Computer vision-based video action recognition has led to significant advancements in sports analytics, streamlining the previous labour-intensive tasks of sensor-based or manual analysis through automated video processing. This paper focuses on applying transformer-based video action recognition models to classify cricket bowling actions. For this, we created a novel dataset named ActionBowl, designed to support multiple specialized classification schemes. We trained and evaluated state-of-the-art transformer based action recognition models- ActionCLIP, TimeSformer and UniFormerV2 on these datasets. This paper aims to highlight the effectiveness of these models in recognizing actions that range from subtle variations to significantly distinct hand movements. Through rigorous evaluation, we provide conclusive evidence of these models’ ability to learn and distinguish this unique set of actions effectively. It presents a comprehensive analysis of the experiments, results, and insights drawn from the study, highlighting the potential for further advancements in cricket analytics through video-based action recognition.