Refactor Player Data Initialization with Consistent Projection Scaling

Align Assist Projection Scaling with Kill Projection Method

Modify Kill Scaling Calculation to Remove Minimum Bound

Add Debug Print Statements for Kill, Death, and Assist Scaling

Update Player Stats Column Names in Team Data Initialization

Simplify Kill, Death, and Assist Projection Scaling

Improve Kill, Death, and Assist Projection Scaling

Revert Assist Projection to Weighted Assist Percentage Calculation

Fix Assist Projection Calculation with Scaled Base Assists

Refine Assist Projection Calculation with Simplified Base Assist Approach

Modify Assist Projection to Use Weighted Assist Percentage

Remove assist projection scaling in player data initialization

Refactor assist projection calculation in player data initialization

Reorder tabs and adjust Gamelogs display column positioning

Enhance Gamelogs filtering with player toggle and improved display logic

Add player and scenario filtering to Gamelogs display

Ensure Fantasy column is converted to float and rounded to two decimal places

Fix column name reference in Gamelogs display filtering

Update Gamelogs display with additional performance metrics and refined column presentation

Enhance Gamelogs display with sorting and column selection

Add Gamelogs tab to display individual game data in app.py

Refactor database connection and data initialization in app.py

Enhance app.py to include additional display formats for probability percentages. Updated the 'Over %' and 'Under %' calculations to round to two decimal places and modified the display logic to utilize the new formatting. This change improves clarity and consistency in the displayed data during simulations.

Refactor app.py to improve conditional rendering of session state data. Updated checks to ensure player summaries, overall simulations, individual game data, and opponent boosts are displayed only when the corresponding data is available. This change enhances user experience by preventing errors and improving clarity during simulations.

Refactor app.py to improve conditional checks for session state data. Updated rendering logic to ensure that player summaries, overall simulations, individual game data, and opponent boosts are only displayed when the corresponding data is available. This change enhances user experience by preventing errors and improving clarity during simulations.

Refactor app.py to improve data display and conditional rendering in simulation tabs. Added checks for session state data before rendering player summaries, overall simulations, individual game data, and opponent boosts. This change enhances user experience by ensuring that only available data is displayed, improving clarity and interaction during simulations.

Refactor app.py to streamline player data processing by utilizing session state for game data and cleaning player names. This change enhances data management and ensures consistent access to player statistics, improving overall performance during simulations.

Refactor app.py to utilize session state for storing team and player data during simulations. This change enhances data management by ensuring consistent access to initialized data across different parts of the application, improving overall performance and user experience.

Refactor display of probability percentages in app.py to remove percentage sign from 'Over %' and 'Under %' columns. This change improves data formatting for better clarity in the displayed dataframe during simulations.

Add Prop Check feature to app.py for statistical analysis of player performance. Users can input a prop value and select a statistic (Kills, Deaths, Assists, CS) to calculate and display the probabilities of exceeding or falling below the specified prop value. This enhancement improves user interaction and provides deeper insights into player statistics during simulations.

Update team data processing in init_player_data function to convert unique team names to a list. This change improves compatibility with subsequent database queries, enhancing the accuracy of team statistics retrieval during simulations.

Enhance team data processing in init_player_data function by adding retrieval of team statistics. Introduced a new query to fetch team data based on unique team names, improving the accuracy of team performance metrics. Updated the logic to conditionally apply team statistics calculations, ensuring proper integration with player data. This change enhances the overall integrity of performance metrics during simulations.

Refactor team data retrieval in init_player_data function to filter by 'position' instead of 'teamname'. This change improves data accuracy for team statistics calculations, enhancing the integrity of player performance metrics during simulations.

Refactor team data retrieval in init_player_data function to use 'teamname' for filtering. Removed unnecessary st.write() calls for player and team data display to streamline output. This change enhances data integrity and improves the clarity of the application during simulations.

Enhance user feedback by displaying initialized player and team data in app.py. Added st.write() to show player tables and removed redundant team data display, improving visibility of loaded data for better understanding and debugging during simulations.

Add display of team data in app.py for improved user feedback

Add data display for team and player initialization in app.py

Fix game number indexing in player data initialization to correctly reference current game data. This change ensures accurate retrieval of player statistics by adjusting the conditional check for kill predictions, enhancing the integrity of player performance metrics during simulations.

Refactor assist projection calculations in app.py to improve accuracy by scaling based on total kills instead of kill predictions. This change enhances the integrity of player performance metrics in both team and player data initialization functions, ensuring more reliable projections.

Refactor assist projection calculations in app.py to improve accuracy. Updated the logic for calculating 'Assist_Proj' to scale based on total kills, ensuring projections do not exceed actual performance metrics. This change enhances the integrity of player performance data in both team and player data initialization functions.

Fix game number indexing in player summary generation to correctly reference previous game data. This change ensures accurate retrieval of player statistics by adjusting the conditional check for kill predictions, enhancing the integrity of player summaries during simulations.

Fix game number indexing in player summary generation by removing unnecessary conditional checks. This change simplifies the logic for retrieving game data, ensuring accurate player statistics are generated consistently across simulations.

Fix game number indexing in player summary generation to support data type selection. Updated logic to correctly reference game numbers based on the selected data type ("Team" or "Player"), ensuring accurate retrieval of player statistics. This change enhances data integrity and improves the clarity of player summaries during simulations.

Refactor init_team_data and init_player_data functions in app.py to accept game_count as a parameter. This change improves function clarity and ensures consistent handling of game count across team and player data initialization. Updated function calls in the simulation logic to reflect this modification.

Merge branch 'main' of https://huggingface.co/spaces/Multichem-PD/LOL_Matchups_Model

Refactor assist projection calculations in app.py to ensure they do not exceed total kills. Introduced scaling for assist projections based on raw assist calculations, improving accuracy in player performance metrics. Additionally, added duplicate removal based on 'position' to enhance data integrity in team data initialization.

Update app.py

Update game simulation settings in app.py to allow selection of the number of games (1-5) instead of match format (BO1, BO3, BO5). Adjusted game count extraction logic to reflect the new selection method, improving user experience and clarity in simulation configurations.

Update player data query in app.py to support multiple player names. Changed the query to use the "$in" operator for playername, allowing for more flexible data retrieval across multiple players. This enhancement improves the accuracy of game log data retrieval for player statistics.

Enhance player data handling in app.py by introducing a new data type selection feature. Users can now choose between "Team" and "Player" data types, allowing for more flexible data analysis. Updated the player data initialization function to support player-specific statistics, including average performance metrics based on game outcomes. This change improves the overall functionality and user experience of the application.