Quantifying individual performance in Cricket — A network analysis of batsmen and bowlers

• Published in: Physica A Vol. 393; pp. 624 - 637
• Main Author: Mukherjee, Satyam
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2014
• Subjects: Balancing; Cricket; Crickets; Domestic; Games; Gradient networks; Network analysis; Networks; Scoring; Social network analysis; Social networks; Sports; Cricket; Gradient networks; Social network analysis; Sports
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2013.09.027

Quantifying individual performance in the game of Cricket is critical for team selection in International matches. The number of runs scored by batsmen and wickets taken by bowlers serves as a natural way of quantifying the performance of a cricketer. Traditionally the batsmen and bowlers are rated on their batting or bowling average respectively. However, in a game like Cricket it is always important the manner in which one scores the runs or claims a wicket. Scoring runs against a strong bowling line-up or delivering a brilliant performance against a team with a strong batting line-up deserves more credit. A player’s average is not able to capture this aspect of the game. In this paper we present a refined method to quantify the ‘quality’ of runs scored by a batsman or wickets taken by a bowler. We explore the application of Social Network Analysis (SNA) to rate the players in a team performance. We generate a directed and weighted network of batsmen–bowlers using the player-vs-player information available for Test cricket and ODI cricket. Additionally we generate a network of batsmen and bowlers based on the dismissal record of batsmen in the history of cricket—Test (1877–2011) and ODI (1971–2011). Our results show that M. Muralitharan is the most successful bowler in the history of Cricket. Our approach could potentially be applied in domestic matches to judge a player’s performance which in turn paves the way for a balanced team selection for International matches. •We construct a network of batsmen as well as bowlers in a team sport — Cricket.•Social network analysis on the networks.•Construction of gradient networks.•PageRank Algorithm to evaluate player performance.•Captures the consensus opinions on player’s performance according to ICC ranking.