Replace dataframe display with table in app.py for improved performance and simplicity. This change enhances the user interface by streamlining the presentation of paginated data.

Enhance stack summary management in app.py by introducing session state checks for 'origin_stack_exposures'. This update ensures accurate stack statistics are displayed based on user settings and improves data handling for stack summaries during portfolio interactions.

Refactor name matching analysis in app.py to streamline the process of matching portfolio and projection names. This update enhances the accuracy of name matching, improves session state management, and ensures proper handling of unmatched names during user interactions.

Refactor name matching analysis in app.py to ensure execution only occurs when 'origin_portfolio' is absent. This update improves the accuracy of player name matching and enhances session state management for portfolio and projections, ensuring proper data handling during user interactions.

ping

Update session state condition in app.py to prevent analysis execution when 'origin_portfolio' is present. This change enhances the accuracy of the name matching analysis by ensuring proper data handling based on user interactions.

Enhance data handling in load_ss_file.py by replacing zeros with NaN and dropping rows with any NaN values. This improves data integrity for CSV and Excel file imports.

Update player name lists in app.py to include additional entries for MLB, enhancing accuracy in player identification and lineup generation.

need matplotlib

Refactor import statements across multiple files to replace 'fuzzywuzzy' with 'rapidfuzz' for improved performance and consistency in string matching functionality. Additionally, clean up unused imports in app.py and related global functions to enhance code clarity and maintainability.

Refine session state condition in app.py to ensure player summary assignment only occurs when the display source is 'Portfolio'. This change enhances the accuracy of player statistics management during user interactions.

Add session state management for 'settings_base' in app.py to improve portfolio handling during user interactions. This change ensures the correct initialization of session state when working with the portfolio, enhancing overall user experience.

Refactor session state management in app.py to enhance portfolio reset functionality and streamline player summary assignment. Introduce logic to toggle 'settings_base' state during form submissions, ensuring accurate handling of player exposures and improving user experience.

Update session state key in app.py from 'origin_player_exposures' to 'player_summary' to improve player statistics management and ensure accurate data handling during lineup generation.

Update player summary management in app.py to ensure accurate session state handling. On portfolio reset, copy player exposures to maintain consistency. Refactor player summary assignment logic to streamline data flow and enhance user experience.

Refactor player statistics calculation in app.py to streamline logic for 'Showdown' and 'Classic' formats. Introduce session state management for player exposures when the source is 'Portfolio', enhancing lineup generation accuracy and user experience.

Update hedging_preset function to double lineup_target when list_size is 5, enhancing lineup generation accuracy by adjusting target values based on portfolio size.

Enhance volatility_preset function to include sport-specific logic for MLB, allowing dynamic adjustments in player selection based on team diversity. This update improves lineup generation accuracy by refining the handling of team constraints and optimizing the portfolio based on lineup edge.

Implement sport-specific logic in small_field_preset for MLB, enhancing lineup management by dynamically adjusting player selection based on 'Weighted Own' values. This update improves the function's ability to handle team-specific constraints and ensures more accurate lineup generation.

Update hedging_preset function to include 'sport' parameter in small_field_preset and large_field_preset calls for enhanced lineup management flexibility. This change aligns with recent updates to improve sport-specific logic in lineup strategies.

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.

Update player name lists in app.py to include 'Joseph Cantillo' and 'Joey Cantillo' for improved accuracy in lineup management. This change ensures correct player identification in the MLB context.

Remove 'Reduce Volatility' option from preset selection in app.py to streamline user choices. This change simplifies the interface for lineup management, focusing on the most relevant presets for users.

Rename function 'volatility_preset' to 'reduce_volatility_preset' for consistency with updated functionality. This change clarifies the purpose of the function in managing portfolio volatility.

Add 'Reduce Volatility' preset option in app.py and implement reduce_volatility_preset function. This update enhances user options for lineup management by allowing users to manage volatility in their selections, improving overall portfolio strategy.

Similarity Score > Diversity

Update sorting variable options in app.py to include 'Own' and 'Weighted Own' for enhanced user selection. This change improves the flexibility of variable sorting, allowing for more tailored data analysis.

