Skip to main content

Analytic for Cricket Match Winner Prediction Through Major Events Quantification

  • Conference paper
  • First Online:
Third International Conference on Image Processing and Capsule Networks (ICIPCN 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 514))

Included in the following conference series:

  • 681 Accesses

Abstract

Cricket is rated as one of the most famous game across the globe. It has all the possibility to grab the attentions of audience and candidates and in the other hand attentions from researchers as well. Numerous monetary organizations are focusing on this interesting game for profit making and are involving man power and resources towards the task of cricket data analytic. In this paper, an attempt has been made to quantify the active events that count towards the final result of a cricket match. Mainly the batsman scoring efficiency (BSP) and the effective bowling skill (EBS) are taken into consideration for the purpose. These two indicators are used to compute the real time efficiency (RTE) of a particular cricket team. A comparison among the RTE measures of two teams playing each other can be utilized to predict the winner of an ongoing match. User defined functions and equations are framed through this proposed work. Simulation of the proposed framework is carried out on sufficient number of samples. The statistical cricket data samples are chosen from ODI (one day international) matches only. The simulation outcomes reveal a satisfactory justification in for of the validity of the proposed work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Manoharan, J.S.: Capsule network algorithm for performance optimization of text classification. J. Soft Comput. Paradigm (JSCP) 3(01), 1–9 (2021)

    Article  Google Scholar 

  2. Pandian, A.P.: Performance evaluation and comparison using deep learning techniques in sentiment analysis. J. Soft Comput. Paradigm (JSCP) 3(02), 123–134 (2021)

    Article  Google Scholar 

  3. Parameswaran, K.: Vector quantization, density estimation and outlier detection on cricket dataset. In: 2013 International Conference on Computer Communication and Informatics, pp. 1–5. IEEE (2013)

    Google Scholar 

  4. Satao, P., Tripathi, A., Vankar, J., Vaje, B., Varekar, V.: Cricket score prediction system (csps) using clustering algorithm. Int. J. Curr. Eng. Sci. Res. 3(04), 43–46 (2016)

    Google Scholar 

  5. Awan, M.J., et al.: Cricket match analytics using the big data approach. Electronics 10(19), 2350 (2021)

    Article  Google Scholar 

  6. Singh, M.P., Ahmad, M.: Performance prediction of players in sports league matches. Int. J. Sci. Res. (IJSR) 4(04), 1–7 (2015)

    Google Scholar 

  7. Jhanwar, M.G., Pudi, V.: Quantitative assessment of player performance and winner prediction in ODI cricket. Int. Inst. Inf. Technol. Hyderabad-500032, Indıa. (2017)

    Google Scholar 

  8. Fister, I., Fister, D., Fong, S.: Data mining in sporting activities created by sports trackers. In: 2013 İnternational Symposium on Computational and Business İntelligence, pp. 88–91. IEEE (2013)

    Google Scholar 

  9. Pileggi, H., Stolper, C.D., Boyle, J.M., Stasko, J.T.: Snapshot: visualization to propel ice hockey analytics. IEEE Trans. Visual Comput. Graph. 18(12), 2819–2828 (2012)

    Article  Google Scholar 

  10. Thakare, I.S., Suyal, S.R., Pandav, K.Y.: Performance evaluation for sports team selection using data mining techniques. AADYA-Natl. J. Manag. Technol. 1(5), 102–108 (2015)

    Google Scholar 

  11. UmaMaheswari, P., Rajaram, M.: A novel approach for mining association rules on sports data using principal component analysis: for cricket match perspective. In: 2009 IEEE International Advance Computing Conference, pp. 1074–1080. IEEE (2009)

    Google Scholar 

  12. Sivaramaraju Vetukuri, V., Rajender, R., Sethi, N.: A multi-aspect analysis and prediction scheme for cricket matches in standard T-20 format. Int. J. Knowl.-based Intell. Eng. Syst. 23(3), 149–154 (2019)

    Google Scholar 

  13. Sivaramaraju, V., Sethi, N., Rajender, R.: Heuristics for winner prediction in international cricket matches. Stat. Optim. Inf. Comput. 8(2), 602–609 (2020)

    Article  Google Scholar 

  14. Vetukuri, V.S., Sethi, N., Rajender, R.: Generic model for automated player selection for cricket teams using recurrent neural networks. Evol. Intel. 14(2), 971–978 (2020). https://doi.org/10.1007/s12065-020-00488-4

