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.

ping

Refactor exposure_spread function to include a new parameter for type_var, enabling sport-specific logic for player eligibility checks and enhancing the accuracy of player replacements based on game type.

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

Reset index for comparable players in exposure_spread function to ensure consistent DataFrame structure, improving accuracy in player comparisons.

Update exposure_spread function to correct stack comparison logic by using the 'team' column instead of 'Stack', ensuring accurate filtering of player rows based on stacking criteria.

Add debug print statement to exposure_spread function to log player rows, aiding in troubleshooting and ensuring accurate filtering of player data.

Enhance exposure_spread function to include stack comparison for player rows, ensuring accurate filtering based on stacking criteria, similar to the logic applied for comparable players.

Add stack comparison logic in exposure_spread function to filter out comparable players based on stacking, enhancing the accuracy of player comparisons.

Reset index for comparable players in exposure_spread function to ensure consistent DataFrame structure, improving subsequent calculations for player comparisons.

Add position eligibility check in exposure_spread function to filter comparable players based on overlapping positions, improving accuracy in player comparisons.

Update comp_projection_low calculation in exposure_spread function to use a 75% reduction instead of 90%, refining the logic for comparable player projections.

Implement change counter in exposure_spread function to limit player replacements based on lineups_to_remove, enhancing control over lineup adjustments.

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.