Temporal evolution is a crucial source of information for many systems, and numerous phenomena occurring in networks are intrinsically temporal. These temporal dynamics contain valuable information, which is often hidden and challenging to represent effectively. Temporal networks offer a means to capture this information, and machine learning techniques such as temporal network embedding have shown promising results in this regard. Our past work has demonstrated that it is possible to successfully construct representations that reveal latent structures in temporal phenomena. Causal analysis is another powerful tool for analyzing temporal phenomena, particularly in understanding the impact of external perturbations on time-dependent dynamics. Utilizing state-of-the-art methods, we demonstrate the ability to quantify the causal impact of extreme events, such as natural disasters, on digital discourse patterns and the spread of misinformation. Building upon these methodologies, we extend our approach to sports analytics, presenting preliminary results that uncover hidden patterns in team dynamics and player performance. This research demonstrates the versatility of network science applications across diverse domains and proposes new avenues for interdisciplinary exploration at the intersection of complex systems analysis and sports analytics.