ELİT KADIN VOLEYBOLCULARDA 20. SAYILAR SONRASINDA ATAKTAN ALINAN SAYIYI ETKİLEYEN DEĞİŞKENLERİN CHAID KARAR AĞACI İLE BELİRLENMESİ

Yıl 2022, Cilt: 13 Sayı: 1, 89 - 100, 15.04.2022

Cengiz Akarçeşme , Nurbanu Bursa

https://doi.org/10.17155/omuspd.972951

Cited By: 1

Öz

Voleybolda maç sonucunu etkileyen en önemli etkenlerden biri, oyuncuların atak becerileri ve maç boyunca sayı ile sonuçlandırdıkları ataklardır. Bu nedenle, oyuncuların atak sayılarını etkileyen değişkenlerin belirlenmesi ve bu değişkenlerin antrenör ve oyuncular tarafından bilinmesi galibiyet için oldukça önemlidir. Bu amaçla çalışmada, 2018-2019 Türkiye Voleybol Federasyonu Sultanlar Voleybol Ligi takımlarındaki oyuncuların 20. sayılar sonrası atak verileri kullanılarak uyruk, mevki, iyi atak sayısı (pozitif atak), kötü atak sayısı (negatif atak), atak hatası ve bloklanan atak değişkenlerinin sayı ile sonuçlanan atak üzerinde etkili olup olmadıkları incelenmiştir. Etkili değişkenlerin belirlenmesi için karar ağacı tekniklerinden biri olan ve kolay yorumlanabilir olması nedeniyle veri madenciliği uygulamalarında sıkça kullanılan CHAID (Chi-squared Automatic Interaction Detection) algoritmasından yararlanılmıştır. CHAID karar ağacı sayesinde sayı ile sonuçlanan
atakları en iyi açıklayan değişkene göre oyuncular gruplandırılarak bu oyuncular için ayrı ayrı bir dizi kararlar oluşturulmuştur. Analizler sonucunda, sayı ile sonuçlanan atakları; oyuncu mevkisinin hiç etkilemediği, kötü atak sayısının ise diğer değişkenler içerisinde en çok etkileyen değişken olduğu belirlenmiştir. 

Anahtar Kelimeler

Voleybol, atak, karar ağacı, CHAID algoritması

DETERMINING THE VARIABLES AFFECTING THE NUMBER OF ATTACKING AFTER THE 20TH NUMBERS IN ELITE WOMEN VOLLEYBALL PLAYERS WITH THE CHAID DECISION TREE

Öz

One of the most important factors affecting the outcome of the match in volleyball is the attacking skills of the players and the attacks that they result in score points (attack score points) throughout the match. In this respect, determining the variables that affect the attack score points and being aware of these variables by the coach and the players, has importance for the victory. For this purpose, in the study, using the attack data after the 20th scores of the players in the 2018-2019 Turkish Volleyball Federation Sultans Volleyball League (TVFSVL) teams; it was examined whether the variables nationality, player position, number of good attacks (positive attacks), number of bad attacks (negative attacks), failure to attack, and blocked attacks, have an effect on the attacks that resulted in score points. CHAID (Chi-squared Automatic Interaction Detection) algorithm, which is one of the decision tree techniques and is frequently used in data mining applications due to its easy interpretation, was used to determine the effective variables. As a result of the analysis, it was determined that the player position does not affect the attack score points at all, and the number of bad attacks is the variable that most affected the attack score points among the other variables.

