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Cricket team selection using data envelopment analysis.

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This study introduces a novel data envelopment analysis (DEA) method for objective cricket team selection. The approach objectively ranks players based on multiple performance capabilities, aiding in forming efficient teams.

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Area of Science:

  • Sports Analytics
  • Operations Research
  • Data Science

Background:

  • Cricket team selection traditionally relies on subjective assessments.
  • Evaluating players across diverse capabilities presents a complex challenge.
  • Objective, data-driven methodologies are needed for optimal team composition.

Purpose of the Study:

  • To propose a novel data envelopment analysis (DEA) formulation for evaluating cricket players.
  • To objectively rank players based on multiple performance outputs and capabilities.
  • To facilitate efficient cricket team selection using a quantitative approach.

Main Methods:

  • Developed a DEA model to assess cricket players using multiple performance metrics as outputs.
  • Applied the DEA formulation to a dataset of Indian Premier League (IPL 2011) players.
  • Utilized linear programming to aggregate player scores and determine efficiency.

Main Results:

  • Identified efficient and inefficient cricket players based on DEA scores.
  • Generated objective rankings of players across various cricketing capabilities.
  • Demonstrated the application of the method for selecting a balanced cricket team.

Conclusions:

  • The proposed DEA method offers an objective alternative to subjective player evaluations.
  • This approach can be effectively used for selecting national or club-level cricket teams.
  • DEA provides a robust framework for optimizing team selection based on multi-dimensional player performance.