Global Average Pooling (GAP) has become a standard aggregation method in deep learning models for Time Series Classification (TSC), yet its effectiveness has recently been questioned by the research community. In this work, we conduct an extensive empirical investigation into the validity of GAP as an aggregation mechanism by comparing it to a diverse set of alternative methods. These include pooling-based, feature-based, and learnable aggregation techniques, evaluated across two well-established univariate (UCR) and multivariate (UEA) TSC benchmarks. Our results reveal that GAP remains highly competitive, consistently achieving strong classification performance with minimal computational overhead. Importantly, none of the alternative methods were able to statistically significantly outperform GAP, either in terms of accuracy or efficiency. Furthermore, we show that parametrized and complex aggregators, such as those based on Recurrent Neural Networks, often degrade performance, reinforcing the principle that simpler, non-parametric methods like GAP are not only sufficient but often preferable. This study reaffirms GAP as a robust and efficient choice for aggregation in deep neural networks for TSC tasks. All of our experimental results and source code are publicly available to ensure the reproducibility of our work and also to allow the community to use the raw results for further research.