Cricket, the second most popular sport in the world, has emerged as one of the most widely played international mainstream sports in the world. With the advent of newer formats such as the Twenty-twenty (T-20) over cricket, the popularity of the game has further soared, spurting out international leagues such as the Indian Premier League (IPL) and the Big Bash League (BBL), over the last one decade. Over the course of more than 450 years since the inception of this sports in south-east England, statistics have played a pivotal role in shaping the structure and format of the game. Of late, there have been considerable advancements in cricket data analytics using IoT-based cricket bat sensors, advanced video analysis using Pitch-Vision, Hawk-Eye and allied technologies. From providing trajectories of the ball, to measuring the speed and impact point of the ball hitting the bat, such stellar breakthroughs have made it possible to fathom and analyze the game in a more refined way. Internet of Things (IoT) and Data Analytics can be the proponents in learning more about the stroke play of players as well as about the myriad facets of the game. In this paper, we have endeavored to discover a novel paradigm of research in cricket analytics known as the timing index, based on a real life IoT-based implementation and case study. The timing index is based on an amalgam of different factors such as bat speed, back lift angle, max. bat speed and impact bat speed. We believe that such a study can help in analyzing a player's training sessions using IoT and data analytics, thereby accurately and holistically analyzing a player's game and going beyond traditional statistical reporting, by ushering in intriguing and insightful information. © 2022 Elsevier B.V.. All rights reserved.