restructuring of own_rank mapping

Add FLEX5 ownership calculations in predict_dupes.py to enhance ownership metrics and improve portfolio analysis.

Refactor predict_dupes.py to remove FLEX5 ownership calculations, simplifying the ownership metrics and improving code clarity.

Add debug print statement for flex rank in predict_dupes.py to enhance troubleshooting capabilities.

Add debug print statement for flex ownerships in predict_dupes.py to assist with troubleshooting

print some debug for showdown

Refactor own_product calculation in predict_dupes.py to remove Contest_Size influence, streamlining the computation of player ownership metrics.

Update dupes calculation in predict_dupes.py by modifying the divisor in the dupes formula from 90 to 120, improving the accuracy of duplicate predictions based on player ownership and contest size.

Refactor own_product calculation in predict_dupes.py to remove Contest_Size dependency, simplifying the computation of player ownership metrics.

Enhance predict_dupes function by adding max_salary parameter for improved salary handling and adjusting sample range slider step in app.py for better user experience.

adjusted own scaling

cleaning up salary subtraction

Update dupes calculation in predict_dupes.py by adjusting salary thresholds from 59700 and 49700 to 60000 and 50000 respectively, enhancing the accuracy of duplicate predictions across different contest sizes.

Refine dupes calculation in predict_dupes.py by updating salary thresholds from 60000 and 50000 to 59700 and 49700 respectively, improving accuracy in duplicate predictions across various contest sizes.

more contest size reductions

REDUCING cONTEST sIZE VARIABLE

ADJUSTED DUPE COUNTS

Update predict_dupes.py functionality

Sort portfolio by 'Dupes' before displaying the top 10 entries in predict_dupes.py to enhance visibility of duplicate player selections.

printing table of predict_dupes

Comment out unnecessary column drops in predict_dupes.py to retain data for potential future use, enhancing flexibility in the duplicate prediction process.

ping

Add 'top_x_presence' calculation to predict_dupes.py for enhanced duplicate prediction accuracy. This new metric counts the presence of top ownership players in the portfolio, refining the dupes calculation by incorporating player ownership dynamics across various contest types.

Update 'own_rank_percentile' calculation in predict_dupes.py to use 'Own' column instead of duplicate count columns. This change enhances the accuracy of the duplicate prediction logic by directly reflecting player ownership rankings in the calculations.

Enhance duplicate prediction logic in predict_dupes.py by introducing 'own_rank_percentile' calculation. This addition improves the accuracy of the dupes calculation by factoring in player ownership rankings, ensuring more precise predictions across different contest types.

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