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Predicting Trends In Sports Betting With Artificial Intelligence & Machine Learning | Altenar

With the rise in the interest and use of AI in many different parts of everyday life, it seems only natural that AI and machine learning would have a place in sports betting, though it might not be the way you would expect. Thanks to Altenar, a sportsbook software provider, operators can see the benefits of AI technologies in the sports betting world. 

Instead of predicting winning outcomes and algorithms of success for players and bettors, it could help many operators in assessing upcoming trends and the preferred type of sports that many players will want to bet on in the future. 

This kind of predictive ability is thanks to AI technologies being able to handle large data sets and information, all from the process of learning its patterns to assess future probable outcomes. 

According to a paper titled ‘Betting models using AI: A review on ANN, SVM, and Markov Chain’ written by Aladár Kollár ‘Various sports leagues around the world provide this information to bookmakers, allowing them to develop better products and improve legal betting.’ This is where AI and machine learning can help provide operators and players with a unique and top-tier experience. 

This article hopes to explain how three AI and machine learning systems can boost products, and improve legal betting and bettor experiences.

Sports Bettings Best Friends ANN and Markov | Altenar 

When looking at AI and machine learning for sports betting, it’s important to consider ANN (Artificial Neural Network) and Markov Chains, these ways of working could be detrimental to the sports betting industry, as it heightens the overall experience for operators and players, and sports betting software providers, like Altenar, understand the importance of AI and machine learning and implementing it to achieve amazing products.

This article will briefly look at the two modes of AI and machine learning to shed some light on how they can achieve brilliance for sports betting. 

Based On Human Thinking | ANN

The human brain consists of around 10 billion neurons and 60 trillion synapses that send information between each other as a response to a host of stimuli. ‘In response to stimulus patterns, neurons show long-term changes in the frequency of their partnerships.’  

The premise of ANN is to follow a similar structure, as written by Kollár, ‘The brain can be used as a parallel information processing system that is both extremely complex and non-linear. In a neural network, data is collected and processed continuously over the entire network, rather than at discrete points. To put it another way, in neural networks, both data and computation are global rather than local.’

The aim is to have machines mimic biological neural networks as demonstrated in images 1 & 2. 

Image 1 

In comparison with the ANN system… 

Image 2 

ANN will take the below steps to predict match results, for instance…

Step 1: Selecting prediction parameter 

Step 2: Designing appropriate NN model

Step 3: Gathering data Step 

Step 4: Predicting the match results 

In line with the below ‘prediction criteria’ …

Random Variables | Markov 

According to Kollár, Markov Chains are ‘a sequence of random variables used to define a Markov chain. The state space is a set of variables, with each variable representing a single state. The Markov chain is the sequence or chain from which the next sample from this state space is sampled. Furthermore, it is presumed that the next state is only based on a finite number of previous states. The first-order Markov chain is the simplest Markov chain. The current state is solely based on the previous state in this case.’ 

Kollár explained how this system can be attributed to sports betting and predicting future trends and outcomes to enhance operator revenue and player experience. Kollár demonstrated the following as an example to be applied to predicting trends instead of … 

In the above example, Kollár wanted to ‘measure a team's strength solely based on a match outcome; if the team's result is positive, the strength is updated with a positive value; if the result is negative, the strength is updated with a negative value. Naturally, this is a condensed example intended to illustrate the definition. Since the conditional probability of the outcome in the given example is the same for all t, P(Rt|Rt1), a conditional probability table is only needed once.’ 

It’s as a result of the two systems, ANN & Markov Chains, that sports betting can predict trends, as well as, bring new technologies to the forefront of a pioneering industry. 

You can benefit from new technologies, features and products from Altenar, a sportsbook software provider. 

Contact the team today to discover more… 


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