Mumbai Cricket Association Turns to AI: Ajinkya Naik Introduces a Technology-Led Coaching Experiment

The next significant change in Mumbai cricket may not begin with a batter scoring a century or a bowler taking five wickets. It could begin during a practice session, where artificial intelligence (AI) helps analyse a batter's bat angle, reaction to the line of a delivery and recurring technical weaknesses. Under president Ajinkya Naik, the Mumbai Cricket Association (MCA) has decided to introduce AI-assisted coaching into its training system. For this initiative, the association has partnered with Singapore-based sports technology company Level Up Sportslytics. The initial centre of activity will be the Sharad Pawar Indoor Cricket Academy in Bandra-Kurla Complex (BKC).

The announcement offers an important signal about the administrative direction of Indian domestic cricket. However, it is more accurate to describe the initiative as a technological experiment in coaching than as an established revolution. MCA intends to use AI during its daily net sessions. The objective is to provide players and coaches with additional information about training, allowing technical improvements to be approached more systematically. Naik has emphasised the need to change with the times. His message is centred on institutional adaptation. The real measure of success, however, will be the accuracy of the information generated and the ability of coaches to convert that information into useful improvements on the field.

The gap between training data and performance in an actual match will be the most important test of this initiative.

MCA's strategy behind adopting technology

The Mumbai Cricket Association is one of the most important administrative organisations in Indian domestic cricket. Mumbai's historic achievements in the Ranji Trophy and its tradition of producing players for Indian cricket have given the association a distinctive position. Its cricket ecosystem extends well beyond the senior team. Age-group cricket, local club competitions, academies and training programmes at different levels together form an extensive talent-development structure. Within such a system, improvements in coaching quality can have long-term consequences.

The decision to introduce AI-assisted coaching must be understood in this context. Traditional cricket coaching relies primarily on a trainer's observations, a player's experience, video analysis and direct feedback during practice. An experienced coach can identify technical issues by examining a batter's footwork, head position, backlift, balance and timing. In a bowler's case, the run-up, release point, body balance and consistency of delivery are important areas of observation.

However, measuring all these aspects during every practice session is difficult. Coaches must monitor several players simultaneously, with batters and bowlers often working in different nets. Some technical changes are so subtle that they are difficult to identify at normal speed. This is where AI-based analysis could become useful.

If the system can examine video and other available training data to identify recurring movement patterns, it could provide coaches with additional context. For example, a batter may repeatedly move the front foot away from the line of the ball or react late to short deliveries. Such patterns could be recorded systematically. Similarly, changes in a bowler's release point or balance during the run-up could be examined.

However, the precise technology, measurement systems, algorithms and analytical methods involved in MCA's initiative have not been publicly detailed. It would therefore be premature to claim that the system will identify any particular technical weakness with certainty. The available information establishes that MCA intends to experiment with AI-assisted coaching during its daily net sessions.

There is also an administrative dimension to this decision. Introducing technology involves more than purchasing equipment. Coaches must understand the system, players must become comfortable working with data, and the association must establish how analytical information will be used. If these processes are managed properly, AI could make the existing training structure more measurable.

Sharad Pawar Indoor Cricket Academy as the initial testing centre

The first phase of the project is planned at the Sharad Pawar Indoor Cricket Academy in BKC. Beginning with one designated training centre is a practical administrative decision. It allows the technology to be evaluated under relatively controlled conditions and makes early operational problems easier to identify.

The indoor environment is also relevant to the experiment. Outdoor grounds are affected by changing weather, wind, lighting and surface conditions. Indoor training generally involves fewer environmental variations. This can make it easier to repeat similar exercises and compare different sessions.

Consider a batter who appears unbalanced against a particular type of delivery in three consecutive practice sessions. If an AI system can record that movement and provide the coach with relevant video or data, the coach can examine the player's technique more closely. A bowler's body position during delivery could be studied in a similar manner. These examples illustrate the potential value of the technology, but they should not be mistaken for actual findings from MCA's pilot.

This distinction matters. Identifying a technical pattern in a controlled environment and correcting that problem in an actual match are two different processes. During indoor practice, a batter has repeated opportunities to work on a specific movement. In a match, the same batter must make decisions according to field placements, scoreboard pressure, bowling strategy and pitch conditions. A technical improvement achieved during practice will not necessarily translate into identical results in competition.

For this reason, MCA would benefit from treating the initial phase as a process of learning and evaluation rather than as proof of technological performance. If the system identifies a weakness, coaches must determine whether the pattern appears consistently or is the result of an unusual practice situation. Differences in physical structure, batting style and individual technique must also be considered.

Applying one technical standard to every player would underestimate the complexity of cricket coaching. A batter's backlift may appear unusual, but if the player maintains balance and produces effective shots, changing the technique simply to achieve uniformity may be counterproductive. AI will be useful only when its analysis is adapted to individual requirements.

