Add FLEX eligibility check in check_golf_position_eligibility function to allow 'G' positions for FLEX columns. This enhances player eligibility logic for golf positions.

Implement error handling in exposure_spread function to ensure robust position extraction. If an error occurs during position parsing, fallback to the original position list, enhancing reliability in player eligibility processing.

Refactor app.py to enhance user interface by updating form button labels to "Portfolio" and "Export" for clarity. Introduce informative messages to guide users on the functionality of each button, improving overall user experience in data submission processes.

Update app.py to correct data assignment by replacing 'working_frame' with 'export_base' for parsed_frame. This change ensures accurate filtering based on duplication and salary criteria, enhancing data integrity in the export process.

Refactor app.py to streamline data handling by removing 'display_frame_source' checks. Update working_frame and export_merge assignments to ensure consistent data processing across functionalities, including filtering, trimming, and exposure management, enhancing overall code clarity and maintainability.

Enhance app.py to differentiate data handling based on 'display_frame_source'. Implement conditional logic for working_frame and export_merge updates in various functionalities, including filtering, trimming, and exposure management, ensuring accurate data processing for both Portfolio and Base sources.

Refactor display frame handling in app.py to use a unified session state variable. Update references to 'display_frame' to ensure consistency and improve data management across various functionalities, including player and stack analysis.

Adjust lineups_to_remove calculation in exposure_spread function to apply a 101% factor, refining accuracy in lineup adjustments based on player exposure and target.

Update default target exposure value in exposure management form from 0.25 to 0.50 for improved user experience.

Add salary, median, and ownership calculations for Classic and Showdown types in exposure_spread function

Adjust lineups_to_remove calculation in exposure_spread function to apply a 105% factor, refining accuracy in lineup adjustments based on player exposure and target.

Enhance exposure_spread function by adding salary_max parameter for improved salary filtering and adjusting lineups_to_remove calculation to a 125% factor, refining player selection logic and ensuring existing players are excluded from comparable options.

Refactor exposure_spread function to streamline comparable player selection logic, ensuring accurate filtering based on salary, median projections, and position overlap, while maintaining team removal functionality.

Refine lineups_to_remove calculation in exposure_spread function to ensure accurate adjustment based on player exposure and target, applying a consistent 150% factor for lineup modifications.

Refactor lineups_to_remove calculation in exposure_spread function to streamline logic, ensuring consistent application of the 150% factor for lineup adjustments.

Adjust lineups_to_remove calculation in exposure_spread function to apply a 150% factor, enhancing accuracy in lineup adjustments for non-zero target exposure.

Refine lineups_to_remove calculation in exposure_spread function to adjust for non-zero target exposure, improving accuracy in lineup adjustments by applying a 75% reduction factor.

Update lineups_to_remove calculation in exposure_spread function to handle zero target exposure and adjust for non-zero targets, improving accuracy in lineup adjustments.

Shuffle player rows in exposure_spread function to ensure random selection of players for lineup adjustments, enhancing variability in exposure calculations.

Refactor exposure_spread function to correctly apply team removal logic to comparable players, ensuring accurate filtering in player comparisons.

Enhance exposure_spread function to include an option for removing specified teams, improving flexibility in player comparison and exposure calculations.

Enhance dupes calculation in predict_dupes function by adjusting the Finish_percentile formula to include ownership ratio and Contest_Size, improving the accuracy of duplicate predictions.

Refactor dupes calculation in predict_dupes function to incorporate ownership ratio adjustment based on Contest_Size, enhancing the accuracy of duplicate predictions. Additionally, modify the calculation to use a salary ratio of 50 for improved precision.

Update dupes calculation in predict_dupes function to multiply ownership product by 10, refining the accuracy of duplicate predictions based on ownership metrics and Contest_Size.

Update dupes calculation in predict_dupes function to multiply ownership product by 100, enhancing the accuracy of duplicate predictions based on ownership metrics and Contest_Size.

Refine dupes calculation in predict_dupes function by adjusting the salary ratio from 100 to 50, enhancing the accuracy of duplicate predictions based on ownership metrics and Contest_Size.

Adjust dupes calculation in predict_dupes function by modifying the salary ratio from 100 to 50, further refining the accuracy of duplicate predictions based on ownership metrics and Contest_Size.

Refine dupes calculation in predict_dupes function by adjusting the formula to prioritize Contest_Size and ownership metrics, enhancing the accuracy of duplicate predictions.

Refine dupes calculation in predict_dupes function by adjusting the salary ratio from 50000 to 49500, improving accuracy in duplicate predictions based on ownership metrics.

Refactor dupes calculation in predict_dupes function to correct ownership ratio adjustments, ensuring accurate predictions based on ownership metrics and Contest_Size. This update enhances the overall accuracy of duplicate predictions.

Update dupes calculation in predict_dupes function to multiply ownership product by 100, improving accuracy in duplicate predictions based on ownership metrics.

Update dupes calculation in predict_dupes function to incorporate ownership ratio adjustment based on Contest_Size, enhancing accuracy in duplicate predictions.

Adjust ownership average calculation in predict_dupes function to a new ratio of 0.33, and add a debug print statement to display key portfolio metrics, enhancing data verification and accuracy in ownership assessments.

Update ownership average calculation in predict_dupes function to reflect a new ratio of 0.66, improving accuracy in ownership metrics for both portfolio and team ownership assessments.

Refactor captain ownership calculation in app.py to simplify logic by removing unnecessary type variable check, ensuring accurate ownership adjustments when captain ownership data is missing.

Remove type conversion for 'Own' column in predict_dupes function and add debug print statement to display the first 10 rows of the working frame, aiding in data verification.

Refactor ownership ratio adjustments in predict_dupes function to streamline logic for CS2 and LOL, ensuring consistent application of nerf values based on sport type, which enhances prediction accuracy.

Update type conversion in predict_dupes function to conditionally apply float32 conversion based on sport variable, enhancing compatibility for non-LOL sports.

Update player column selection in predict_dupes function to include an additional player, ensuring compatibility with the updated League of Legends (LOL) logic for enhanced prediction accuracy.

Add support for 7 players in League of Legends (LOL) in predict_dupes function, updating salary cap and player column logic to accommodate sport-specific requirements, enhancing prediction accuracy.

Refactor type conversion in predict_dupes function to handle exceptions gracefully, ensuring robust data processing without interruption.

Add debug print statements in predict_dupes function to display portfolio columns and the first 10 rows, aiding in troubleshooting and data verification.

Refactor predict_dupes function to enhance League of Legends (LOL) support, adding specific calculations for ownership percentages and ranks, while maintaining existing logic for CS2. This improves accuracy in player predictions and ensures consistent handling of duplicate calculations across both sports.

Enhance predict_dupes function to include League of Legends (LOL) alongside CS2 for sport-specific logic, updating conditions for duplicate count calculations and own ratio nerf adjustments to improve accuracy in player predictions.

Add support for LOL in app.py and related functions, updating sport selection and eligibility checks across multiple modules to include League of Legends, enhancing overall functionality and user experience.

Update exposure selection logic in app.py to conditionally display stack maintenance option based on the presence of 'Stack' column in the working frame, defaulting to 'No' if absent.

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Refactor player statistics calculation in exposure_spread function to handle GOLF sport-specific logic, ensuring accurate lineup counts and averages for players across Captain and FLEX positions.

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Update exposure_spread function call in app.py to include type_var parameter, ensuring sport-specific logic is applied during player eligibility checks and enhancing the accuracy of player replacements.