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

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py