James McCool commited on
Commit
9b875c4
·
1 Parent(s): 89bf41c

Add 'Top Score' to scoring percentages in app.py and calculate DK and FD LevX metrics based on this new column, enhancing player analysis capabilities.

Browse files
Files changed (1) hide show
  1. app.py +4 -2
app.py CHANGED
@@ -17,7 +17,7 @@ def init_conn():
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  db, db2 = init_conn()
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- game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
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  'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
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  player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
@@ -84,7 +84,7 @@ def init_baselines():
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  cursor = collection.find()
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  team_frame = pd.DataFrame(cursor)
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  scoring_percentages = team_frame.drop(columns=['_id'])
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- scoring_percentages = scoring_percentages[['Names', 'Avg First Inning', 'First Inning Lead Percentage', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage', 'Avg Score', '8+ runs', 'Win Percentage', 'Slate']]
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  scoring_percentages['8+ runs'] = scoring_percentages['8+ runs'].replace('%', '', regex=True).astype(float)
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  scoring_percentages['Win Percentage'] = scoring_percentages['Win Percentage'].replace('%', '', regex=True).astype(float)
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  dk_hitters_only = dk_roo[dk_roo['pos_group'] != 'Pitchers']
@@ -97,6 +97,8 @@ def init_baselines():
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  scoring_percentages = scoring_percentages.merge(fd_team_ownership, left_on='Names', right_on='Team', how='left')
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  scoring_percentages.rename(columns={'Own%': 'FD Own%'}, inplace=True)
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  scoring_percentages.drop('Team', axis=1, inplace=True)
 
 
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  return roo_data, sd_roo_data, scoring_percentages, dk_roo, fd_roo
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  db, db2 = init_conn()
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+ game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', 'Top Score': '{:.2%}',
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  'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
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  player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
 
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  cursor = collection.find()
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  team_frame = pd.DataFrame(cursor)
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  scoring_percentages = team_frame.drop(columns=['_id'])
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+ scoring_percentages = scoring_percentages[['Names', 'Avg First Inning', 'First Inning Lead Percentage', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage', 'Avg Score', '8+ runs', 'Win Percentage', 'Slate', 'Top Score']]
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  scoring_percentages['8+ runs'] = scoring_percentages['8+ runs'].replace('%', '', regex=True).astype(float)
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  scoring_percentages['Win Percentage'] = scoring_percentages['Win Percentage'].replace('%', '', regex=True).astype(float)
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  dk_hitters_only = dk_roo[dk_roo['pos_group'] != 'Pitchers']
 
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  scoring_percentages = scoring_percentages.merge(fd_team_ownership, left_on='Names', right_on='Team', how='left')
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  scoring_percentages.rename(columns={'Own%': 'FD Own%'}, inplace=True)
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  scoring_percentages.drop('Team', axis=1, inplace=True)
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+ scoring_percentages['DK LevX'] = scoring_percentages['Top Score'].rank(pct=True) - scoring_percentages['DK Own%'].rank(pct=True)
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+ scoring_percentages['FD LevX'] = scoring_percentages['Top Score'].rank(pct=True) - scoring_percentages['FD Own%'].rank(pct=True)
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  return roo_data, sd_roo_data, scoring_percentages, dk_roo, fd_roo
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