Back in the 1990s, if you suggested the three-point corner was the best shot in basketball, you might have been laughed at in the gym.

The game was still largely dominated by a fleet of seven-foot centers, most of whom couldn’t shoot more than a few feet from the basket. Even the game’s best player, Michael Jordan, was a midrange specialist who averaged less than two three-point attempts per game during his career.

Fast forward to today, and the best players average about a dozen long ball attempts per game – usually favoring corner shots.

What changed ? Analytic.

“When I started in the profession, 10 to 12 years ago, data analysis was almost non-existent in the training rooms,” says Adam Petway, director of strength and conditioning for basketball. male at the University of Louisville. “Today we have force platform technology, we have speed-based training, we have GPS tracking during games and in training, all to get more objective analysis to help our athletes, so it has increased exponentially.

Petway, who previously worked on the coaching staff of the NBA’s Philadelphia 76ers and Washington Wizards, has a bachelor’s degree in sports science, an MBA with a major in sports management and a Ph.D. in sports science. Recently, he extended his education through the Applied Data Science Program (ADSP) at MIT Professional Education.

“The impetus behind enrolling in ADSP was primarily a curiosity to learn and a desire to improve,” says Petway. “During my time in professional and college sports, we’ve had entire departments dedicated to data science, so I know that’s a skill set I’ll need in the future.”

Apply new skills

Petway took classes in a live online format. Although he was the only strength and conditioning trainer in his cohort — learning alongside lawyers, professors and corporate executives — he says the focus on data has given all of his comrades class a kind of common language.

“In many people’s minds, the worlds of data science and NCAA strength and conditioning training may not intersect. We see that there are many other professional and industry sectors that can benefit from data science and analytics, which is why we’re seeing an ever-increasing number of professionals around the world enrolling in our applied data science program,” says Bhaskar Pant, executive director of MIT Professional Education: “It’s exciting to hear how changemakers like Adam are using the insights they’ve gained through the program to address their most pressing challenges using data science tools.”

“Having access to top practitioners in the field of data science has been something that I’ve found very, very helpful,” says Petway. “The chance to interact with my classmates, and the chance to interact in small groups with professionals and professors, was amazing. When you write code in Python, you might mess up a semicolon and a comma, insert 200 characters into the code, and find out that it won’t work. So the ability to stop and ask questions, and really get into the thick of it with a cohort of peers from different industries, that was really helpful.

Petway points to its new abilities to code in Python and run data through artificial intelligence programs that use unsupervised learning techniques as key takeaways from its experience. Sports teams produce a wealth of data, he notes, but coaches need to be able to process that information in a way that results in actionable insights.

“Now I am able to create decision trees, visualize data, and perform principal component analysis,” says Petway. “So instead of relying on third-party companies to come and tell me what to do, I can take all that data and release the results myself, which not only saves me time, but also a lot of money.”

As well as giving him new abilities in his role as a coach, the skills were crucial to the research of an article that Petway and a team of several other authors published in the International Journal of Strength and Conditioning This year. “The data comes from my PhD program about five years ago,” notes Petway. “I already had the data, but I couldn’t visualize and analyze it properly until I took the MIT professional training course.”

“MIT’s motto is ‘mens et manus’ (‘mind and hand’), which translates to experiential learning. As such, there has been a great deal of thought given to how the applied data science curriculum is structured. It is expected that each participant will not only acquire fundamental skills, but also learn to apply this knowledge in real-world scenarios. We are excited to see the learning from our course applied to high-level college basketball,” says Munther Dahleh, Director of the Institute for Data, Systems and Society, William A. Coolidge Professor of Electrical Engineering and computer science at MIT, and one of the ADSP instructors.

The growing role of data in sport

The analytics push the field of strength and conditioning well beyond the days when coaches simply told players to do a certain number of reps in the weight room, Petway says. Wearable devices help track the distance athletes cover on the ground during training, as well as their average speed. Data from a force rig helps Petway analyze how hard basketball players jump (and land), and even determine how much force an athlete generates from each leg. Using a tool called a Linear Position Transducer, Petway can measure how fast athletes move a prescribed load during weightlifting exercises.

“Instead of telling someone to do 90 percent of their squat max, we tell them to squat 200 kg and move it at a speed of over one meter per second,” explains Petway. “So it’s more about power and speed than your traditional weight training.”

The goal isn’t just to improve athlete performance, says Petway, but also to create training programs that minimize the risk of injury. Sometimes that means deviating from the worn-out sports clichés of “giving 110%” or “leaving it all on the pitch”.

“There’s a misconception that doing more is always better,” says Petway. “One of my mentors always said, ‘Sometimes you have to have the courage to do less.’ The most important thing for our athletes is to be available for competition. We can now use data analysis to predict the early onset of fatigue. If we find that their power output in the weight room is decreasing, we may need to intervene with rest before things get worse. It’s about using the information to make more objective decisions.

The ability to create visuals from data, says Petway, has dramatically improved his ability to communicate with athletes and other coaches about what he sees in the numbers. “It’s a really powerful tool that can take a bunch of data points and show that things are going up or down and what intervention we’re going to have to take based on what the data suggest,” he said.

Ultimately, Petway notes, coaches are primarily interested in one data point: wins and losses. But as more sports professionals see that data science can lead to more wins, he says, analytics will continue to gain a foothold in the industry. “If you can show that preparing in some way increases the likelihood of the team winning, that really speaks the language of the coaches,” he says. “They just want to see results. And if data science can help deliver those results, they’re going to be bought. »