Cricket team selection using data envelopment analysis

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.