Why the Level Up Sportslytics partnership matters

MCA has entered into a partnership with Singapore-based Level Up Sportslytics for the initiative. The agreement can be viewed as an example of the growing connection between Indian domestic cricket and the international sports technology industry. The role of data analysis in cricket has expanded considerably in recent years. Video analysis, performance statistics and technical assessment are already used by international teams and major franchise competitions. Technology is now increasingly capable of reaching domestic associations and academies.

However, the financial details of this partnership are unavailable. The contract value, duration, exclusivity arrangements, equipment costs and conditions for future expansion have not been publicly disclosed. It would therefore be inaccurate to describe the agreement as a major financial investment or an expensive technology acquisition. For now, it should be understood as a limited technology partnership and pilot initiative.

An important question concerns the ownership of training data. If a foreign company's technology analyses the training data of Indian players, who owns that information? Will players provide informed consent for the use of their personal information and video recordings? Will MCA have independent access to all analytical reports? Can the company use the data to develop other products?

The announcement does not answer these questions. That does not establish that there is a problem with the partnership. However, a technology project requires a clear data-management policy. Information about a player's technique, physical movements and training history could eventually become part of an individual sporting profile. Access rights, limits on usage and data security should therefore be addressed from the beginning.

Another challenge for MCA concerns technological dependence. If the entire system relies on a single service provider, the association's independence could be restricted if costs increase or the service needs to be changed. A stronger model would combine external technological expertise with the ability of MCA's own coaches and analysts to understand the system. This would prevent the association's use of technology from becoming entirely dependent on the vendor.

Ajinkya Naik's message about change and its administrative implications

MCA president Ajinkya Naik has emphasised that organisations must change with the times. His position helps explain the broader administrative objective of the project. Sports organisations have long faced the challenge of adopting new technologies while preserving the strengths of traditional coaching systems. Mumbai cricket has historically been associated with competitive domestic cricket, a strong club culture and the development of technically skilled players. Introducing new technology into this environment is a significant institutional decision.

Naik's position indicates that MCA wants its coaching structure to evolve. However, a president's support for technology is not evidence of success. The real administrative test will be to establish clear objectives, provide adequate training to coaches and ensure an impartial assessment of results.

The initiative could also create opportunities beyond senior cricket. If AI-assisted coaching proves useful, it may eventually be considered for age-group cricket and the development of younger players. Identifying technical habits early and guiding players towards appropriate improvements could be beneficial over the long term. However, this remains a potential opportunity rather than an announced expansion plan or an established outcome.

Talent shortages are frequently presented as the principal problem in Indian domestic cricket. In reality, identifying talent, providing appropriate coaching and helping players progress through different competitive levels are equally important. Mumbai has a large pool of players across its cricket ecosystem. A systematic record of individual development could help coaches manage that workload. AI could support this process, provided it remains an assistant to coaching expertise rather than a replacement for it.

AI and traditional coaching need collaboration, not competition

A common assumption about sports technology is that access to more data automatically produces better decisions. In coaching, however, several stages separate information from a decision. Video or sensors may identify that a batter's body is rotating late towards the ball. But a single technical measurement cannot explain why the problem occurs. Physical ability, mental preparation, practice fatigue and natural batting style may all influence the result.

An experienced coach can assess these factors together. The coach can speak to the player, observe reactions against different deliveries and modify the training programme. An AI system can organise these observations and provide additional information. However, if a coach accepts an algorithm's recommendation without examining it, technology could also create problems.

My years of observing cricket have reinforced one point: a technical movement cannot be evaluated separately from its outcome. An unusual batting position is not automatically a weakness, and a different bowling release point does not always indicate poor control. Several successful players have used techniques that differ from conventional coaching standards. AI should therefore avoid forcing every player into the same technical framework.

The most useful AI system will be one that helps coaches ask better questions rather than supplying predetermined answers for players.

For example, if a batter repeatedly loses balance against deliveries outside the off stump during net sessions, the system may identify the pattern. But the coach must determine whether the problem lies in footwork, late recognition of the delivery or the player's focus on a particular practice shot. Data becomes the starting point for investigation, not the final conclusion.

In bowling, the relationship between run-up speed and release position could be examined. If both appear inconsistent, the coach must determine whether the cause is a technical habit or fatigue during practice. Making changes on the basis of one session would be risky. A conclusion becomes more reliable when the same pattern appears across several sessions.

The claim of being the first association needs verification

MCA's initiative has been presented as making it the first cricket association to adopt AI coaching. The claim attracts attention, but independent verification is unavailable. Establishing that an organisation is the first requires clarity about which organisations are being compared and how AI coaching is defined.

Does the use of machine learning in video analysis qualify as AI coaching? Should private academies using technical systems be included? Should the activities of the National Cricket Academy and IPL franchises be considered when assessing the claim? Without answers to these questions, it is difficult to establish that one organisation is definitively the first.

Indian cricket has already used technology and data analysis at several levels. It would therefore be more balanced to describe MCA's initiative as an important step at the domestic-association level. If documentary evidence and comparative information later establish its first-mover status, that can be recognised separately. Until then, the claim should be treated as an institutional announcement rather than an independently established fact.

