Using A1 to detect the gentle nuances of a backlift

USING video only, artificial intelligence (AI) can now distinguish batters with a straight backlift from those using a lateral backlift, a technique by one of cricket’s legends, Sir Donald Bradman, that left the lovers of the game divided.

Thanks to University of Johannesburg (UJ) researchers who built the first deep-learning computer vision AI model to do this, coaches may give more detailed feedback to players using such technology or easily identify players with lateral backlift components in future.

The technique involves the batter initiating a very subtle movement in a split second just before a bowler releases the ball at the crease. This is when the batter starts moving his/her bat into position.

They select the shot depending on the format of the game and the type of bowler approaching them. Most fast bowlers tend to bowl at between 140 and 150km/h, says Prof Habib Noorbhai, director of the Biomedical Engineering and Healthcare Technology (BEAHT) Research Centre at UJ.

He is also a sports scientist and has worked with local and international cricket teams since 2010.

His doctoral thesis was the first research to theorise the lateral batting backlift technique in cricket.

Another factor is the batter’s instincts and training. Will they bring up the bat straight with its face towards the ground or the stumps? Or will they angle the face of the bat outward towards the second slip or the wicketkeeper?

The researchers, Tevin Moodley, Professor Dustin van der Haar and Noorbhai all played cricket at school.

Moodley, a doctoral student, used AI to recognise different batting strokes in previous research.

For this study, he focused on getting AI to do the subtle task of identifying batting backlift style.

“I was wondering if I can automate that process. Why does a coach have to think, is that straight or lateral? If they know, they can say ‘You have a lateral backlift, I can now help you in this manner’,” said Moodley.

Moodley had to find video footage for their AI to learn from, since no open datasets were available. He combed through online video footage of first-class International Cricket Tests. He used the years 1995 to 2021.

From there he selected 11 batters with a straight backlift: Babar Azam, Temba Bavuma, Rahul Dravid, JP Duminy, Dean Elgar, Mahela Jayawardene, Ajinkya Rahane, Joe Root, Rory Burns, Ben Stokes and David Warner.

And another 10 with a lateral backlift: AB de Villiers, Hashim Amla, Quinton de Kock, Faf du Plessis, Kevin Pietersen, Kumar Sangakkara, Brian Lara, Ricky Ponting, Steve Smith and Virat Kohli.

In total, Moodley manually selected 160 frames (images) from the video clips as the AI’s “training class”. He selected another 40 as the “testing class”.

“The beauty of deep learning in AI is that you don’t have to tell the AI what to look for. A coach would need to focus on where the bat’s face is, the angle of the bat, where the batter’s feet and head are, and so on,” said Moodley.

The neural network in a deep-learning AI can learn by itself, figuring out what factors are involved with each backlift style.

The model using the Xception AI architecture emerged as the most accurate. It could correctly distinguish players with a lateral backlift technique, from those who tend to play with a straight backlift, 98.2% of the time. Close on its heels was Inception Resnet V2 with accuracy of 96.1%.

“I don’t think this research is about analysing more players.

“At the very start of a batter’s innings, we find that they have more of a straight backlift. Once they develop confidence in their innings, they start to open up, then their backlift goes more in a lateral direction with an open face of the bat,” said Noorbhai.