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A Novel Graph Based Approach to Predict Man of the Match for Cricket

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Machine Learning, Image Processing, Network Security and Data Sciences (MIND 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1241))

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Abstract

The advent of computational intelligence has only accelerated the motive to automate manual processes. In sports, the award of the best player is questionable in many cases and requires the opinion of multiple distinguished experts in the sport. This often results in an unfair judgement of the situation, given the time constraint for making this decision, and the dependence on pure human skill and reasoning. This calls for a reliable and fair system to fairly award the man of the match, taking into account the numerous variables. Though there are machine learning based approaches trying to model the game for predicting win/loss outcome, it is hard to model the “man of the match” in a cricket game using the same approach. We propose a novel graph based approach to award the “man of the match” to a player for a cricket game. We model the game as a directed graph with each player as a node and an interaction between two players as an edge. We use ball by ball delivery details to construct this graph with a heuristics to calculate the edge weights and compute node centrality of every player using the Personalized Pagerank algorithm to find the most central player in the game. A high centrality indicates a good overall performance of the player. Comparison with existing game data showed encouraging results.

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References

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

    Article  Google Scholar 

  2. Ali, F., Khusro, S.: Player ranking: a solution to the duckworth/lewis method problems. In: 2018 14th International Conference on Emerging Technologies (ICET), pp. 1–4. IEEE (2018)

    Google Scholar 

  3. Bandulasiri, A.: Predicting the winner in one day international cricket. J. Math. Sci. Math. Educ. 3(1), 6–17 (2008)

    Google Scholar 

  4. Blum, A., Chan, T.H., Rwebangira, M.R.: A random-surfer web-graph model. In: 2006 Proceedings of the Third Workshop on Analytic Algorithmics and Combinatorics (ANALCO), pp. 238–246. SIAM (2006)

    Google Scholar 

  5. Charlton, B.G.: The bowling equivalent of the batting average: quantitative evaluation of the contribution of bowlers in cricket using a novel statistic of ‘extra runs saved per match’(ERS/M). OR Insight 20(4), 3–9 (2007)

    Article  Google Scholar 

  6. Chaudhary, R., Bhardwaj, S., Lakra, S.: A dea model for selection of indian cricket team players. In: 2019 Amity International Conference on Artificial Intelligence (AICAI), pp. 224–227. IEEE (2019)

    Google Scholar 

  7. Cho, Y., Yoon, J., Lee, S.: Using social network analysis and gradient boosting to develop a soccer win-lose prediction model. Eng. Appl. Artif. Intell. 72, 228–240 (2018)

    Article  Google Scholar 

  8. Godin, F., Zuallaert, J., Vandersmissen, B., De Neve, W., Van de Walle, R.: Beating the bookmakers: leveraging statistics and twitter microposts for predicting soccer results. In: KDD Workshop on Large-Scale Sports Analytics (2014)

    Google Scholar 

  9. Heit, E., Price, P.C., Bower, G.H.: A model for predicting the outcomes of basketball games. Appl. Cogn. Psychol. 8(7), 621–639 (1994)

    Article  Google Scholar 

  10. Hodge, V.J., Devlin, S.M., Sephton, N.J., Block, F.O., Cowling, P.I., Drachen, A.: Win prediction in multi-player Esports: live professional match prediction. IEEE Trans. Games (2019)

    Google Scholar 

  11. Hossain, M.J., Kashem, M.A., Islam, M.S., Marium, E., et al.: Bangladesh cricket squad prediction using statistical data and genetic algorithm. In: 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), pp. 178–181. IEEE (2018)

    Google Scholar 

  12. Huang, K.Y., Chang, W.L.: A neural network method for prediction of 2006 world cup football game. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2010)

    Google Scholar 

  13. 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 

  14. Kaluarachchi, A., Aparna, S.V.: Cricai: A classification based tool to predict the outcome in ODI cricket. In: 2010 Fifth International Conference on Information and Automation for Sustainability, pp. 250–255. IEEE (2010)

    Google Scholar 

  15. Khan, M.Z., Hassan, M.A., Farooq, A., Khan, M.U.G.: Deep CNN based data-driven recognition of cricket batting shots. In: 2018 International Conference on Applied and Engineering Mathematics (ICAEM), pp. 67–71. IEEE (2018)

    Google Scholar 

  16. Leung, C.K., Joseph, K.W.: Sports data mining: predicting results for the college football games. Procedia Comput. Sci. 35, 710–719 (2014)

    Article  Google Scholar 

  17. Loeffelholz, B., Bednar, E., Bauer, K.W.: Predicting NBA games using neural networks. J. Quant. Anal. Sports 5(1), 1–17 (2009)

