Add team filter and quick fill options in app.py to enhance lineup management. Introduced UI elements for auto-filling players based on selected team and size, improving user experience and efficiency in lineup building.

Refactor lineup display in app.py by removing '2x%' column from background gradient styling, enhancing clarity in player data presentation and maintaining focus on key statistics.

Remove '2x%' column from display in Handbuilder tab of app.py to streamline player data presentation and enhance clarity in lineup management.

Remove specific CSS styling for text input height in app.py to streamline the user interface and maintain consistency across components.

Adjust CSS styling in app.py to set a specific height for text input fields within the Handbuilder tab, enhancing the visual consistency of the user interface.

Refactor lineup display logic in app.py to ensure that the player data is only processed when the filled lineup is not empty. This change enhances the efficiency of the display logic and maintains clarity in the representation of player statistics.

Update Handbuilder lineup DataFrame in app.py to include '2x%' column, enhancing player data management and display. Adjust related logic for player selection and summary calculations to ensure accurate representation of player statistics, improving user experience and clarity.

Update subheader in Handbuilder tab of app.py to "Lineup Build" for improved clarity and context in lineup management.

Update Handbuilder tab in app.py to replace empty string values with NaN for 'Order', 'Salary', 'Median', and 'Own%' columns in the lineup DataFrame. Adjust background gradient logic for improved visual representation of player data, enhancing clarity and user experience.

Enhance DataFrame display in Handbuilder tab of app.py by applying background gradients to player selection and lineup tables. This improves visual clarity and user experience when reviewing player data.

Implement single-row selection for lineup removal in Handbuilder tab of app.py, allowing users to easily remove players from the lineup. Update DataFrame display logic to enhance user interaction and streamline lineup management.

Comment out player eligibility filtering in Handbuilder tab of app.py to temporarily disable player selection logic, allowing for further testing and adjustments without affecting current functionality.

Update Handbuilder lineup DataFrame in app.py to include 'Order' column for improved player management and display. Adjust related logic to ensure consistent handling of player data across the application, enhancing clarity and user experience.

Enhance lineup display in Handbuilder tab of app.py by implementing an explicit order for player slots. Update DataFrame to show filled and unfilled slots clearly, and adjust summary calculations to ensure accurate representation of total salary, median, and ownership percentage. This improves user experience and data clarity.

Refactor player selection logic in Handbuilder tab of app.py to improve eligibility checks based on position limits. Update the DataFrame to include a 'Slot' column for better lineup management and ensure players are only added to available positions, enhancing user experience and data integrity.

Add 'Position' column to summary row in Handbuilder tab of app.py to provide a comprehensive overview of player data, enhancing clarity and user experience.

Implement position limits and filtering logic in Handbuilder tab of app.py to manage player selection more effectively. This update enhances lineup management by preventing selection of players whose positions have reached their maximum count, improving user experience.

Add position limits and filtering for player selection in Handbuilder tab of app.py. Implement logic to exclude players whose positions have reached their maximum count, enhancing lineup management and user experience.

Refine player selection DataFrame in Handbuilder tab of app.py by adding duplicate removal based on 'Player' and 'Team', ensuring data integrity and enhancing user experience.

Update player selection DataFrame in Handbuilder tab of app.py to remove duplicate entries based on 'Player' and 'Team', enhancing data integrity and improving user experience.

Sort player selection DataFrame by 'Order' in Handbuilder tab of app.py to enhance display clarity and ensure consistent ordering of players based on their designated order, improving user experience.

Add session state management for player selection in Handbuilder tab of app.py by introducing a unique key to reset selection upon player choice. This enhancement improves user experience by ensuring accurate player selection and preventing stale selections during interactions.

Implement lineup management in Handbuilder tab of app.py by introducing a session state for the lineup, allowing players to be added without duplicates. Update summary calculations to reflect the current lineup and add a "Clear Lineup" button for user convenience, enhancing overall functionality and user experience.

Refactor player selection handling in Handbuilder tab of app.py by removing unnecessary session state variables and simplifying the selection logic. Update the display of selected players to directly reflect current selections, enhancing clarity and user experience.

