Skip to main content

Advertisement

SpringerLink
Log in

Optimization of team selection in fantasy cricket: a hybrid approach using recursive feature elimination and genetic algorithm

  • Original - OR Modeling/Case Study
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Fantasy Sports allows individuals to assemble a virtual team to participate in free or paid tournaments and earn rewards. Selecting a good team forms a crucial decision in fantasy cricket. Existing team selection methods cater only to professional cricket and are not suited well to accommodate the differences between fantasy cricket and the on-field game. This paper proposes a two-step methodology for player assessment and team selection in fantasy cricket. Player assessment is carried out using recursive feature elimination in random forest, in which context relevant player metrics are considered and the selection of players is based on modified genetic algorithm. We illustrate the efficacy of the proposed method on Dream11, a popular fantasy sports application. The results show that the proposed method outshines the traditional team selection process in fantasy sports, which is based on hit and trial. Furthermore, we provide a typology to analyse the proposed algorithm along the dimensions of reward distribution and entry fee.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig.8
Fig.9
Fig.10
Fig. 11

Similar content being viewed by others

Availability of data and material

Player statistics is freely available. Platform specific data is visible only to the participants of the game.

Notes

  1. Based on feedback, player’s age was included as a player evaluation characteristic. It was eventually dropped due to comparatively negligible MDA values (0.0008 for batsmen and 0.0011 for bowlers).

  2. Weightage of fielding points of a player is halved during the calculation of player talent (\({\mathrm{PT}}_{\mathrm{k}})\)

References

  • Adhikari, A., Majumdar, A., Gupta, G., et al. (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, 284, 1–32. https://doi.org/10.1007/s10479-018-3088-4

    Article  Google Scholar 

  • Agarwal, S., Yadav, L., & Mehta, S. (2017). Cricket team prediction with Hadoop: Statistical modeling approach. Procedia Computer Science, 122, 525–532.

    Google Scholar 

  • Ahmed, F., Deb, K., and Jindal, A. (2011). Cricket team selection using evolutionary multi-objective optimization. In proceedings of the international conference on Swarm, Evolutionary and Memetic Computing. 7077, 71–78.

  • Ahmed, F., Deb, K., & Jindal, A. (2013). Multi-objective optimization and decision-making approaches to cricket team selection. Applied Soft Computing, 13(1), 402–414.

    Google Scholar 

  • Amin, G. R., & Sharma, S. K. (2014). Cricket team selection using data envelopment analysis. European Journal of Sports Science, 14(sup1), S369–S376..

    Google Scholar 

  • Andreff, W. (2008). Globalization of the sports economy. Diritto Ed Economia Dello Sport, 4(3), 14–34.

    Google Scholar 

  • Arkes, H., Herren, L., & Isen, A. (1988). The role of potential loss in the influence on risk-taking behaviour. Organizational Behaviour and Human Development Process, 42(2), 181–193.

    Google Scholar 

  • Baerg, A. (2009). Just a fantasy? Exploring fantasy sports. The Electronic Journal of Communication. 19(3&4).

  • Barr, G., Holdsworth, C., & Kantor, B. (2008). Evaluating performances at the 2007 Cricket World cup. South African Statistical Journal, 42(2), 125–142.

    Google Scholar 

  • Barr, G., & Kantor, B. (2004). A criterion for comparing and selecting batsmen in limited overs cricket. Journal of the Operational Research, 55(12), 1266–1274.

    Google Scholar 

  • Bhattacharjee, D., & Saikia, H. (2016). An objective approach of balanced cricket team selection using binary integer programming method. Journal of Operations Research, 53, 225–247.

    Google Scholar 

  • Billings, A. C., & Ruihley, B. J. (2013). Why we watch, why we play: The relationship between fantasy sport and fan-ship motivations. Mass Communication and Society, 16(1), 5–25. https://doi.org/10.1080/15205436.2011.635260

    Article  Google Scholar 

  • Cea, S., Duran, G., Guajardo, M., et al. (2020). An analytics approach to the FIFA ranking procedure and the World cup final draw. Annals of Operations Research, 286, 119–146.

    Google Scholar 

  • Chantelat, P. (1999). An overview of some recent perspectives on the socioeconomics of sport. International Review for the Sociology of Sport, 34(1), 59–68.

    Google Scholar 

  • Cordes, V., & Olfman, L. (2016). Sports analytics: predicting athletic performance with a genetic algorithm. Decision Support and Analytics., 13, 131–145.

