Researchers' Algorithm Aims for Economical, High-Performing Cricket Bat - 3 minutes read
Researchers' Algorithm Aims for Economical, High-Performing Cricket Bat
A new cricket bat designed using machine learning at the University of British Columbia could put a high-performing bat into young kids' hands and ultimately bring more people into the sport.
The bat resembles the top-rated bats on the market but can be made more economically using cheaper materials than English willow.
"At least a million people play cricket and 2.5 billion people watch the game, making it the world's second most popular sport after football," says the leader of the project, UBC forestry professor Phil Evans, himself a fan of the game since his early days playing cricket in the fields of England. "But for young kids just starting out, the cost of a high-quality bat can be prohibitive."
Evans and colleague Sadegh Mazloomi used machine learning and genetic algorithms to teach a computer to maximize the performance of a cricket bat. The result closely resembles some of the finest available bats that sell for hundreds and sometimes thousands of dollars.
"The back of the bat is uniquely shaped so that it does what it's supposed to do—it minimizes the vibration and maximizes the rebound energy when it makes contact with the ball, allowing the batsman to transfer full power to the shot," says Mazloomi, a Ph.D. researcher who wrote the algorithms.
"It's fascinating that our cricket bat, which was designed based on physics and machine learning techniques, actually resembles the best commercial bat designs, which evolved by trial and error over hundreds of years," Mazloomi says.
The researchers say cricket bat manufacturers can use this technique to produce a great bat out of cheaper woods like Kashmiri willow or even poplar.
"English willow is the best wood for bats, but there is room for alternatives, as long as the bat performance stays the same," says Evans, the BC Leadership Chair in Advanced Wood Products Manufacturing at UBC. "Manufacturers could optimize the design of the bat to match the unique characteristics of a particular species of wood—and our technique can make that possible."
However, batsmen won't be hitting sixes with the new bat (nicknamed Algobat by its creators, for "algorithmically optimized") anytime soon. Evans plans to first test the prototype and compare its performance with high-end commercial bats.
"We hope that manufacturers can use this method to either make the world's best cricket bat, or to make them out of cheaper woods while maintaining the quality and the performance of the bat," Evans says. "Our ultimate goal is to put high-quality bats in the hands of all the young kids in Australia, England, India, and elsewhere who cannot currently afford one."
Source: Acm.org
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Keywords:
Research • Algorithm • Cricket bat • Cricket bat • Machine learning • University of British Columbia • Cricket bat • Child • Sport • Cricket bat • Salix alba • Cricket • Sport • Football • Forestry • Phil Evans (footballer, born 1980) • Cricket • England • Machine learning • Genetic algorithm • Computer • Cricket bat • Vibration • Energy • Batting (cricket) • Doctor of Philosophy • Cricket bat • Physics • Machine learning • Skill • Design • Evolution • Trial and error • Cricket bat • Kashmir • Populus • Salix alba • Wood • Cricket bat • Wood • Manufacturing • University of British Columbia • Manufacturing • Cricket bat • Cricket bat • Prototype • Manufacturing • Cricket bat • Australia • England • India national cricket team •
A new cricket bat designed using machine learning at the University of British Columbia could put a high-performing bat into young kids' hands and ultimately bring more people into the sport.
The bat resembles the top-rated bats on the market but can be made more economically using cheaper materials than English willow.
"At least a million people play cricket and 2.5 billion people watch the game, making it the world's second most popular sport after football," says the leader of the project, UBC forestry professor Phil Evans, himself a fan of the game since his early days playing cricket in the fields of England. "But for young kids just starting out, the cost of a high-quality bat can be prohibitive."
Evans and colleague Sadegh Mazloomi used machine learning and genetic algorithms to teach a computer to maximize the performance of a cricket bat. The result closely resembles some of the finest available bats that sell for hundreds and sometimes thousands of dollars.
"The back of the bat is uniquely shaped so that it does what it's supposed to do—it minimizes the vibration and maximizes the rebound energy when it makes contact with the ball, allowing the batsman to transfer full power to the shot," says Mazloomi, a Ph.D. researcher who wrote the algorithms.
"It's fascinating that our cricket bat, which was designed based on physics and machine learning techniques, actually resembles the best commercial bat designs, which evolved by trial and error over hundreds of years," Mazloomi says.
The researchers say cricket bat manufacturers can use this technique to produce a great bat out of cheaper woods like Kashmiri willow or even poplar.
"English willow is the best wood for bats, but there is room for alternatives, as long as the bat performance stays the same," says Evans, the BC Leadership Chair in Advanced Wood Products Manufacturing at UBC. "Manufacturers could optimize the design of the bat to match the unique characteristics of a particular species of wood—and our technique can make that possible."
However, batsmen won't be hitting sixes with the new bat (nicknamed Algobat by its creators, for "algorithmically optimized") anytime soon. Evans plans to first test the prototype and compare its performance with high-end commercial bats.
"We hope that manufacturers can use this method to either make the world's best cricket bat, or to make them out of cheaper woods while maintaining the quality and the performance of the bat," Evans says. "Our ultimate goal is to put high-quality bats in the hands of all the young kids in Australia, England, India, and elsewhere who cannot currently afford one."
Source: Acm.org
Powered by NewsAPI.org
Keywords:
Research • Algorithm • Cricket bat • Cricket bat • Machine learning • University of British Columbia • Cricket bat • Child • Sport • Cricket bat • Salix alba • Cricket • Sport • Football • Forestry • Phil Evans (footballer, born 1980) • Cricket • England • Machine learning • Genetic algorithm • Computer • Cricket bat • Vibration • Energy • Batting (cricket) • Doctor of Philosophy • Cricket bat • Physics • Machine learning • Skill • Design • Evolution • Trial and error • Cricket bat • Kashmir • Populus • Salix alba • Wood • Cricket bat • Wood • Manufacturing • University of British Columbia • Manufacturing • Cricket bat • Cricket bat • Prototype • Manufacturing • Cricket bat • Australia • England • India national cricket team •