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An Overview of Beach Soccer, Sepak Takraw and the Application of Machine Learning in Team Sports

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Machine Learning in Team Sports

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

This chapter shall provide an overview of the sport of beach soccer as well as sepak takraw. It then provides a brief review on existing literature that employs machine learning on team sports. The clustering techniques, as well as machine learning models that are used in the book, are also deliberated. The participants (subjects) and the performance measures utilised in the study are also reflected in this chapter.

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References

  1. J. Castellano, D. Casamichana, Heart rate and motion analysis by GPS in beach soccer. J. Sports Sci. Med. 9, 98–103 (2010). http://www.doaj.org/doaj?func=openurl&genre=article&issn=13032968&date=2010&volume=9&issue=1&spage=98

  2. W.S.S. Leite, Physiological demands in football, futsal and beach soccer: a brief review. Eur. J. Phys. Educ. Sport Sci. 2, 1–10 (2016). https://doi.org/10.5281/ZENODO.205160

    Article  Google Scholar 

  3. R. Sanitate, J. Harney, M. Schiro, D. Wollbrinck, M. Carrigg, C. Buell, Takraw: a global sport. Strategies 11, 29–33 (1998)

    Article  Google Scholar 

  4. M.N. Jawis, R. Singh, H.J. Singh, M.N. Yassin, Anthropometric and physiological profiles of sepak takraw players. Br. J. Sports Med. 39, 825–829 (2005). https://doi.org/10.1136/bjsm.2004.016915

    Article  Google Scholar 

  5. B. Ulmer, M. Fernandez, Predicting soccer match results in the English Premier League, 2013

    Google Scholar 

  6. C. Peace, E. Okechukwu, An improved prediction system for football a match result. IOSR J. Eng. 04, 2250–3021 (2014)

    Google Scholar 

  7. C.P. Igiri, Support vector machine-based prediction system for a football match result. IOSR J. Comput. Eng. 17, 21–26 (2015). https://doi.org/10.9790/0661-17332126

    Article  Google Scholar 

  8. R.G. Martins, A.S. Martins, L.A. Neves, L.V. Lima, E.L. Flores, M.Z. do Nascimento, Exploring polynomial classifier to predict match results in football championships. Expert Syst. Appl. 83, 79–93 (2017). https://doi.org/10.1016/J.ESWA.2017.04.040

    Article  Google Scholar 

  9. A. Joseph, N.E. Fenton, M. Neil, Predicting football results using Bayesian nets and other machine learning techniques. Knowl.-Based Syst. 19, 544–553 (2006). https://doi.org/10.1016/J.KNOSYS.2006.04.011

    Article  Google Scholar 

  10. N. Razali, A. Mustapha, F.A. Yatim, R. Ab Aziz, Predicting football matches results using Bayesian networks for English Premier League (EPL). IOP Conf. Ser. Mater. Sci. Eng. 226, 012099 (2017). https://doi.org/10.1088/1757-899X/226/1/012099

    Article  Google Scholar 

  11. R.M. Musa, A.P.P. Abdul Majeed, M.A. Mohd Razman, M.A.H. Shaharudin, Match outcomes prediction of six top English Premier League clubs via machine learning technique, in Communications in Computer and Information Science (Springer Verlag, 2019), pp. 236–244. https://doi.org/10.1007/978-981-13-7780-8_20

    Chapter  Google Scholar 

  12. A.E. SaricaoÄŸlu, A. Aksoy, T. Kaya, Prediction of Turkish Super League match results using supervised machine learning techniques (2020). https://doi.org/10.1007/978-3-030-23756-1_34

    Google Scholar 

  13. G. Xiaohong, W. Yu, Analysis of basketball training model optimization based on artificial intelligence and computer aided model (2020). https://doi.org/10.1007/978-3-030-25128-4_257

    Google Scholar 

  14. M.S. Oughali, M. Bahloul, S.A. El Rahman, Analysis of NBA players and shot prediction using random forest and XGBoost models, in 2019 International Conference on Computer and Information Sciences, ICCIS 2019 (Institute of Electrical and Electronics Engineers Inc., 2019). https://doi.org/10.1109/ICCISci.2019.8716412