    Google Scholar 

  • Darst, B. F., Malecki, K. C., & Engelman, C. D. (2018). Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genetics., 19, 65. https://doi.org/10.1186/s12863-018-0633-8

    Article  Google Scholar 

  • Das, S., Gleason, S., Shen, S., Goddard, S., and Perez, L.C. (2005). 2D tracking performance evaluation using the cricket location support system. In the proceedings of the 2005 IEEE international conference on electro information technology, 6–10.

  • Davis, N. W., & Duncan, M. (2006). Sports knowledge is power: Reinforcing masculine privilege thorough fantasy sport league participation. Journal of Sport and Social Issues, 30(3), 244–264.

    Google Scholar 

  • Deb, P. K., Pratap, A., & Agarwal, S. (2002). A fast and elitist multi-objective genetic algorithm: NSGA II. IEEE Transactions on Evolutionary Computing, 6(2), 182–197.

    Google Scholar 

  • Deb, K. (2011). Multi-objective optimisation using evolutionary algorithms: an introduction. In Multi-objective evolutionary optimisation for product design and manufacturing (pp. 3–34). Springer, London.

  • Dey, P. K., Ghosh, D. N., & Mondal, A. C. (2011). A MCDM approach for evaluating bowler’s performance in IPL. Journal of Emerging Trends in Computing and Information Sciences, 11, 563–573.

    Google Scholar 

  • Dream11 (2020). Fantasy Points System. Retrieved, October 15, 2020 from https://www.dream11.com/games/point-system.

  • Dwyer, B., & Lecrom, C. W. (2013). Is fantasy trumping reality? The redefined national football league experience of novice fantasy football participants. Journal of Contemporary Athletics., 7(3), 119–139.

    Google Scholar 

  • Fantasy Sports Trade Association (2018). Fantasy sports conference demographic survey shows continued growth. Retrieved October 21, 2020 from http://fsta.org/news/pressreleases/PRWeb-FantasySportsConference0807.pdf.

  • Farquhar, L. K., & Meeds, R. (2007). Types of fantasy sports users and their motivations. Journal of Computer-Mediated Communication, 12(4), 1208–1228. https://doi.org/10.1111/j.1083-6101.2007.00370

    Article  Google Scholar 

  • Fisher, E. (2015). Fantasy players spend big. Retrieved December 8, 2020 from http://www.sportsbusinessjournal.com/article/60598

  • Gerber, H., & Sharp, G. D. (2006). Selecting a limited overs cricket squad using an integer programming model. South African Journal for Research in Sports, Physical Education and Recreation, 28(2), 81–90.

    Google Scholar 

  • Goldsmith, A., & Walker, M. (2015). The NASCAR experience : Examining the influence of fantasy sports participation on ‘non-fans.’ Sports Management Review, 18(2), 231–243.

    Google Scholar 

  • Granitto, P., Furlanello, C., Biasoli, F., & Gasperi, F. (2006). Recursive feature elimination with random forest for PTR_MS analysis of agroindustrial products. Chemo Metrics and Intelligent Laboratory Systems, 83(2), 83–90.

    Google Scholar 

  • Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.

    Google Scholar 

  • Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1), 382–422.

    Google Scholar 

  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.

    Google Scholar 

  • ICC. (2019). Men’s ODI Player Rankings (2019). Retrieved, October 15, 2020, from https://www.icc-cricket.com/rankings/mens/player-rankings/odi

  • Iyer, S., & Sharda, R. (2009). Prediction of athletes’ performance using neural networks: An application in team selection. Expert Systems with Application, 36(3), 5510–5522.

    Google Scholar 

  • Juan, A. A., Keenan, P., Marti, R., et al. (2021). A review of the role of heuristics in stochastic optimization: From metaheuristics to learn heuristics. Annals of Operation Research. https://doi.org/10.1007/s10479-021-04142-9

    Article  Google Scholar 

  • Kamble, A. G., Rao, R. V., Kale, A. V., & Samant, S. P. (2011). Selection of cricket players using analytical hierarchical process. International Journal of Sports Science and Engineering, 5, 207–212.

    Google Scholar 

  • Kissane, R. J., & Winslow, S. (2016a). Bonding and abandoning: Gender, social interaction, and relationships in fantasy sports. Social Currents, 3(3), 256–272.

    Google Scholar 

  • Kissane, R. J., & Winslow, S. (2016b). ‘You’re underestimating me, and you shouldn’t’: Women’s agency in fantasy sports. Gender & Society., 30(5), 819–841.