Adjust 'Finish_percentile' calculation in predict_dupes.py to halve the result for improved accuracy. This change refines the formula, ensuring more precise percentile outcomes by modifying the final output of the calculation.

Refine 'Finish_percentile' calculation in predict_dupes.py by adjusting the denominator to improve accuracy. This change enhances the formula by using a fixed multiplier for median values, ensuring more precise percentile results.

Update calculation for 'Finish_percentile' in predict_dupes.py to use Contest_Size for improved accuracy. This change enhances the formula by incorporating Contest_Size into the denominator, ensuring more precise percentile calculations.

Enhance column configuration in app.py by adding 'Similarity Score' for projected lineup diversity. This update includes help text, width specifications, and value constraints to improve user interaction and clarity.

Refactor column configuration in app.py by removing unnecessary dictionary structure for 'Lineup Edge'. This change streamlines the code and enhances readability while maintaining functionality.

Add column configurations for 'Finish_percentile' and 'Lineup Edge' in app.py to enhance DataFrame display. This update includes help text, width specifications, and value constraints for improved user interaction and clarity.

Update portfolio reset functionality in app.py to restore working frame from base frame. This change ensures that the portfolio resets correctly by copying the base frame, enhancing the user experience during portfolio management.

Refactor sidebar and container layout in app.py to improve user interface. Moved trimming options to the sidebar and adjusted the export section to use a container with a border. Updated DataFrame display settings for better visibility and reduced height.

Refactor lineup generation logic in hedging_preset.py to improve processing efficiency. Introduced an initialization counter for lineup adjustments and added checks for empty DataFrames before generating lineups. Enhanced debug messages for better visibility into the lineup concatenation process.

Update lineup generation logic in hedging_preset.py to adjust lineup target calculations. Changed divisor from 3 to 4 in small_field_preset and large_field_preset functions to improve accuracy in lineup adjustments.

Update volatility_preset function to use 'Similarity Score' for row filtering instead of 'median'. This change improves the accuracy of lineup adjustments by ensuring the correct comparison for ownership type maximums.

Fix condition in volatility_preset function to correctly identify rows to drop based on median values. This change ensures accurate lineup adjustments by comparing current ownership type maximums with median values.

Add volatility preset option in app.py and implement volatility_preset function. Updated lineup generation logic to include new preset for enhanced lineup edge management.

Update preset options in app.py to improve lineup generation accuracy. Changed 'Large Field (Finish Percentile / Edge)' to 'Large Field (Manage Similarity)' and adjusted corresponding logic in the form submission process.

Enhance lineup processing in hedging_preset.py by adding checks for empty DataFrames before generating lineups. This update prevents errors when no lineups are available and improves the clarity of debug messages related to lineup generation.

Refactor lineup selection logic in large_field_preset.py to improve accuracy and efficiency. Replaced the previous iterative approach with a method that calculates evenly spaced target similarity scores, ensuring a more precise selection of lineups based on similarity while avoiding duplicates.

Enhance lineup generation in hedging_preset.py by adding debug print statements for removed and team lineups. This update improves visibility into the lineup processing and ensures all relevant lineups are concatenated for final output.

Add debug print statements in hedging_preset.py to output the count of removed and locked lineups during processing. This change enhances visibility into lineup generation and aids in debugging the lineup filtering logic.

Add team lineup filtering in hedging_preset.py to enhance lineup generation. Introduced logic to separate teams and calculate removed lineups, improving the accuracy of team-based strategies.

Update sorting and ownership calculations in large_field_preset.py to use 'median' instead of 'Finish_percentile' and 'Own'. This change improves accuracy in lineup generation by aligning calculations with the updated data structure.

Remove unused import statement from hedging_preset.py to clean up the code and improve maintainability. This change enhances clarity by eliminating unnecessary dependencies.

Add team ownership calculation in hedging_preset.py to enhance lineup generation. Introduced a new function to sum hitter ownership by team and adjusted lineup target calculations for improved accuracy. This change allows for better team-based lineup strategies.

Adjust lineup target calculations in hedging_preset.py to divide by double the list size, improving accuracy in lineup generation. This change enhances the filtering process for both removed and locked lineups.