Enhance player selection state management in Handbuilder tab of app.py by initializing and maintaining a session state for selected indices. Implement logic to handle out-of-range selections and update the display based on current selections, improving user experience and selection accuracy.

Refactor player selection display in Handbuilder tab of app.py to utilize a dataframe for selected players, improving selection accuracy and user experience. Update summary row calculation to align with new selection method, ensuring accurate display of total salary, median, and ownership percentage.

Refactor player selection logic in Handbuilder tab of app.py to initialize session state DataFrame for player selections, ensuring all players are included when teams change. Update filtering mechanism to improve selection accuracy and user experience.

Add summary row to player selection display in Handbuilder tab of app.py, calculating total salary, median, and ownership percentage, and displaying the most common team for improved data insight and user experience.

Adjust team filter UI in Handbuilder tab of app.py to spread toggle switches across fewer columns, improving layout and user experience.

Add team filter UI in Handbuilder tab of app.py to allow users to toggle team selections, enhancing player selection flexibility and improving user experience.

Enhance "Clear Data" button functionality in Handbuilder tab of app.py by introducing a session state variable to control the key, ensuring a proper reset of the player selection editor on each rerun, thereby improving user interactivity and experience.

Refactor "Clear Data" button functionality in Handbuilder tab of app.py to ensure proper data reset on rerun, enhancing user interactivity and maintaining a clean state for player selections.

Update "Clear Data" button functionality in Handbuilder tab of app.py to trigger a rerun, improving data refresh behavior and enhancing user interactivity.

Refactor player selection logic in Handbuilder tab of app.py by removing the "Clear All Selections" button and simplifying the state management. Introduce a "Clear Data" button to reset selections, enhancing user experience and interactivity.

Implement "Clear All Selections" button in Handbuilder tab of app.py to reset player selections, enhancing user interactivity. Update session state management to preserve user selections across reruns, improving overall functionality.

Enhance Handbuilder tab in app.py by increasing the height of the player selection display for better visibility and adding a "Clear Data" button to reset player selections, improving user experience and interactivity.

Update player selection display in Handbuilder tab of app.py to remove unnecessary columns from the DataFrame, enhancing clarity and maintaining the overall functionality of the lineup presentation.

Refactor selected players DataFrame in Handbuilder tab of app.py to simplify the structure by removing unnecessary columns, enhancing clarity in player selection display.

Refactor lineup display in Handbuilder tab of app.py to use a separate DataFrame for selected players, enhancing clarity and maintaining functionality in the user interface.

Update player selection DataFrame in Handbuilder tab of app.py to include 'Order' and 'Hand' columns, and adjust lineup display settings for improved user experience and clarity.

Refactor player selection logic in Handbuilder tab of app.py to streamline DataFrame handling and improve user experience by ensuring fresh data on each rerun, while maintaining lineup display functionality.

Refactor player selection handling in Handbuilder tab of app.py to improve session state management and enhance user experience by separating lineup display and player selection editor.

Add Handbuilder tab in app.py to allow user selection of players for lineup creation, enhancing user interactivity and lineup management capabilities.

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Add Pitcher_Info integration in app.py to enhance player data processing by including LHH and RHH attributes, improving data analysis capabilities for hitters.

Enhance player ROO DataFrame styling in app.py by expanding the background gradient subset to include 'Small Field Own%', 'Large Field Own%', and 'Cash Own%' for improved visual representation and clarity.

Refactor background gradient styling in app.py for scoring percentages and player ROO DataFrames to enhance visual clarity by including 'Salary' and 'Own%' in the gradient subset.

Update scoring percentages DataFrame in app.py to include 'Order' in the background gradient styling for improved visual clarity and data representation.

Update 'Order' and 'Hand' column handling in app.py to replace NaN with 0 for 'Order' and default 'Hand' to 'Pitcher' for improved data consistency and clarity in player data processing for hitters.

Update 'Order' column handling in app.py to replace empty strings with NaN for improved data integrity, and ensure proper type conversion for consistency in player data processing.