Mumbai Cricket Association Turns to AI: Ajinkya Naik Takes Coaching into a New Era, but the Real Test Lies Ahead

Mumbai's Indoor Nets Enter a New Technological Phase

At the Sharad Pawar Indoor Cricket Academy in Mumbai's Bandra Kurla Complex, batters and bowlers will now have a new technological companion alongside their coaches. It is not another specialist coach or an overseas player, but artificial intelligence-based coaching technology. The Mumbai Cricket Association (MCA), under president Ajinkya Naik, has decided to introduce AI into its coaching programme through a partnership with Singapore-based sports technology company Level Up Sportslytics.

The importance of this initiative lies less in any immediate sporting outcome and more in the institutional signal it sends. One of India's most influential domestic cricket centres is exploring ways to supplement traditional coaching, which has historically depended on observation, experience and hours spent in the nets, with technological assistance. However, a distinction must be maintained between announcing AI-assisted coaching and demonstrating that AI produces better cricketers. MCA has announced a pilot project. Its actual value can only be assessed once the results become available.

I have watched cricket from grounds, press boxes and television studios over several decades. That experience has taught me a simple rule: an administration's announcement must be judged by its results, not its vocabulary. The press list said no. The story said otherwise. The same standard must apply when evaluating technology.

From Traditional Coaching to Data-Assisted Training

The Mumbai Cricket Association occupies an important position in Indian domestic cricket. Its historical achievements in the Ranji Trophy and its contribution to the national team's talent pool give the organisation considerable influence. A technological change within such an institution therefore carries significance beyond one academy.

Traditional cricket coaching has relied primarily on observation, experience and repetition. A coach watches a batter's bat swing, head position, footwork, response to different lengths and shot selection. For bowlers, attention goes to the run-up, release point, wrist position and direction of delivery. The greatest strength of this system is the human understanding of an experienced coach, who can assess a player's mental condition and circumstances alongside technical execution.

Human observation, however, has limitations. A coach can monitor only a limited number of players at a time. Small technical errors can escape attention during long sessions, while maintaining accurate comparisons of a player's technique across different days is difficult. AI-assisted analysis could help address some of these limitations.

The partnership with Level Up Sportslytics represents a step in that direction. According to the available information, the objective is to use AI for technical analysis during training sessions and provide additional information to coaches and players. The project is centred on MCA's Sharad Pawar Indoor Cricket Academy in BKC.

MCA president Ajinkya Naik has emphasised the need to adapt to changing times. His message is that cricket coaching must evolve alongside technology. He has also expressed the intention to adopt AI in daily net sessions. That distinction matters: the current initiative concerns practice, not the use of AI during live competitive matches.

The Real Change Will Be in Coaching Decisions

The most important question is how much better AI can make a coach. Collecting cricket data is becoming increasingly straightforward, but converting that information into useful training decisions remains difficult. A system may identify changes in a batter's swing, differences in delivery direction or inconsistencies in footwork. Explaining how the player should correct those problems remains dependent on coaching expertise.

AI's greatest potential lies in identifying technical errors and measuring how frequently they recur and whether they improve over time. For example, if a young batter repeatedly reaches away from the body to play deliveries outside off stump, an AI-based system could identify a recurring pattern across numerous practice attempts. A coach could then assess consistent technical behaviour rather than relying on one or two unsuccessful shots.

The same principle applies to fast bowlers. Changes in pace must be considered alongside release position, body balance and consistency at the point of delivery. If AI provides reliable information on these aspects, coaches may be able to adjust training programmes according to individual requirements. However, MCA's announcement has not specified particular sensors, camera systems, software models or performance indicators. These possible applications must therefore not be presented as proven outcomes of the project.

In cricket, technical information becomes valuable only when it changes a player's behaviour. Telling a batter that his head is moving away from the line of the ball can be useful. The coach must also explain why the same mistake reappears under match pressure. That is the difference between practice and competition.

The most interesting aspect of this project is that AI coaching should not be understood solely as video analysis of batting. Its potential applications could include technical consistency, decision-making habits during practice and the mechanics of bowling. However, MCA has not yet provided detailed evidence of its intended applications in these areas.

Mumbai's real advantage will emerge if technological information becomes a shared development system spanning age-group cricket and senior domestic cricket. Identifying a talented Under-19 player's technical weakness early, documenting the correction process and comparing it with subsequent domestic performances could become particularly valuable. Coaches and selectors would then have a longer-term record of a player's development.

That will require consistent data quality, continuous analysis and standardised training benchmarks. Buying expensive technology alone does not develop domestic cricket. Trained personnel, regular evaluation and clear accountability are equally necessary.

