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Estimation of American Basketball League (NBA) Match Results by Artificial Neural Networks

Year 2018, Volume: 3 Issue: 1, 40 - 53, 25.03.2018

Abstract

In
this study, American National Basketball League (NBA) match results were tried
to be predicted using artificial neural networks by using 2015-2016 season
data. This prediction can be defined as a classification because it will take 0
to 1 value as the win or lose. In the study, 596 matches selected from
different matchday of the NBA 2015-2016 season were modeled to have 11
different input parameters and 1 output parameter. 396 of them were trained and
200 of them were randomly selected as test data, and an artificial neural
network with 3 hidden neurons was designed on both two layers. Logarithmic
sigmoid was used as transfer function and purelin function was used as output
function, and the values produced by the network are rounded to zero or one for
clustering as win or lose. Artificial neural networks have performed well in
this model, which is about 90% success. Moreover, by examining the confusion
matrix, it has been shown that network faults are distributed regular.

References

  • Hsu, K. L., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall‐runoff process. Water resources research, 31(10), 2517-2530.
  • Hu, F., & Zidek, J. V. (2004). Forecasting NBA basketball playoff outcomes using the weighted likelihood. Lecture Notes-Monograph Series, 385-395.
  • Karaatli, M., Helvacioğlu, Ö. C., Ömürbek, N., & Tokgöz, G. (2012). Yapay Sinir Ağları Yöntemi İle Otomobil Satış Tahmini. Uluslararası Yönetim İktisat ve İşletme Dergisi, 8(17), 87-100.
  • Karayel, D. (2009). Prediction and control of surface roughness in CNC lathe using artificial neural network. Journal of materials processing technology, 209(7), 3125-3137.
  • Meireles, M. R., Almeida, P. E., & Simões, M. G. (2003). A comprehensive review for industrial applicability of artificial neural networks. IEEE transactions on industrial electronics, 50(3), 585-601.
  • Mohaghegh, S., Arefi, R., Ameri, S., Aminiand, K., & Nutter, R. (1996). Petroleum reservoir characterization with the aid of artificial neural networks. Journal of Petroleum Science and Engineering, 16(4), 263-274.
  • Öztemel, E. (2003). Yapay Sinir Ağlari. PapatyaYayincilik, Istanbul.
  • Pardee, M. (1999). An artificial neural network approach to college football prediction and ranking. University of Wisconsin–Electrical and Computer Engineering Department.
  • Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., & Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE transactions on Power Systems, 6(2), 442-449.
  • Pomerleau, D. A. (1991). Efficient training of artificial neural networks for autonomous navigation. Neural Computation, 3(1), 88-97.
  • Ahn, B. S., Cho, S. S., & Kim, C. Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert systems with applications, 18(2), 65-74.
  • Radovanović, S., Radojičić, M., & Savić, G. (2014). Two-phased DEA-MLA approach for predicting efficiency of NBA players. Yugoslav Journal of Operations Research, 24(3), 347-358.
  • Rehman, S., & Mohandes, M. (2008). Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy, 36(2), 571-576.
  • Staudenmayer, J., Pober, D., Crouter, S., Bassett, D., & Freedson, P. (2009). An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. Journal of Applied Physiology, 107(4), 1300-1307.
  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G.,& Dieleman, S. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489.
  • Wilson, R. L. (1995). Ranking college football teams: A neural network approach. Interfaces, 25(4), 44-59.
  • Wong, M. D., & Nandi, A. K. (2004). Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Processing, 84(2), 351-365.
  • Yazici, A. C., Öğüş, E., Ankarali, S., Canan, S., Ankarali, H., & Akkuş, Z. (2007). Yapay Sinir Ağlarına Genel Bakış. Turkiye Klinikleri Journal of Medical Sciences, 27(1), 65-71.
  • Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European journal of operational research, 116(1), 16-32.
  • Zhou, C. C., Yin, G. F., & Hu, X. B. (2009). Multi-objective optimization of material selection for sustainable products: artificial neural networks and genetic algorithm approach. Materials & Design, 30(4), 1209-1215.
  • Akkaya, G. C., Demireli, E., & Yakut, Ü. H. (2009). İşletmelerde finansal başarısızlık tahminlemesi: Yapay sinir ağları modeli ile İMKB üzerine bir uygulama. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 10(2), 187-216.
  • Arslan, A., & Ince, R. (1996). The neural network approximation to the size effect in fracture of cementitious materials. Engineering Fracture Mechanics, 54(2), 249-261.
  • Civalek, Ö., (1999) Dairesel Plakların Nöro-Fuzzy Tekniği ile Analizi, DEÜ Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 1(2), 13-31.
  • Çelik, M. K. (2010). Bankaların Finansal Başarısızlıklarının Geleneksel ve Yeni Yöntemlerle Öngörüsü. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(2), 129-143.
  • Firat, M., & Güngör, M. (2004). Askı madde konsantrasyonu ve miktarının yapay sinir ağları ile belirlenmesi. Teknik Dergi, 15(73), 3267-3283.
  • Floyd, C. E., Lo, J. Y., Yun, A. J., Sullivan, D. C., & Kornguth, P. J. (1994). Prediction of breast cancer malignancy using an artificial neural network. Cancer, 74(11), 2944-2948.
  • Hamzaçebi, C. (2011). Yapay sinir ağları: tahmin amaçlı kullanımı MATLAB ve Neurosolutions uygulamalı. Ekin Basım Yayın Dağıtım, Bursa.
  • Haykin, S. (1994). Neural networks: A comprehensive foundation: Macmillan college publishing company. New York.

