Observation is a highly recommended approach in game analysis as it helps form a better understanding for the types of relations within the game. The aim of this study is to present a new approach for predicting competitions results which are based on game analysis by the use of Modular Forward Neural Networks (MFNN). The data of 80 games were analyzed (i.e. Fast break, Breakthrough, different type of shot…). The Data used to train Modular Feed Forward networks include 21 processing elements (PEs) as input, one element as output, 2 hidden layers, 100 epochs - termination Cross Validation, random initial weights, and weight update batch. The MFNN test contains single output case threshold 0, 5 on level 1000. Results show significant correlation between game results and neural network output 0.93, 0.96. Actual network output was 0, 91. Normalized Root Mean Square Error was 0,078. Final mean squared error was 0.9. The variables mostly affecting the results of (MFNN) were: fast breaks, and blocked shots. Using MFNN in predicting game results based on game details is considered a novel approach for evaluating the level of teams and competitors and for improving the training plans and tactics
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