Performance Analysis of Pakistan Super League Players Using Principle Component Analysis Approach

Performance Analysis of Pakistan Super League Players Using Principle Component Analysis Approach

Where there is sport there is statistics and cricket is no exception to this. The game of cricket has a wide wealth of complex statistical data associated with the game. This study provides an outstanding application of Principle Component Analysis in evaluating the performance analysis of Cricket data. This study probes the systematic covariation among various dimensions pertaining to batting and bowling capabilities of Players of Pakistan Super League PSL T-20 (2016-2017) using the advanced statistical technique Principle Component Analysis. In the present study PCA is used to rank the batsmen and bowlers of PSL based on their contributions to their teams during these competitive seasons. The findings of this study showed the best top ten ranked batsmen and bowlers who performed well during the series also we can concluded that batting capability dominates over bowling capability. This conclusion coincides with the general opinion of several cricketing enthusiasts and experts. This research is a first study in Pakistan that highlights the features of PSL.

___

  • [1]. Shah, S., Hazarika, P. J., & Hazarika, J. (2017). A study on performance of cricket players using factor analysis approach. International Journal of Advanced Research in Computer Science, 8(3). [2]. Perera, H., Davis, J., & Swartz, T. B. (2016). Optimal lineups in Twenty20 cricket. Journal of Statistical Computation and Simulation, 86(14), 2888-2900. [3]. Richardson, M. (2009). Principal component analysis. URL: http://people. maths. ox. ac. uk/richardsonm/SignalProcPCA. pdf (last access: 3.5. 2013). Aleš Hladnik Dr., Ass. Prof., Chair of Information and Graphic Arts Technology, Faculty of Natural Sciences and Engineering, University of Ljubljana, Slovenia ales. hladnik@ ntf. uni-lj. si, 6, 16. [4]. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement, 20(1), 141-151