    Google Scholar 

  • Koulis, T., Muthukumarana, S., & Briercliffe, C. D. (2014). A Bayesian stochastic model for batting performance evaluation in one-day cricket. Journal of Quantitative Analysis in Sport, 10(1), 1–13.

    Google Scholar 

  • Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021). Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights, 1(1), 1–14.

    Google Scholar 

  • Kumarasiri, I., & Perrera, S. (2017). Optimal one day cricket team selection by genetic algorithm. Journal of Sports Analytics, 36(4), 213–221.

    Google Scholar 

  • Kushwaha, Kar, A. K., and Dwivedi, Y. K. (2021). Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2), 100017, 1–17.

  • Kwak, D. H., Lim, C. H., Lee, W. Y., & Mahan, J. E. (2010). How confident are you to win your fantasy league: Exploring the antecedents and consequences of winning expectancy? Journal of Sport Management, 24(4), 416–433.

    Google Scholar 

  • Larkin, B. A., & Fink, J. S. (2016). Fantasy sport, FoMO, and traditional fandom: How second-screen use of social media allows fans to accommodate multiple identities. Journal of Sport Management, 30(6), 643–655.

  • Lee, J., Ruihley, B. J., Brown, N., & Billings, A. C. (2013). The effects of fantasy football participation on team identification, team loyalty and NFL fandom. Journal of Sports Media, 8(1), 207–227. https://doi.org/10.1353/jsm.2013.0008

    Article  Google Scholar 

  • Lee, S., Seo, W. J., & Green, B. C. (2013). Understanding why people play fantasy sport: Development of the fantasy sport motivation inventory (FanSMI). European Sport Management Quarterly, 13(2), 166–199. https://doi.org/10.1080/16184742.2012.752855

    Article  Google Scholar 

  • Lemmer, H. (2013). Team selection after a short cricket series. European Journal of Sports Science, 13(2), 200–206.

    Google Scholar 

  • Lewis, A. (2005). Towards a fairer measure of play. Journal of Operational Research Society, 56, 804–815.

    Google Scholar 

  • Li, H., & Zhang, Q. (2009). Multiobjective optimisation problems with complicated pareto sets and NSGA II. IEEE Transactions on Evolutionary Computation, 13(2), 284–302. https://doi.org/10.1109/TEVC.2008.925798

    Article  Google Scholar 

  • Lourens, M. (2009). Integer optimisation for the selection of a Twenty20 cricket team. (Master thesis, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa). Retrieved from https://core.ac.uk/download/pdf/49241155.pdf.

  • Malek M., Badhan, R., Shesir, M., and Nazmuk, F. (2018). Squad selection for cricket teams using machine learning algorithms (Doctoral Thesis, Brac University, Bangladesh). Retrieved from http://hdl.handle.net/10361/12296.

  • Marchica, L., & Derevensky, J. (2016). Fantasy sports: A growing concern among college student athletes. International Journal of Mental Health and Addiction, 14(5), 635–645.

    Google Scholar 

  • Martin, R. J., & Nelson, S. (2014). Fantasy sports, real money: Exploration of the relationship between fantasy sports participation and gambling-related problems. Addictive Behaviours, 39(10), 1377–1382.

    Google Scholar 

  • Moreno, P., & Lozano, S. (2014). A network DEA assessment of team efficiency in the NBA. Annals of Operations Research, 214, 99–124. https://doi.org/10.1007/s10479-012-1074-9

    Article  Google Scholar 

  • Nikolaidis, Y. (2015). Building a basketball game strategy through strategic analysis of data. Annals of Operations Research, 227, 137–159. https://doi.org/10.1007/s10479-013-1309-4

    Article  Google Scholar 

  • Oates, T. P. (2009). New media and the repackaging of NFL fandom. Sociology of Sport Journal, 26(1), 31–49.

    Google Scholar 

  • Omkar, S.N., and R, Verma. (2003). Cricket team selection using genetic algorithm. In Proceedings of the international congress on sports dynamics, 1–9.

  • Patravardhan, C., & Singh, S. (2016). A new machine learning based deep performance index for ranking IPL T20 cricketers. International Journal of Computer Applications, 137(10), 887–975.

    Google Scholar 

  • Pickering, D., Blaszczynski, A., Hartmann, M., & Keen, B. (2016). Fantasy sports: Skill, gambling, or are these irrelevant issues? Current Addiction Reports, 3(3), 307–313.

    Google Scholar 

  • Rohde, N. (2011). An “economic” ranking of batters in test cricket. Journal of Applied Economics and Policy, 30(4), 455–465.