Amerika Basketbol Ligi (NBA) Maç Sonuçlarının Yapay Sinir Ağları ile Tahmini

Year 2018, Volume: 3 Issue: 1, 40 - 53, 25.03.2018

Abstract

Bu
çalışmada, Amerika Ulusal Basketbol Ligi (NBA) 2015-2016 sezonunun maç
verilerinden yararlanılarak maç sonuçları yapay sinir ağları kullanılarak tahmin
edilmeye çalışılmıştır. Bu tahmin maçı kimin kazanacağı bilgisini hedeflediği
için 0 yada 1 değeri alacağından sınıflandırma olarak tanımlanabilir. Çalışmada
NBA 2015-2016 sezonunun farklı dilimlerinden seçilen 596 maç 11 farklı girdi
parametresi ve 1 çıktı parametresi oluşacak şekilde modellenmiştir. Bu
verilerden 396 tanesi eğitim kalan 200 tanesi ise test verisi olarak rastgele
seçilerek her iki katmanında da 3 gizli nöron olan bir yapay sinir ağı
tasarlanmıştır. Tasarlanan ağın transfer fonksiyonu olarak logaritmik sigmoid,
çıktı fonksiyonu olarak ise purelin fonksiyonu kullanılmıştır, ayrıca ağın
ürettiği değerler 0 ya da 1 e yuvarlanarak sınıflandırılması sağlanmıştır.
Yaklaşık olarak yüzde 90 başarı yakalanan bu model özelinde yapay sinir ağları
başarılı bir performans göstermiştir. Ayrıca hata matrisi incelenerek ağın
hatalarının dengeli dağıldığı gösterilmiştir.

