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.

Enhance portfolio filtering and trimming options in app.py: introduce expandable sections for filter and trimming options, update portfolio trimming logic to support geomean calculations, and improve export functionality with stack count adjustments.

Add 'Product Own' calculation to portfolio in predict_dupes.py

Initial Commit