Update salary multiplier calculations in MMA ROO functions

function_hold/MMA_functions.py

@@ -208,27 +208,27 @@ def DK_MMA_ROO_Build(projections_file, std_var, distribution_type):
 
     players_only=players_only.drop(['Player'], axis=1)
     
-    salary_4x_check = (overall_file - (salary_file*4))
-    salary_5x_check = (overall_file - (salary_file*5))
-    salary_6x_check = (overall_file - (salary_file*6))
-    gpp_check = (overall_file - ((salary_file*5)+10))
+    salary_10x_check = (overall_file - (salary_file*10))
+    salary_11x_check = (overall_file - (salary_file*11))
+    salary_12x_check = (overall_file - (salary_file*12))
+    gpp_check = (overall_file - ((salary_file*11)+10))
 
     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['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-    players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
-    players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['100+%'] = overall_file[overall_file >= 100].count(axis=1)/float(total_sims)
+    players_only['10x%'] = salary_10x_check[salary_10x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['11x%'] = salary_11x_check[salary_11x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['12x%'] = salary_12x_check[salary_12x_check >= 1].count(axis=1)/float(total_sims)
     players_only['GPP%'] = gpp_check[gpp_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+%', '4x%', '5x%', '6x%', 'GPP%']]
+    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '100+%', '10x%', '11x%', '12x%', 'GPP%']]
 
     final_Proj = pd_merge(hold_file, final_outcomes, on="Player")
-    final_Proj = final_Proj[['Player', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+    final_Proj = final_Proj[['Player', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '100+%', '10x%', '11x%', '12x%', 'GPP%']]
     
     final_Proj['Own'] = final_Proj['Player'].map(own_dict)
     final_Proj['Small_Own'] = final_Proj['Player'].map(small_own_dict)
@@ -238,7 +238,7 @@ def DK_MMA_ROO_Build(projections_file, std_var, distribution_type):
     final_Proj['LevX'] = ((final_Proj[['Top_finish', '6x%', 'Top_5_finish']].mean(axis=1))*100) - final_Proj['Own']
     final_Proj['ValX'] = ((final_Proj[['5x%', '6x%']].mean(axis=1))*100) + final_Proj['LevX']
 
-    final_Proj = final_Proj[['Player', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
+    final_Proj = final_Proj[['Player', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '100+%', '10x%', '11x%', '12x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
     final_Proj = final_Proj.sort_values(by='Median', ascending=False)
     
     return final_Proj.copy()
@@ -435,27 +435,27 @@ def FD_MMA_ROO_Build(projections_file, std_var, distribution_type):
 
     players_only=players_only.drop(['Player'], axis=1)
         
-    salary_4x_check = (overall_file - (salary_file*4))
-    salary_5x_check = (overall_file - (salary_file*5))
-    salary_6x_check = (overall_file - (salary_file*6))
-    gpp_check = (overall_file - ((salary_file*5)+10))
+    salary_10x_check = (overall_file - (salary_file*10))
+    salary_11x_check = (overall_file - (salary_file*11))
+    salary_12x_check = (overall_file - (salary_file*12))
+    gpp_check = (overall_file - ((salary_file*11)+10))
 
     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['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-    players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
-    players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['100+%'] = overall_file[overall_file >= 100].count(axis=1)/float(total_sims)
+    players_only['10x%'] = salary_10x_check[salary_10x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['11x%'] = salary_11x_check[salary_11x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['12x%'] = salary_12x_check[salary_12x_check >= 1].count(axis=1)/float(total_sims)
     players_only['GPP%'] = gpp_check[gpp_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+%', '4x%', '5x%', '6x%', 'GPP%']]
+    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '100+%', '10x%', '11x%', '12x%', 'GPP%']]
 
     final_Proj = pd_merge(hold_file, final_outcomes, on="Player")
-    final_Proj = final_Proj[['Player', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+    final_Proj = final_Proj[['Player', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '100+%', '10x%', '11x%', '12x%', 'GPP%']]
     
     final_Proj['Own'] = final_Proj['Player'].map(own_dict)
     final_Proj['Small_Own'] = final_Proj['Player'].map(small_own_dict)
@@ -465,7 +465,7 @@ def FD_MMA_ROO_Build(projections_file, std_var, distribution_type):
     final_Proj['LevX'] = ((final_Proj[['Top_finish', '6x%', 'Top_5_finish']].mean(axis=1))*100) - final_Proj['Own']
     final_Proj['ValX'] = ((final_Proj[['5x%', '6x%']].mean(axis=1))*100) + final_Proj['LevX']
 
-    final_Proj = final_Proj[['Player', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
+    final_Proj = final_Proj[['Player', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '100+%', '10x%', '11x%', '12x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
     final_Proj['Salary'] = final_Proj['Salary'].astype(int)
     final_Proj = final_Proj.sort_values(by='Median', ascending=False)