Machine learning (ML) algorithms are becoming a standard way of improving decisions, whether this is a finance, health or gaming sector. Sports betting is one of those areas where ML technologies are becoming a standard decision support tools. The betting market is a massive industry, and simple means like statistics do not work anymore. Betting operators are focusing on machine learning-based methods to increase the expected return on investment. It is necessary to make accurate in-house predictions of sports events because all business mode success depends on this. It is essential for a betting operator to increase its accuracy in sports prediction to be an attractive company and be profitable.
Professional betters, also are using machine learning algorithms to formulate strategies for accurate prediction. Using an ML is a two-way game, which depends on how well big data processing algorithms work on each side.
To make a good prediction, this is necessary to consider all possible data. This include historical data of sports team performance, data of each player and team, geographical, seasonal, and other related data can be relevant in making the final prediction. Machine learning algorithms don’t use simple logic and rules. Instead – they are learning from data. With more complete and accurate data, better predictions are possible.
There are many machine-learning algorithms available depending on desired results, data, and learning methods. There are three major types of ML algorithms:
- Supervised Learning
- Unsupervised Learning
- Reinforced Learning
The supervised learning method is working best when we want to predict some target value from available known outcomes. Usually, this is a historical performance of teams, statistical metrics, and other data that correlates with past results. The main algorithms in this group are Regression, Neural Networks, and Decision Trees, Ensemble methods such as Random Forest, Boosting and Voting. Supervised Learning algorithms try to find relations between all independent value with dependent and according to this predict new incoming data.
Unsupervised learning methods come in handy when there is no outcome value available. They are good at finding unknown patterns in so-called data clustering. Cluster algorithms it is possible to group data and detect some similarities between different items, e.g., teams.
Reinforcement learning is similar to supervised. The difference is that machine-learning algorithms train itself continuously using prediction error analysis. It learns from past experiences and tries to find the best possible model to make the most accurate predictions.
During the past years, Artificial Neural Networks (ANNs) are becoming the most popular. Since the processing power of modern computers is vast, they are showing amazing results. Along with ANNs, there is a Deep Learning term used. As name states, ANNs have multiple interconnections with linear and nonlinear activation functions mimicking the human brain. They are capable of discovering deep relations within data and give even better prediction results. There are more derivative NNs that can dig even more data from multiple time instances. These are called Recurrent Neural Networks (RNN). They can analyze several past events using dynamic behavior analysis. They are more sophisticated and require in-depth knowledge on how to build, train and apply to data.
The reality is that probably all major companies are using machine learning algorithms as standard tools for risk management, predictions, forecasting and analyzing vast amounts of data. ML is a modern tool that makes a difference. It gives a stimulus for improving and creating newer and better algorithms because there is a sharp edge between winning and losing.