Optimization of team selection in fantasy cricket: a hybrid approach using recursive feature elimination and genetic algorithm. (English) Zbl 07712032
Summary: 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.
MSC:
| 62Pxx | Applications of statistics |
| 90Bxx | Operations research and management science |
| 90Cxx | Mathematical programming |
References:
| [1] | Adhikari, A.; Majumdar, A.; Gupta, G., 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 (2020) · doi:10.1007/s10479-018-3088-4 |
| [2] | Agarwal, S.; Yadav, L.; Mehta, S., Cricket team prediction with Hadoop: Statistical modeling approach, Procedia Computer Science, 122, 525-532 (2017) |
| [3] | 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. |
| [4] | Ahmed, F.; Deb, K.; Jindal, A., Multi-objective optimization and decision-making approaches to cricket team selection, Applied Soft Computing, 13, 1, 402-414 (2013) |
| [5] | Amin, G.; Sharma, SK, Cricket team selection using data envelopment analysis, European Journal of Sports Science, 14, 1, 369-376 (2014) |
| [6] | Andreff, W., Globalization of the sports economy, Diritto Ed Economia Dello Sport, 4, 3, 14-34 (2008) |
| [7] | Arkes, H.; Herren, L.; Isen, A., The role of potential loss in the influence on risk-taking behaviour, Organizational Behaviour and Human Development Process, 42, 2, 181-193 (1988) |
| [8] | Baerg, A. (2009). Just a fantasy? Exploring fantasy sports. The Electronic Journal of Communication. 19(3&4). |
| [9] | Barr, G.; Holdsworth, C.; Kantor, B., Evaluating performances at the 2007 Cricket World cup, South African Statistical Journal, 42, 2, 125-142 (2008) · Zbl 1397.62618 |
| [10] | Barr, G.; Kantor, B., A criterion for comparing and selecting batsmen in limited overs cricket, Journal of the Operational Research, 55, 12, 1266-1274 (2004) · Zbl 1088.90545 |
| [11] | Bhattacharjee, D.; Saikia, H., An objective approach of balanced cricket team selection using binary integer programming method, Journal of Operations Research, 53, 225-247 (2016) · Zbl 1360.90158 |
| [12] | Billings, AC; Ruihley, BJ, Why we watch, why we play: The relationship between fantasy sport and fan-ship motivations, Mass Communication and Society, 16, 1, 5-25 (2013) · doi:10.1080/15205436.2011.635260 |
| [13] | Cea, S.; Duran, G.; Guajardo, M., An analytics approach to the FIFA ranking procedure and the World cup final draw, Annals of Operations Research, 286, 119-146 (2020) · Zbl 1493.62633 |
| [14] | Chantelat, P., An overview of some recent perspectives on the socioeconomics of sport, International Review for the Sociology of Sport, 34, 1, 59-68 (1999) |
| [15] | Cordes, V.; Olfman, L., Sports analytics: predicting athletic performance with a genetic algorithm, Decision Support and Analytics., 13, 131-145 (2016) |
| [16] | Darst, BF; Malecki, KC; Engelman, CD, Using recursive feature elimination in random forest to account for correlated variables in high dimensional data, BMC Genetics., 19, 65 (2018) · doi:10.1186/s12863-018-0633-8 |
| [17] | 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. |
| [18] | Davis, NW; Duncan, M., Sports knowledge is power: Reinforcing masculine privilege thorough fantasy sport league participation, Journal of Sport and Social Issues, 30, 3, 244-264 (2006) |
| [19] | Deb, PK; Pratap, A.; Agarwal, S., A fast and elitist multi-objective genetic algorithm: NSGA II, IEEE Transactions on Evolutionary Computing, 6, 2, 182-197 (2002) |
| [20] | 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. |
| [21] | Dey, PK; Ghosh, DN; Mondal, AC, A MCDM approach for evaluating bowler’s performance in IPL, Journal of Emerging Trends in Computing and Information Sciences, 11, 563-573 (2011) |
| [22] | Dream11 (2020). Fantasy Points System. Retrieved, October 15, 2020 from https://www.dream11.com/games/point-system. |
| [23] | Dwyer, B.; Lecrom, CW, Is fantasy trumping reality? The redefined national football league experience of novice fantasy football participants, Journal of Contemporary Athletics., 7, 3, 119-139 (2013) |
