Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2020, Cilt: 11 Sayı: 3, 258 - 268, 15.12.2020

Hasan Aka , Zait Burak Aktuğ , Faruk Kılıç

https://doi.org/10.17155/omuspd.761660
Cited By: 2

Öz

This study, artificial neural networks (ANN) model using the Turkey Super League season-ending ranking of teams according to the number of input variables thrown and the renewed goal was conducted to predict. Working under the Turkey Super League in the 2015/2016, 2016/2017 and 2017/2018 a total of 918 matches played in the season; The data of the number of goals scored and defeated were evaluated. In the Turkey Super League, it was determined that seasonal data for 2015/2016 and 2016/2017 were as input variables, and seasonal data for 2017/2018 were output variables. The data analyzed in the study were separated randomly for training and testing purposes. The league order of the teams was modeled with numerical values between 0 (zero) and 1 (one). According to the results of the analysis conducted through the ANN model, the end-of-season team order in the Turkey Super League was estimated at high accuracy for several teams (above 99%) in the test dataset. Turkey Super League at the end of the season the team ranking is determined that directly affect the number of discarded and renewed goals. Estimating the end-of-season team ranking in football with the machine learning method can enable clubs to set transfer policies according to their destination in the end-of-season league ranking.

Anahtar Kelimeler

Artificial Neural Networks Soccer Turkey Super League

Kaynakça

  • 1. Harper DJ, Carling C, Kiely J. High-intensity acceleration and deceleration demands in elite team sports competitive match play: A systematic review and meta-analysis of observational studies. Sports Medicine, 2019; 49(12): 1923-1947.
  • 2. Sarmento H, Anguera MT, Pereira A, Araújo D. Talent identification and development in male football: A systematic review. Sports Medicine, 2018; 48(4): 907-931.
  • 3. Brito de Souza D, López-Del Campo R, Blanco-Pita H, Resta R, Del Coso J. An extensive comparative analysis of successful and unsuccessful football teams in La Liga. Frontiers in Psychology, 2019; 10: 1-8.
  • 4. Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A League: Effect of fatigue and competitive level. Journal of Science and Medicine in Sport, 2009; 12(1): 227-233.
  • 5. Carling C, Williams AM, Reilly T. Handbook of soccer match analysis: A systematic approach to improving performance. New York: Routledge; 2007.
  • 6. Baacke H. Voleybol antrenmanı üst düzey takımlar için el kitabı 2. İstanbul: Çağrı Baskı; 2005.
  • 7. O'Donoghue P. What is sports performance analysis, In: O'Donoghue P, editor, An introduction to performance analysis of sport. New York: Routledge; 2015.
  • 8. Setterwall D. Computerised video analysis of football-technical and commercial possibilities for football coaching. centre for user oriented it design. Department of numerical analysis and computer science. 2003.
  • 9. Ayyıldız E. Amerika Basketbol Ligi (NBA) maç sonuçlarının yapay sinir ağları ile tahmini. Gaziantep Üniversitesi Spor Bilimleri Dergisi, 2018; 3(1): 40-53.
  • 10. Bartlett R. Artificial intelligence in sports biomechanics: New dawn or false hope. Journal of Sports Science and Medicine, 2006; 5(4): 474-479.
  • 11. Öztemel E. Yapay sinir ağları. Türkiye: Papatya Yayınevi; 2003.
  • 12. Sağıroğlu Ş, Beşdok E, Erler M. Mühendislikte yapay zeka uygulamaları 1 / Yapay sinir ağları. Kayseri: Ufuk Kitap Kırtasiye –Yayıncılık Tic. Ltd. Şti; 2003.
  • 13. Arabzad A, Araghi M, Soheil S. Football match results prediction using artificial neural networks: The case of Iran pro league. International Journal of Applied Research on Industrial Engineering, 2014; 1(3): 159-179.
  • 14. Özden S, Kılıç F. Performance evaluation of GSA, SOS, ABC and ANN algorithms on linear and quadratic modelling of eggplant drying kinetic. Food Science and Technology. 2019.
  • 15. Sözen A, Arcaklioğlu E, Özkaymak M. Turkey’s net energy consumption. Applied Energy, 2005; 81(2): 209-221.
  • 16. Salman MS, Kukrer O, Hocanin A. Recursive inverse algorithm: Mean-square-error analysis. Digital Signal Processing, 2017; 66: 10-17.
  • 17. Menet F, Berthier P, Gagnon M, Fernandez JM. Spartan Networks: Self-feature-squeezing neural networks for increased robustness in adversarial settings. Computers & Security, 2020; 88: 1-17.
  • 18. Aka H. Yapay sinir ağları modeli ile futbolda takım sıralamasının tahmin edilmesi. Spor Bilimleri Alanında Akademik Çalışmalar-2. Ankara: Gece Kitaplığı Yayın Evi; 2020.
  • 19. Tümer AE, Koçer S. Prediction of team league’s rankings in volleyball by artificial neural network method. International Journal of Performance Analysis in Sport, 2017; 17(3): 202-211.
  • 20. Igiri CP, Nwachukwu EO. An improved prediction system for football a match result. IOSR Journal of Engineering, 2014; 4: 12-20.
  • 21. Ivankovic Z, Rackovic M, Markoski B, Radosav D, Ivankovic, M. Analysis of basketball games using neural networks. In Computational Intelligence and Informatics (CINTI), 11th International Symposium on (pp. 251–256), Obuda University Budapest, Hungary. IEEE. 2010.
  • 22. McCabe A, Trevathan J. Artificial intelligence in sports prediction. In information technology: New generations, ITNG 2008 Fifth International Conference Las Vegas. 2008: 1194–1197.
  • 23. Arslan A, İnce R. The neural network approximation to the size effect in fracture of cementitious materials. Engineering Fracture Mechanics, 1996; 54(2): 249-261.