    Google Scholar 

  • Ruihley, B. J., Billings, A. C., & Rae, C. (2014). As time goes by: Deciphering the fantasy sport playing teenager. Sport Marketing Quarterly, 23(4), 187–197.

    Google Scholar 

  • Shah, P., & Shah, M. (2014). Pressure index in cricket. IOSR Journal of Sports and Physical Education, 5(1), 9–11.

    Google Scholar 

  • Shapiro, S. L., Drayer, J., & Dwyer, B. (2014). Exploring fantasy baseball consumer behavior: Examining the relationship between identification, fantasy participation, and consumption. Journal of Sport Behaviour, 37(1), 77–93.

    Google Scholar 

  • Sharp, G., Brettenny, W. J., Gonsalves, J. W., & Lourens, M. (2010). Integer optimisation for the selection of Twenty20 cricket team. Journal of Operational Research Society, 62(9), 1688–1694.

    Google Scholar 

  • Shuai, Y., Yang, C., Yuan, C., and Song, T. A hybrid electronic components detection parameter sequence optimization method based on random forest and mean decrease accuracy. In Proceedings of the 2019 IEEE 10th international conference on software engineering and service sciences, pp. 1–4.

  • Singh, G., Bhatia, N., and Singh, S. (2011). Fuzzy Logic based cricket player performance evaluator. IJCS Special Issue on “Artificial Intelligence Techniques” – Novel Approaches and Practical Applications. pp.11–16.

  • Smith, B., Sharma, P., & Hooper, P. (2006). Decision making in online fantasy sports communities. Interactive Technology and Smart Education, 3(4), 347–360. https://doi.org/10.1108/17415650680000072

    Article  Google Scholar 

  • Suh, Y. I., Lim, C., Kwak, D. H., & Pedersen, P. M. (2010). Examining the psychological factors associated with involvement in fantasy sports: An analysis of participants’ motivations and constraints. International Journal of Sport Management, Recreation and Tourism, 5, 1–28. https://doi.org/10.5199/ijsmart-1791-874X-5a

    Article  Google Scholar 

  • Tacon, R., & Vainker, S. (2017). Fantasy sport: A systematic review and new research directions. European Sport Management Quarterly, 17(5), 558–589.

    Google Scholar 

  • Toffolo, T. A. M., Christiaens, J., Speiksma, F. C. R., et al. (2019). The sports team grouping problem. Annals of Operations Research, 275, 223–243. https://doi.org/10.1007/s10479-017-2595-z/

    Article  Google Scholar 

  • Yang, C. H., Lin, H. Y., & Chen, C. P. (2014). Measuring the efficiency of NBA teams: Additive efficiency decomposition in two stage DEA. Annals of Operations Research, 217, 565–589. https://doi.org/10.1007/s10479-014-1536-3

    Article  Google Scholar 

  • Yusoff, Y., Ngadiman, M. S., & Zain, M. A. (2011). Overview of NSGA II for optimizing machine process parameters. Procedia Engineering, 15, 3978–3983.

    Google Scholar 

  • Zhang, J., et al. (2018). The sports industry in growing economies: Critical issues and challenges. International Journal of Sports Marketing and Sponsorship, 19(2), 110–126.

    Google Scholar 

Download references

Funding

The authors have no relevant financial interests to disclose.

Author information

Authors and Affiliations

  1. Department of Management Studies, Indian Institute of Technology Delhi, Delhi, India

    Apurva Jha, Arpan Kumar Kar & Agam Gupta

Authors
  1. Apurva Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arpan Kumar Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Agam Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The first, second and third authors have contributed equally to the study conception and design. Material preparation, data collection and analysis were performed by Apurva Jha. Review, editing and supervision were performed by Dr. Arpan Kar and Dr. Agam Gupta. All authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Apurva Jha.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A

See Tables

Table 5 Participant demographics for the initial exploratory study
Full size table

5,

Table 6 Batting and Bowling Performance Indicators
Full size table

6.

Appendix B.1

See Fig. 

Fig. 12
figure 12

Flowchart for team selection in Dream11

Full size image

12.

Appendix B.2

See Table

Table 7 Contest Types
Full size table

7.

Appendix B.3

See Table

Table 8 Algorithmic team selection vs Team by Rank 1
Full size table

8.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jha, A., Kar, A.K. & Gupta, A. Optimization of team selection in fantasy cricket: a hybrid approach using recursive feature elimination and genetic algorithm. Ann Oper Res 325, 289–317 (2023). https://doi.org/10.1007/s10479-022-04726-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-022-04726-z

Keywords

Search

Navigation