SUMMARY
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.?Extended English summary is in the end of Full Text PDF (TURKISH) file. Özet Bu çalisma 2016 Rio Olimpiyatlarina voleybol bransindan katilma hakki kazanan ülkelerin eleme gruplarindaki maçlarinin, gelistirilen yapay sinir aglari (YSA) ve dogrusal esitlik modeli ile analiz edilerek olimpiyat siralamasinin tahmin edilmesi amaci ile yapilmistir. Çalismada 2016 Rio Olimpiyatlarina voleybol bransindan katilan (11 kadin ve 11 erkek voleybol takimi) toplam 22 takimin grup elemelerinde oynadigi tüm maçlar (n=324) zorluk derecesi ve Uluslararasi Voleybol Federasyonu (FIVB) siralama puani göz önüne alinarak degerlendirilmistir. Çalismada dokuz farkli giris degiskenine göre gelistirilen YSA modeli ile modellemede iki gizli katmana sahip ileri yayilimli ag yapisi tercih edilmistir. Ayrica çalismada YSA’na göre çok daha basit bir hesaplama saglayan dogrusal modelleme yöntemi de, MATLAB’de bulunan “regress” komutu ile gerçeklestirilmistir. Kadinlar grubunda; test verilerine bakildiginda modellerin yüzde ortalama hata degeri, YSA modelinde 18.86, dogrusal modelde 4.53 olarak; erkekler grubunda ise YSA modelinde 19.34, dogrusal modelde 0.74 olarak hesaplanmistir. Çalismada elde edilen modelleme sonuçlarina göre; hem kadin hem de erkek voleybol takimlarinin sonuçlari dogrusal model ile daha yüksek dogrulukla modellenmistir. Sonuç olarak, kadinlar kategorisinde 2016 Rio Olimpiyat Oyunlarinda voleybol bransinin takim siralamasi, YSA modelleme regresyon sonuçlari ve dogrusal modelleme regresyon sonuçlari ile ayri ayri %98’in üstünde dogrulukla tahmin edilmistir. Erkek voleybol maçlarinda ise YSA modelleme regresyon sonuçlari %98’in üstünde, dogrusal modelleme regresyon sonuçlari ise %99’un üstünde dogrulukla tahmin edilmistir. Voleybolda Olimpiyatlara katilan ülkelerin eleme gruplarinda oynadiklari maçlarin zorluk derecesi ve FIVB siralama puanlarinin Olimpiyat siralamasinin belirlenmesine önemli etkisi olan degiskenlerden oldugu söylenebilir.