Waterhouse VC: Inside the Mind

Every month, industry investors Waterhouse VC publish an article that spotlights different aspects of the ecosystem. This month, Tom Waterhouse delves inside the mind.

What separates extraordinary betting professionals from the average punter? In our exclusive webinar, we gained unprecedented access to the strategic thinking of Tom Dry, one of the world's elite tennis bettors. Professional betting - a sector that can generate consistent returns regardless of economic conditions - represents a compelling but often overlooked investment category. Tom's approach combines analytical rigour with deep domain expertise, offering valuable lessons not just for bettors, but for investors seeking uncorrelated returns in today's volatile markets.

During our in-depth conversation with Tom Dry, we explored the disciplined, analytical, and occasionally counterintuitive realm of professional betting. Although he kept his “secret sauce” under wraps, Tom spoke openly about the mindset, processes, and curiosity that have guided his success.

Early Days

Tom’s journey began fittingly, with a winner. His first ever wager, an 8/1 shot at Goodwood, obliged when he was just nine years old. Still, he didn’t set out to become a professional bettor. Tom studied History and French at Durham University, and like many graduates, initially looked towards more conventional career paths. It wasn’t until after university when Tom would start dabbling in football betting, and he was quickly drawn in by the analytical challenge it offered.

Fate intervened when Tom’s father spotted a recruitment advert for Starlizard, the data-driven gambling syndicate founded by Tony Bloom - widely recognised as the world’s best football bettor and the owner of Brighton & Hove Albion Football Club. As Tom did his own research, he was quickly convinced that working on betting edges was a far more appealing career choice. Tom applied, and was offered the position.

The Data Approach

At Starlizard, as with most betting syndicates, the focus is firmly on data. Much like a quant hedge fund, these operations build statistical models to derive accurate probabilities. Everything is factored into these models: form, venue, weather conditions, historical performance, and more. When the model’s implied probability differs from market prices, there’s a potential edge to exploit. Over thousands of bets a year, those edges compound - assuming the models are continually refined and executed with iron discipline.

Bloom’s data-backed philosophy also underpins his success as owner of Brighton & Hove Albion Football Club. Under his stewardship, Brighton has gone from League One to the top tier of English football, and in 2023, the club reached new heights by qualifying for the Europa League for the first time. Their transfer dealings are a testament to the approach:

Brentford FC, owned by Matthew Benham - founder of the Smartodds, and a former colleague of Bloom, reflects a similar story. Benham and Bloom pioneered the now-ubiquitous ‘expected goals’ metric, profiting from it in their betting.

Benham has invested around £100M in Brentford over the past 15 years, and the club is now reportedly valued at over £400M, with Rothschild appointed to explore a potential sale of his majority stake (Source: Sky News). Despite operating with a fraction of the budgets of Premier League giants, both clubs continue to outperform.

Starlizard Days

When Tom joined Starlizard in 2017, his role centred on fine-tuning the football model to capture factors that are difficult for a model to incorporate: team and player motivation, injuries, squad rotation, off-pitch dynamics. In short, adding human insight and sense-checking machine-calculated probabilities. That, Tom argues, is when models become truly valuable.

Tom credits much of his development to the culture at Starlizard. It was flat, ideas-driven, and there was no such thing as a stupid idea, as the only goal was to improve the model. In other words, if you thought a factor was relevant in determining outcomes, there was a chance to test it.

During his time there, Tom began betting personally at a more serious level, focusing on golf and snooker. In snooker, he developed a computer vision model that mapped ball positions and calculated shot difficulty. The edge was significant, but the markets lacked the liquidity to accommodate his desired volume. By this point, Tom knew that betting for himself was the long-term goal - the bets were becoming increasingly meaningful.

Going Solo

‘The foundation of a winning model is unique exclusive, data, that has been crafted by people who really understand the sport’

Tennis was a natural fit. Tom had played from a young age with his brother, and knew the sport intimately. It offered global scale, year-round events, deeper liquidity and an opportunity to build an edge.

In today’s AI-driven world, scraping public data is easy. Everyone has access to the same surface-level stats. But that’s not an edge. Real edge comes from understanding what actually matters in determining outcomes - and building proprietary datasets to test those hypotheses, based on insights others aren’t looking for.

Know your Strengths

Tom puts a huge emphasis on domain expertise. His team is made up of people with thousands of hours of tennis experience - former players, coaches, and obsessive students of the game. Interviews are conducted by watching matches together over Zoom, where candidates must demonstrate a deep understanding of point construction, momentum shifts, and the tactical nuances that shape results.

He’s focused on what he knows. When asked if he’d ever consider betting on cricket, he jokes: “My top score is 13.” Even in tennis, his early attempts at modelling the women’s game fell short. It was a reminder that edge doesn’t come from data alone. It comes from understanding the game on a level others don’t.

Mentality

Tom’s approach is best captured by a line he now lives by: “It’s better to sleep well than to eat well.” He prioritises long-term sustainability over short-term gain, favouring discipline and pragmatism over theory.

He’s sceptical of rigid frameworks - both in staking, like the Kelly Criterion, and in modelling - arguing that while models are useful, they’re only reliable up to a point. “You need at least 1,000 IQ points to beat trial and error,” he jokes. The key, he believes, is staying flexible and letting experience drive adjustments.

His reading list for aspiring bettors:

● The Black Swan – Nassim Nicholas Taleb

● The Signal and the Noise – Nate Silver

For Tom, a thriving betting operation doesn’t hinge on one “secret sauce.” It’s about constant refinement - synthesising data, expert insights, and a measured mindset to spot opportunities where others see only noise.