TÜRKİYE SÜPER LİG SEZON SONU TAKIM SIRALAMASININ GELİŞTİRİLEN YAPAY SİNİR AĞLARI MODELİ İLE TAHMİN EDİLMESİ

Yıl 2020, Cilt: 11 Sayı: 3, 258 - 268, 15.12.2020

Hasan Aka , Zait Burak Aktuğ , Faruk Kılıç

https://doi.org/10.17155/omuspd.761660
Cited By: 2

Öz

Bu çalışma yapay sinir ağları (YSA) modeli kullanılarak Türkiye Süper Lig sezon sonu takım sıralamasının, atılan ve yenilen gol sayısı giriş değişkenlerine göre tahmin edilmesi amacıyla yapılmıştır. Çalışma kapsamında Türkiye Süper Liginde 2015/2016, 2016/2017 ve 2017/2018 sezonlarında oynanan toplam 918 maçta; atılan ve yenilen gol sayısı değişkenlerine ait veriler değerlendirilmiştir. Türkiye Süper Liginde 2015/2016 ve 2016/2017 sezonlarında oynanan maçların analizi yapılarak 2017/2018 sezon sonu lig sıralaması tahmin edilmiştir. Çalışmada değerlendirilen veriler eğitim ve test için rastgele yöntemle ayrılmıştır. Takımların lig sıralaması 0 (sıfır) ile 1 (bir) aralığındaki sayısal değerlerle modellenmiştir. Geliştirilen YSA modeli ile yapılan analizlere göre Türkiye Süper Lig takım sıralaması birçok takım için (test veri kümesi) % 99’un üzerinde doğruluk oranıyla tahmin edilmiştir. Türkiye Süper Liginde sezon sonu takım sıralamasını atılan ve yenilen gol sayılarının doğrudan etkilediği belirlenmiştir. Futbolda sezon sonu takım sıralamasının makine öğrenme yöntemi ile tahmin edilmesi, kulüplerin sezon sonu lig sıralamasında hedefledikleri yerlere göre transfer politikaları belirlemelerini sağlayabilir.

