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IPL auction modeled as an optimization problem. We aim to maximize the overall performance of the team within a given budget.

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DyuthiVivek/IPL-auction-optimization-model

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Optimization-project

Instructions to run code

Generating the files to be used by the Optimizer

Input files:

  1. deliveries.csv: ball-by-ball IPL complete data
  2. IPLPLayerAuctionData.csv: auction data
  3. retained_players.csv: players who played in RCB team, 2021

player_performance.ipynb calculates the player performance and merges it with auction data to produce 2 files: final.csv and retained_players_final.csv.

  1. final.csv: Player name, Role, Amount, Player Origin, Average batting performance index, Average bowling performance index of players in the auction pool in 2022.
  2. retained_players_final.csv: Player, Player Origin, Role, Average batting performance index, Average bowling performance index, Capped status for the players in the team of RCB 2021 (they can be retained)

Running the Optimizer

  1. Run solution_general.py for the list of players to be chosen for a team starting from scratch, assuming no players will be retained.
  2. Run solution_rcb.py for the list of new players from the auction as well as the list of retained players to be chosen for RCB.

In both cases, a file called player_names.txt is generated containing the list of players to be chosen.

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IPL auction modeled as an optimization problem. We aim to maximize the overall performance of the team within a given budget.

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