Evaluating the Performance of Test Cricket Players Using Principal Component Analysis and the Weighted Average Method

Year : 2025 | Volume : 02 | Issue : 02 | Page : 20 30
    By

    Arjun Prakash Mane,

  • Jaydip Yuvaraj Patil,

  1. Biostatistician, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India
  2. Assistant Professor, SNBP College of Arts, Commerce, Science and Management of Studies, Pune, Maharashtra, India

Abstract

This study evaluates cricket player performance using Principal Component Analysis (PCA) and a weighted average approach. In order to achieve this, we analyzed detailed batting and bowling datasets from the International Cricket Council (ICC) to calculate player performance based on various performance indicators. The datasets included comprehensive statistics from multiple matches and tournaments, allowing for an in-depth evaluation of players’ skills and contributions. PCA ranked players according to their participation level, with the first three principal components accounting for nearly 90% of the data’s variability, which demonstrates the robustness of the approach in capturing the majority of information inherent in the data. Our analysis reveals Sachin Tendulkar and Muthaiya Muralidharan as top-ranked batsmen and bowlers, respectively, based on the combined assessment of their performance metrics. This study provides a comprehensive evaluation of cricket player performance, highlighting the effectiveness of both PCA and weighted average approaches in producing accurate and meaningful rankings. The findings clearly demonstrate the applicability of this methodology in selecting the best cricket squad, while also offering detailed insights into the strengths and weaknesses of top players. Overall, this research makes a significant contribution to the field of sports analytics by providing a robust and reliable framework for evaluating cricket player performance. The study further informs decision-making processes in team selection and strategy development, supporting coaches, analysts, and managers in making data-driven choices for optimizing team performance and achieving competitive success.

Keywords: PCA, Weighted Average Method, Heat Map, Scree Plot, Ranking

[This article belongs to Recent Trends in Sports ]

How to cite this article:
Arjun Prakash Mane, Jaydip Yuvaraj Patil. Evaluating the Performance of Test Cricket Players Using Principal Component Analysis and the Weighted Average Method. Recent Trends in Sports. 2025; 02(02):20-30.
How to cite this URL:
Arjun Prakash Mane, Jaydip Yuvaraj Patil. Evaluating the Performance of Test Cricket Players Using Principal Component Analysis and the Weighted Average Method. Recent Trends in Sports. 2025; 02(02):20-30. Available from: https://journals.stmjournals.com/rts/article=2025/view=227047


