Fix indentation in exposure_spread function to ensure proper flow control during player replacement, enhancing the logic for lineup adjustments.

Refactor exposure_spread function to adjust the iteration order for player replacements, ensuring that all player rows are processed before making changes, which enhances the logic for lineup adjustments.

Refactor player location retrieval in exposure_spread function to iterate over columns for locating exposure_player, enhancing accuracy in player replacement logic.

Add debug print statements in exposure_spread function to log player rows and working frame, enhancing troubleshooting capabilities during player replacement process.

Refactor exposure_spread function to iterate over player rows for replacements, enhancing the logic for player adjustments and ensuring all relevant lineups are updated with comparable players.

Add debug print statement in exposure_spread function to log player assignments, aiding in troubleshooting during player replacement process.

Refactor exposure_spread function to simplify player assignment by using the .at method for direct access to player locations, enhancing code readability and performance.

Enhance exposure_spread function to assign the selected comparable player to both the player location and a new 'player_change' column, improving tracking of player adjustments in lineups.

Refactor player location retrieval in exposure_spread function to use list index method, improving clarity and ensuring accurate player replacement from the comparable player list.

Refactor exposure_spread function to improve player replacement logic by directly accessing player locations using column indices, enhancing code clarity and performance.

Update exposure_spread function to ensure the number of lineups to remove is rounded up using math.ceil, enhancing the accuracy of player replacement from the comparable player list.

Refactor exposure_spread function to iterate over the number of lineups to remove, ensuring correct player replacement from the comparable player list. This change improves the logic for player adjustments in lineups.

Add debug print statements in exposure_spread function to log player rows and locations for improved troubleshooting during player replacement process.

Update exposure_spread function to utilize numpy for player location identification, enhancing accuracy in lineup adjustments by ensuring the correct player is replaced with a comparable player from the list.

Refactor exposure_spread function to access salary and median values directly from the first row of comparable players, improving clarity and ensuring correct data retrieval for player comparisons.

Refactor exposure_spread function to use median projections instead of raw projections for better accuracy in player comparisons. Update comparable player list to reference 'player_names' for consistency.

Add exposure management feature in app.py and implement exposure_spread function

Add stratification functionality and enhance portfolio handling in app.py

Refactor predict_dupes.py to implement vectorized calculations for ownership and similarity scores, improving performance. Introduce new functions for weighted ownership and player similarity, while maintaining backward compatibility. Update data type handling for portfolio results to optimize memory usage.

Increase the number of top combos analyzed from 50 to 100 in the analyze_player_combos function, enhancing the depth of player combination statistics available for analysis.

Rename analyze_player_combinations function to analyze_player_combos for consistency and clarity in the player combination analysis module.

Add player combination analysis feature in app.py and implement analyze_player_combos function. This update introduces a new tab for analyzing player combinations, allowing users to select combo sizes and view statistics on the most common player pairings or triplets, enhancing the analytical capabilities of the application.

Remove NASCAR table display from app.py to streamline portfolio presentation and improve user experience. Update name mapping logic in load_dk_fd_file.py to enhance ID handling and ensure cleaner data output.

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.

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.

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.

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

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.

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.