Multichem commited on
Commit
9eb164a
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1 Parent(s): 3fb8a3d

Update app.py

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Files changed (1) hide show
  1. app.py +67 -4
app.py CHANGED
@@ -363,6 +363,33 @@ with tab5:
363
  prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
364
  prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
365
  df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
366
  elif prop_type_var == "Total Bases (Hitters)":
367
  player_df = hitter_stats
368
  prop_df = prop_frame[prop_frame['prop_type'] == 'batter_total_bases']
@@ -381,6 +408,15 @@ with tab5:
381
  prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
382
  prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
383
  df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
 
 
 
 
 
 
 
 
 
384
 
385
  prop_dict = dict(zip(df.Player, df.Prop))
386
  over_dict = dict(zip(df.Player, df.Over))
@@ -392,8 +428,14 @@ with tab5:
392
 
393
  if prop_type_var == "Strikeouts (Pitchers)":
394
  df['Median'] = df['Ks']
 
 
395
  elif prop_type_var == "Total Outs (Pitchers)":
396
  df['Median'] = df['Outs']
 
 
 
 
397
  elif prop_type_var == "Total Bases (Hitters)":
398
  df['Median'] = df['Total Bases']
399
  elif prop_type_var == "Stolen Bases (Hitters)":
@@ -402,28 +444,49 @@ with tab5:
402
  flex_file = df
403
  if prop_type_var == 'Strikeouts (Pitchers)':
404
  flex_file['Floor'] = flex_file['Median'] * .20
405
- flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
406
  flex_file['STD'] = flex_file['Median'] / 4
407
  flex_file['Prop'] = flex_file['Player'].map(prop_dict)
408
  flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
409
 
410
  elif prop_type_var == 'Total Outs (Pitchers)':
411
  flex_file['Floor'] = flex_file['Median'] * .20
412
- flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
413
  flex_file['STD'] = flex_file['Median'] / 4
414
  flex_file['Prop'] = flex_file['Player'].map(prop_dict)
415
  flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
416
 
417
  elif prop_type_var == 'Total Bases (Hitters)':
418
  flex_file['Floor'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * .20, 0)
419
- flex_file['Ceiling'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] + (flex_file['Median'] * .80), flex_file['Median'] * 4)
420
  flex_file['STD'] = flex_file['Median'] / 1.5
421
  flex_file['Prop'] = flex_file['Player'].map(prop_dict)
422
  flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
423
 
424
  elif prop_type_var == 'Stolen Bases (Hitters)':
425
  flex_file['Floor'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * .20, 0)
426
- flex_file['Ceiling'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] + (flex_file['Median'] * .80), flex_file['Median'] * 4)
427
  flex_file['STD'] = flex_file['Median'] / 1.5
428
  flex_file['Prop'] = flex_file['Player'].map(prop_dict)
429
  flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
 
363
  prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
364
  prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
365
  df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
366
+ elif prop_type_var == "Earned Runs (Pitchers)":
367
+ player_df = pitcher_stats
368
+ prop_df = prop_frame[prop_frame['prop_type'] == 'pitcher_earned_runs']
369
+ prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
370
+ prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
371
+ prop_df = prop_df.loc[prop_df['Prop'] != 0]
372
+ prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
373
+ prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
374
+ df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
375
+ elif prop_type_var == "Hits Against (Pitchers)":
376
+ player_df = pitcher_stats
377
+ prop_df = prop_frame[prop_frame['prop_type'] == 'pitcher_hits_allowed']
378
+ prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
379
+ prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
380
+ prop_df = prop_df.loc[prop_df['Prop'] != 0]
381
+ prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
382
+ prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
383
+ df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
384
+ elif prop_type_var == "Walks Allowed (Pitchers)":
385
+ player_df = pitcher_stats
386
+ prop_df = prop_frame[prop_frame['prop_type'] == 'pitcher_walks']
387
+ prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
388
+ prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
389
+ prop_df = prop_df.loc[prop_df['Prop'] != 0]
390
+ prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
391
+ prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
392
+ df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
393
  elif prop_type_var == "Total Bases (Hitters)":
394
  player_df = hitter_stats
395
  prop_df = prop_frame[prop_frame['prop_type'] == 'batter_total_bases']
 