References

1. Yin W, Ye Z, Shah WUH. Indices Development for Player’s Performance Evaluation through the Super-SBM Approach in Each Department for All Three Formats of Cricket. Sustainability. 2023; 15(4):3201. https://doi.org/10.3390/su15043201
2. Kapil Gupta, an integrated batting performance analytics model for women’s cricket using Principal Component Analysis and Gini scores, Decision Analytics Journal, Volume 4, 2022, 100109, ISSN 2772-6622, https://doi.org/10.1016/j.dajour.2022.100109.
3. Mohammad Reza Mahmoudi, Mohammad Hossein Heydari, Sultan Noman Qasem, Amirhosein Mosavi, Shahab S. Band, Principal component analysis to study the relations between the spread rates of COVID-19 in high risks countries, Alexandria Engineering Journal, Volume 60, Issue 1, 2021, Pages 457-464, ISSN 1110-0168, https://doi.org/10.1016/j.aej.2020.09.013.
4. Shah, Parag & Patel, M. (2018). Ranking the cricket captains using principal component analysis. 10.13140/RG.2.2.33455.38564.
5. Manage, Ananda & Scariano, Stephen. (2013). An Introductory Application of Principal Components to Cricket Data. Journal of Statistics Education. 21. 10.1080/10691898.2013.11889689.
6. Zhang Z, Castelló A. Principal components analysis in clinical studies. Ann Transl Med. 2017 Sep;5(17):351. doi: 10.21037/atm.2017.07.12. PMID: 28936445; PMCID: PMC5599285.
7. Das, Nayan & Mukherjee, Imon & Paul, Goutam & Priya, Ratna. (2023). A Multiple Criteria Decision Making Approach for Ranking Cricket Captains. 10.1109/GCON58516.2023.10183420.
8. Jadwani, Yash & Denholm-Price, James & Hunter, Gordon. (2023). A Machine Learning-based Approach to Analyse Player Performance in T20 Cricket Internationals.
9. Yenuga, B. R. (2022). Selection of Best Players in ODI Cricket using Ranking Based Indexing Method (Doctoral dissertation, Dublin, National College of Ireland).
10. Gløersen Ø, Myklebust H, Hallén J, Federolf P. Technique analysis in elite athletes using principal component analysis. J Sports Sci. 2018 Jan;36(2):229-237. doi: 10.1080/02640414.2017.1298826. Epub 2017 Mar 13. PMID: 28287028.
11. León-Mantero C, Casas-Rosal JC, Pedrosa-Jesús C, Maz-Machado A (2020) Measuring attitude towards mathematics using Likert scale surveys: The weighted average. PLoS ONE 15(10): e0239626. https://doi.org/10.1371/journal.pone.0239626
12. Pessanha Santos N. The Expansion of Data Science: Dataset Standardization. Standards. 2023; 3(4):400-410. https://doi.org/10.3390/standards3040028
13. Hitesh Mangtani (2021); Futuristic accession of Test Cricket: A Statistical Tour; International Journal of Scientific and Research Publications (IJSRP) 11(2) (ISSN: 2250-3153), DOI: http://dx.doi.org/10.29322/IJSRP.11.02.2021.p11004
14. Holubova, Halyna. (2023). A comparative analysis of the principal component method and parallel analysis in working with official statistical data. Statistics in Transition new series. 24. 199-212. 10.59170/stattrans-2023-011.
15. Manage, Ananda & Kafle, Ram & Wijekularathna, Danush. (2020). Classification of all-rounders in limited over cricket – a machine learning approach. Journal of Sports Analytics. 6. 1-12. 10.3233/JSA-200467.
16. Khan, Sheharyar & Ishtiaq, Muhammad & Bibi, Kainat & Amin, Rashid & Ahmed, Adeel & Chaudhary, Iqra. (2021). Statistical Analysis of Cricket Leagues Using Principal Component Analysis. Journal of Engineering and Applied Sciences. Vol 2 No 1 (2021). 30-40. 10.33897/fujeas.v2i1.451.
17. Hussain, Akbar & Qiang, Yan & Ullah, Inam & Ullah, Najeeb & Qudoos, Abdul. (2019). Region-based Teams Ranking in the Game of Cricket using PageRank Algorithm. International Journal of Computer Applications. 177. 10-15. 10.5120/ijca2019919458.
18. Patil, R., Duraphe, A., Motarwar, P., Suganya, G., Premalatha, M. (2023). Cluster-Centric Based Hybrid Approach for Cricket Sports Analytics Using Machine Learning. In: Kottursamy, K., Bashir, A.K., Kose, U., Uthra, A. (eds) Deep Sciences for Computing and
19. Communications. IconDeepCom 2022. Communications in Computer and Information Science, vol 1719. Springer, Cham. https://doi.org/10.1007/978-3-031-27622-4_21
20. Souza, A.S., Bezerra, M.A., Cerqueira, U.M.F.M. et al. An introductory review on the application of principal component analysis in the data exploration of the chemical analysis of food samples. Food Sci Biotechnol 33, 1323–1336 (2024). https://doi.org/10.1007/s10068-023-01509-5
21. Akhtar, S., Scarf, P. & Rasool, Z. Rating players in test match cricket. J Oper Res Soc 66, 684–695 (2015). https://doi.org/10.1057/jors.2014.30
22. Bharadwaj, F., Saxena, A., Kumar, R., Kumar, R., Kumar, S., Stević, Ž. (2024). Player performance predictive analysis in cricket using machine learning. Revue d’Intelligence Artificielle, Vol. 38, No. 2, pp. 449-457. https://doi.org/10.18280/ria.380208

Regular Issue Subscription Original Research
Volume 02
Issue 02
Received 11/06/2025
Accepted 10/09/2025
Published 25/10/2025
Publication Time 136 Days
439

Login


My IP

PlumX Metrics