Automatically extracting key events from sports videos is extremely useful for teams, coaches, fans, and anybody who only wants to watch the highlights of a match. In our work, we propose a solution to automate the detection of key events in GAA hurling matches. In particular, our solution focuses on the detection of the four important events in the sport: puckouts, kickouts, scores, and wides. Due to both the wide variety of detectable events and the scarce availability of training data, we did not use common techniques for video classification & detection tasks (i.e. LSTM, 3D-CNNs). Instead, we designed handcrafted features capable of capturing discriminative information using alternative techniques (i.e. optical flow, people detection & tracking). We then fused these features together in order to predict the probability that a specific frame relates to a specific target event.