Anahtar Kelimeler

Yapay Sinir Ağları, Futbol, Türkiye Süper Lig,

Kaynakça

  • 1. Harper DJ, Carling C, Kiely J. High-intensity acceleration and deceleration demands in elite team sports competitive match play: A systematic review and meta-analysis of observational studies. Sports Medicine, 2019; 49(12): 1923-1947.
  • 2. Sarmento H, Anguera MT, Pereira A, Araújo D. Talent identification and development in male football: A systematic review. Sports Medicine, 2018; 48(4): 907-931.
  • 3. Brito de Souza D, López-Del Campo R, Blanco-Pita H, Resta R, Del Coso J. An extensive comparative analysis of successful and unsuccessful football teams in La Liga. Frontiers in Psychology, 2019; 10: 1-8.
  • 4. Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A League: Effect of fatigue and competitive level. Journal of Science and Medicine in Sport, 2009; 12(1): 227-233.
  • 5. Carling C, Williams AM, Reilly T. Handbook of soccer match analysis: A systematic approach to improving performance. New York: Routledge; 2007.
  • 6. Baacke H. Voleybol antrenmanı üst düzey takımlar için el kitabı 2. İstanbul: Çağrı Baskı; 2005.
  • 7. O'Donoghue P. What is sports performance analysis, In: O'Donoghue P, editor, An introduction to performance analysis of sport. New York: Routledge; 2015.
  • 8. Setterwall D. Computerised video analysis of football-technical and commercial possibilities for football coaching. centre for user oriented it design. Department of numerical analysis and computer science. 2003.
  • 9. Ayyıldız E. Amerika Basketbol Ligi (NBA) maç sonuçlarının yapay sinir ağları ile tahmini. Gaziantep Üniversitesi Spor Bilimleri Dergisi, 2018; 3(1): 40-53.
  • 10. Bartlett R. Artificial intelligence in sports biomechanics: New dawn or false hope. Journal of Sports Science and Medicine, 2006; 5(4): 474-479.
  • 11. Öztemel E. Yapay sinir ağları. Türkiye: Papatya Yayınevi; 2003.
  • 12. Sağıroğlu Ş, Beşdok E, Erler M. Mühendislikte yapay zeka uygulamaları 1 / Yapay sinir ağları. Kayseri: Ufuk Kitap Kırtasiye –Yayıncılık Tic. Ltd. Şti; 2003.
  • 13. Arabzad A, Araghi M, Soheil S. Football match results prediction using artificial neural networks: The case of Iran pro league. International Journal of Applied Research on Industrial Engineering, 2014; 1(3): 159-179.
  • 14. Özden S, Kılıç F. Performance evaluation of GSA, SOS, ABC and ANN algorithms on linear and quadratic modelling of eggplant drying kinetic. Food Science and Technology. 2019.
  • 15. Sözen A, Arcaklioğlu E, Özkaymak M. Turkey’s net energy consumption. Applied Energy, 2005; 81(2): 209-221.
  • 16. Salman MS, Kukrer O, Hocanin A. Recursive inverse algorithm: Mean-square-error analysis. Digital Signal Processing, 2017; 66: 10-17.
  • 17. Menet F, Berthier P, Gagnon M, Fernandez JM. Spartan Networks: Self-feature-squeezing neural networks for increased robustness in adversarial settings. Computers & Security, 2020; 88: 1-17.
  • 18. Aka H. Yapay sinir ağları modeli ile futbolda takım sıralamasının tahmin edilmesi. Spor Bilimleri Alanında Akademik Çalışmalar-2. Ankara: Gece Kitaplığı Yayın Evi; 2020.
  • 19. Tümer AE, Koçer S. Prediction of team league’s rankings in volleyball by artificial neural network method. International Journal of Performance Analysis in Sport, 2017; 17(3): 202-211.
  • 20. Igiri CP, Nwachukwu EO. An improved prediction system for football a match result. IOSR Journal of Engineering, 2014; 4: 12-20.
  • 21. Ivankovic Z, Rackovic M, Markoski B, Radosav D, Ivankovic, M. Analysis of basketball games using neural networks. In Computational Intelligence and Informatics (CINTI), 11th International Symposium on (pp. 251–256), Obuda University Budapest, Hungary. IEEE. 2010.
  • 22. McCabe A, Trevathan J. Artificial intelligence in sports prediction. In information technology: New generations, ITNG 2008 Fifth International Conference Las Vegas. 2008: 1194–1197.
  • 23. Arslan A, İnce R. The neural network approximation to the size effect in fracture of cementitious materials. Engineering Fracture Mechanics, 1996; 54(2): 249-261.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Sporda Yönetim ve Organizasyon
Yazarlar

Hasan Aka 0000-0003-0603-9478

Zait Burak Aktuğ 0000-0002-5102-4331

Faruk Kılıç 0000-0002-9978-1972

Yayımlanma Tarihi 15 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 11 Sayı: 3

Kaynak Göster

APA Aka, H., Aktuğ, Z. B., & Kılıç, F. (2020). TÜRKİYE SÜPER LİG SEZON SONU TAKIM SIRALAMASININ GELİŞTİRİLEN YAPAY SİNİR AĞLARI MODELİ İLE TAHMİN EDİLMESİ. Spor Ve Performans Araştırmaları Dergisi, 11(3), 258-268. https://doi.org/10.17155/omuspd.761660

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