Anahtar Kelimeler

Volleyball, attack, decision tree, CHAID algorithm

Kaynakça

• Akarçeşme, C. (2017). Is it possible to estimate match result in volleyball: A new prediction model. Central European Journal of Sport Sciences and Medicine, 19, 5-17.
• Akarcesme, C., Sahin, M., Varol, Y. K., & Colakoglu, F. F. (2018). Examining the attacks after the 20th scores in volleyball according to nationality and positions. Journal of Education and Learning, 7(6).
• Aktaş, S. (2017). ETSO kayıtlı işletmelerin işçi istihdamlarının Elazığ ekonomisine etkilerinin CHAID analizi ile incelenmesi, Yüksek Lisans Tezi, Fırat Üniversitesi Fen Bilimleri Enstitüsü. Elazığ.
• Barzouka, K. (2018). Comparison and assessment of the setting zone choices by elite male and female volleyball setters in relation to the reception quality. Journal of Physical Education and Sport, 18, 2014.
• Challoumas, D. & Artemiou, A. (2018). Predictors of attack performance in high-level male volleyball players. International Journal of Sports Physiology and Performance, 13(9), 1230-1236.
• Chiou, C. C., Lin, T. M., Liu, Y. T., Su, T. Y., Tsai, J. C., & Pi, C., L. (2016). “The effects of volleyball attacking on score points: a case study of 2014 TVL in Taiwan”. 34th International Conference on Biomechanics in Sports, Tsukuba, Japan.
• Costa, G., Afonso, J., Brant, E., & Mesquita, I. (2012). Differences in game patterns between male and female youth volleyball. Kinesiology, 44(1).
• Costa, G. C., Castro, H. O., Evangelista, B. F., Malheiros, L. M., Greco, P. J., & Ugrinowitsch, H. (2017). Predicting factors of zone 4 attack in volleyball. Perceptual and Motor Skills, 124(3), 621-633.
• Diepen, V. M. & Franses, H.F. (2006). Evaluating chi-squared automatic interaction detection, Information Systems, 31, 814-831.
• 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.
• Drikos, S., Ntzoufras, I., & Apostolidis, N. (2019). Bayesian analysis of skills importance in World Champions Men’s Volleyball across ages. International Journal of Computer Science in Sport, 18(1), 24-44.
• Echeverría, C., Ortega, E., & Palao, J. M. (2019). Evolution of floor defense efficacy and execution in women’s volleyball from national u-14 to senior international. European Journal of Human Movement, 42, 108-122.
• Fellingham, G., Hinkle, L., & Hunter, I. (2013). Importance of attack speed in volleyball. Journal of Quantitative Analysis in Sports, 9, 87-96.
• Horner, B.S., Fireman, D.G., & Wang, W.E. (2010). The relation of student behavior, peer status, race and gender to decisions about school discipline using CHAID decision trees and regression modelling. Journal of School Psycohology, 48(2), 135-161.
• Inkinen, V., Häyrinen, M., & Linnamo, V. (2013). Technical and tactical analysis of women’s volleyball. Biomedical Human Kinetics, 5(1).
• Karasar, N. (2020). Bilimsel araştırma yöntemleri. Nobel Akademik Yayıncılık.
• Kass, V.G. (1980). An explaratory technique for investigating large quantities of categorical data, Applied Statistics, 29(2), 119-127.
• Kountouris, P., Drikos, S., Aggelonidis, I., Laios, A., & Kyprianou, M. (2015). Evidence for differences in men's and women's volleyball games based on skills effectiveness in four consecutive olympic tournaments. Comprehensive Psychology, 4, 30.
• Leo, F. (2019). Statistics and Volleyball: detection of the most significant skills and their importance in the results prediction. Ph.D. Thesis, Politecnico di Torino.
• Marcelino, R. O., Sampaio, J. E., & Mesquita, I. M. (2012). Attack and serve performances according to the match period and quality of opposition in elite volleyball matches. The Journal of Strength & Conditioning Research, 26(2), 3385-3391.
• Millan-Sanchez, A., Rabago, Morante Rabago, J. C., & Urena Espa, A. (2017). Differences in the success of the attack between outside and opposite hitters in high level men’s volleyball. Journal of Human Sport and Exercise, 12(2), 251-256.
• Oğuzlar, A. (2004). CART Analizi ile hane halkı işgücü anketi sonuçlarının özetlenmesi. Atatürk Üniversitesi İkdisadi ve İdari Bilimler Dergisi, 18 (3-4), 79-90.
• Palao, J., Santos, J. A., & Ureña Espa, A. (2004). Effect of team level on skill performance in volleyball. International Journal of Performance Analysis in Sport, 4, 50-60.
• Papageorgiou, A., Willy, S., & Christ, R. (2002). Volleyball a handbook for coaches and players. Meyer & Meyer Verlag.
• Patsiaouras, A., Moustakidis, A., Charitonidis, K., & Kokaridas, D. (2011). Technical skills leading in winning or losing volleyball matches during Beijing Olympic Games. Journal of Physical Education and Sport, 11(2), 149.
• Rodríguez-Ruiz, D., Quiroga, M. E., Miralles, J. A., Sarmiento, S., de Saá, Y., & García-Manso, J. M. (2011). Study of the technical and tactical variables determining set win or loss in top-level European Men’s Volleyball. Journal of Quantitative Analysis in Sports, 7(1), 1-15.
• Sanchez-Moreno, J, Mesquita, I., Afonso, J., Millan-Sanchez, A., & Urena, A. (2018). Effect of rally length on performance according to the final action and the playing level in high-level men’s volleyball. Revista Internacional de Ciencias del Deporte, 52, 136-147.
• Silva, M., Lacerda, D., & João, P. V. (2014). Game-related volleyball skills that influence victory. Journal of Human Kinetics, 41(1), 173-179.
• Singh, B. A. & Rathore, V. S. (2013). Kinematic factors of off-speed and power spike techniques in volleyball. Journal of Education and Practice, 4(7), 112-117.
• Software, D. S. (2007). Software for the scouting and analysis of volleyball matches (Version 3.6.6.). Erişim adresi: dataproject.com
• Sullivan, W. (2017). Machine Learning For Beginners: Algorithms, Decision Tree & Random Forest Introduction, Springer International Publisher.
• Üngüren, E. & Doğan, H. (2010). Beş yıldızlı konaklama işletmelerinde çalışanların iş tatmin düzeylerinin CHAID analiz yöntemiyle değerlendirilmesi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 11(2), 39-52.
• Zetou, E., Tsigilis, N., Moustakidis, A., & Komninakidou, A. (2006). Playing characteristics of men’s Olympic volleyball teams in complex II. International Journal of Performance Analysis in Sport, 6(1), 172-177.
• Zeybekoğlu, Ş. (2021). PISA 2015 Türkiye örneklemi fen okuryazarlığını açıklayan değişkenlerin CHAID analizi ile incelenmesi. Yüksek Lisans Tezi, Akdeniz Üniversitesi Eğitim Bilimleri Enstitüsü, Antalya.