Novel method for ranking batsmen in Indian Premier League

Abstract

Sports analytics have benefited immensely from the growth and popularity of artificial intelligence and machine learning. These techniques enable sports analysts to evaluate player performance more effectively. A literature review of player performance evaluation methods shows the need to develop a new performance evaluation index for Twenty20 (T20) cricket. A novel framework was proposed to evaluate batsman strength based on individual performance, role in the team, and team interactions. Traditionally, proposed ranking systems are derived from static networks, that is, the aggregation of game results over time. However, the scores of the players (or teams) fluctuate over time. Intuitively, defeating a renowned player during peak performance is more rewarding than defeating the same player during other periods. To account for this, we propose a new method and apply it to the T20 format Indian Premier League. The method serves three main purposes: First, it creates a new performance index for players to rank them more accurately and effectively. Second, the players are clustered based on their expertise. In the third phase, a social network analysis approach is applied to visualize and analyze crickets as a network to gain better insights into players’ team interactions. This novel approach is a helpful index for sports coaches, analysts, cricket fans, and managers to evaluate player performance and rank for future aspects.