  15. S. Valero, Predicting win-loss outcomes in MLB regular season games—a comparative study using data mining methods. Int. J. Comput. Sci. Sport 15 (2016). https://doi.org/10.1515/ijcss-2016-0007

    Article  Google Scholar 

  16. B. Tolbert, T. Trafalis, Predicting Major League Baseball championship winners through data mining. Athens J. Sports 3(4), 239 (2016). https://doi.org/10.30958/ajspo.3.4.1

    Article  Google Scholar 

  17. N. Pathak, H. Wadhwa, Applications of modern classification techniques to predict the outcome of ODI cricket. Procedia Comput. Sci. 55–60 (2016). https://doi.org/10.1016/j.procs.2016.05.126

    Article  Google Scholar 

  18. J. Kumar, R. Kumar, P. Kumar, Outcome prediction of ODI cricket matches using decision trees and MLP networks, in ICSCCC 2018—1st International Conference on Secure Cyber Computing and Communications (Institute of Electrical and Electronics Engineers Inc., 2019), pp. 343–347. https://doi.org/10.1109/ICSCCC.2018.8703301

  19. M.M. Rahman, M.O.F. Shamim, S. Ismail, An analysis of Bangladesh One Day International cricket data: a machine learning approach, in 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET) (IEEE, 2018), pp. 190–194

    Google Scholar 

  20. R.M. Musa, M.R. Abdullah, A.B.H.M. Maliki, N.A. Kosni, M. Haque, The application of principal components analysis to recognize essential physical fitness components among youth development archers of Terengganu, Malaysia. Indian J. Sci. Technol. 9 (2016)

    Google Scholar 

  21. Z. Taha, M. Haque, R.M. Musa, M.R. Abdullah, A.B.H.M. Maliki, N. Alias, N.A. Kosni, Intelligent prediction of suitable physical characteristics toward archery performance using multivariate techniques. J. Glob. Pharma Technol. (2017)

    Google Scholar 

  22. M.R. Abdullah, A.B.H.M. Maliki, R.M. Musa, N.A. Kosni, H. Juahir, M. Haque, Multi-hierarchical pattern recognition of athlete’s relative performance as a criterion for predicting potential athletes. J. Young Pharm. 8, 463 (2016)

    Article  Google Scholar 

  23. O. Maimon, L. Rokach, Data Mining and Knowledge Discovery Handbook (2005). https://doi.org/10.1007/b107408

    MATH  Google Scholar 

  24. R.M. Musa, M.R. Abdullah, A.B.H.M. Maliki, N.A. Kosni, S.M. Mat-Rasid, A. Adnan, H. Juahir, Supervised pattern recognition of archers’ relative psychological coping skills as a component for a better archery performance. J. Fundam. Appl. Sci. 10, 467–484 (2018)

    Google Scholar 

  25. R. Muazu Musa, A.P.P. Abdul Majeed, Z. Taha, M.R. Abdullah, A.B. Husin Musawi Maliki, N. Azura Kosni, The application of Artificial Neural Network and k-Nearest Neighbour classification models in the scouting of high-performance archers from a selected fitness and motor skill performance parameters. Sci. Sports (2019). https://doi.org/10.1016/j.scispo.2019.02.006

    Article  Google Scholar 

  26. C. Wu, R.C. Gudivada, B.J. Aronow, A.G. Jegga, Computational drug repositioning through heterogeneous network clustering. BMC Syst. Biol. 7, S6 (2013). https://doi.org/10.1186/1752-0509-7-S5-S6

    Article  Google Scholar 

  27. V.D. Blondel, J. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of community hierarchies in large networks. J. Stat. Mech. Theory Exp. 2008 (2008). https://doi.org/10.1088/1742-5468/2008/10/P10008

