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.

ping

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.

ping

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.

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.

fixed exposure_player default var