Big data has taken over the globe, from Facebook and Amazon to its use in sports. The use of data analytics in sports is not a new concept. It was brought to the mainstream in the famous novel Moneyball, which detailed the use of statistics and AI in American baseball. It even became a hit Hollywood movie.
Now, data science is used in all sports, from cricket to MMA. Teams and players are looking for every advantage they can get. The application of technology in sports has propelled teams and players to play better than ever. Sports have evolved alongside technology.
Artificial intelligence is being used to improve cricket strategy. It can also be used to help accurately predict match results. Simple analysis has always had a place in cricket. Statistics like runs, batting average, bowling average, and centuries have been tracked for quite some time. However, as of late, the level of analysis has reached an all-time high.
In cricket, mathematical formulas like the Duckworth-Lewis system are used to help teams reset targets in matches affected by rain. It’s a simple formula to help the team batting second set a goal. The Duckworth-Lewis method uses two main factors: the overs remaining and the chasing side’s wickets lost. This type of analytics is used mainly for the team batting second.
Additionally, cricket squads can use machine learning algorithms to discover patterns from compiled statistical data. This machine learning can help strategists predict future occurrences. Techniques such as Neural Network and Random Forest are used to create models.
For example, let’s think about all the data science that can be applied to a single batsman. Artificial intelligence can be implemented to take into account factors like the number of runs scored, the number of balls faced, the number of fours and sixes, strike rate against particular powers, runs scored against specific bowlers, and the list can go on and on. All of this can be used to help accurately predict match outcomes.
This can also be used with bowlers. The number of wickets the bowler took, runs given, overs bowled, runs given, bowling average. This is all useful for cricket teams looking for that extra edge.
The Winning and Scoring Prediction is an application of analytics that applies machine learning techniques to help predict the chasing team’s chances at winning in the second innings. This technique was developed by two New Zealanders, Scott Brooker and Seamus Hogan. This technique takes into consideration the venue’s history, weather, pitch, as well as scoring rates to make predictions.
Although data science is used more extensively in other sports, it has begun to make its mark in cricket. IBM developed a technique that utilized AI called “ScoreWithData,” which was able to successfully predict that Imran Tahir would be South Africa’s power bowler hours before the 2015 World Cup Quarter Final.
The data collected can get even more specific. Now, there is technology available that can collect information on the ways in which batters swing to help bowlers. Data can even display how batters respond to the bowler’s delivery.
All in all, AI implementation in cricket is a little behind when compared to other sports. However, the potential is there to make an in-depth analysis and create new tactics that will further develop the game. This can aid captains in their decision-making process. International teams and clubs are finding new ways to apply these innovative techniques to help improve their match results. As technology continues to develop, so shall the ability to predict future outcomes in sports. Soon enough, there may be a Moneyball for cricket.
