Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 7 Sayı: 1, 43 - 48, 31.03.2023
https://doi.org/10.30516/bilgesci.1097014

Öz

Kaynakça

  • Akpınar, H. (2014). Data: Veri Madenciliği. İstanbul: Papatya Yayıncılık.
  • Aygün D, Ulucenk E. (2019). Futbol Kulüplerinde İnsan Kaynakları Faaliyetlerinin Muhasebeleştirilmesi. Muhasebe Ve Vergi Uygulamaları Dergisi , 689–710.
  • Behravan, I., Razavi, S. M. (2021). A Novel Machine Learning Method For Estimating Football Players’ Value İn The Transfer Market. Soft Computing, 25(3), 2499–2511. Https://Doi.Org/10.1007/S00500-020-05319-3
  • Bruzzone, L. , Prieto, F. (2002). An Adaptive Semiparametric and Context-Based Approach to unsupervised Change Detection in Multitemporal Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 11 (4): 452-466, 2002.
  • Choudhary, A. (2016). Survey on K-Means and Its Variants. International Journal of Innovative Research in Computer and Communication Engineering, 4(1), ss.949-952.
  • Dempster, A. P., Laird, N. M., Rubin, D. B. (1977). Maximum Likelihood From Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), ss.1–22.
  • Gemici, B. (2012). Veri Madenciliği ve Bir Uygulaması. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Ekonometri Anabilim Dalı Ekonometri Programı Yüksek Lisans Tezi, İzmir.
  • Kangalli, S. G. (2014). Oecd Ülkelerinde Ekonomik Özgürlük: Bir Kümeleme Analizi Economic Freedom İn Oecd Countries: A Cluster Analysis. In Uluslararası Alanya İşletme Fakültesi Dergisi International Journal Of Alanya Faculty Of Business Yıl (Vol. 6, Issue 3). Http://Www.Heritage.Org/Index/,
  • Kawasaki, T., Sakaue, K., Matsubara, R., Ishizaki, S. (2019). Football Pass Network Based On The Measurement Of Player Position By Using Network Theory And Clustering. International Journal Of Performance Analysis İn Sport, 19(3), 381–392. Https://Doi.Org/10.1080/24748668.2019.1611292
  • Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., Liu, H. (2019). Expectation-MaximizationAttention Networks for Semantic Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), ss.9167-9176.
  • Mahmoud, M., Xia, Y. (2014). Expectation Maximization. In Networked Filtering and Fusion in Wireless Sensor Networks.
  • Narizuka, T., Yamazaki, Y. (2019). Clustering Algorithm For Formations İn Football Games. Scientific Reports, 9(1). Https://Doi.Org/10.1038/S41598-019-48623-1
  • Öntürk, Y., Karacabey, K., Özbar, N. (2019). Günümüzde Spor Denilince İlk Akla Neden Futbol Gelir? Sorusu Üzerine Bir Araştırma. Ankara Üniversitesi Beden Eğitimi Ve Spor Yüksekokulu Spormetre Beden Eğitimi Ve Spor Bilimleri Dergisi, 17(2), 1–12. Https://Doi.Org/10.33689/Spormetre.533739
  • Özkan, Y. (2015). Veri Madenciliği Yöntemleri. İstanbul: Papatya Yayıncılık.
  • Savaş, S., Topaloğlu, N., Yilmaz, M. (2012). Veri Madenciliği Ve Türkiye’deki Uygulama Örnekleri. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi Yıl:11 Sayı: 21 Bahar 2012 S. 1-23.
  • Stübinger, J., Mangold, B., Knoll, J. (2020). Machine Learning İn Football Betting: Prediction Of Match Results Based On Player Characteristics. Applied Sciences (Switzerland), 10(1). Https://Doi.Org/10.3390/App10010046 Talimciler, A. (2008). Futbol Değil İş: Endüstriyel Futbol. İletişim Kuram Ve Araştırma Dergisi Sayı 26 Kış-Bahar, 89–114. Tuğbay İ. (2007). Türkiye’deki Futbol Kulüplerinin Gişe Gelirlerini Arttırmaya Yönelik Uygulamaların İncelenmesi. Çukurova Üniversitesi Sağlık Bilimleri Enstitüsü Beden Eğitimi Ve Spor Anabilim Dalı. Yetim, A. A. (2000). Sporun Sosyal Görünümü. Gazi Beden Eğitimi Ve Spor Bilimleri Dergisi (Gazi Besbd), 1, 63–72.

Examination of Player Positions by Cluster Analysis

Yıl 2023, Cilt: 7 Sayı: 1, 43 - 48, 31.03.2023
https://doi.org/10.30516/bilgesci.1097014

Öz

Today, the football industry stands out among the sports branches. Especially with the development of technology and its integration into football, different tactical understandings and formations emerge. With these developments, the current positions of the players and the other positions they are prone to play can be revealed as a result of the analysis. In this way, club management and technical team aim to establish the best team according to the current budget and tactical game understanding. Therefore, it is very important for the teams to play the players in the right position or to transfer the right player to the required position. In football competitions where 11 players are involved in the game, tactical changes can be made within the game according to the tactical arrangement and tactical understanding of the opposing team, and the player can be played in different positions. In this study, the player data of Turkey and the leagues of Germany, England, France, Spain, Italy, which are considered to be the five big leagues, for the years 2020-2021 were obtained from the website named “whoscored”. In the data set obtained, the players who stayed on the field for a minimum of 1500 minutes were taken as a basis and clustering analysis was performed with the data of 985 players. Players are clustered on four basic positions: goalkeeper, defender, midfielder and attacker. In the study, Expectation Maximization, one of the clustering analysis algorithms, was used and a success rate of 81 percent was achieved.

