The Growing Implementation of AI in Sports Betting

AI is something that everyone is aware of now, with a 2023 McKinsey Global survey stating that last year, one-third of the company’s polled individuals stated that they have used AI tools in some work capacity. Per Hostinger, around 35% of companies worldwide are now utilizing artificial intelligence in their day-to-day operations, and 42% are exploring this possibility. Experts project that the sector will reach a staggering market size of $407 billion in the next three years. That would represent a $320 billion jump compared to two years ago. Hence, there is little doubt that we will be hearing more and more about this tech in each facet of life, and we believe that it will dramatically change the world of sports betting.

While many people see artificial intelligence as something new, its roots can get traced back to the 1940s and mathematician Alan Turing, who published a paper in 1950 called Computing Machinery and Intelligence, which referenced machine intelligence and introduced a now-famous test on how to rate a machine’s ability to exhibit intelligence indistinguishable from that of a human being. That said, it was not until the 2000s that this sphere saw significant advancements thanks to sizeable improvements in computing power and the availability of substantial data sets. It was at this time experimenting with AI sports betting started, primarily through betting syndicates and consultancy firms that supply computer/data-driven advice to professional bettors.

Today, several high-end analytic services like Opta exist implementing machine learning to create predictive models that try to forecast future events with a reasonable degree of accuracy. In the subheadings below, we dive deeper into the ever-expanding role AI will have on our favorite pastime – wagering on sporting outcomes.    

How AI Gets Used in Betting

It primarily gets implemented for predictive analytics, or algorithms that can run through vast data libraries, incorporating statistical probabilities to deduce the most likely outcomes. Via neural networks and reinforcement learning, these pieces of software can provide more accurate outcomes than what human intuition can deliver.

Not only is the quality of the decisions AI makes higher, but this tech can also get fed real-time happenings, odds fluctuations, and more to give gamblers the best competitive edge possible. It can even tailor advice using set preferences and most desirable strategies. Therefore, these algorithms have access to diverse data sources, creating a personalized experience for bettors through predictive modeling and trend analysis, helping them manage risk better. Bookmakers also use them for these same things and utilize similar modes for fraud detection, constantly monitoring transactions and patterns to identify suspicious activities.

The cool thing about these types of betting software is they can adjust on the fly, modifying their implemented patterns from incoming novel metrics. According to TMCneet, new predictive AI models can churn out guesses that boast a 90% accuracy, which should mean that no one should ever have to rely on their gut, as there are sophisticated methods that predict game results with far more precision.

It will be interesting to see how bookmakers respond to bettors who use technology on their side to boost their wagering efficiency. Will margins go up, limits go down, or will bookies come up with something out of left field?

The Downsides of AI Use for Bettors

We live in an age where people turn to Google and AI as authorities on everything. Leaning so much on technology is not the perfect solution for anything, as human intuition and expertise in strategic decision-making feature nuances that tech has not yet mastered. The absence of these aspects can hinder the entire wagering process, as AI cannot recognize when it gets fed inaccurate data, which can lead to incorrect odds calculations and skewed betting patterns. It also struggles to account for external factors like weather conditions, injuries, mood, and pressure.  It displays an overreliance on past patterns.

Moreover, the software has programmed biases that can originate from historical data discrepancies, developer subjectivity, errors in data labeling, and so on. These systems can also be prone to tampering and manipulation, compromising the integrity and quality of the information they provide.

Probable Future Trends

We expect to see a rise in conversational AI and the emergence of services that allow users to get personalized recommendations that they can question and ask for the offered approaches to get tweaked. The level of automation regarding implemented tactics should also grow and factor in a broader range of variables. Next-generation models are going to leverage deep learning techniques at a higher level to analyze intricate patterns in-game data, and they should feature responsible gambling practices that warn bettors when they make ill-advised decisions.

Good AI Advice Services

If you are a frequent GOSUBETTING reader, then you know that we posted AI-produced tips. Statistic Sports is a popular choice with a library of over ten years of historical data, and it processes more than two thousand games weekly, helping users spot games worth wagering on. Rebel Betting is a well-known value wagering service we have talked about quite a bit here. Zcode System is a famous score predictor incorporating over eighty parameters to create a formula that performs thousands of game simulations en route to help gamblers confidently anticipate scores. We suggest you check it out if you are into football betting.

Privacy and IP Considerations for AI Services

Something that many people curious about developing AI predictors do not think about is that the integration of AI for betting carries notable legal risks. The most notable ones are in the spheres of privacy and intellectual property. Managing privacy concerns is essential in this arena due to laws/regulations like the EU General Data Protection Regulation or the California Consumer Privacy Act. These cover personally identifiable information, and we must note that proprietary data, which doesn’t fall under the personal label, is also a sizeable legal consideration. Many sports metrics providers have specific terms regarding the use of their data. Not all data services permit the info they supply to get processed by AI-like software for betting advice. So, companies planning to operate in this area must ensure they partner with data partners who are comfortable with the services they deliver. And to who? They must ensure that acquisition methods, downloading, scraping, etc, respect the terms set by data providers.

Another sizeable consideration for service is that intellectual property rights are another critical area in betting-related AI creation/deployment. AI algorithms and the methods used to train and infer from them may be eligible for IP protection, meaning the AI processes that generate recommendations could be patented. There was actually such a court case in the US that demonstrated that novel AI techniques in betting meet the criteria for patentability in the United States. That means that sector players must be super careful when creating their algorithms not to infringe on existing IP rights. Furthermore, if open-source software has gotten used in developing these algorithms, the associated open-source licenses could restrict how the software gets used, modified, and distributed.

While data itself is not patentable, copyright can protect certain aspects. Although sports statistics are not enforceable as IP, copyright infringement claims are still possible. That is why services may transform the data used in algorithms or use small subsets to avoid copyright issues. It is pivotal that when someone creates an AI model for commercial betting advice, they ensure any IP rights associated with the algorithms get assigned instantly through ownership agreements. That is a proactive step that minimizes the risk of future disputes.

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