median_gpu, overall_std_gpu) elif distribution_type == 'poisson': # Poisson distribution - using median as lambda result_gpu = np_random.poisson(overall_median_gpu) elif distribution_type == 'bimodal': # Bimodal distribution - mixture of two normal distributions # First peak centered at 80% of median, second at 120% of median if np_random.random() < 0.5: result_gpu = np_random.normal(overall_floor_gpu, overall_std_gpu) else: result_gpu = np_random.normal(overall_ceiling_gpu, overall_std_gpu) else: raise ValueError("Invalid distribution type. Must be 'normal', 'poisson', or 'bimodal'") sim_array[:, x] = result_gpu add_array = sim_array overall_file = overall_file.reset_index(drop=True) df2 = DataFrame(add_array, columns=list(range(0, total_sims))) check_file = pd_concat([overall_file, df2], axis=1) except: for x in range(0,total_sims): if distribution_type == 'normal': overall_file[x] = np_random.normal(overall_file['Median'], overall_file['STD']) elif distribution_type == 'poisson': overall_file[x] = np_random.poisson(overall_file['Median']) elif distribution_type == 'bimodal': # Bimodal distribution fallback if np_random.random() < 0.5: overall_file[x] = np_random.normal(overall_file['Median'] * 0.8, overall_file['STD']) else: overall_file[x] = np_random.normal(overall_file['Median'] * 1.2, overall_file['STD']) check_file = overall_file.copy() overall_file=check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) players_only = hold_file[['Player']] raw_lineups_file = players_only for x in range(0,total_sims): maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) players_only[x] = raw_lineups_file[x].rank(ascending=False) players_only=players_only.drop(['Player'], axis=1) salary_2x_check = (overall_file - (salary_file*2)) salary_3x_check = (overall_file - (salary_file*3)) salary_4x_check = (overall_file - (salary_file*4)) players_only['Average_Rank'] = players_only.mean(axis=1) players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) players_only['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] final_Proj = pd_merge(hold_file, final_outcomes, on="Player") final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Small_Field_Own'] = final_Proj['Player'].map(small_own_dict) final_Proj['Large_Field_Own'] = final_Proj['Player'].map(large_own_dict) final_Proj['Cash_Field_Own'] = final_Proj['Player'].map(cash_own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own']] final_Proj['Salary'] = final_Proj['Salary'].astype(int) final_Proj = final_Proj.sort_values(by='Median', ascending=False) return final_Proj.copy()