Refactor dupes calculation in app.py to ensure proper data type handling. Updated the Dupes metric calculation to explicitly cast values to float32, enhancing accuracy in ownership analysis and contest lineup simulations.

Update dupes calculation in app.py to incorporate scaling factor based on strength variable. This change enhances the accuracy of the Dupes metric by adjusting the calculation to reflect contest size dynamics, improving player ownership analysis and contest lineup simulations.

Update contest size input in app.py to allow a broader range of values. Changed the number input for contest size from a fixed placeholder to accept values between 1 and 100,000, enhancing user flexibility in contest configuration.

Refactor ownership calculations in app.py to enhance accuracy for FanDuel and DraftKings. Updated dupes calculation to incorporate a scaling factor for Contest_Size, and streamlined ownership metrics by adjusting relevant columns. This improves the precision of player ownership analysis and contest lineup simulations.

Refactor ownership calculations in app.py to streamline dupes logic for FanDuel and DraftKings. Introduced new columns for 'own_product', 'avg_own_rank', and 'dupes_calc' to enhance clarity and accuracy in ownership metrics. This update improves the precision of player ownership analysis and contest lineup simulations.

Refactor ownership calculations in app.py to improve accuracy and clarity. Adjusted the calculation of ownership products and dupes for both FanDuel and DraftKings by removing unnecessary divisions and ensuring proper grouping in the formulas. This change enhances the precision of player ownership analysis and contest lineup simulations.

Refactor ownership calculations in app.py to use specific mapping for CPT ownership metrics. Updated 'CPT_Own_percent_rank' and 'CPT_Own' to utilize 'cpt_Own_map' for improved accuracy in player ownership analysis. This change enhances the clarity and precision of ownership data for contest simulations.

Update ownership calculations in app.py to divide ownership metrics by 100 for accurate percentage representation. This change enhances the clarity of ownership data for each roster position, improving the overall analysis of player ownership in contest simulations.

Enhance ownership calculations in app.py by adding new columns for ownership metrics at each roster position. Implemented dupes calculation for FanDuel and DraftKings based on these metrics, improving the accuracy of player ownership analysis and contest lineup simulations.

testing

Add ownership percent rank calculations and dupes logic for FanDuel and DraftKings in app.py

Add auxiliary seed frame functions for NFL and NBA in app.py

reverting change for vectorizing names

Standardize player names in app.py to improve data consistency across DraftKings and FanDuel simulations. This change applies the standardization function to the 'Player' column in raw baseline data, enhancing the accuracy of player data handling for contest simulations.

Implement player name standardization in app.py to enhance data consistency across simulations. Added a new function to remove common suffixes from player names and updated relevant mappings to apply this standardization. This change improves the accuracy of player data handling for contest simulations on DraftKings and FanDuel.

Fix ownership calculation for 'Small_Field_Own' in app.py by correcting the reference from 'Large_Own' to 'Small_Own'. This change enhances the accuracy of player ownership projections for small contest sizes, ensuring consistency across ownership metrics.

Refactor raw_display filtering in app.py to streamline player data selection for DraftKings and FanDuel. This change ensures that only relevant player data is processed for each site, enhancing the accuracy of ownership calculations and improving overall clarity in the data handling logic.

Refactor salary mapping in app.py to consistently apply salary calculations to the 'flex_working' DataFrame for both NFL and NBA players across DraftKings and FanDuel. This change enhances the accuracy of player salary projections by ensuring the correct mapping logic is applied uniformly, improving overall clarity in the salary calculation process.

Refactor salary mapping logic in app.py to differentiate between NFL and NBA players for DraftKings and FanDuel. Updated the salary calculation to apply a specific divisor for NFL while maintaining the original mapping for NBA, enhancing the accuracy of player salary projections based on sport selection.

Refactor salary calculation logic in app.py to apply a specific multiplier for NBA players, ensuring accurate salary projections based on sport selection. This change enhances the clarity and correctness of player salary mappings, particularly for NBA contests.