    Article  Google Scholar 

  28. A. Motevalli, S.A. Naghibi, H. Hashemi, R. Berndtsson, B. Pradhan, V. Gholami, Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater. J. Clean. Prod. 228, 1248–1263 (2019). https://doi.org/10.1016/j.jclepro.2019.04.293

    Article  Google Scholar 

  29. M.A.M. Razman, G.A. Susto, A. Cenedese, A.P.P. Abdul Majeed, R.M. Musa, A.S. Abdul Ghani, F.A. Adnan, K.M. Ismail, Z. Taha, Y. Mukai, Hunger classification of Lates calcarifer by means of an automated feeder and image processing. Comput. Electron. Agric. 163 (2019). https://doi.org/10.1016/j.compag.2019.104883

    Article  Google Scholar 

  30. K.J. Luken, R.P. Norris, L.A.F. Park, Preliminary results of using k-nearest neighbor regression to estimate the redshift of radio-selected data sets. Publ. Astron. Soc. Pacific. 131, 108003 (2019). https://doi.org/10.1088/1538-3873/aaea17

    Article  Google Scholar 

  31. F. Martínez, M.P. Frías, M.D. Pérez, A.J. Rivera, A methodology for applying k-nearest neighbor to time series forecasting. Artif. Intell. Rev. (2017). https://doi.org/10.1007/s10462-017-9593-z

    Article  Google Scholar 

  32. T.M. Cover, P.E. Hart, Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967). https://doi.org/10.1109/TIT.1967.1053964

    Article  MATH  Google Scholar 

  33. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  34. W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943). https://doi.org/10.1007/BF02478259

    Article  MathSciNet  MATH  Google Scholar 

  35. I.M. Yusri, A.P.P. Abdul Majeed, R. Mamat, M.F. Ghazali, O.I. Awad, W.H. Azmi, A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel. Renew. Sustain. Energy Rev. (2018). https://doi.org/10.1016/j.rser.2018.03.095

    Article  Google Scholar 

  36. A. El-Sawy, A.P.P. Abdul Majeed, R.M. Musa, M.A. Mohd Razman, M.H.A. Hassan, A.A. Jaafar, The flexural strength prediction of porous Cu-Sn-Ti composites via artificial neural networks, in Lecture Notes in Mechanical Engineering (Pleiades Publishing, 2020), pp. 403–407. https://doi.org/10.1007/978-981-13-8323-6_34

    Google Scholar 

  37. M.A. Abdullah, M.A.R. Ibrahim, M.N.A.B. Shapiee, M.A. Mohd Razman, R.M. Musa, A.P.P. Abdul Majeed, The classification of skateboarding trick manoeuvres through the integration of IMU and machine learning (2020). https://doi.org/10.1007/978-981-13-9539-0_7

    Google Scholar 

  38. N.Q. Radzuan, M.H.A. Hassan, A.P.P. Abdul Majeed, R.M. Musa, M.A. Mohd Razman, K.A. Abu Kassim, Predicting serious injuries due to road traffic accidents in Malaysia by means of artificial neural network (2020). https://doi.org/10.1007/978-981-13-9539-0_8

    Google Scholar 

  39. L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  40. M.R. Abdullah, R.M. Musa, A.B.H.M. Maliki, N.A. Kosni, P.K. Suppiah, Development of tablet application based notational analysis system and the establishment of its reliability in soccer. J. Phys. Educ. Sport 16, 951–956 (2016). https://doi.org/10.7752/jpes.2016.03150

    Article  Google Scholar 

  41. K. McGuigan, M. Hughes, D. Martin, Performance indicators in club level Gaelic football. Int. J. Perform. Anal. Sport 18, 780–795 (2018). https://doi.org/10.1080/24748668.2018.1517291

    Article  Google Scholar 

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Correspondence to Anwar P. P. Abdul Majeed .

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Muazu Musa, R., P. P. Abdul Majeed, A., Kosni, N.A., Abdullah, M.R. (2020). An Overview of Beach Soccer, Sepak Takraw and the Application of Machine Learning in Team Sports. In: Machine Learning in Team Sports. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-15-3219-1_1

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