The Estimation of the Set Results in 2016/2017 Vestel Venus Sultans League Games by Artificial Neural Network


  • Hasan Aka aka
  • Cengiz AKARÇEŞME
  • Zait Burak AKTUĞ
  • Semih ÖZDEN



artificial neural network, estimation, volleyball


The objective of this study is to estimate the set result via Artificial Neural Network (ANN) by considering the scores of the volleyball teams at technical time-outs (8th and 16th points) and 21st point. In the study, 132 games, 984 sets and 4152 points that were played and scored during a season by 12 teams playing in 2016/2017 Vestel Venus Sultans League were examined separately. 85% of all sets that teams played in one season were randomly reserved for training and 15% for test. Verbally winning or losing was modeled as 0 (zero) or 1 (one) numerically. Since the produced value was between the ranges of 0 – 1, for a trained network, it was multiplied with 100 and thus the possibility of winning was obtained. Consequently, it was determined that the developed model estimated the set result for many teams (test dataset) with an accuracy rate over 95%. By means of competition analysis to be made using ANN model in volleyball, it is thought that technical officers can reach fast and accurate conclusions at the moment of the set is played. It can be said that these conclusions will provide technical officers with a warning mechanism to take necessary technical and tactical measures while the set is being played.


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Ayyıldız E. (2018). Estimation of American Basketball League (NBA) match results by artificial neural networks. Gaziantep University Journal of Sports Science, 3(1), 40-53.

Baacke H. (2005). Voleybol antrenmanı üst düzey takımlar için el kitabı 2. İstanbul: Çağrı Baskı.

Drikos S., Kountouris P., Laios A., & Laios Y. (2009). Correlates of Team Performance in Volleyball. International Journal of Performance Analysis in Sport, 9(2), 149-156.

Fernandez-Echeverria C., Mesquita I., González-Silva J., Claver F., & Moreno, M. P. (2017). Match analysis within the coaching process: A critical tool to improve coach efficacy. International Journal of Performance Analysis in Sport, 17(1-2), 149-163.

FIVB. (2019). International volleyball federation picture of the game. Available:

Grehaigne J. F., Bouthier D., & David B. (1997). Dynamic-system analysis of opponent relationships in collective actions in soccer. Journal of Sports Sciences, 15(2), 137-149.

João P. V., Vaz L., & Mota M. P. (2019). The statistics which qualified Portugal for the European Volleyball Championship 2019. Motricidade, 15, 139-139.

Jörg M., , Perl J. J., & Schöllhorn W. (2017). Analysis of players’ configuration by means of artifical neural Networks. International Journal of Performance Analysis in Sport, 7(3), 90-105.

Kautz T., Groh B. H., Hannink J., Jensen U., Strubberg H., & Eskofier B. M. (2017). Activity recognit

ion in beach volleyball using a deep convolutional neural network. Data Mining and Knowledge Discovery, 31, 1678-1705.

Koch C., & Tilp M. (2009). Analysis of beach volleyball action sequences of female top athletes. Journal of Human Sport & Exercise, 4(3), 272-283.

Öztemel, E. (2003). Yapay sinir ağları. Türkiye: Papatya Yayınevi.

Palao J., & Hernández-Hernández E. (2014). Game statistical system and criteria used by Spanish volleyball coaches. International Journal of Performance Analysis in Sport, 14(2), 564-573.

Sağıroğlu Ş., Beşdok E., & Erler M. (2003). Mühendislikte yapay zeka uygulamaları– 1:yapay sinir ağları. Ufuk Kitap Kırtasiye-Yayıncılık Tic. Ltd. Şti: Kayseri, 299-426.

Sánchez-Moreno J., Marcelino R., Mesquita I., & Ureña A. (2015). Analysis of the rally length as a critical incident of the game in elite male volleyball. International Journal of Performance Analysis in Sport, 15(2), 620-631.

Sarmento H., Bradley P., & Travassos B. (2015). The transition from match analysis to intervention: optimising the coaching process in elite futsal. International Journal of Performance Analysis in Sport, 15(2), 471-488.

Tümer A. E., & Koçer, S. (2017). Prediction of team league’s rankings in volleyball by artificial neural network method. International Journal of Performance Analysis in Sport, 17(3), 202-211.

Wright C., Carling C., & Collins D. (2014). The wider context of performance analysis and it application in the football coaching process. International Journal of Performance Analysis in Sport, 14(3), 709-733.






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