| [24] | 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. |
| [25] | Farquhar, LK; Meeds, R., Types of fantasy sports users and their motivations, Journal of Computer-Mediated Communication, 12, 4, 1208-1228 (2007) · doi:10.1111/j.1083-6101.2007.00370 |
| [26] | Fisher, E. (2015). Fantasy players spend big. Retrieved December 8, 2020 from http://www.sportsbusinessjournal.com/article/60598 |
| [27] | Gerber, H.; Sharp, GD, 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 (2006) |
| [28] | Goldsmith, A.; Walker, M., The NASCAR experience : Examining the influence of fantasy sports participation on ‘non-fans’, Sports Management Review, 18, 2, 231-243 (2015) |
| [29] | Granitto, P.; Furlanello, C.; Biasoli, F.; Gasperi, F., Recursive feature elimination with random forest for PTR_MS analysis of agroindustrial products, Chemo Metrics and Intelligent Laboratory Systems, 83, 2, 83-90 (2006) |
| [30] | Guyon, I.; Elisseeff, A., An introduction to variable and feature selection, Journal of Machine Learning Research, 3, 1157-1182 (2003) · Zbl 1102.68556 |
| [31] | Guyon, I.; Weston, J.; Barnhill, S.; Vapnik, V., Gene selection for cancer classification using support vector machines, Machine Learning, 46, 1, 382-422 (2002) · Zbl 0998.68111 |
| [32] | Holland, JH, Genetic algorithms, Scientific American, 267, 1, 66-73 (1992) |
| [33] | ICC. (2019). Men’s ODI Player Rankings (2019). Retrieved, October 15, 2020, from https://www.icc-cricket.com/rankings/mens/player-rankings/odi |
| [34] | Iyer, S.; Sharda, R., Prediction of athletes’ performance using neural networks: An application in team selection, Expert Systems with Application, 36, 3, 5510-5522 (2009) |
| [35] | Juan, AA; Keenan, P.; Marti, R., A review of the role of heuristics in stochastic optimization: From metaheuristics to learn heuristics, Annals of Operation Research (2021) · Zbl 1511.90427 · doi:10.1007/s10479-021-04142-9 |
| [36] | Kamble, AG; Rao, RV; Kale, AV; Samant, SP, Selection of cricket players using analytical hierarchical process, International Journal of Sports Science and Engineering, 5, 207-212 (2011) |
| [37] | Kissane, RJ; Winslow, S., Bonding and abandoning: Gender, social interaction, and relationships in fantasy sports, Social Currents, 3, 3, 256-272 (2016) |
| [38] | Kissane, RJ; Winslow, S., ‘You’re underestimating me, and you shouldn’t’: Women’s agency in fantasy sports, Gender & Society., 30, 5, 819-841 (2016) |
| [39] | Koulis, T.; Muthukumarana, S.; Briercliffe, CD, A Bayesian stochastic model for batting performance evaluation in one-day cricket, Journal of Quantitative Analysis in Sport, 10, 1, 1-13 (2014) |
| [40] | Kumar, S.; Kar, AK; Ilavarasan, PV, Applications of text mining in services management: A systematic literature review, International Journal of Information Management Data Insights, 1, 1, 1-14 (2021) |
| [41] | Kumarasiri, I.; Perrera, S., Optimal one day cricket team selection by genetic algorithm, Journal of Sports Analytics, 36, 4, 213-221 (2017) |
| [42] | 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. |
| [43] | Kwak, DH; Lim, CH; Lee, WY; Mahan, JE, 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 (2010) |
| [44] | 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. |
| [45] | Lee, J.; Ruihley, BJ; Brown, N.; Billings, AC, The effects of fantasy football participation on team identification, team loyalty and NFL fandom, Journal of Sports Media, 8, 1, 207-227 (2013) · doi:10.1353/jsm.2013.0008 |
| [46] | Lee, S.; Seo, WJ; Green, BC, Understanding why people play fantasy sport: Development of the fantasy sport motivation inventory (FanSMI), European Sport Management Quarterly, 13, 2, 166-199 (2013) · doi:10.1080/16184742.2012.752855 |
| [47] | Lemmer, H., Team selection after a short cricket series, European Journal of Sports Science, 13, 2, 200-206 (2013) |
| [48] | Lewis, A., Towards a fairer measure of play, Journal of Operational Research Society, 56, 804-815 (2005) · Zbl 1084.90513 |
