The Central Park walk that could change cricket forever

A lot can happen over a walk in New York City's Central Park. On a serene summer morning, for a few fleeting moments, the park feels wonderfully detached from the city it anchors. The park has an uncanny way of making improbable ideas seem possible.

A couple of years ago, on one such June morning on the eve of the memorable India-Pakistan T20 World Cup clash in New York, two lifelong cricket tragics set off on what appeared to be an ordinary stroll. In reality, it may well prove to be one of the most consequential walks in cricket's technological evolution.

Those "cricket tragics" were anything but ordinary. One was Anand Rajaraman, Silicon Valley entrepreneur, venture capitalist, co-founder of Rocketship VC and co-owner of the San Francisco Unicorns. The other was Vishal Misra, Vice Dean of Computing and AI at Columbia University, pioneering computer scientist and one of the founding team members of Cricinfo in the 1990s.

By the time the walk ended, the seeds had been planted for what would eventually become one of cricket's most ambitious artificial intelligence projects: SFU AI.

"Getting a chance to get involved with the operations of a professional team seemed very intriguing and exciting to me," Misra recalled.

Misra was offered the opportunity not only to become a minority owner in the Unicorns but also to lead their Modelling and Data (MAD) team, the initiative that would ultimately build SFU AI.

"Anand and Venky (Hariharan) are computer science PhDs. They wanted to run this team based on data. That was the foremost idea behind getting a professional cricket team," Misra said.

Misra had previously authored a research paper on a predictive technique that today forms the nucleus of SFU AI. Rajaraman had already assembled the majority of the MAD team — a group of cricket fanatics who were all Stanford PhDs working in big tech in Silicon Valley. The team quickly programmed the methodology into a workable tool. The results, according to those involved, were startling.

Rajaraman and Misra were both present at the India-Pakistan thriller in New York during the 2024 T20 World Cup. For all practical purposes, India appeared out of the contest at the halfway stage. "I was at the ground and it didn't feel like India were out of the contest," said Misra, one of the few among the 25,000 spectators and billions watching worldwide who felt the game remained evenly poised.

As it turned out, SFU AI agreed with him. Popular win predictors such as WinWiz and Cricinfo showed Pakistan having more than 95% chance of victory during the chase. SFU AI, however, viewed the contest very differently, keeping India and Pakistan neck-to-neck throughout the chase except for a couple of overs when Fakhar Zaman threatened momentarily.

Even after Mohammad Rizwan's dismissal, with Pakistan requiring roughly 40 runs from six overs and still possessing six wickets in hand, SFU AI believed India held the upper hand.

The difference lay in Misra's philosophy. "That technique looks at previous games that proceeded similarly. We have a historical database of all games that have happened, and we map and create a sort of simulated version of the current game based on past games. Create a digital twin in AI parlance," Misra explained.

The concept of the digital twin lies at the heart of modern AI-driven predictive analysis. In cricketing terms, a digital twin is a data-driven virtual alter ego of a player, team or match that can be used to predict, simulate and optimize decision-making.

The win-loss predictor is merely the tip of the iceberg. Every probability generated by the model is the cumulative outcome of thousands of microscopic calculations. Those capabilities can broadly be categorized into three areas: draft strategy, pre-game strategy and in-game strategy.

Perhaps SFU AI's most groundbreaking work lies in player acquisition. The platform can identify deficiencies within a squad and recommend precisely the type of player required to address those shortcomings — for instance, a left-handed top-order batter or a death-overs specialist.

According to Misra, there is currently no cricket tool capable of comprehensively identifying squad deficiencies, evaluating available talent pools and quantifying the impact of potential acquisitions.

In what is believed to be a first in cricket analytics, SFU AI can also translate performances across competitions. A player's performances in domestic cricket can be projected onto leagues such as the IPL, Major League Cricket or even international cricket.

The system accounts for variables such as the quality of opposition, playing conditions and the presence of elite players to estimate how a player's numbers might translate at a higher level.

It also functions as a dynamic draft assistant. If a team identifies a left-handed middle-order batter as its preferred target and another franchise selects him first, SFU AI immediately recalibrates and produces the next-best option.

One of SFU AI's proprietary metrics is Average Delta Win Probability, particularly for batters chasing targets. The "delta" represents the difference between a player's peak win probability — the difference between the highest point of win expectancy reached while he was at the crease and the minimum win probability experienced during his innings.

By that measure, Vaibhav Sooryavanshi emerged as an extraordinary outlier during IPL 2026. According to SFU AI's analysis, Sooryavanshi had an average delta-win percentage of 22 percent per game. Among players who featured in at least ten games, the second-best was Prabhsimran Singh at 11 percent.

What could prove even more transformative is SFU AI's long-term ambition to tackle player auctions. "That's a roadmap item. We have all the ingredients ready," Misra said.

SFU AI has already transformed the way coaches prepare for games, particularly through Cricket Lens, a natural language interface built on top of SFU AI. Coaches can directly interrogate the system in plain English — ask what strategies should be devised against specific opponents, generate graphics at the click of a button or create visualisations ranging from wagon wheels to comparative performance charts.

They can even generate bespoke video playlists through simple prompts. A coach might type: "Show me every time Rachin Ravindra was beaten off the back foot by a left-arm pacer" or "Show me every inswinger of Mitchell Starc that resulted in either a bowled or LBW dismissal against right-handers."

Field placements provide another compelling example. Ahead of IPL 2026, SFU AI identified several unconventional catching positions for some of the world's best batters. For Rohit Sharma, it highlighted short fine leg. For Nicholas Pooran and Hardik Pandya, it identified short third man. For Shubman Gill, it pointed towards short cover. Each position was identified before the season began and subsequently validated by actual dismissals during IPL 2026.

The platform's in-game capabilities are equally sophisticated. SFU AI can recommend the optimal bowler for the next over, identify the most suitable batter to send in next and advise whether a side should adopt an aggressive or conservative approach.

"We want to automate as many of these things as possible, and have AI do a lot of the planning. Ultimately, we want the AI to be the co-pilot of a coach — the AI will keep telling you, okay, do this, do that, and the coach can decide yes or no," Misra said.

Yet, for all its sophistication, SFU AI still confronts significant challenges. The availability of comprehensive data remains the biggest limitation. Factors such as wind, dew, boundary asymmetry and pitch deterioration can be incorporated, but emotional intelligence remains far more elusive.

"Someone is going through a personal problem, their mother is very sick back home, or someone's career is on the line. Data doesn't know that," Misra said.

Similarly, if a player has picked up a niggle or is battling fatigue, those variables may not yet form part of the modelling process.

Those limitations may not remain forever. As part of the San Francisco Unicorns support staff, Misra now has access to some of the game's intangibles — dugout discussions, the emotional quotient of players and direct insight into the minds of elite cricketers in real time.

Following an extended interaction with Ravichandran Ashwin during a Unicorns game, Misra took to social media: "His insights are incredible. Spending three hours in the dugout with Ashwin was the most intense learning experience for me — how the mind of a top-tier professional cricketer works and what kind of data and insights he is looking for!"

For SFU AI, there can scarcely be a richer source of intelligence. Ashwin is now widely regarded as one of cricket's foremost analytical minds. Misra, meanwhile, helped usher cricket into the internet age and now finds himself, alongside SFU AI and Ashwin, trying to shepherd it into the age of artificial intelligence.

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