adjusted own scaling

cleaning up salary subtraction

Update dupes calculation in predict_dupes.py by adjusting salary thresholds from 59700 and 49700 to 60000 and 50000 respectively, enhancing the accuracy of duplicate predictions across different contest sizes.

Refine dupes calculation in predict_dupes.py by updating salary thresholds from 60000 and 50000 to 59700 and 49700 respectively, improving accuracy in duplicate predictions across various contest sizes.

more contest size reductions

REDUCING cONTEST sIZE VARIABLE

ADJUSTED DUPE COUNTS

Update predict_dupes.py functionality

Sort portfolio by 'Dupes' before displaying the top 10 entries in predict_dupes.py to enhance visibility of duplicate player selections.

printing table of predict_dupes

Comment out unnecessary column drops in predict_dupes.py to retain data for potential future use, enhancing flexibility in the duplicate prediction process.

ping

Add 'top_x_presence' calculation to predict_dupes.py for enhanced duplicate prediction accuracy. This new metric counts the presence of top ownership players in the portfolio, refining the dupes calculation by incorporating player ownership dynamics across various contest types.

Update 'own_rank_percentile' calculation in predict_dupes.py to use 'Own' column instead of duplicate count columns. This change enhances the accuracy of the duplicate prediction logic by directly reflecting player ownership rankings in the calculations.

Enhance duplicate prediction logic in predict_dupes.py by introducing 'own_rank_percentile' calculation. This addition improves the accuracy of the dupes calculation by factoring in player ownership rankings, ensuring more precise predictions across different contest types.

Remove debug statements from player filtering logic in app.py to streamline code. This cleanup enhances readability and focuses on the core functionality of the filtering process.

Refactor player filtering logic in app.py to simplify mask conditions. Updated the include and remove masks to use row.values instead of converting to lists, improving code efficiency and readability.

Update player filtering logic in app.py to change the include mask condition from 'any' to 'all'. This modification ensures that lineups are only included when all specified players are present, enhancing the accuracy of player management.

Add debug statements to player filtering logic in app.py to display mask results. This enhancement aids in verifying the accuracy of the include and remove masks during lineup filtering, improving debugging capabilities.

Update player filtering logic in app.py to change the include mask condition from 'all' to 'any'. This adjustment ensures that lineups are accurately filtered based on user-defined selections, enhancing the functionality of player management.

ping

Update player filtering logic in app.py to correct the remove mask condition. Changed from using 'all' to 'any' for player removal criteria, ensuring accurate filtering of lineups based on user-defined selections.

Refactor player filtering logic in app.py to combine include and remove masks into a single operation. This change simplifies the filtering process, improving code efficiency and maintaining accurate lineup management based on user-defined criteria.

Refactor player filtering logic in app.py to correct mask application for removing players. Updated the remove mask condition to ensure accurate filtering of lineups based on user-defined criteria, enhancing functionality and maintainability.

Refactor player filtering logic in app.py to consolidate mask creation for including and removing players. This change enhances code readability and maintains the functionality of dynamic player selection based on user-defined criteria.

Refactor player filtering logic in app.py to utilize dynamic player column selection. Updated include and remove masks to improve accuracy in filtering lineups based on user-defined criteria, enhancing overall functionality and maintainability.

Refactor lineup filtering logic in app.py to streamline player selection process. Updated multiselect options for removing and including players, enhancing clarity and user experience. Improved filtering conditions to ensure accurate lineup management based on user selections.

ping

Refactor lineup filtering logic in app.py to improve clarity and maintainability. Updated variable names for multiselect options to enhance user understanding when managing player and stack filters, ensuring a more intuitive experience.

Refactor lineup filtering options in app.py to enhance clarity in user prompts. Updated multiselect labels to provide context based on selected variables, improving the user experience when managing lineups.

Enhance user interaction in app.py by implementing a form for selecting variables to remove from lineups. This change allows users to apply their selections more intuitively, improving the overall lineup management experience.

Update lineup filtering options in app.py to replace multiselect with selectbox for improved user experience

Add Conditional Manager to app.py for lineup filtering options

ping

Refactor reassess_edge function in reassess_edge.py to improve efficiency by concatenating modified and base frames, simplifying the extraction of updated rows, and enhancing clarity in the logic for processing modified data.

Refactor app.py to replace predict_dupes calls with reassess_edge for both working and export frames, enhancing the accuracy of edge assessments by utilizing the updated reassess_edge function in reassess_edge.py.

Replace reassess_edge function calls with predict_dupes in app.py to streamline duplicate prediction logic for working and export frames, enhancing data processing accuracy.

Refactor dupe adjustment logic in reassess_dupes function of reassess_edge.py to conditionally apply adjustments based on salary_diff, ensuring accurate calculations for positive salary differences while maintaining original dupes for non-positive differences.

Update reassess_finish_percentile calculation in reassess_edge.py to use max() instead of min(), ensuring that the finish_percentile does not fall below the sum of a minimum threshold and 'Win%', thereby enhancing the accuracy of lineup assessments.

Update reassess_finish_percentile calculation in reassess_edge.py to ensure it does not exceed the sum of a minimum threshold and 'Win%', enhancing accuracy in lineup assessments.

Update return logic in reassess_lineup_edge function of reassess_edge.py to use 'Lineup Edge' instead of 'Finish_percentile', ensuring accurate calculations of lineup edge assessments.

Refactor dupe rate calculation in reassess_lineup_edge function of reassess_edge.py to use min() instead of clip(), improving clarity and ensuring accurate handling of zero previous dupes.

Update reassess_lineup_edge function in reassess_edge.py to handle cases where previous dupes are zero, ensuring robust calculations by clipping the dupe rate. This change enhances the accuracy of lineup edge assessments.

Enhance reassess_lineup_edge function in reassess_edge.py to incorporate previous finish_percentile and dupes for improved lineup edge calculations. Update the return logic to better reflect the relationship between finish_percentile and lineup edge, ensuring more accurate assessments.

Refactor reassess_lineup_edge function in reassess_edge.py to improve clarity by using local variables for calculations. Update references to 'Edge' to 'Lineup Edge' for consistency, and adjust column drop logic to retain necessary data for further processing.

Update reassess_finish_percentile calculation in reassess_edge.py to adjust ownership impact by changing the divisor for own_diff from 2 to 200, improving accuracy in finish_percentile adjustments.

Comment out the column drop logic in reassess_edge function to retain additional data for debugging purposes, enhancing flexibility in data handling.

Refactor reassess_finish_percentile function in reassess_edge.py to remove unnecessary parameters, streamlining the calculation of finish_percentile. Update references in reassess_edge function to align with the new function signature, enhancing code clarity and maintainability.

Refactor reassess_finish_percentile function in reassess_edge.py to simplify calculations by using ownership and median differences, improving accuracy in finish_percentile adjustments. Update column drop logic to remove unnecessary columns for cleaner data handling.

Implement reassess_finish_percentile function in reassess_edge.py to recalculate finish_percentile based on ownership and contest size, enhancing accuracy in ownership adjustments. Remove debug print statements from app.py for cleaner code.