Can AI Help New Betting Syndicates Compete?

Simply put, AI is the backbone behind a rapidly growing analytics ecosystem, and the new syndicates out there have transformed the betting experience, making them more monetizable.

To a certain extent, AI is democratising analysis at surface level, but is there greater parity in the playing field, or will it just continue to empower established sharps? This piece attempts to grapple with this question.

Lower barriers to entry

In an age of information and technology, AI is becoming a fundamental part of the toolkit. The success of tomorrow’s betting products, it seems, ultimately lies within advanced automation and modelling.

Betting syndicates nowadays have access to high volumes of enriching data, while the well-known sharps have accumulated data through years of global trading across the top leagues. Through certain AI models, syndicates can benefit from more precise pricing and informed risk management at scale.

Those betting groups that understand and effectively scrutinise the latency of data models will be more likely to spot underlying flaws within systems. AI is starting to feed through more granular data, but those syndicates that used outdated public information will be more susceptible to making bad assumptions.

In essence, timing is critical. The barriers to creating robust models are low, as it is cheaper to compile data and write code. However, the new syndicates that will stand out will be the ones that can manipulate liquid markets, execute quickly, and generate bigger returns in the long term.

Hard decisions to consider

Inevitably, meeting the demands of an upwardly mobile betting industry can be testing. Whether it is trying to identify value wagers or making betting more personalised through bet builders or prop-style wagers, betting syndicates and sharps alike need to be more disciplined to reap the rewards.

Perhaps what is more intriguing is that AI is resonating with a younger demographic, which should give betting syndicates food for thought. A YouGov poll published last year indicated that 33% of those within the 18-34 age group had used AI for gambling, and a further 19% said they would think about using it. Moreover, the poll highlighted that 26% of all bettors would use AI for making sports betting decisions in the next 12 months ^[1].

Inevitably, there are difficult decisions to take. The challenge facing a new betting syndicate team is whether they can generate at scale, scoop bigger profits, and do so without sacrificing their betting philosophies.

Cutting the gap to the sharps

The sharps, or professional bettors as they are more commonly known, possess a far superior bankroll, and they can eke out small edges by refreshing datasets and flagging any anomalies found with market prices.

For weaker syndicates, AI can make these betting groups sound clued up, but that won’t necessarily be the case if there are gaps in their knowledge. New syndicates that are likely to flourish will have carefully built infrastructure and robust data systems to narrow the chasm to the sharp bettors, who have a proven track record in the field.