James McCool
Enhance preset functions in app.py and related modules to include 'sport' parameter for improved flexibility in lineup management. This update modifies the small_field_preset, large_field_preset, volatility_preset, hedging_preset, and reduce_volatility_preset functions to accommodate sport-specific logic, enhancing user experience and accuracy in lineup strategies.
206da8c
| import pandas as pd | |
| import numpy as np | |
| def large_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str): | |
| excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity'] | |
| player_columns = [col for col in portfolio.columns if col not in excluded_cols] | |
| concat_portfolio = portfolio.copy() | |
| concat_portfolio = concat_portfolio.sort_values(by='Diversity', ascending=True).reset_index(drop=True) | |
| # Calculate target similarity scores for linear progression | |
| similarity_floor = concat_portfolio['Diversity'].min() | |
| similarity_ceiling = concat_portfolio['Diversity'].max() | |
| # Create evenly spaced target similarity scores | |
| target_similarities = np.linspace(similarity_floor, similarity_ceiling, lineup_target) | |
| # Find the closest lineup to each target similarity score | |
| selected_indices = [] | |
| for target_sim in target_similarities: | |
| # Find the index of the closest similarity score | |
| closest_idx = (concat_portfolio['Diversity'] - target_sim).abs().idxmin() | |
| if closest_idx not in selected_indices: # Avoid duplicates | |
| selected_indices.append(closest_idx) | |
| # Select the lineups | |
| concat_portfolio = concat_portfolio.loc[selected_indices].reset_index(drop=True) | |
| return concat_portfolio.sort_values(by='median', ascending=False) | |