The 'First Association' Claim Needs Evidence

One of the more attractive elements of the announcement is the claim that MCA is the first cricket association to adopt AI-based coaching. It sounds impressive, but an administrative superlative must first be clearly defined. Does it mean MCA is the first state association to introduce an external AI company into daily net sessions? Or is the claim intended to cover the entire Indian cricket ecosystem?

Those are substantially different propositions. The National Cricket Academy, IPL franchises and other professional cricket organisations have already used various forms of video analysis, data models and technological assistance. It is also technically inaccurate to describe every analytical system as AI. Until independently verified, MCA's claim should therefore be treated as institutional positioning rather than an established fact.

I returned my IPL media accreditation in 2026, when the spot-fixing scandal raised serious questions about the relationship between cricket administration and journalism. My principle then was to separate an institution's announcements from its actual work. The same discipline is necessary when evaluating technology today.

Another important question concerns the financial terms of the initiative. The financial conditions of the agreement between MCA and Singapore-based Level Up Sportslytics, the contract duration, equipment costs and service charges have not been publicly established. It would therefore be premature to describe this as a major financial investment or an expensive technology contract.

For now, the project is a limited pilot. MCA intends to test the technology in a controlled training environment before deciding what should follow. From an administrative perspective, that is a sensible approach. A limited experiment is more practical than changing the entire coaching structure before evaluating the results.

How Much of Indoor-Nets Success Will Transfer to the Ground?

The most significant technical challenge is the difference between indoor training and actual match conditions. The BKC indoor academy provides a controlled environment. Weather, changing light and external conditions are less variable, while repeating similar practice exercises is relatively straightforward. This makes the facility a convenient location for an initial AI trial.

The real examination of cricket is different. Wind, pitch characteristics, the condition of the ball, a bowler's strategy and field placements all influence a batter's decisions on an outdoor ground. In a match, the batter must also read the score, wickets remaining and time available with every delivery. A technically correct practice shot does not automatically become the correct match decision.

After years of watching cricket, I have repeatedly observed this distinction. A batter may play the cover drive successfully throughout a net session, but the same shot could be the wrong choice in a match when the bowler has placed a fielder in that area. AI may assess technical execution, but understanding the situation also requires cricketing context.

MCA should therefore evaluate more than the number of errors identified by the system. It must establish whether technical suggestions improve practice performance and whether those improvements remain visible in outdoor cricket.

A useful assessment model could compare videos before and after training, obtain independent coaching assessments, record player feedback and measure improvements during actual practice. If feasible, a comparable group continuing with conventional coaching could also be monitored. This would help isolate the contribution of AI-assisted training. Such a model is a recommendation, not a confirmed component of MCA's announced project.

Who Owns the Player Data?

AI coaching introduces another issue that cricket administrators should clarify from the beginning: who controls the player's data? If the system collects video recordings, technical profiles and practice information, there must be a clear understanding of the rights and responsibilities of MCA, the technology company and the players themselves.

Consent and privacy are particularly important for young cricketers. Information about technical weaknesses, fitness and practice habits can support development, but inappropriate use could also affect selection and future opportunities. Because the collaboration involves a Singapore-based company, it is also important to establish where data will be stored, who will have access and how long it will be retained.

The available announcement does not answer these questions. That does not imply any wrongdoing. It simply identifies the governance standards that should accompany any modern sports technology project.

Third Umpire began in a spare room in Ahmedabad with one chair. That experience taught me that launching a new platform is relatively easy; making it credible requires consistent work and public accountability. The same principle applies to AI coaching. MCA should publish an account of the results alongside its technology partnership.

What Should Be Measured After the Pilot?

MCA has indicated that the project will begin as a pilot and that feedback from coaches and players will inform future decisions. That is a sensible starting point, but feedback must eventually become measurable evidence. If coaches find AI useful, it is important to establish which specific aspects have improved. If players respond positively, their technical development and decision-making must also be evaluated.

Three clear tests should guide the project. First, can AI identify technical errors during training that are difficult to detect consistently through ordinary observation? Second, can coaches translate those findings into practical training plans? Third, do improvements extend beyond indoor nets into competitive outdoor cricket?

Only after these questions are answered will it be possible to determine whether MCA has simply tested a new technology or genuinely improved its coaching structure.

I will put one prediction on the record: the strongest evidence of success will appear in comparative training data published by MCA, not in another promotional announcement. If the association releases pilot results, feedback from coaches and players, and outdoor validation within the next six to twelve months, the initiative could become a useful model for Indian domestic cricket. If the evidence remains unpublished, discussion of AI will eventually disappear beneath the next administrative headline.

Ajinkya Naik and MCA have opened the door to technology. The real question now is whether better cricket will walk through it and appear on the field.