Refactor exposure_spread function to handle player replacement logic based on lineups_to_remove value. Separate logic for positive and negative adjustments to ensure accurate player exposure management and enhance lineup flexibility.

Update lineups_to_remove calculation in exposure_spread function to handle cases where exposure_target is zero, ensuring accurate lineup adjustments based on player exposure.

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.

Update position eligibility checks in check_position_eligibility function to correct sport name cases for MMA, Golf, and Tennis. This ensures consistent handling of player positions across different sports.

Add FLEX eligibility check in check_golf_position_eligibility function to allow 'G' positions for FLEX columns. This enhances player eligibility logic for golf positions.

Implement error handling in exposure_spread function to ensure robust position extraction. If an error occurs during position parsing, fallback to the original position list, enhancing reliability in player eligibility processing.

Adjust lineups_to_remove calculation in exposure_spread function to apply a 101% factor, refining accuracy in lineup adjustments based on player exposure and target.

Adjust lineups_to_remove calculation in exposure_spread function to apply a 105% factor, refining accuracy in lineup adjustments based on player exposure and target.

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.

Refactor exposure_spread function to streamline comparable player selection logic, ensuring accurate filtering based on salary, median projections, and position overlap, while maintaining team removal functionality.

Refine lineups_to_remove calculation in exposure_spread function to ensure accurate adjustment based on player exposure and target, applying a consistent 150% factor for lineup modifications.

Refactor lineups_to_remove calculation in exposure_spread function to streamline logic, ensuring consistent application of the 150% factor for lineup adjustments.

Adjust lineups_to_remove calculation in exposure_spread function to apply a 150% factor, enhancing accuracy in lineup adjustments for non-zero target exposure.

Refine lineups_to_remove calculation in exposure_spread function to adjust for non-zero target exposure, improving accuracy in lineup adjustments by applying a 75% reduction factor.

Update lineups_to_remove calculation in exposure_spread function to handle zero target exposure and adjust for non-zero targets, improving accuracy in lineup adjustments.

Shuffle player rows in exposure_spread function to ensure random selection of players for lineup adjustments, enhancing variability in exposure calculations.

Refactor exposure_spread function to correctly apply team removal logic to comparable players, ensuring accurate filtering in player comparisons.

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

Enhance dupes calculation in predict_dupes function by adjusting the Finish_percentile formula to include ownership ratio and Contest_Size, improving the accuracy of duplicate predictions.

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.

Update dupes calculation in predict_dupes function to multiply ownership product by 10, refining the accuracy of duplicate predictions based on ownership metrics and Contest_Size.

Update dupes calculation in predict_dupes function to multiply ownership product by 100, enhancing the accuracy of duplicate predictions based on ownership metrics and Contest_Size.

Refine dupes calculation in predict_dupes function by adjusting the salary ratio from 100 to 50, enhancing the accuracy of duplicate predictions based on ownership metrics and Contest_Size.

Adjust dupes calculation in predict_dupes function by modifying the salary ratio from 100 to 50, further refining the accuracy of duplicate predictions based on ownership metrics and Contest_Size.

Refine dupes calculation in predict_dupes function by adjusting the formula to prioritize Contest_Size and ownership metrics, enhancing the accuracy of duplicate predictions.

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 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.

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.