Source: NDTV Sports

The 2015 Cricket World Cup semi-final. India, chasing a formidable target against Australia, found themselves in a precarious position, hopes dwindling with each fallen wicket. The roar of the crowd, once fervent, began to wane, giving way to a collective sigh of resignation across millions of living rooms. Yet, for a significant segment of the Indian audience, the remote control remained firmly in hand. What kept them glued to a match that seemed all but over? The answer, as revealed by CricViz Head of Analytics Services Sankar Rajgopal, lay in a single, perfectly timed data graphic featuring MS Dhoni.

As Rajgopal recounted, at a moment when India’s chase looked ‘hopeless’, a graphic was aired, asserting that ‘as long as Dhoni was still at the crease, India retained a real chance’. This wasn’t mere conjecture; it was backed by his formidable record of ‘near-unbeaten chases’. The impact was immediate and measurable: CricViz tracked a ‘noticeable spike in viewer retention’ right after that graphic appeared. This single instance encapsulates the seismic shift in modern cricket coverage, where advanced analytics don’t just inform, but actively shape fan engagement and even the emotional trajectory of a match.

The Ascent of Analytics: From Backroom to Centre Stage

Cricket, once a sport largely interpreted through the ‘eye-test’ and traditional statistics, has entered an era where artificial intelligence and advanced analytics are as critical as talent and technique. This transformation, highlighted by experts like Rajgopal, began subtly but has accelerated dramatically, influencing everything from team selection and tactical decisions to the very way we consume the game.

In the early days of data in cricket, analytics was a rudimentary affair, confined to the basic vocabulary of the game: averages, strike rates, and economy rates for Tests and ODIs. However, the advent of T20 cricket proved to be the catalyst for fragmentation. As Rajgopal explains, you can no longer judge every batsman or bowler with a single set of numbers. An opener, tasked with setting a platform, solves fundamentally ‘different problems’ than a finisher responsible for late-inning acceleration. This necessitated the development of role-specific metrics, offering a nuanced understanding of player performance that traditional stats simply couldn’t provide.

Beyond the evolution of metrics, the biggest shift has been analytics’ transition from a ‘back-room, post-facto exercise’ to a ‘live decision-making tool’. Coaches now demand data ‘before making a call, not after’. Critically, players themselves have embraced this change. While a decade ago, ‘real skepticism’ from dressing rooms was common, today, most players are acutely ‘aware of matchups’ and understand the analytical rationale behind tactical decisions, such as why a particular bowler is being targeted at them. This sentiment is echoed by cricketing stalwarts like R Ashwin, who has been vocal about players who ‘don’t get on top of AI and analytics risking a shorter career’, underscoring the irreversible nature of this data-driven revolution.

Engineering Engagement: How Data Redefines Broadcasts

For broadcasters, the ultimate goal is ‘keeping the audience hooked through rich storytelling’. Data analytics, and increasingly AI, have become indispensable in achieving this. CricViz provides a fascinating insight into a typical broadcast day, which is meticulously planned long before the first ball is bowled.

A day prior to a match, producers and commentators receive a detailed ‘preview document’ from analysts, flagging ‘main talking points’ and ‘storylines worth building the broadcast around’. These narratives are pre-fed into graphics engines via APIs, ensuring that ‘the moment a commentator wants to bring up a story, the graphic is already sitting there ready to go, no scrambling required’. This seamless integration ensures that insights are delivered at the optimal moment, enhancing viewer comprehension and engagement.

During live play, the ‘Centurion match page’ runs in the background, offering a real-time summary of the game, key insights, upcoming milestones, and granular details on player strengths and weaknesses, all updating ball by ball. Furthermore, Hawk-Eye ball-tracking data provides critical, otherwise invisible, information about ‘how a pitch is behaving on the day’—its pace, bounce, deviation, and spin. This constant stream of ‘story hooks’ ensures that ‘there’s always another layer to keep viewers engaged, even when the game itself goes quiet’.

AI’s Role in Unearthing Hidden Narratives

AI and pattern recognition have taken broadcast analytics to a new level, enabling the surfacing of ‘trends that aren’t obvious to the naked eye’. Rajgopal cites the example of a Virat Kohli fifty where ‘every boundary has come through the off side’. This pattern instantly appears on the Centurion match page, relaying a crucial insight to commentators and producers in real time. Such granular observations don’t just add depth; they offer tactical windows into a player’s current form or strategy.

Even before AI became a widespread ‘industry buzzword’, data science models like WinViz (live win probability), PredictViz (score prediction), and PitchViz (pitch behaviour) were already in play. More recently, Skill Scales, providing format-specific player skill ratings, have been developed. These sophisticated tools are all ‘built for the same purpose: deepening viewer engagement through insight, not just information’. They transform passive viewing into an active, analytical experience, allowing fans to understand the probabilities and potential outcomes unfolding before them.

A Decade of Data-Driven Dialogue: The 2012 Turning Point

The pivot towards heavy reliance on data in cricket coverage can be traced back to approximately 2012. Before this period, commentary was largely ‘reactive’—commentators would respond to the action as they saw it, with data limited to ‘the occasional statistical record being read out’. It was a descriptive rather than a predictive or deeply analytical dialogue.

From 2012 onwards, the proper integration of ‘ball-by-ball data’ into broadcasts empowered commentators with the ability to ‘analyse player strengths and weaknesses in real time’ and ‘offer predictive insight based on historical patterns’. This marked a significant departure, moving beyond mere description to proactive analysis that enriched the viewer’s understanding of the game’s unfolding narrative.

This evolution reached another milestone in 2018 with Star Sports’ launch of ‘Select Dugout’. This was an ‘analytics-driven’ feed, specifically designed to ‘weave data into the broadcast as a storytelling layer rather than a standalone graphic’. It signified a complete embrace of data not as an add-on, but as an integral thread in the fabric of cricketing narrative.

Beyond Broadcast: Data’s Impact on Team Building and Strategy

The influence of data extends far beyond broadcast booths, deeply impacting team formation and tactical blueprints. Rajgopal’s experience as an R&D data consultant for the IPL team, Punjab Kings, offers a compelling illustration. During the 2022 auction, the squad was strategically built around ‘six-hitting and market inefficiencies’.

What struck Rajgopal wasn’t just the increasing trend of boundary-hitting across IPL seasons, but a crucial nuance revealed by the data: ‘how selective the best hitters were’. They ‘picked their moments to attack and left the rest’, demonstrating a ‘discipline’ that the ‘eye-test just reads as all-out hitting’. This insight underscores how data can uncover hidden layers of strategy and skill that are otherwise imperceptible, proving invaluable in squad construction and in-game tactical adjustments. It highlights the sophistication required in T20 batting, moving beyond brute force to intelligent, situation-aware power-hitting.

The Algorithmic Future of Cricket

The journey of cricket from a traditional sport to a data-rich spectacle is far from over. The MS Dhoni graphic in 2015 was more than just a fleeting image; it was a potent demonstration of how data, wielded strategically, can defy conventional wisdom and rekindle hope, holding an audience captive through sheer statistical possibility. It exemplifies how analytics has matured from a supplementary tool to an essential component of modern cricket’s appeal.

As the sport continues its inexorable march into an era increasingly shaped by AI, data will remain the critical differentiator for players, teams, and broadcasters alike. Its capacity to deepen understanding, enhance engagement, and reveal hidden truths ensures that the algorithmic future of cricket will be one of ever-increasing insight and unparalleled storytelling.

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Disclaimer: Cricket Mantra builds on reporting from NDTV Sports for this story, adding independent analysis and broader context for readers.