References

  • Hsu, K. L., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall‐runoff process. Water resources research, 31(10), 2517-2530.
  • Hu, F., & Zidek, J. V. (2004). Forecasting NBA basketball playoff outcomes using the weighted likelihood. Lecture Notes-Monograph Series, 385-395.
  • Karaatli, M., Helvacioğlu, Ö. C., Ömürbek, N., & Tokgöz, G. (2012). Yapay Sinir Ağları Yöntemi İle Otomobil Satış Tahmini. Uluslararası Yönetim İktisat ve İşletme Dergisi, 8(17), 87-100.
  • Karayel, D. (2009). Prediction and control of surface roughness in CNC lathe using artificial neural network. Journal of materials processing technology, 209(7), 3125-3137.
  • Meireles, M. R., Almeida, P. E., & Simões, M. G. (2003). A comprehensive review for industrial applicability of artificial neural networks. IEEE transactions on industrial electronics, 50(3), 585-601.
  • Mohaghegh, S., Arefi, R., Ameri, S., Aminiand, K., & Nutter, R. (1996). Petroleum reservoir characterization with the aid of artificial neural networks. Journal of Petroleum Science and Engineering, 16(4), 263-274.
  • Öztemel, E. (2003). Yapay Sinir Ağlari. PapatyaYayincilik, Istanbul.
  • Pardee, M. (1999). An artificial neural network approach to college football prediction and ranking. University of Wisconsin–Electrical and Computer Engineering Department.
  • Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., & Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE transactions on Power Systems, 6(2), 442-449.
  • Pomerleau, D. A. (1991). Efficient training of artificial neural networks for autonomous navigation. Neural Computation, 3(1), 88-97.
  • Ahn, B. S., Cho, S. S., & Kim, C. Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert systems with applications, 18(2), 65-74.
  • Radovanović, S., Radojičić, M., & Savić, G. (2014). Two-phased DEA-MLA approach for predicting efficiency of NBA players. Yugoslav Journal of Operations Research, 24(3), 347-358.
  • Rehman, S., & Mohandes, M. (2008). Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy, 36(2), 571-576.
  • Staudenmayer, J., Pober, D., Crouter, S., Bassett, D., & Freedson, P. (2009). An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. Journal of Applied Physiology, 107(4), 1300-1307.
  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G.,& Dieleman, S. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489.
  • Wilson, R. L. (1995). Ranking college football teams: A neural network approach. Interfaces, 25(4), 44-59.
  • Wong, M. D., & Nandi, A. K. (2004). Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Processing, 84(2), 351-365.
  • Yazici, A. C., Öğüş, E., Ankarali, S., Canan, S., Ankarali, H., & Akkuş, Z. (2007). Yapay Sinir Ağlarına Genel Bakış. Turkiye Klinikleri Journal of Medical Sciences, 27(1), 65-71.
  • Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European journal of operational research, 116(1), 16-32.
  • Zhou, C. C., Yin, G. F., & Hu, X. B. (2009). Multi-objective optimization of material selection for sustainable products: artificial neural networks and genetic algorithm approach. Materials & Design, 30(4), 1209-1215.
  • Akkaya, G. C., Demireli, E., & Yakut, Ü. H. (2009). İşletmelerde finansal başarısızlık tahminlemesi: Yapay sinir ağları modeli ile İMKB üzerine bir uygulama. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 10(2), 187-216.
  • Arslan, A., & Ince, R. (1996). The neural network approximation to the size effect in fracture of cementitious materials. Engineering Fracture Mechanics, 54(2), 249-261.
  • Civalek, Ö., (1999) Dairesel Plakların Nöro-Fuzzy Tekniği ile Analizi, DEÜ Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 1(2), 13-31.
  • Çelik, M. K. (2010). Bankaların Finansal Başarısızlıklarının Geleneksel ve Yeni Yöntemlerle Öngörüsü. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(2), 129-143.
  • Firat, M., & Güngör, M. (2004). Askı madde konsantrasyonu ve miktarının yapay sinir ağları ile belirlenmesi. Teknik Dergi, 15(73), 3267-3283.
  • Floyd, C. E., Lo, J. Y., Yun, A. J., Sullivan, D. C., & Kornguth, P. J. (1994). Prediction of breast cancer malignancy using an artificial neural network. Cancer, 74(11), 2944-2948.
  • Hamzaçebi, C. (2011). Yapay sinir ağları: tahmin amaçlı kullanımı MATLAB ve Neurosolutions uygulamalı. Ekin Basım Yayın Dağıtım, Bursa.
  • Haykin, S. (1994). Neural networks: A comprehensive foundation: Macmillan college publishing company. New York.
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Sports Medicine
Journal Section Hareket ve Antrenman Bilimleri
Authors

Ertuğrul Ayyıldız 0000-0002-6358-7860

Publication Date March 25, 2018
Submission Date February 1, 2018
Published in Issue Year 2018 Volume: 3 Issue: 1

Cite

APA Ayyıldız, E. (2018). Amerika Basketbol Ligi (NBA) Maç Sonuçlarının Yapay Sinir Ağları ile Tahmini. Gaziantep Üniversitesi Spor Bilimleri Dergisi, 3(1), 40-53.

ISSN: 2536-5339

Gaziantep Üniversitesi Spor Bilimleri Dergisi

16157

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