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Indices Development for Player’s Performance Evaluation through the Super-SBM Approach in Each Department for All Three Formats of Cricket

Author

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  • Wei Yin

    (Physical Education College, Taishan University, 525 Dongyue Street, Daiyue District, Tai’an 271000, China)

  • Zhixiao Ye

    (Property Management Department, School of Management, Zhejiang Shuren University, Hangzhou 310015, China)

  • Wasi Ul Hassan Shah

    (School of Management, Zhejiang Shuren University, Hangzhou 310015, China)

Abstract

Player performance evaluations in all three formats of cricket have been a topic of great concern for sports analysts and research experts. This study proposed a comprehensive performance estimation tool that incorporates all the essential inputs–outputs and evaluates a cricketer’s overall performance. This research introduced three different estimation indices for player efficiency in all three formats of cricket for batting, bowling, and fielding. Further, this research employed the DEA Super-SBM model to evaluate the player’s efficiency in batting, bowling, and fielding departments of all three formats. The study estimates the most efficient batsman, bowler, and fielder in cricketing history by using the data of international cricketers (1877–2019). The results indicate that, compared to the traditional parameters, the proposed study indices are more accurate and comprehensive in nature. The most efficient batsman, bowler, and fielder in all three formats are given, respectively: (i) Sir Bradman, Sachin Tendulkar, and Virat Kohli; (ii) Muralitharan, Mitchell Starc, and Umar Gul; and (iii) Saleem Yousuf, Luke Ronchi, and Scott Edwards. For teams, England, Australia, and India were determined to be the most efficient in batting for all three formats; the West Indies, Australia, and Pakistan are the most efficient in bowling; and the Australian (Test & ODIs) and South African teams are efficient in the fielding department.

Suggested Citation

  • Wei Yin & Zhixiao Ye & Wasi Ul Hassan Shah, 2023. "Indices Development for Player’s Performance Evaluation through the Super-SBM Approach in Each Department for All Three Formats of Cricket," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3201-:d:1063469
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    References listed on IDEAS

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    1. J M Norman & S R Clarke, 2007. "Dynamic programming in cricket: optimizing batting order for a sticky wicket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1678-1682, December.
    2. Dibyojyoti Bhattacharjee & Hemanta Saikia, 2016. "An objective approach of balanced cricket team selection using binary integer programming method," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 225-247, June.
    3. G D I Barr & B S Kantor, 2004. "A criterion for comparing and selecting batsmen in limited overs cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1266-1274, December.
    4. Liam J.A. Lenten & Wayne Geerling & László Kónya, 2012. "A hedonic model of player wage determination from the Indian Premier League auction: Further evidence," Sport Management Review, Taylor & Francis Journals, vol. 15(1), pages 60-71, January.
    5. Li, Yongjun & Lei, Xiyang & Dai, Qianzhi & Liang, Liang, 2015. "Performance evaluation of participating nations at the 2012 London Summer Olympics by a two-stage data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 243(3), pages 964-973.
    6. A J Lewis, 2008. "Extending the range of player-performance measures in one-day cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 729-742, June.
    7. G. Villa & S. Lozano, 2018. "Dynamic Network DEA approach to basketball games efficiency," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1738-1750, November.
    8. Arnab Adhikari & Adrija Majumdar & Gaurav Gupta & Arnab Bisi, 2020. "An innovative super-efficiency data envelopment analysis, semi-variance, and Shannon-entropy-based methodology for player selection: evidence from cricket," Annals of Operations Research, Springer, vol. 284(1), pages 1-32, January.
    9. Dibyojyoti Bhattacharjee & Hemanta Saikia, 2014. "On Performance Measurement of Cricketers and Selecting an Optimum Balanced Team," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 14(1), pages 262-275, April.
    10. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    11. Herbert F. Lewis, 2014. "Performance Measurement of Major League Baseball Teams Using Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 475-535, Springer.
    12. S R Clarke & J M Norman, 2003. "Dynamic programming in cricket: choosing a night watchman," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 838-845, August.
    13. J M Norman & S R Clarke, 2010. "Optimal batting orders in cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 980-986, June.
    14. Shantonu Islam Shanto & Nabil Awan, 2019. "A sequential principal component-based algorithm for optimal lineup and batting order selection in one day international cricket for Bangladesh," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(4), pages 567-583, July.
    15. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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