How Data Science is Changing Sports

From player performance to the fan experience, data science has changed every sport. This change happened from the field to the back offices, where data scientists collect and analyze more data than ever before. While statistics has always been a major factor, it’s no longer a secret that you need to do something more than just look at the numbers. No sports team, whether it be professional or amateur, can afford to ignore data collection and analysis if they want to be the best and continuously improve, from practices to the biggest sporting event of the season.

Data analytics answers questions that both fans and coaches are always eager to answer as they combine new metrics to gain insights that were never before imagined or possible. With the amount of data being collected changing each year and the sophistication in which it can be analyzed, the possibilities for new innovations and breakthroughs are endless. Below are how various sporting events and professional teams are making the most out of data science and all it brings to the table.

Cycling

Nearly 1.3 billion people watch the Tour de France every year. This year, data analysts set out to offer real-time insights to those who follow the race to the Champs Elysées. This was no easy task, as the Tour has 21 stages with 198 rides crossing over 2,200 miles.

To get the data from the bikes to the viewers, 100-gram sensors were attached under the saddle of each bike containing a GPS receiver, a radio frequency transmitter, and a battery that needs to be charged daily. Each one of these sensors transmits its GPS position every second. This device connects via radio frequency to one of the many helicopters that follow the race, which is then sent to the TV trucks on the race courses. From there the data is transmitted to its final destination – the Dimension Data truck, which is waiting at the finish line.

This data provides viewers with enhanced insights regarding the time to finish, live speed, and riders distribution, which are broadcasted both on the Tour de France’s website and their social media. When it combines with weather conditions and road gradients, commentators can provide more relevant information to cycling fans as the race unfolds.

Baseball

Data-based decision making wasn’t taken seriously until Michael Lewis published the book Moneyball: The Art of Winning an Unfair Game in 2003. Since then, every Major League Baseball team has engaged in data-driven analytics, especially the Houston Astros, who won the World Series in 2017 against the Los Angeles Dodgers.

The journey into data science began when Jim Crane purchased the Astros in 2011 and completely changed the team’s structure with the hiring of experts to make data-driven judgments based on predictive analytics for both player assessment and in-game decisions. Beyond scouting for players, data analytics has also assisted managers to make smarter on-the-field calls regarding lineups, field positions, and relief pitching.

Regarding the 2017 World Series, Vijay Mehrotra, associate professor at the University of San Francisco and an analytics consultant, said, “You saw two sharp contrasts. The Dodgers were a team that was very much bought. It’s the highest paid team in baseball. The Astros were much more data-driven. They had to be because they didn’t have the benefit of the huge payroll. That’s your Moneyball parallel”.

This year, the MLB renewed their partnership with Amazon Web Services, which began in 2014. Amazon helps power Statcast, the high-tech player tracking system that uses data to measure every play during every game. In turn, it produces statistics regarding pitching velocity, launch angles of home runs, acceleration of baserunners, and more. The MLB also is predicted to begin using Amazon SageMaker to predict pitches by analyzing the batter, catcher, pitcher, and situation.

Basketball

Over the last 10 years, data analysts have made major headway in the NBA, as nearly every team now has an analyst on their staff working directly with coaches and player evaluators with the goal of maximizing the talent of athletes.

In 2009, the NBA began using a video system that tracked the movement of every player on the court, in addition to the ball, 25 times a second, to assess which player was doing the most to achieve a win. In addition, data also began being used to analyze three-point shots, as it showed that while players only had an average of 35% chance of making the shot, it led to more points than a two-point shot that was taken closer to the basket. As a result, three-point shots increased 50% from 2012 to 2017. Data was also used to evaluate defense, as analysts can identify which players are best at altering three-pointers and dunk shots, as well as establish how much better a team’s defense is when a certain player is on the court.

To ensure that the NBA constantly works to uncover new data analyst talent, an annual Hackathon takes place for students, statisticians, developers, and engineers that build tools to solve important and challenging problems the NBA faces.

Football

The NFL has fully embraced the technology side of the game, as they have hired their first Chief Information Officer, in addition to developing their own platform for all 32 teams. Thanks to a partnership with the tech firm Zebra Technologies, RFID (radio-frequency identification) data sensors will be installed in players’ shoulder pads to collect data involving the movement of everyone on the field. This will include top speed, longest completed pass distance, and the fastest sack, among others.

These sensors will also be embedded inside footballs, placed under the laces during the manufacturing process, to capture data points 25 times per second. These tags weigh a tenth of an ounce and are roughly the size of a nickel, so the aerodynamics and inflation of the ball aren’t affected.  The New Orleans Saints have begun to use these sensors in team practices, with their coach Sean Payton saying, “We use the Zebra Sports practice system to track our players and monitor their participation and performance throughout the season. The information provided by Zebra has proven to be a vital asset to our staff in evaluating and training our entire squad. Adding tracking capabilities to the ball will take things to another level for us.”

Beyond sensors, the Seattle Seahawks utilize machine learning and predictive analytics technology to help predict and prevent injury with Microsoft’s Sports Performance Platform. “What sets Sports Performance Platform from Microsoft apart is the availability of machine learning and artificial intelligence capabilities to help predict likely outcomes, and use those insights to make better coaching decisions,” noted Microsoft GM Jeff Hansen.

Golf

One of the first sports to adopt data innovation was golf when they debuted the ShotLink System back in 2001. Using a combination of lasers, 3D mapping software, and various employees and volunteers, this system is able to calculate exact locations and distances between any two coordinates. The PGA Tour was one of the first tournaments to utilize ShotLink to track various statistics and rank players based on this slew of new metrics.

From the very beginning, ShotLink was a way to revolutionize the fan experience by using lasers to record every move of the shot, from tee to green. Technology has advanced immensely since 2001, and ShotLink can now use cameras instead of lasers to identify and indicate the speed, break, and distance of shots taken. Because of this advancement, not only can ShotLink know that a player has made a 5-foot putt to achieve birdie, but now can also track the line and the speed of every putt.

These changes to ShotLink have the potential to collect a staggering amount of new information. For instance, a single shot from a laser is about 10 bytes of data, whereas the same single second from these new cameras is roughly 400 megabytes of data. Of these changes, Jeff Howell, the circuit director of ShotLink Technologies, said, “The volunteers do great, they are the reason we’re up in the 97% [accuracy] of every shot, but it does take away the human element, thinking they are shooting the golf ball with the laser when you may be overshooting it and hitting a couple feet away. Now the camera can see it more clearly.”

Conclusion

Data and analytics have the potential to continue to benefit everyone in sports. With advancements and innovations happening all the time, the most exciting breakthrough in sports could be just around the corner. Like those working with sports analytics, the data scientists at Egen are consistently working with big data and analytics to capture every last insight from every drop of data that is available. If your business is looking to gain new understandings and observations regarding data that can provide a fresh perspective, contact us at Egen to get the process started.