Refactor salary calculations in app.py to differentiate between NFL and NBA for DraftKings. Updated the salary mapping logic to apply a specific divisor for NFL players while maintaining the original mapping for NBA players. This change enhances the accuracy of player salary projections based on the selected sport.

Update app.py to include 'player_id' in ownership calculations, enhancing data accuracy for player projections. This change improves the integrity of the 'raw_display' DataFrame by ensuring all relevant metrics are accounted for, particularly in relation to contest sizes.

Update ownership calculations in app.py to improve accuracy for 'small_CPT_Own' metrics. Adjusted the calculation of 'small_cpt_own_var' from 100 to 200, enhancing consistency in player ownership projections across contest sizes.

Enhance ownership calculations in app.py for 'Small' contest size by adding references to 'Small_Field_Own' and 'small_CPT_Own'. This update improves accuracy in player ownership projections, ensuring consistency across contest sizes.

Fix ownership calculation for 'Small_Field_Own' in app.py by correcting the reference from 'Large_Own' to 'Large_Field_Own'. This change ensures accurate player ownership projections in the context of contest sizes, maintaining consistency across the ownership metrics.

Enhance ownership calculations in app.py by introducing 'Small_Field_Own' and 'small_CPT_Own' metrics. Updated calculations to improve accuracy in player ownership projections for both DraftKings and FanDuel, ensuring consistent handling of contest sizes. This refactor streamlines the logic while maintaining clarity in ownership metrics.

Refactor contest size handling in app.py by removing redundant ownership calculations for 'Small' and 'Large' contest sizes. This streamlines the logic and improves clarity in the ownership metrics without altering the overall functionality.

Trying this calc again

Refactor ownership calculations in app.py to improve accuracy for 'CPT_Own_raw' and 'CPT_Own' metrics. Updated the calculation of 'cpt_own_var' to use 'raw_baselines['Own'].sum()' instead of 'raw_baselines['CPT_Own_raw'].sum()' for better alignment with contest size handling.

Enhance contest size handling in app.py by adding ownership calculations for 'Small' and 'Large' contest sizes. Updated 'CPT_Own_raw' and 'CPT_Own' metrics to improve accuracy in player ownership projections based on selected contest size.

Update 'cpt_Median' calculation in app.py to improve projection accuracy. Adjusted multiplier from 1.5 to 1.25 for both DraftKings and FanDuel dataframes, ensuring more precise ownership metrics in the init_baselines function.

Refactor 'cpt_STDev' to 'CPT_STDev' in app.py for consistency in projection calculations. Updated variable naming in the init_baselines function to enhance clarity and maintain uniformity across ownership metrics.

Experiementing with a high cpt stdev

Update projection calculations in app.py to ensure accurate ownership metrics. Adjusted 'Proj Own' calculations for both 'cpt_working' and 'flex_working' DataFrames by dividing by 100, enhancing the precision of player ownership projections.

Enhance projection calculations by adding 'cpt_Own_map' to mapping logic in app.py. Updated 'Proj Own' calculations for both 'cpt_working' and 'flex_working' DataFrames to utilize the new mapping, improving accuracy in player ownership projections.

Refactor projection logic in sim_contest function to improve clarity and performance. Updated handling of the first column using 'vec_cpt_projection_map' and adjusted the application of 'vec_projection_map' for remaining columns, ensuring consistent simulation calculations.

Fix typo in vectorization of 'cpt_projection_map' in sim_contest function for consistency in simulation calculations.

Added vectorization for 'cpt_Projection_map' in sim_contest function to enhance performance and maintain consistency in simulation calculations.

Enhance player median calculations and projections in init_baselines and sim_contest functions. Added 'cpt_Median' field to raw_display DataFrame for both DraftKings and FanDuel, and updated projection logic to utilize 'cpt_projection_map' for improved accuracy in simulations.

replacing first 7 columns of lineup export with player_ids

maybe not an array in export L471

yeah wasn't an array

referenced the wrong frame, this might break if not an array

aiming to export player IDs

removed use of gspread and transferred to mongo

added some keys to variables

added var for init baselines

Aimed to add NBA as an option for the seed frame source

Update app.py

Update app.py