There is a practical reason for this caution. In sports administration, the success of a new technology is measured through results rather than publicity. If MCA makes expansive claims, expectations for measurable evidence will increase. The association would be better served by setting limited objectives and publishing the results of its experiment from the beginning.

The difficult transition from indoor nets to competitive cricket

Even if the technology performs well, the biggest challenge will be transferring insights from indoor training to actual cricket conditions. During net sessions, batters generally understand the bowler's line, pace and training objective. In a match, they must make decisions according to the opposition captain's strategy, fielding arrangements, pitch conditions and scoreboard.

A player may demonstrate better footwork during practice, but there is no guarantee that the improvement will remain consistent under match pressure. Similarly, a bowler may repeatedly hit a particular length during training, but the batter's response and playing conditions will influence the outcome in competition. AI-generated technical improvements must therefore be evaluated against performance in real matches.

MCA could consider a multi-stage evaluation system. The first stage would examine technical consistency during indoor nets. The second could assess outdoor nets and practice on different surfaces. Only after these stages should the association study the potential effect on technical stability and performance in competitive matches.

The data must be compared carefully. If a player improves, it would be incorrect to attribute the entire improvement to AI. Training quality, coaching changes, age, fitness and the level of competition also influence performance. These factors must be considered separately to estimate the technology's actual contribution.

How should the pilot project be measured?

MCA has described the initiative as a pilot project and indicated that feedback will be collected from coaches and players. This is important because the technology can be evaluated before wider implementation. Early feedback is useful, but satisfaction and convenience alone should not determine success.

The first requirement is accuracy. If the AI system incorrectly identifies a movement, coaching changes based on that information could be harmful. The second requirement is operational efficiency. MCA should measure whether the system saves coaches time and makes their work easier. A technology that produces complicated reports or requires excessive preparation time may prove impractical.

The third area is player development. Did technical consistency improve after repeated practice? Did an identified weakness become less frequent? Did the coach receive clearer information for designing individual training programmes? These questions should be examined through measurable indicators.

The fourth consideration is whether players of different ages and experience levels receive similar benefits. An experienced batter and a beginner may have very different technical requirements. A single success benchmark would therefore be inappropriate for the entire group.

MCA should record baseline information before the trial begins. Training data and coaching observations can then be compared over a defined period. Where possible, the progress of players who do not receive AI-assisted training could also be examined. This would help provide a more reliable estimate of the technology's contribution.

Publishing a concise evaluation report at the end of the pilot would strengthen the association's credibility. The report should include the trial period, number of participating players, limitations of the technology, feedback from coaches and the results obtained. Weak results should also be disclosed. An unsuccessful experiment can still generate useful institutional knowledge if the lessons are documented.

Wider implications for Indian domestic cricket

The potential significance of MCA's initiative could extend beyond Mumbai. State associations play important roles in developing players and organising competitions across Indian domestic cricket. Not all organisations have equal financial resources, training facilities or analytical expertise. If AI-assisted coaching proves effective at a manageable cost, other associations may consider adopting similar models.

However, there is also a risk of technological inequality. Larger associations may be able to purchase better equipment and specialist services, leaving players at smaller associations with fewer facilities. A shared technology-resource model involving the BCCI and state associations could help reduce this gap. Common training standards and secure data-sharing arrangements could make technological development more accessible across domestic cricket.

For Mumbai, the opportunity is to modernise its established talent-development system. However, a technology initiative cannot be considered successful simply because it is being used at a prestigious academy. The real questions are whether players make better decisions, identify weaknesses earlier and perform more consistently in different conditions.

Any future effect on broadcasting, franchise cricket and the commercial side of the sport would be indirect and long-term. The available information does not provide a basis for concluding that the partnership will change broadcasting agreements, player valuations or the commercial value of competitions. Its immediate focus remains coaching and talent development.

The real test will be how the technology is used

Under Ajinkya Naik, the Mumbai Cricket Association has taken a notable step towards AI-assisted coaching. Its partnership with Level Up Sportslytics and the proposed pilot at the Sharad Pawar Indoor Cricket Academy reflect the growing importance of sports technology in Indian domestic cricket. The experiment could provide coaches with additional information and make player development more systematic.

However, no published performance figures, player-level improvements or independent evaluation are currently available. The initiative should therefore be treated as an experiment rather than an announcement of success. The claim of being the first association to adopt AI also requires verification. Clear policies on data protection, technological dependence and the transfer of insights from indoor practice to match conditions will be necessary.

The future of technology in cricket will not be determined by how many cameras, sensors or algorithms reach the training ground. What matters is how much better coaching decisions they help produce.

MCA's most important next step should be to measure and publish the results of the pilot. If the technology improves technical consistency, makes coaches more effective and proves useful across different conditions, Mumbai's experiment could become a practical model for Indian domestic cricket. If no clear performance standards emerge, AI may remain an attractive phrase associated with administrative modernisation. Domestic cricket needs evidence of technological value more than another technological claim.