<![CDATA[This study aims to enhance the accuracy of predicting English Premier League football match outcomes by utilizing a partially updated Artificial Neural Network (ANN) model based on match outcome data from the period 2017 to 2021. In this research, various statistical features such as the number of goals scored in the first half and the number of shots on target were incorporated as inputs to the ANN model. The match outcome data was normalized to improve the model's performance. The ANN model employed multiple hidden layers with ReLU (Rectified Linear Unit) activation functions and was trained using the Backpropagation algorithm. Throughout the training process, the model was periodically updated to reflect changes in match patterns over time. The research findings reveal that the ANN model with partial updates can predict football match outcomes with an accuracy of 77.89% in the final iteration, with a Mean Squared Error (MSE) of 0.769 and a Mean Absolute Error (MAE) of 0.689. Additionally, the prediction results are visualized in the form of a distribution graph comparing actual match outcomes with the predictions from the final iteration, providing a visual representation of the model's performance. This study makes a significant contribution to the development of modeling techniques for forecasting football match outcomes and underscores the importance of partial updates in adapting to changes in match patterns over time, offering potential for improvements in football match analysis and prediction in the future]]>
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