Abstract
This paper represents the use of machine learning techniques in predicting the fantasy scores for future baseball matches. A dataset is prepared from Korean Baseball Organization (KBO) matches of past few years, the dataset includes various players’ statistics and match details that could affect players’ performance. It is presented as a form of multi-layer perceptron, initiating numerous features that strive to apprehend the quality for KBO baseball teams. The system was not only implemented using neural networks but was also experimented with other learning models like decision trees and Support vector machines. The main aim behind this approach is to compare the regression and neural models in respect to their cost function values for predicting the fantasy sport results.
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References
Beal R, Norman TJ, Ramchurn SD (2019) Artificial intelligence for team sports: a survey. Knowl Eng Rev. https://doi.org/10.1017/S0269888919000225
Baseball before we knew it: a search for the roots of the game. Choice Rev. Online (2005). https://doi.org/10.5860/choice.42-5927
Gillette G, Palmer P (2006) The 2006 ESPN baseball encyclopedia: the most comprehensive single-volume reference in print. Libr J
Hong J (2011) Sport fans’ sponsorship evaluation based on their perceived relationship value with a sport property. Int J Sport Manag Mark. https://doi.org/10.1504/IJSMM.2011.040260
Mahan III JE, Drayer J, Sparvero E (2012) Gambling and fantasy: an examination of the influence of money on fan attitudes and behaviors. Sport Mark Q
Halverson ER, Halverson R (2008) Fantasy baseball: the case for competitive fandom. Games Cult. https://doi.org/10.1177/1555412008317310
Cochran JJ (2005) The numbers game: baseball’s lifelong fascination with statistics. Am Stat. https://doi.org/10.1198/000313005x43551
Min B, Kim J, Choe C, Eom H, Bob McKay RI (2008) A compound framework for sports results prediction: a football case study. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2008.03.016
McCabe A, Trevathan J (2008) Artificial intelligence in sports prediction. https://doi.org/10.1109/ITNG.2008.203
Smith L, Lipscomb B, Simkins A (2007) Data mining in sports: predicting Cy young award winners. J Comput Sci Coll
Atlas M, Zhang YQ (2004) Fuzzy neural agents for online NBA scouting. https://doi.org/10.1109/WI.2004.10047
Polese G, Troiano M, Tortora G (2002) A data mining based system supporting tactical decisions. https://doi.org/10.1145/568760.568877
Miljković D, Gajić L, Kovačević A, Konjović Z (2010) The use of data mining for basketball matches outcomes prediction. https://doi.org/10.1109/SISY.2010.5647440
Maszczyk A, Gołaś A, Pietraszewski P, Roczniok R, Zając A, Stanula A (2014) Application of neural and regression models in sports results prediction. Procedia Soc Behav Sci. https://doi.org/10.1016/j.sbspro.2014.02.249
Hill GM (2016) Youth sport participation of professional baseball players. Sociol Sport J. https://doi.org/10.1123/ssj.10.1.107
Fialho G, Manhães A, Teixeira JP (2019) Predicting sports results with artificial intelligence—a proposal framework for soccer games. https://doi.org/10.1016/j.procs.2019.12.164
Russell S, Norvig P (2010) Artificial intelligence a modern approach, 3rd edn
de Myttenaere A, Golden B, Le Grand B, Rossi F (2016) Mean absolute percentage error for regression models. Neurocomputing. https://doi.org/10.1016/j.neucom.2015.12.114
Fürnkranz J et al (2011) Mean squared error. In: Encyclopedia of machine learning
Nau R (2015) What’s a good value for R-squared? In: Linear regression models. Duke University
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Harikrishnan, V.K., Deore, H., Raju, P., Agrawal, A., Sharma, M.M. (2021). Predictive Analysis Using Machine Learning Techniques for Fantasy Games. In: Manik, G., Kalia, S., Sahoo, S.K., Sharma, T.K., Verma, O.P. (eds) Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-0942-8_65
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