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

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  • Mukherjee, Satyam

Abstract

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.

Suggested Citation

  • Mukherjee, Satyam, 2014. "Quantifying individual performance in Cricket — A network analysis of batsmen and bowlers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 624-637.
  • Handle: RePEc:eee:phsmap:v:393:y:2014:i:c:p:624-637
    DOI: 10.1016/j.physa.2013.09.027
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    References listed on IDEAS

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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, September.
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    Cited by:

    1. Calzada-Infante, Laura & Lozano, Sebastián, 2016. "Analysing Olympic Games through dominance networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1215-1230.
    2. Sahadeb Sarkar & Subhasis Mishra & Sanjeev Kumar, 2022. "Development of a Comprehensive Multi-Factor Method for Comparing Batting Performances in One-Day International Cricket," IIM Kozhikode Society & Management Review, , vol. 11(1), pages 92-108, January.
    3. Ma, Yinghong & He, Jiaoyang & Yu, Qinglin, 2019. "Modeling on social popularity and achievement: A case study on table tennis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 235-245.
    4. Petersen, Alexander M. & Penner, Orion, 2020. "Renormalizing individual performance metrics for cultural heritage management of sports records," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    5. Johan Joubert & Sumarie Meintjes, 2015. "Computational considerations in building inter-firm networks," Transportation, Springer, vol. 42(5), pages 857-878, September.

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