Sort working portfolio by 'median' before resetting index in distribute_preset.py to enhance player selection accuracy. This change ensures that the highest median values are prioritized in the distribution process.

Update player column selection logic in distribute_preset.py to reference the correct portfolio DataFrame. This change ensures that the player filtering process accurately reflects the current portfolio structure, enhancing the overall functionality of the distribution process.

Refactor player removal logic in distribute_preset.py to utilize a list of players with the lowest lineup counts. This change enhances the portfolio distribution process by ensuring that the least favorable players are excluded from the lineup, improving overall selection accuracy.

Refactor player selection logic in distribute_preset.py to improve clarity by explicitly defining columns used for player masking. This change enhances the readability of the code and ensures that only relevant columns are considered during the player filtering process.

Enhance portfolio distribution logic in distribute_preset.py by adding player exposure calculations and refining the handling of 'Similarity Score'. This update improves the accuracy of player selection and provides a summary of player exposure in the final output, ensuring a more comprehensive analysis of the lineup distribution process.

Refactor sorting logic in small_field_preset.py to ensure accurate portfolio filtering by utilizing 'Own' for sorting. This change enhances the portfolio distribution process by maintaining consistency in data handling across teams.

Refactor portfolio distribution logic in distribute_preset.py to group by 'Stack' and update sorting criteria to 'median'. This change improves the accuracy of portfolio filtering by ensuring the correct metrics are used for both selection and final output.

Update sorting logic in distribute_preset.py to prioritize 'Finish_percentile' over 'median' for improved portfolio filtering accuracy. This change enhances the effectiveness of the distribution process by ensuring the correct metric is used for sorting.

Update portfolio distribution logic in distribute_preset.py to utilize 'Similarity Score' for maximum ownership type calculations. This change improves the accuracy of portfolio filtering by ensuring the correct comparison metric is applied, enhancing the overall trimming process.

Enhance portfolio trimming logic in trim_portfolio.py by adding calculation for maximum ownership type value when conditions are not met. This adjustment improves the accuracy of the trimming process by ensuring all relevant ownership metrics are considered.

Fix logic for 'Similarity Score' comparison in trim_portfolio.py to ensure correct filtering of portfolios based on performance thresholds. This change enhances the accuracy of the trimming process.

Add 'Similarity Score' to data handling in app.py

Enhance trimming variable options in app.py and update portfolio trimming logic in trim_portfolio.py to accommodate 'Similarity Score'. This change allows for more flexible data handling and improves the trimming process based on the selected variable.

Optimize player similarity score calculation in predict_dupes function

Refactor player similarity score calculation in predict_dupes function

Update excluded columns in app.py to include 'Similarity Score' for improved data handling in portfolio processing.

Refactor player similarity score calculation in predict_dupes function

Enhance player selection handling in predict_dupes function

Add player similarity score calculation to predict_dupes function

Limit concatenation in distribute_preset function to the top 10 entries of the working portfolio, removing debug print statements for cleaner output and improved performance in portfolio processing.

Refactor handling of working portfolio in distribute_preset function to ensure proper checks for empty portfolios and maintain accurate filtering logic. This change improves robustness and clarity in the portfolio processing flow.

Fix lower threshold calculation in distribute_preset function to ensure accurate filtering based on finishing range. This change enhances the clarity and correctness of the threshold logic.

Update debug print statements in distribute_preset function to include finish and lower threshold values, and sort the top 10 entries by 'Finish_percentile' for enhanced traceability and clarity during execution.

Refactor threshold logic in distribute_preset function to improve clarity and accuracy in filtering. Introduced a lower threshold variable for better readability and consistency in handling 'Finish_percentile' across all finishing ranges.

Add debug print statement in distribute_preset function to display the top 10 entries of the working portfolio for improved traceability during execution.

Remove limit on concatenation in distribute_preset function to include all entries of the working portfolio, enhancing data completeness while maintaining sorting by 'Finish_percentile'.

Limit concatenation in distribute_preset function to the top 5 entries of the working portfolio to enhance performance and maintain focus on the most relevant data.

Enhance portfolio concatenation in distribute_preset function by increasing the limit to the top 10 entries and adding a step to remove duplicates based on 'median', 'salary', and 'Own' for improved data relevance and accuracy.

Limit concatenation in distribute_preset function to the top 5 entries of the working portfolio to further enhance performance and focus on the most relevant data.

Limit concatenation in distribute_preset function to the top 10 entries of the working portfolio to enhance performance and maintain focus on the most relevant data.

Update preset label in app.py for clarity and consistency

Update debug print statement in distribute_preset function to display only the top 10 entries of the working portfolio for improved clarity during execution.

Add debug print statement in distribute_preset function to output working portfolio for better traceability during execution.

Refactor filtering logic in distribute_preset function to improve handling of 'Finish_percentile' thresholds. Introduced a variable for finish threshold to enhance code readability and maintain accurate portfolio selection across all finishing ranges.

Fix variable reference in distribute_preset function to correctly use 'Weighted Own' for filtering logic, ensuring accurate portfolio selection based on ownership thresholds.

Refactor distribute_preset function to improve filtering logic for 'Finish_percentile' by handling edge cases for the first and last finishing ranges, ensuring accurate portfolio selection and maintaining sorting by 'median' in descending order.

Update sorting logic in distribute_preset function to sort 'median' in descending order for improved portfolio selection accuracy

Update preset option in app.py to correct wording for 'Distributed' selection

Add distribute_preset function and update app.py for preset selection

Refactor large_field_preset function to streamline player removal logic by eliminating unnecessary iterations and simplifying the exposure tracking process. Ensure sufficient lineups are generated before finalizing selections, enhancing overall efficiency and accuracy.

Enhance large_field_preset function to improve player exposure tracking by implementing a mechanism to remove high-exposure players across iterations, ensuring lineup integrity and preventing excessive ownership. Adjust logic to check for sufficient lineups before finalizing selections.

Refactor large_field_preset function to streamline the portfolio selection process by removing unnecessary iterations and simplifying the logic for team-based filtering, enhancing efficiency and lineup accuracy.

Enhance small_field_preset and large_field_preset functions to accept an additional parameter for excluded columns, improving filtering capabilities and lineup accuracy. Update app.py to reflect these changes in preset selections.

Refactor large_field_preset function to adjust filtering criteria, replacing 'Finish_percentile' with 'Own' for team-based selection, enhancing accuracy in lineup targeting and improving the logic for dropping rows based on ownership thresholds.

Refactor large_field_preset function to simplify ranking logic by removing median and finish percentile rankings, and adjust filtering criteria to focus on 'Finish_percentile' for team-based selection, enhancing accuracy in lineup targeting.

Remove filtering of working_portfolio by 'Lineup Edge' in large_field_preset function to allow for broader selection of portfolios, enhancing lineup options.

Update ranking logic in large_field_preset function to sort 'median' in descending order and 'Finish_percentile' in ascending order, enhancing accuracy in team-based filtering and lineup targeting.

Refactor large_field_preset function to improve team-based filtering by introducing median and finish percentile rankings for enhanced sorting accuracy. Clean up temporary columns after processing to maintain DataFrame integrity.

Refactor large_field_preset and small_field_preset functions to use 'Stack' instead of 'Team' for team-based filtering, ensuring consistency in portfolio adjustments and improving accuracy in lineup targeting.

Refactor large_field_preset and small_field_preset functions to enhance lineup targeting: increase slack_var range, implement team-based filtering, and consolidate results into a single DataFrame for improved accuracy in portfolio adjustments.