| [49] | Li, H.; Zhang, Q., Multiobjective optimisation problems with complicated pareto sets and NSGA II, IEEE Transactions on Evolutionary Computation, 13, 2, 284-302 (2009) · doi:10.1109/TEVC.2008.925798 |
| [50] | 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. |
| [51] | 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. |
| [52] | Marchica, L.; Derevensky, J., Fantasy sports: A growing concern among college student athletes, International Journal of Mental Health and Addiction, 14, 5, 635-645 (2016) |
| [53] | Martin, RJ; Nelson, S., Fantasy sports, real money: Exploration of the relationship between fantasy sports participation and gambling-related problems, Addictive Behaviours, 39, 10, 1377-1382 (2014) |
| [54] | Moreno, P.; Lozano, S., A network DEA assessment of team efficiency in the NBA, Annals of Operations Research, 214, 99-124 (2014) · Zbl 1303.90016 · doi:10.1007/s10479-012-1074-9 |
| [55] | Nikolaidis, Y., Building a basketball game strategy through strategic analysis of data, Annals of Operations Research, 227, 137-159 (2015) · doi:10.1007/s10479-013-1309-4 |
| [56] | Oates, TP, New media and the repackaging of NFL fandom, Sociology of Sport Journal, 26, 1, 31-49 (2009) |
| [57] | Omkar, S.N., and R, Verma. (2003). Cricket team selection using genetic algorithm. In Proceedings of the international congress on sports dynamics, 1-9. |
| [58] | Patravardhan, C.; Singh, S., A new machine learning based deep performance index for ranking IPL T20 cricketers, International Journal of Computer Applications, 137, 10, 887-975 (2016) |
| [59] | Pickering, D.; Blaszczynski, A.; Hartmann, M.; Keen, B., Fantasy sports: Skill, gambling, or are these irrelevant issues?, Current Addiction Reports, 3, 3, 307-313 (2016) |
| [60] | Rohde, N., An “economic” ranking of batters in test cricket, Journal of Applied Economics and Policy, 30, 4, 455-465 (2011) |
| [61] | Ruihley, BJ; Billings, AC; Rae, C., As time goes by: Deciphering the fantasy sport playing teenager, Sport Marketing Quarterly, 23, 4, 187-197 (2014) |
| [62] | Shah, P.; Shah, M., Pressure index in cricket, IOSR Journal of Sports and Physical Education, 5, 1, 9-11 (2014) |
| [63] | Shapiro, SL; Drayer, J.; Dwyer, B., Exploring fantasy baseball consumer behavior: Examining the relationship between identification, fantasy participation, and consumption, Journal of Sport Behaviour, 37, 1, 77-93 (2014) |
| [64] | Sharp, G.; Brettenny, WJ; Gonsalves, JW; Lourens, M., Integer optimisation for the selection of Twenty20 cricket team, Journal of Operational Research Society, 62, 9, 1688-1694 (2010) |
| [65] | 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. |
| [66] | 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. |
| [67] | Smith, B.; Sharma, P.; Hooper, P., Decision making in online fantasy sports communities, Interactive Technology and Smart Education, 3, 4, 347-360 (2006) · doi:10.1108/17415650680000072 |
| [68] | Suh, YI; Lim, C.; Kwak, DH; Pedersen, PM, 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 (2010) · doi:10.5199/ijsmart-1791-874X-5a |
| [69] | Tacon, R.; Vainker, S., Fantasy sport: A systematic review and new research directions, European Sport Management Quarterly, 17, 5, 558-589 (2017) |
| [70] | Toffolo, TAM; Christiaens, J.; Speiksma, FCR, The sports team grouping problem, Annals of Operations Research, 275, 223-243 (2019) · Zbl 1411.90302 · doi:10.1007/s10479-017-2595-z/ |
| [71] | Yang, CH; Lin, HY; Chen, CP, Measuring the efficiency of NBA teams: Additive efficiency decomposition in two stage DEA, Annals of Operations Research, 217, 565-589 (2014) · Zbl 1308.62209 · doi:10.1007/s10479-014-1536-3 |
| [72] | Yusoff, Y.; Ngadiman, MS; Zain, MA, Overview of NSGA II for optimizing machine process parameters, Procedia Engineering, 15, 3978-3983 (2011) |
| [73] | Zhang, J., The sports industry in growing economies: Critical issues and challenges, International Journal of Sports Marketing and Sponsorship, 19, 2, 110-126 (2018) |
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