Kaynakça

  • Akpınar, H. (2014). Data: Veri Madenciliği. İstanbul: Papatya Yayıncılık.
  • Aygün D, Ulucenk E. (2019). Futbol Kulüplerinde İnsan Kaynakları Faaliyetlerinin Muhasebeleştirilmesi. Muhasebe Ve Vergi Uygulamaları Dergisi , 689–710.
  • Behravan, I., Razavi, S. M. (2021). A Novel Machine Learning Method For Estimating Football Players’ Value İn The Transfer Market. Soft Computing, 25(3), 2499–2511. Https://Doi.Org/10.1007/S00500-020-05319-3
  • Bruzzone, L. , Prieto, F. (2002). An Adaptive Semiparametric and Context-Based Approach to unsupervised Change Detection in Multitemporal Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 11 (4): 452-466, 2002.
  • Choudhary, A. (2016). Survey on K-Means and Its Variants. International Journal of Innovative Research in Computer and Communication Engineering, 4(1), ss.949-952.
  • Dempster, A. P., Laird, N. M., Rubin, D. B. (1977). Maximum Likelihood From Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), ss.1–22.
  • Gemici, B. (2012). Veri Madenciliği ve Bir Uygulaması. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Ekonometri Anabilim Dalı Ekonometri Programı Yüksek Lisans Tezi, İzmir.
  • Kangalli, S. G. (2014). Oecd Ülkelerinde Ekonomik Özgürlük: Bir Kümeleme Analizi Economic Freedom İn Oecd Countries: A Cluster Analysis. In Uluslararası Alanya İşletme Fakültesi Dergisi International Journal Of Alanya Faculty Of Business Yıl (Vol. 6, Issue 3). Http://Www.Heritage.Org/Index/,
  • Kawasaki, T., Sakaue, K., Matsubara, R., Ishizaki, S. (2019). Football Pass Network Based On The Measurement Of Player Position By Using Network Theory And Clustering. International Journal Of Performance Analysis İn Sport, 19(3), 381–392. Https://Doi.Org/10.1080/24748668.2019.1611292
  • Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., Liu, H. (2019). Expectation-MaximizationAttention Networks for Semantic Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), ss.9167-9176.
  • Mahmoud, M., Xia, Y. (2014). Expectation Maximization. In Networked Filtering and Fusion in Wireless Sensor Networks.
  • Narizuka, T., Yamazaki, Y. (2019). Clustering Algorithm For Formations İn Football Games. Scientific Reports, 9(1). Https://Doi.Org/10.1038/S41598-019-48623-1
  • Öntürk, Y., Karacabey, K., Özbar, N. (2019). Günümüzde Spor Denilince İlk Akla Neden Futbol Gelir? Sorusu Üzerine Bir Araştırma. Ankara Üniversitesi Beden Eğitimi Ve Spor Yüksekokulu Spormetre Beden Eğitimi Ve Spor Bilimleri Dergisi, 17(2), 1–12. Https://Doi.Org/10.33689/Spormetre.533739
  • Özkan, Y. (2015). Veri Madenciliği Yöntemleri. İstanbul: Papatya Yayıncılık.
  • Savaş, S., Topaloğlu, N., Yilmaz, M. (2012). Veri Madenciliği Ve Türkiye’deki Uygulama Örnekleri. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi Yıl:11 Sayı: 21 Bahar 2012 S. 1-23.
  • Stübinger, J., Mangold, B., Knoll, J. (2020). Machine Learning İn Football Betting: Prediction Of Match Results Based On Player Characteristics. Applied Sciences (Switzerland), 10(1). Https://Doi.Org/10.3390/App10010046 Talimciler, A. (2008). Futbol Değil İş: Endüstriyel Futbol. İletişim Kuram Ve Araştırma Dergisi Sayı 26 Kış-Bahar, 89–114. Tuğbay İ. (2007). Türkiye’deki Futbol Kulüplerinin Gişe Gelirlerini Arttırmaya Yönelik Uygulamaların İncelenmesi. Çukurova Üniversitesi Sağlık Bilimleri Enstitüsü Beden Eğitimi Ve Spor Anabilim Dalı. Yetim, A. A. (2000). Sporun Sosyal Görünümü. Gazi Beden Eğitimi Ve Spor Bilimleri Dergisi (Gazi Besbd), 1, 63–72.
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makaleleri
Yazarlar

Okan Dağ 0000-0001-9756-722X

Asım Sinan Yüksel 0000-0003-1986-5269

Şerafettin Atmaca 0000-0003-2407-1113

Erken Görünüm Tarihi 25 Mart 2023
Yayımlanma Tarihi 31 Mart 2023
Kabul Tarihi 29 Ocak 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 1

Kaynak Göster

APA Dağ, O., Yüksel, A. S., & Atmaca, Ş. (2023). Examination of Player Positions by Cluster Analysis. Bilge International Journal of Science and Technology Research, 7(1), 43-48. https://doi.org/10.30516/bilgesci.1097014