Özet:

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 a easier calculation than artificial neural networks, was performed by "regress" instruction in MATLAB. In the female group, the percentage average 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 the 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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