James McCool commited on
Commit
6a3a743
·
1 Parent(s): 211070a

Refactor salary calculations in app.py to differentiate between NFL and NBA for DraftKings. Updated the salary mapping logic to apply a specific divisor for NFL players while maintaining the original mapping for NBA players. This change enhances the accuracy of player salary projections based on the selected sport.

Browse files
Files changed (1) hide show
  1. app.py +8 -2
app.py CHANGED
@@ -519,7 +519,10 @@ with tab1:
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  freq_working['Freq'] = freq_working['Freq'].astype(int)
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  freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
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  if sim_site_var1 == 'Draftkings':
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- freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
 
 
 
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  elif sim_site_var1 == 'Fanduel':
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  freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
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  freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
@@ -536,7 +539,10 @@ with tab1:
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  columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
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  cpt_working['Freq'] = cpt_working['Freq'].astype(int)
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  cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
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- cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
 
 
 
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  cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
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  cpt_working['Exposure'] = cpt_working['Freq']/(1000)
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  cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
 
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  freq_working['Freq'] = freq_working['Freq'].astype(int)
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  freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
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  if sim_site_var1 == 'Draftkings':
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+ if sim_sport_var1 == 'NFL':
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+ freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
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+ elif sim_sport_var1 == 'NBA':
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+ freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
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  elif sim_site_var1 == 'Fanduel':
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  freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
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  freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
 
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  columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
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  cpt_working['Freq'] = cpt_working['Freq'].astype(int)
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  cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
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+ if sim_sport_var1 == 'NFL':
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+ cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) * 1.5
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+ elif sim_sport_var1 == 'NBA':
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+ cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
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  cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
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  cpt_working['Exposure'] = cpt_working['Freq']/(1000)
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  cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']