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.

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.

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.

Similarity Score > Diversity

Adjust 'Finish_percentile' calculation in predict_dupes.py to halve the result for improved accuracy. This change refines the formula, ensuring more precise percentile outcomes by modifying the final output of the calculation.

Refine 'Finish_percentile' calculation in predict_dupes.py by adjusting the denominator to improve accuracy. This change enhances the formula by using a fixed multiplier for median values, ensuring more precise percentile results.

Update calculation for 'Finish_percentile' in predict_dupes.py to use Contest_Size for improved accuracy. This change enhances the formula by incorporating Contest_Size into the denominator, ensuring more precise percentile calculations.

Optimize player similarity score calculation in predict_dupes function

Refactor player similarity score calculation in predict_dupes function

Refactor player similarity score calculation in predict_dupes function

Enhance player selection handling in predict_dupes function

Add player similarity score calculation to predict_dupes function

Update salary input limits in app.py: increase the maximum acceptable salary from 60,000 to 100,000 to accommodate a wider range of salary options for users. Adjust conditional checks in predict_dupes.py to use 'elif' for improved clarity in type_var evaluations.

Update weighted ownership calculation in predict_dupes.py: adjust the multiplication factor for ownership values and change the return value to a larger scale, enhancing the accuracy of percentage representation in ownership metrics.

Refactor calculate_weighted_ownership function in predict_dupes.py: remove debug print statements, adjust ownership value calculations to handle percentages, and ensure the return value is in percentage form, improving clarity and accuracy of ownership metrics.

Add debug statements in calculate_weighted_ownership function: include print statements to trace input values and intermediate calculations, aiding in the debugging process for ownership metrics.

Add weighted ownership calculation in predict_dupes.py: implement a new function to calculate weighted ownership values, enhancing the accuracy of ownership metrics in portfolio analysis.

Update ownership nerf logic in predict_dupes.py: implement sport-specific adjustments for CS2, allowing for differentiated ownership ratio calculations based on sport type, enhancing accuracy in duplication predictions.

Enhance ownership calculations in predict_dupes.py: add support for an additional FLEX5 ownership column, improving the accuracy of duplication predictions for CS2 portfolios and ensuring comprehensive analysis of player ownership metrics.

Refactor ownership calculations in predict_dupes.py: remove redundant ownership columns for CS2, streamlining the duplication prediction logic and enhancing performance in portfolio analysis.

Refactor portfolio duplication prediction in predict_dupes.py: enhance the function to accommodate sport-specific logic for CS2, improving ownership calculations and ensuring accurate duplication metrics based on player ownership and salary. Update app.py to pass sport_var to predict_dupes for better context in calculations.

Fix typo in geometric mean calculation in predict_dupes: rename 'Goemean' to 'Geomean' for consistency and clarity in portfolio analysis.

Add geometric mean calculation in predict_dupes: introduce a new column 'Goemean' to compute the geometric mean of ownership percentages, enhancing portfolio analysis capabilities.

Enhance trimming options in app.py: add informational prompt for filtering before trimming and update portfolio copy logic to ensure correct data handling during download, improving user guidance and functionality.

Update 'Weighted Own' calculation in predict_dupes.py: adjust formula to account for the difference between maximum and minimum ownership values for improved accuracy in portfolio analysis.

Enhance portfolio reset functionality in app.py: implement comprehensive filtering options for salary, finish percentile, and player selections, and update predict_dupes function to calculate 'Weighted Own' for improved accuracy in portfolio management.

Enhance portfolio trimming logic in app.py: add comprehensive filtering options based on salary, finish percentile, and player selections, and update predict_dupes function to adjust 'Product Own' calculation for improved accuracy.

Remove unused import of gmean from app.py and predict_dupes.py for cleaner code and improved performance.

Update trimming options in app.py: replace 'Geomean' with 'Product Own' for portfolio calculations, and ensure trimming logic is executed only upon form submission for improved user experience.