Any lucrative business that wants to thrive in a competitive market will want to implement as many of that market’s technological trends. Progress never stops going forward, especially when that progress can all but guarantee possible scaling, sustainable profitability, and brand legacy with a lot of importance and heft.
The most important part is to use that technological advancement in a way that is both cost-effective and showcases overall efficiency, and, when bringing innovation, to disrupt the market. This is how some big names with tech-oriented approaches have become the leaders in their field.
One can make a direct parallel between what we’ve said and the emergence of remote gambling, especially in its online format. Once sports betting and casino gaming became feasible for online use, it started to show its fangs in terms of its capacity to disrupt the entertainment industrial bloc. As the global population continued to adopt internet usage, the market grew exponentially.
Nowadays, sports betting generates incredible numbers across the world. Statista’s sports betting & lottery report up to 2023 shows an immense level of rise over the last decade plus, proving that the model is popular among consumers, but also incentivizing operators to come up with efficiency plans.
Machine learning has become one of the hottest methods of turning this industry into a certifiable behemoth, showcasing that upside can come from the handling of operations rather than just the product itself. Given that sportsbook operators are those who act as middle entities between a sport and its audience, they need to move fast, efficiently, and decisively. Machine learning is how it can do so.
What is machine learning? Is it the same as AI?
Machine learning is a technological process that stems from computer science, leveraging as much data as possible in order to create algorithms (the machine). In order for the machine to work by itself and sustain its own process, it needs to learn what conclusion to draw.
The data that developers ‘feed’ the machine with is its learning process. By giving it as much ‘raw’ data as it can, the machine can analyze and emulate certain types of behavior, results, and make predictions based on the data. More specifically, it refers to what that data entails. When the machine has enough data to be able to draw conclusions, it becomes able to also suggest theoretical outcomes, or even answers.
Artificial intelligence is a high-powered evolution of machine learning. Its development process is machine learning, and the end product is an ability to generate complex answers and solve complex problems. Its perceived ability to go beyond the realm of numbers and outcomes, all while generating human-like responses, is what makes it particularly endearing and efficient.
Naturally, its extreme processing speed is the source of its impressiveness, showcasing a penchant for incredible upside. It also uses incredibly large and varied bunches of datasets, including markers of human behavior that the developer collates and sorts as data.
Creating a model for sports betting
When it comes to machine learning applicability in sports betting, the principle is quite simple because it mostly deals with numbers. Sports betting requires a feasible model that can calculate odds, especially in real time. We’ll address this particularity in the next section.
However, the situation regarding sports betting machine learning is that there must be a balance between data bias and unpredictability, especially when there are factors that can influence outcomes without being part of the sport itself.
You have team and individual statistics that are simple to collate and turn into quality datasets. You also have an immense historical record that can be as recent as the very last event before the one that is yet to happen. Moreover, you can add elements like injuries, which a well-trained machine can consider as a new data entry point that can affect the theoretical outcome in real time.
As such, it’s very important that the data sets that go into a machine learning process for sportsbooks are high-quality data. We’re talking about as much detail as possible, even if there is a risk that these very niche circumstances can become patterns for the machine, even if there is none.
Overall, calculations are important as trends and performances continue to change the status quo. For bookmaking, it’s more than essential that the algorithms behind the machine learning process have enough base training so that they can develop over time by adding more data. However, such a system must also be ready for the market launch if it is to have any relevance.
Odds shape-shifting and timely changes
Applications can vary, as we will explain in the following sections.
We’ve alluded to the importance of shaping odds, as they are the markers of sustainability and profitability for bookmakers. However, it’s just as important that the customer finds the odds fair and sustainable if it chooses an ML-based sportsbook as its solution. Today’s industry is full of such solutions, but the strength/viability of each model is what can differentiate it from the others.
So, what does an ML model need if it is to work fine, and why is it so important for the operator market?
Firstly, you want to look into the very real truth that live betting has become one of the most popular and endearing types of betting. When there are real-time changes within an event, the bookie can adjust their odds in order to cover itself. At the same time, bettors can look at this situation and decide to change up their bet because it needs to cover new ground.
How about circumstances when there is a reality of the field (visible form) that does not appear yet in the stats sheet? The operator would want to transform such situations into realistic odds.
In short, machine learning models can examine the data that a system feeds into them, rely on their well-trained algorithm, and then make real-time changes. When calculations require automated efficiency, good ML systems are crucial for expedited oddsmaking.
Marketing endeavors
Nowadays, we look at marketing endeavors in online gambling as both macro and micro endeavors. The macro-ones are the universal campaigns that aim to reach a broad sector of the market share, while the micro-ones aim to address customers on an individual level. Each has its own merit, but direct marketing has a certain appeal when done efficiently.
When you, the bookmaker, try to market yourself as a viable option to bettors, you are trying to convince them to choose you to spend their money via bets. There’s always the possibility that you need to pay them, but volumetric player bases also mean a high margin of error, collated vigorish, and overall scalability opportunities.
Using machine learning for micro marketing makes use of the same dataset principles. The bookmaker, having ethically (via consent) obtained the ability to handle the customer’s data, can do a couple of essential things: create a demographic portrait that it puts in a dataset, and create a smaller dataset made up of betting behavior.
In this case, the bookmaker can use its ML model and create player archetypes that it can overlap with their betting dynamics (money and betting preferences). As such, they can identify what kinds of tendencies they have, giving rise to marketing opportunities.
What kind of opportunities? By essentializing these types of behavior, the ML can provide bespoke offers and suggestions for the user, creating a more efficient rapport. This comes in handy when the gambling platform is particularly hybrid; 30 free spins on sign up may work as an entry for gaming aficionados, but not for sports bettors.
Security and fraud-prevention
When it comes to this topic, two main elements come to mind.
One would be the obvious entry of fraud-prevention. In most cases of collusive activity that is against he rules of sports betting, we are talking about very specific and highly unusual proposition bets. They are the ones that make bookies the most problems.
When they are niche-enough, they might seem untraceable. However, MLs can identify this type of unusual combination of large stakes for niche propositions, bringing out flags and and allerting live operators that supervise the operation. When there is clearance, the ML can know what to do based on what the developer inputs as agreeable decisions.
You also have usage against problem gambling. If there are datasets from problem gamblers, their behavior can turn into possible patterns. An efficient ML can identify a player’s behavior as being prone to/linked to problem gambling when the conditions from these behavioral datasets start to overlap.
Conclusion
In the end, all this computer gibberish is all about creating an efficient model that turns a business into a commercial success, and your time and money into a truly entertaining experience. If you’re the type who likes an efficient sportsbook, just remember to bet responsibly!
