Quantifying team precedence in the game of cricket

Precedence of cricket teams depicts the authority of teams over the counter peers. The existing ad-hoc ranking systems either incorporate the count of won or lost matches, or just consider the winning margins. The batting and bowling productivity at team level along with the reward for each win and penalty against each lost never adopted for extracting the supremacy of teams over the others. The intuition of this paper is to address the aforementioned limitations while presenting an effective mechanism. With this aim, first of all, effective features are explicitly formulated for finding batting and bowling productivity precedence. Subsequently, these features are combined to devise the team productivity metric. Moreover, an efficient productivity precedence algorithm is presented that incorporates the defined features to retrieve the batting, bowling and team precedences in one day international matches. Extensive experiments are performed for this purpose, the results of which show that the presented method renders quite promising insights. Further, the batting, bowling and team evolution is also presented to depict the precedences of different spans. The presented method can be explicitly adopted for cricket team rankings.

[1]  P. Allsopp,et al.  Rating teams and analysing outcomes in one‐day and test cricket , 2004 .

[2]  Ali Daud,et al.  Ranking Cricket Teams through Runs and Wickets , 2013, AMT.

[3]  Satyam Mukherjee Identifying the greatest team and captain—A complex network approach to cricket matches , 2012 .

[4]  T. Swartz,et al.  Optimal lineups in Twenty20 cricket , 2016 .

[5]  V. Borooah,et al.  The "Bradman Class": An Exploration of Some Issues in the Evaluation of Batsmen for Test Matches, 1877-2006 , 2010 .

[6]  T. Swartz,et al.  Applications: Estimation of the Magnitude of Victory in One‐day Cricket RMIT University, Mayo Clinic Rochester and Simon Fraser University , 2001 .

[7]  T. Swartz,et al.  A Simulator for Twenty20 Cricket , 2015 .

[8]  Jianfeng Ma,et al.  Identifying opinion leaders in social networks with topic limitation , 2017, Cluster Computing.

[9]  Satyam Mukherjee,et al.  Quantifying individual performance in Cricket — A network analysis of batsmen and bowlers , 2012, 1208.5184.

[10]  Ilgu Cho,et al.  Technological-level evaluation using patent statistics: model and application in mobile communications , 2014, Cluster Computing.

[11]  Ali Daud,et al.  Ranking cricket teams , 2015, Inf. Process. Manag..

[12]  Sri Devi Ravana,et al.  Estimating reliability of the retrieval systems effectiveness rank based on performance in multiple experiments , 2017, Cluster Computing.

[13]  F. C. Duckworth,et al.  A successful operational research intervention in one-day cricket , 2004, J. Oper. Res. Soc..

[14]  Sungyoung Lee,et al.  Accurate multi-criteria decision making methodology for recommending machine learning algorithm , 2017, Expert Syst. Appl..

[15]  Paul J Bracewell,et al.  A Parametric Control Chart for Monitoring Individual Batting Performances in Cricket , 2009 .

[16]  Donghai Guan,et al.  Semi-supervised learning using frequent itemset and ensemble learning for SMS classification , 2015, Expert Syst. Appl..

[17]  T. Swartz,et al.  A SIMULATOR FOR TWENTY 20 CRICKET , 2015 .

[18]  Sujeet Kumar Sharma,et al.  Measuring batting parameters in cricket: A two-stage regression-OWA method , 2014 .

[19]  Yixian Yang,et al.  Prediction of Rising Stars in the Game of Cricket , 2017, IEEE Access.

[20]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.