Refactor predict_dupes function call in app.py to use parsed_frame and salary_max, and add reassess_edge function in reassess_edge.py for recalculating metrics based on differences in salary, median, and ownership.

Update exposure_spread function call in app.py to use export_base instead of working_frame, ensuring correct data handling for exposure calculations.

Update stratification sample range slider in app.py to allow a range input from 0.0 to 100.0, enhancing user control over sample selection.

Add stratification sample range slider to app.py and update stratification_function to accept threshold parameters for improved player selection control.

Add app.py to implement a Streamlit application for player analysis and portfolio management, featuring data loading, name matching, and exposure management functionalities.

Add application.py to implement Streamlit app for player analysis and portfolio management, including data loading, name matching, and exposure management features.

Remove debug print statements from app.py and exposure_spread.py to clean up code and improve performance during exposure evaluations.

Update exposure_player selection in app.py to use unique player names from projections_df, ensuring accurate data representation in exposure management.

Reinstate settings_base assignment in app.py after exposure_player debug print statement to ensure session state integrity during exposure evaluations.

Reinstate settings_base assignment in app.py after exposure_player debug print statement to maintain session state integrity during exposure evaluations.

Add debug print statement for exposure_player in app.py to assist with tracking state during exposure evaluations.

Refactor salary, median, and ownership calculations in app.py to use parsed_frame instead of session state, improving data handling consistency and clarity in exposure evaluations.

Refactor exposure_player handling in app.py to use session state for consistent data access during exposure evaluations.

Update working_frame and export_base assignments in exposure_spread function to ensure consistent data handling and improve clarity in exposure evaluations.

Enhance app.py to include F1 in sport selection and implement position and team variable assignments based on selected sport. Update projections to reflect these variables for GOLF, TENNIS, MMA, NASCAR, and F1, improving data handling for player eligibility.

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.

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

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.

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

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.

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.

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.

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.

Enhance exposure_spread function to include sport-specific position eligibility checks, ensuring accurate player replacements based on their eligible positions across various sports.

fixed exposure_player default var

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

Add stratification functionality and enhance portfolio handling in app.py

Update MLB player name mappings in app.py to include 'Temi Fágbénlé' and 'Temi Fagbenle', improving accuracy in player recognition.

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.

Update player name mappings in app.py for MLB to include 'Jakob Bauers' and 'Jake Bauers', enhancing accuracy in name recognition.

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.

Add table display for NASCAR portfolios in app.py to enhance data visibility for NASCAR users.

Update sport selection in app.py to include 'NASCAR' in the options, enhancing user choice for sports selection.

Change dataframe display from table to interactive dataframe in app.py for enhanced user experience and improved data visualization. This update maintains the existing styled formatting and background gradients.

Enhance dataframe display in app.py by applying styled formatting and column configurations for improved visual representation. This update includes background gradients and custom number columns to better convey statistical data in the user interface.

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.

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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.