Using Machine Learning for Predicting Cricket Matches

The future has always been shrouded in mystery and magic. However, recent advances in machine learning and data analysis have transformed predictions from magical myths to surprisingly accurate science. Best of all, machine learning can be applied to every area, from stock projections to who will win an upcoming sports event.

Sports is, in fact, one of the biggest industries that machine learning is regularly tested on. Not only because of the love of sports by many worldwide but also because many types of sports have a wealth of historical information that can be used to achieve proper analysis.

That being the case, let’s look at how machine learning can be applied to one of the most popular sports: cricket.

What Is Machine Learning?

Before applying this technology to cricket, let’s delve into what machine learning is and how it works.

Simply put, machine learning is a type of artificial intelligence (AI) that helps a computer learn from historical data and determine an algorithm that can be used (alongside this data) to make predictions for the future. 

The concept of machine learning (ML) is not as recent as most people might believe. One of the first instances of machine learning dates back to 1943. During this time, the concept was formalised to help map a mathematical model of neural networks by neuroscientist Warren McCulloch.

Since then, particularly in recent years, much research and development has been put into the science. Due to this, machine learning today is a viable concept that has proven results across many applications.

How Does Machine Learning Work?

As mentioned, the core of ML is data. Almost all instances of ML require extensive amounts of historical data that the computer can sift through and analyse. During this process, the AI in the program can identify trends, patterns, and outcomes. 

This uncovered information is then used to formulate an algorithm that can be applied to future events and to predict the scenario’s outcome.

It should be said, however, that this process takes plenty of time, especially in the beginning stages. The leading cause for this is that most of the historical data the machine will use for learning purposes needs to be “cleaned” beforehand.

This process, which can only be automated up to a point, requires removing any data that will be irrelevant, confusing, or not used for the learning process. The final product of this process is the move from “dirty” data to information that can be easily processed and mapped out.

Machine Learning and Cricket Matches

Due to cricket’s immense popularity, its data has been fed into many machine-learning tests. Aside from using the sport as a base to test new machine learning methods, there has been much pursuit in predicting the expected outcomes of games.

The applications of this have ranged from determining which team lineup is most likely to be victorious against another specific lineup to predicting a winner to help place more accurate bets on upcoming games. 

Amongst the big research groups that have applied ML to cricket is the Department of Computer Science and IT at the Amity University Jharkhand Ranchi in India. 

The group, using the most popular T20 body, the IPL (Indian Premier League), tried to apply machine learning to the extensive data available for past matches to determine who would win upcoming games. 

Using six different types of machine learning, the group set 17 critical data points for evaluation during the ML process. Across the variants of machine learning, the group received an accuracy rate in predicting the outcome of upcoming events of 90%.

Another test by Towards Data Science was conducted to try and determine the winner of the 2019 ICC Cricket World Cup. The test employed data comprising player statistics and performances in previous World Cup events and ODI (one-day international) statistics and results from 2011 to 2017.

Also, using around six different formats of machine learning, the final result of the prediction by the algorithm was that England would go on to win the World Cup—which they did, in fact, do just a short while later. 

Machine Learning and Cricket in the Future

There is much debate and speculation as to whether or not machine learning can actively be used to improve the outcomes of cricket matches or even help bettors gain more by helping make more accurate bets. 

While tests like those above demonstrate that ML can provide a substantial level of accuracy, the answer is not so clear-cut. Suppose multiple teams use this innovation to determine what player lineups will beat each other. In that case, the algorithms will be made redundant—with each team constantly adjusting until they are positioned with the (predicted) winning players.

Because of this, the future of ML being applied to cricket is sceptical at best. Regardless of the broad application of uses it could be put to, contrasting machine learning methods and the fact that they can be readily obtained detract from the marvel of their predictive abilities.

Conclusion

At least for now, cricket is likely to continue as it has for centuries. Teams will continue to select players based on current performance and ability, and people placing wagers on games will need to continue relying on betting tips and luck.

If, however, there are even more significant advances in machine learning and the AI behind the science in the future, there is no doubt that ML may become a part of almost every sport.