    MathSciNet  Google Scholar 

  18. Vaz de Melo, P.O., Almeida, V.A., Loureiro, A.A.: Can complex network metrics predict the behavior of NBA teams? In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 695–703. ACM (2008)

    Google Scholar 

  19. Vaz de Melo, P.O., Almeida, V.A., Loureiro, A.A., Faloutsos, C.: Forecasting in the NBA and other team sports: network effects in action. ACM Trans. Knowl. Discovery Data (TKDD) 6(3), 13 (2012)

    Google Scholar 

  20. O’Donoghue, P., Dubitzky, W., Lopes, P., Berrar, D., Lagan, K., Hassan, D., Bairner, A., Darby, P.: An evaluation of quantitative and qualitative methods of predicting the 2002 FIFA world cup. J. Sports Sci. 22(6), 513–514 (2004)

    Google Scholar 

  21. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1999)

    Google Scholar 

  22. Roy, S., Dey, P., Kundu, D.: Social network analysis of cricket community using a composite distributed framework: From implementation viewpoint. IEEE Trans. Comput. Soc. Syst. 5(1), 64–81 (2017)

    Article  Google Scholar 

  23. Saikia, H., Bhattacharjee, D., Lemmer, H.H.: Predicting the performance of bowlers in IPL: an application of artificial neural network. Int. J. Perform. Anal. Sport 12(1), 75–89 (2012)

    Article  Google Scholar 

  24. Sankaranarayanan, V.V., Sattar, J., Lakshmanan, L.V.: Auto-play: a data mining approach to odi cricket simulation and prediction. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 1064–1072. SIAM (2014)

    Google Scholar 

  25. Saraswat, D., Dev, V., Singh, P.: Analyzing the performance of the indian cricket team using weighted association rule mining. In: 2018 International Conference on Computing, Power and Communication Technologies (GUCON), pp. 161–164. IEEE (2018)

    Google Scholar 

  26. Singh, T., Singla, V., Bhatia, P.: Score and winning prediction in cricket through data mining. In: 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), pp. 60–66. IEEE (2015)

    Google Scholar 

  27. Sinha, S., Dyer, C., Gimpel, K., Smith, N.A.: Predicting the NFL using twitter. arXiv preprint arXiv:1310.6998 (2013)

  28. Solanki, U.J., Vala, J.: Selection for balanced cricket team fourth coming ICC championship 2017. In: 2017 2nd International Conference on Communication and Electronics Systems (ICCES), pp. 794–797. IEEE (2017)

    Google Scholar 

  29. Somaskandhan, P., Wijesinghe, G., Wijegunawardana, L.B., Bandaranayake, A., Deegalla, S.: Identifying the optimal set of attributes that impose high impact on the end results of a cricket match using machine learning. In: 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), pp. 1–6. IEEE (2017)

    Google Scholar 

  30. Tripathy, R.M., Bagchi, A., Jain, M.: Complex network characteristics and team performance in the game of cricket. In: Bhatnagar, V., Srinivasa, S. (eds.) BDA 2013. LNCS, vol. 8302, pp. 133–150. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03689-2_9

    Chapter  Google Scholar 

  31. Uddin, M.A., Hasan, M., Halder, S., Ahamed, S., Acharjee, U.K.: CRICRATE: a cricket match conduction and player evaluation framework. In: Abraham, A., Dutta, P., Mandal, J.K., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security. AISC, vol. 755, pp. 491–500. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1951-8_44

    Chapter  Google Scholar 

  32. Yu, S., Kak, S.: A survey of prediction using social media. arXiv preprint arXiv:1203.1647 (2012)

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Authors and Affiliations

  1. PES University, Bengaluru, India

    Kaushik Ravichandran, Lalit Gattani, Advithi Nair & Bhaskarjyoti Das

Authors
  1. Kaushik Ravichandran
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  2. Lalit Gattani
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  3. Advithi Nair
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  4. Bhaskarjyoti Das
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Corresponding author

Correspondence to Kaushik Ravichandran .

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Editors and Affiliations

  1. National Institute of Technology Silchar, Silchar, India

    Arup Bhattacharjee

  2. National Institute Of Technology Silchar, Silchar, India

    Samir Kr. Borgohain

  3. National Institute of Technology Silchar, Silchar, India

    Badal Soni

  4. National Institute of Technology Kurukshetra, Kurukshetra, India

    Gyanendra Verma

  5. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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Ravichandran, K., Gattani, L., Nair, A., Das, B. (2020). A Novel Graph Based Approach to Predict Man of the Match for Cricket. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_48

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  • DOI: https://doi.org/10.1007/978-981-15-6318-8_48

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