Estimating the volleyball team ranking in the 2016 Rio Olympics by artificial neural network and linear model

Abstract

This study was conducted to estimate the Olympic ranking of the games played in the qualifying groups by the countries that were qualified for the 2016 Rio Olympics in volleyball branch by analyzing with the developed artificial neural networks (ANN) and linear equation model. In the study, the difficulty level of all games (n=324) that total 22 teams played in the qualifying for the 2016 Rio Olympics in volleyball branch (11 female and 11 male volleyball teams) and International Volleyball Federation (FIVB) ranking score was evaluated separately. Feedforward network structure having two hidden layers in the modeling with ASS developed for 9 different input variables was preferred in the study. In addition, linear modeling method, which provides an easier calculation than artificial neural networks, was performed by “regress” instruction in MATLAB. In the female group, the percentage mean error value of the models was calculated as 18.86 by ANN model, and as 4.53 by linear model. In male groups, it was calculated as 19,34 by ANN model, and as 0,74 by linear model. According to the modeling results obtained in the study, both female and male volleyball teams’ results were modeled with a higher accuracy by linear model. As a result, team rankings of the volleyball branch in the women's group in the 2016 Rio Olympic Games was estimated with an accuracy over 98% separately by ANN modeling regression results and linear modeling regression results. In men’s volleyball games, it was estimated with an accuracy over 98% by ANN modeling regression results, and with an accuracy over 99% by linear modeling regression results. It can be stated that the difficulty level of the games that countries participating in Olympics in volleyball branch played in the qualifying groups and FIVB ranking scores are among the variables that have a significant effect on determining the Olympic ranking.
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 Özet
 
 Bu çalışma 2016 Rio Olimpiyatlarına voleybol branşından katılma hakkı kazanan ülkelerin eleme gruplarındaki maçlarının, geliştirilen yapay sinir ağları (YSA) ve doğrusal eşitlik modeli ile analiz edilerek olimpiyat sıralamasının tahmin edilmesi amacı ile yapılmıştır. Çalışmada 2016 Rio Olimpiyatlarına voleybol branşından katılan (11 kadın ve 11 erkek voleybol takımı) toplam 22 takımın grup elemelerinde oynadığı tüm maçlar (n=324) zorluk derecesi ve Uluslararası Voleybol Federasyonu (FIVB) sıralama puanı göz önüne alınarak değerlendirilmiştir. Çalışmada dokuz farklı giriş değişkenine göre geliştirilen YSA modeli ile modellemede iki gizli katmana sahip ileri yayılımlı ağ yapısı tercih edilmiştir. Ayrıca çalışmada YSA’na göre çok daha basit bir hesaplama sağlayan doğrusal modelleme yöntemi de, MATLAB’de bulunan “regress” komutu ile gerçekleştirilmiştir. Kadınlar grubunda; test verilerine bakıldığında modellerin yüzde ortalama hata değeri, YSA modelinde 18.86, doğrusal modelde 4.53 olarak; erkekler grubunda ise YSA modelinde 19.34, doğrusal modelde 0.74 olarak hesaplanmıştır. Çalışmada elde edilen modelleme sonuçlarına göre; hem kadın hem de erkek voleybol takımlarının sonuçları doğrusal model ile daha yüksek doğrulukla modellenmiştir. Sonuç olarak, kadınlar kategorisinde 2016 Rio Olimpiyat Oyunlarında voleybol branşının takım sıralaması, YSA modelleme regresyon sonuçları ve doğrusal modelleme regresyon sonuçları ile ayrı ayrı %98’in üstünde doğrulukla tahmin edilmiştir. Erkek voleybol maçlarında ise YSA modelleme regresyon sonuçları %98’in üstünde, doğrusal modelleme regresyon sonuçları ise %99’un üstünde doğrulukla tahmin edilmiştir. Voleybolda Olimpiyatlara katılan ülkelerin eleme gruplarında oynadıkları maçların zorluk derecesi ve FIVB sıralama puanlarının Olimpiyat sıralamasının belirlenmesine önemli etkisi olan değişkenlerden olduğu söylenebilir.

La estimación de los resultados establecidos en los juegos de la liga Vestel Venus Sultans 2016/2017 por red neuronal artificial

“…No study on the evaluation of the set results in volleyball by ANN model was found in the literature, and the studies for the evaluation of the games of other branches were limited. In a similar study, Akarçeşme et al, (2020) predicted the volleyball team ranking in the Rio Olympics via the ANN model developed according to nine different input variables. Volleyball team ranking in the Rio Olympics was predicted with an accuracy of over 98% in women's category and over 99% in men's via the ANN model developed as a result of the study by Akarçeşme et al, (2020).…”

“…In a similar study, Akarçeşme et al, (2020) predicted the volleyball team ranking in the Rio Olympics via the ANN model developed according to nine different input variables. Volleyball team ranking in the Rio Olympics was predicted with an accuracy of over 98% in women's category and over 99% in men's via the ANN model developed as a result of the study by Akarçeşme et al, (2020). In another study, Tümer & Koçer (2017) predicted Turkish Volleyball League with an accuracy of 98% by using ANN model (Tümer & Koçer, 2017).…”

Serving Up Success: Unveiling the Power of Machine Learning for Volleyball League Prediction

“…Since particularly the total scores had a high correlation with most of the other variables, the total scores in question (match, set, t_pt) were not included in the model. Consequently, the feedforward regression model that best estimated (with the lowest error rates: RMSE, MAD and the highest accuracy values) the league standing variable through sigmoid activation function (preferred due to its ability to output values within the 0-1 range) was determined from the input layer that Gazi Journal of Physical Education and Sports Sciences, 2024, 29 (3), [202][203][204][205][206][207][208][209] was generated by using the total 23 input variables and the output variable. The training set included the results of the first 4 seasons and the test set includes that of the last season.…”