Introduction

How are AI solutions, particularly machine learning models, being developed and used to predict game outcomes and player performance?

Industries like sports betting have triggered a lot of interest in building and utilizing reliable systems that can predict the results of sports games. The reason is simple: maximizing the chances of profit by obtaining a prediction with better chances of getting it right than betting at random.

Betting industries are merely one of the many real-world scenarios that motivated the application of predictive machine learning models to predict the outcomes of sports competitions, tournaments, and games, both at the match level and at the individual level by sportsmen.

This article overviews how different machine learning solutions have been leveraged to predict sports outcomes in diverse application domains.

Predicting Results

Machine learning (ML) uses algorithms and models that learn by analyzing complex datasets to perform inference tasks by themselves, such as predictions. In sports, ML technologies are transforming analytical processes to offer deeper and more accurate information about the results of sports events and the performance of individuals and teams.

The combined use of ML and computer vision to predict fighting sports results based on real-time movement tracking

When it comes to predicting results, a few salient examples are:

• One of the most conventional solutions studied IS predicting win and loss in sports involving two competing teams or individuals, such as in basketball, rugby, and tennis. ML models addressing this problem are typically built as binary classifiers trained upon past-game historical statistics. The study can be found here.
• Despite players’ physical condition having been analyzed as an important factor correlated with performance and final results, few studies incorporate these data in ML modeling for predicting outcomes. One notable exception is the work by Minami et al.. Their study uses EEG data reflecting players’ brain condition to increase the accuracy of predicting match outcomes
• Other works like this one for predicting basketball game results put together multiple game performance metrics, including team strength, home team advantage, resting levels, and momentum. They try and compare diverse ML techniques like logistic regression classifiers, SVMs, decision trees, random forests, and XGBoost, achieving a reasonable predictive power in competitions like TBL (Turkish Basketball League).
• A particularly interesting and innovative approach, as showcased in this video is to combine ML and computer vision principles to predict the outcomes of fighting sports like boxing, by tracking the movements of participants in real-time.
• In sports heavily influenced by the physical environment, like F1 racing, there also are industry solutions integrated with computer vision to analyze real-time car and weather conditions and provide actionable insights to optimize strategies and performance during a race competition.

The above examples highlight the significance of ML as a powerful ally in predicting sports outcomes by leveraging large amounts of data to identify key predictor patterns that might be invisible to the “human eye”. While ML models in this context can help enhance decision-making and provide valuable insights, they also have their limits. Most studies to date suggest that the accuracy of their predictions depends significantly on the quantity and quality of the data collected, often struggling with unpredictability situations due to the highly complex nature of sports events where plenty of intricate factors influence results: motivation and state of mind of players, injuries or risk of suffering them, psychological pressure, environmental conditions, etc.

These solutions are not exempt from a critical view of their responsible and ethical implications. For example, to what extent can certain data sources about players’ health and physical condition be used or disclosed? What are the implications of making publicly available a sports outcome predictor for consumers in the sports betting industry?

Predicting Performance

Besides predicting the final result of a sports event, it is often interesting to also predict how well each team or individual performs in a game or competition. Let’s quickly review a few example studies and applications in this direction:

• This IEEE paper analyzes how the joint use of ML and wearable technology could help improving players’ performance by predicting it. Another paper focuses on using deep neural networks to predict players’ performance in the Algerian football selection and recommending best team alignments based on the performance predictions made.
• Predicting injury risk by certain players can be key to implementing injury prevention strategies and minimizing the chances of a fatal outcome in team sports through informed planning before a match. This and other factors have been widely investigated and applied to sports analytics processes to optimize athletes performance.

Wrapping Up

This article provided a glimpse of studies and real-world applications of machine learning to predict outcomes in sports events, along with other aspects like players’ performance.

At present, while ML offers extraordinary capabilities, in the scope of sports predictions it should be used as a complementary tool for analytical purposes rather than as a standalone and infallible solution for predicting outcomes.