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

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