    Article  Google Scholar 

  15. Raju, V.S., Sethi, N., Rajender, R.: A review of data analytic schemes for prediction of vivid aspects in ınternational cricket matches. In: 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), 19 September 2019, pp. 1–4. IEEE (2019)

    Google Scholar 

  16. Bhattacherjee, S., Sahoo, J., Goswami, A.: Association rule mining approach in strategy planning for team İndia in icc world cup 2015. In: 2015 Second International Conference on Advances in Computing and Communication Engineering, 1 May 2015, pp. 616–621. IEEE (2015)

    Google Scholar 

  17. Abraham, A., Dutta, P., Mandal, J.K., Bhattacharya, A., Dutta, S.: Emerging technologies in data mining and information security. In: Proceedings of IEMIS-2018 (2018)

    Google Scholar 

  18. Shah, P., Patel, M.N.: Ranking the cricket captains using principal component analysis. Int. J. Physiol. Nutr. Phys. Educ. 3(2), 477–483 (2018)

    Google Scholar 

  19. Al-Shboul, R., Syed, T., Memon, J., Khan, F.: Automated player selection for sports team using competitive neural networks. Int. J. Adv. Comput. Sci. Appl. 8(8), 457–460 (2017)

    Google Scholar 

  20. Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., Matthews, I.: Large-scale analysis of soccer matches using spatiotemporal tracking data. In: 2014 IEEE İnternational Conference on Data Mining,14 December 2014, pp. 725–730. IEEE (2014)

    Google Scholar 

  21. Sankaran, S.: Comparing pay versus performance of IPL bowlers: an application of cluster analysis. Int. J. Perform. Anal. Sport 14(1), 174–187 (2014)

    Article  Google Scholar 

  22. Iyer, S.R., Sharda, R.: Prediction of athletes performance using neural networks: an application in cricket team selection. Expert Syst. Appl. 36(3), 5510–5522 (2009)

    Article  Google Scholar 

  23. Silva, R.M., Manage, A.B., Swartz, T.B.: A study of the powerplay in one-day cricket. Eur. J. Oper. Res. 244(3), 931–938 (2015)

    Article  Google Scholar 

  24. Pathak, N., Wadhwa, H.: Applications of modern classification techniques to predict the outcome of ODI cricket. Procedia Comput. Sci. 1(87), 55–60 (2016)

    Article  Google Scholar 

  25. Asif, M., McHale, I.G.: In-play forecasting of win probability in one-day international cricket: a dynamic logistic regression model. Int. J. Forecast. 32(1), 34–43 (2016)

    Article  Google Scholar 

  26. Norton, H., Gray, S., Faff, R.: Yes, one-day international cricket in-play trading strategies can be profitable! J. Bank. Finance 1(61), S164–S176 (2015)

    Article  Google Scholar 

  27. Akhtar, S., Scarf, P.: Forecasting test cricket match outcomes in play. Int. J. Forecast. 28(3), 632–643 (2012)

    Article  Google Scholar 

  28. Ahmad, H., Daud, A., Wang, L., Hong, H., Dawood, H., Yang, Y.: Prediction of rising stars in the game of cricket. IEEE Access. 14(5), 4104–4124 (2017)

    Article  Google Scholar 

  29. Bharathan, S., Sundarraj, R.P., Abhijeet, S., Ramakrishnan, S.: A self-adapting intelligent optimized analytical model for team selection using player performance utility in cricket. In: 9th MIT Sloan Sports Analytics Conference, MIT, Boston, pp. 1–11 (2015)

    Google Scholar 

  30. Ahmed, F., Deb, K., Jindal, A.: Evolutionary multi-objective optimization and decision making approaches to cricket team selection. Swarm Evol. Memetic Comput. SEMCCO (2011)

    Google Scholar 

  31. Kumarasiri, S.I.: Optimal one day international cricket team selection by genetic algorithm. J. Sports Anal. 36(4) (2017)

    Google Scholar 

  32. Jayanth, S.B., Anthony, A., Abhilasha, G., Shaik, N., Srinivasa, G.: A team recommendation system and outcome prediction for the game of cricket. J. Sports Anal. 4(4), 263–273 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. Sivaramaraju Vetukuri .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vetukuri, V., Sethi, N., Rajender, R., Reddy, S.S. (2022). Analytic for Cricket Match Winner Prediction Through Major Events Quantification. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_14

Download citation

Publish with us

Policies and ethics