Remove unnecessary break statement in exposure_spread function to enhance lineup modification logic and improve code clarity.

Refactor exposure_spread function to streamline lineup modification logic, ensuring that changes are applied correctly based on the current change counter and improving overall code clarity.

Refactor exposure_spread function to improve list comparison for comparable_player_list, ensuring clarity and consistency in checks for non-empty lists.

fixing exposure management

recent changes

updating for recent changes

Enhance Lineup Edge calculation in predict_dupes.py by normalizing the edge values based on the maximum and minimum edges. This adjustment improves the scaling of the Lineup Edge, leading to more accurate portfolio predictions.

ping

Correct Lineup Edge calculation in predict_dupes.py by changing the adjustment from addition to subtraction based on duplicate ownership. This modification refines the edge computation, improving the accuracy of portfolio predictions.

Update Lineup Edge calculation in predict_dupes.py to include a normalized adjustment based on duplicate ownership. This change improves the accuracy of the edge computation by factoring in the average number of duplicates, enhancing overall portfolio predictions.

Refactor similarity score calculation in predict_dupes.py by removing the reference to duplicate adjustments. This change clarifies the computation of player selection diversity while preserving the overall prediction model's integrity.

Refactor Lineup Edge calculation in predict_dupes.py by removing the duplicate adjustment and simplifying the formula. This change enhances clarity in the computation while maintaining the integrity of the prediction model.

ping

Refactor Lineup Edge calculation in predict_dupes.py to incorporate both raw edge and adjusted edge values. This change enhances the accuracy of the prediction model by averaging the adjusted edge with the raw edge, improving overall portfolio predictions.

Adjust Lineup Edge_Raw calculation in predict_dupes.py to include a constant offset, enhancing the accuracy of the edge computation for portfolio predictions.

Refactor Lineup Edge calculation in predict_dupes.py to utilize the raw edge value for duplicate adjustment. This change clarifies the computation and maintains the integrity of the prediction model.

Update Lineup Edge calculation in predict_dupes.py to separate raw edge computation. Adjusted the formula to remove the scaling based on duplicates, enhancing clarity and maintaining prediction accuracy.

ping

Refine Lineup Edge calculation in predict_dupes.py to adjust the edge based on the mean of duplicates. This update improves the accuracy of the prediction model by incorporating a scaling factor related to the average number of duplicates.

Refine Lineup Edge calculation in predict_dupes.py to average the adjusted edge based on the number of duplicates. This update enhances the prediction model's accuracy and performance.

diverting

Refine Lineup Edge calculation in predict_dupes.py to improve accuracy. Adjusted the formula to incorporate a divisor of (Dupes + 1) and modified the scaling factor, enhancing the prediction model's performance.

ping

Refine Lineup Edge calculation in predict_dupes.py to enhance accuracy. Updated the formula to adjust the edge based on the number of duplicates, improving the prediction model's performance.

Refactor Lineup Edge calculation in predict_dupes.py to improve accuracy. Updated the formula to average the adjusted edge based on the number of duplicates, enhancing the overall performance of the prediction model.

Implement vectorized calculations for salary, median, and ownership in app.py to enhance performance and memory efficiency. Refactor reassess_edge and stratification_function to minimize DataFrame copies and improve memory management. Update filtering logic to use boolean masks for better efficiency.

Add memory-efficient ranking calculation in predict_dupes.py

Update diversity score calculation to use chunked processing for improved performance in predict_dupes.py

Implement chunked processing for player similarity score calculation to enhance memory efficiency in predict_dupes.py

removed prints

finalized dupe math, maintaining on staging

another small adj

SMALL adj

Update dupes calculation in 'predict_dupes.py' by changing the denominator in the scaling factor for CPT_Own_percent_rank from 100 to 90, aiming to further enhance prediction accuracy.

Adjust dupes calculation in 'predict_dupes.py' by modifying the salary scaling factor from 0.96 to 0.98, aiming to enhance prediction accuracy.

ping

contintuing with dupe prediction adj

Remove table display in 'app.py' and adjust dupes calculation in 'predict_dupes.py' to include a new scaling factor for CPT_Own_percent_rank, enhancing prediction accuracy.

ping

Update dupes calculation in 'predict_dupes.py' to adjust the scaling factor for CPT_Own_percent_rank, enhancing the precision of predictions.

Refine dupes calculation in 'predict_dupes.py' by incorporating CPT_Own_percent_rank for improved accuracy in predictions.

printing a check frame

printing check frame

debugging

ping

continuing to adjust dupe predictions

continuing to adjust dupe predictions

Update dupes calculation formula in 'predict_dupes.py' to use salary for scaling instead of max_salary, improving accuracy of predictions.

adjusting dupe prediction formula

Add debug print statements in 'predict_dupes.py' to log key variables during calculation, aiding in the validation of the dupes calculation process.