408
  prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
409
  prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
410
  df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
411
+ elif prop_type_var == "Hits (Hitters)":
412
+ player_df = hitter_stats
413
+ prop_df = prop_frame[prop_frame['prop_type'] == 'batter_hits']
414
+ prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
415
+ prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
416
+ prop_df = prop_df.loc[prop_df['Prop'] != 0]
417
+ prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
418
+ prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
419
+ df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
420
 
421
  prop_dict = dict(zip(df.Player, df.Prop))
422
  over_dict = dict(zip(df.Player, df.Over))
 
428
 
429
  if prop_type_var == "Strikeouts (Pitchers)":
430
  df['Median'] = df['Ks']
431
+ elif prop_type_var == "Earned Runs (Pitchers)":
432
+ df['Median'] = df['ERs']
433
  elif prop_type_var == "Total Outs (Pitchers)":
434
  df['Median'] = df['Outs']
435
+ elif prop_type_var == "Hits Against (Pitchers)":
436
+ df['Median'] = df['Hits']
437
+ elif prop_type_var == "Walks Allowed (Pitchers)":
438
+ df['Median'] = df['BB']
439
  elif prop_type_var == "Total Bases (Hitters)":
440
  df['Median'] = df['Total Bases']
441
  elif prop_type_var == "Stolen Bases (Hitters)":
 
444
  flex_file = df
445
  if prop_type_var == 'Strikeouts (Pitchers)':
446
  flex_file['Floor'] = flex_file['Median'] * .20
447
+ flex_file['Ceiling'] = flex_file['Median'] * 1.8
448
  flex_file['STD'] = flex_file['Median'] / 4
449
  flex_file['Prop'] = flex_file['Player'].map(prop_dict)
450
  flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
451
 
452
  elif prop_type_var == 'Total Outs (Pitchers)':
453
  flex_file['Floor'] = flex_file['Median'] * .20
454
+ flex_file['Ceiling'] = flex_file['Median'] * 1.8
455
+ flex_file['STD'] = flex_file['Median'] / 4
456
+ flex_file['Prop'] = flex_file['Player'].map(prop_dict)
457
+ flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
458
+
459
+ elif prop_type_var == 'Earned Runs (Pitchers)':
460
+ flex_file['Floor'] = flex_file['Median'] * .20
461
+ flex_file['Ceiling'] = flex_file['Median'] * 1.8
462
+ flex_file['STD'] = flex_file['Median'] / 4
463
+ flex_file['Prop'] = flex_file['Player'].map(prop_dict)
464
+ flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
465
+
466
+ elif prop_type_var == 'Hits Against (Pitchers)':
467
+ flex_file['Floor'] = flex_file['Median'] * .20
468
+ flex_file['Ceiling'] = flex_file['Median'] * 1.8
469
+ flex_file['STD'] = flex_file['Median'] / 4
470
+ flex_file['Prop'] = flex_file['Player'].map(prop_dict)
471
+ flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
472
+
473
+ elif prop_type_var == 'Walks Allowed (Pitchers)':
474
+ flex_file['Floor'] = flex_file['Median'] * .20
475
+ flex_file['Ceiling'] = flex_file['Median'] * 1.8
476
  flex_file['STD'] = flex_file['Median'] / 4
477
  flex_file['Prop'] = flex_file['Player'].map(prop_dict)
478
  flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
479
 
480
  elif prop_type_var == 'Total Bases (Hitters)':
481
  flex_file['Floor'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * .20, 0)
482
+ flex_file['Ceiling'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * 1.8, flex_file['Median'] * 4)
483
  flex_file['STD'] = flex_file['Median'] / 1.5
484
  flex_file['Prop'] = flex_file['Player'].map(prop_dict)
485
  flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
486
 
487
  elif prop_type_var == 'Stolen Bases (Hitters)':
488
  flex_file['Floor'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * .20, 0)
489
+ flex_file['Ceiling'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * 1.8, flex_file['Median'] * 4)
490
  flex_file['STD'] = flex_file['Median'] / 1.5
491
  flex_file['Prop'] = flex_file['Player'].map(prop_dict)
492
  flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]