How AI spots legendary cricket batting backlift using video footage
The greatest cricket batsman – Sir Donald Bradman – used a batting technique that fans loved, but some critics slammed. A critical component was his lateral batting backlift technique. Highly successful batters use it to this day.
Artificial Intelligence (AI) can now distinguish batters with a straight backlift from those using a lateral backlift, using video only.
Researchers from the University of Johannesburg (UJ) built the first deep learning computer vision AI model to do this. Their research is published in Nature Scientific Reports.
In future, coaches may give more detailed feedback to players using such technology. Or more easily identify players with lateral backlift components.
In that split second just before a cricket bowler releases a ball at the crease, the batter initiates a very subtle movement. This is when the batter starts moving their bat into position. They select it depending on the format of the game and the type of bowler approaching them. Most fast bowlers tend to bowl at between 140 and 150 km/h (87 to 93 mph), says Prof Habib Noorbhai.
Noorbhai is currently Director of the Biomedical Engineering and Healthcare Technology (BEAHT) Research Centre at the University of Johannesburg (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 second slip or the wicket keeper? This is the technique legendary batter Sir Donald Bradman used. Successful Test cricket batters also use it to this day.
AI helping coaches
The researchers – Mr Tevin Moodley, Prof Dustin van der Haar and Prof Habib Noorbhai – all played cricket at school.
Moodley is a doctoral student and Van der Haar a pattern recognition researcher. Both are at the Academy of Computer Science and Software Engineering (ACSSE), University of Johannesburg.
Moodley played provincial cricket in primary school and continued playing cricket throughout high school. He has always shared a love for the sport and has combined AI with cricket with the goal of making meaningful contributions within the cricketing world.
He 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,” says Moodley.
“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’.”
Record-setting and self-taught
Sir Bradman was born in 1908 in the tiny rural town of Cootamundra in Australia about 350km from Sydney. He was self-taught. He exploded onto the Australian Test team as a teen with eye-opening scores.
Bradman’s unconventional lateral batting backlift batting technique was somewhat controversial in his time. He was harshly criticized by some. But spectators loved what he brought to the game, and he set records that still stand.
To this day, a lateral backlift can be a significant advantage at international Test level cricket. But the batting style is mostly not recognised as part of standard training. Players at cricket academies are mainly trained the straight backlift only, with others not placing much attention on the backlift.
Finding batters for the AI
First, 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 from 1995 to 2021.
From there he selected 10 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, Quintin de Kock, Faf du Plessis, Kevin Pieterson, Kumar Sangakara, 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,” says Moodley. “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.”
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%.
Telling grass from arms and legs
A coach analysing video footage to better assist a player, has to deal with a lot of ‘noise’ and distractions, says Van der Haar. There are other players on the field, along with other objects and subtle movements.
Similarly, if the AI model only looks at texture in the video frame to distinguish between grass and a batter, it will come up short a lot of the time.
“What makes deep learning AI so phenomenal, is that it doesn’t worry about all that. It figures out if an area of the video frame is relevant or not,” says Van der Haar.
The neural network used for this AI model has several layers, he says. The first layer looks at low level stuff such as texture, or transitions from light to dark and represents it as gradients in a neural network.
Each subsequent layer of the neural net looks at something a bit closer to what we would look at. At the highest level, the neural net is looking at things like arms and legs and bat angles.
Once a video frame passes through the different layers in a neural net, it can then say ‘this is a lateral or straight backlift, says Van der Haar.
Coaching insight from AI
Conventionally, cricket coaches rely on analysis tools to see what their players are doing. It is a three or four-step process. They have to take a camera and record the players. They then export that footage to analysis software. Then they analyse the footage and provide recommendations to the players, says Noorbhai.
“I don’t think this research is about analysing more players. I think it is about providing coaches and players with more insight than they are currently getting,” says Noorbhai.
Switching from straight to lateral
Some highly successful batters use both a straight and lateral backlift.
“At the very start of a batter’s innings, we find that they have more of 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,” says Noorbhai.
Moodley, Van der Haar and Noorbhai’s research show how it is already possible to automate some cricket coaching. Their model can help coaches with promising and established cricket players, using video alone.
The legendary Bradman’s backlift was called a ‘loop backlift’ or ‘rotary batting technique’ in his time. Today we recognise it as a lateral backlift. The style is used instinctively by untrained players – and strategically or inherently by successful professionals.