Blood, Sweat and Data A four-part series on the revolutionary rise of technology in sports .

CHAPTER 1 The Search for

the Next Yellow Line Moneyball statistics are hard. Data visualization makes them easy.

Late in the 2013 NFL season, losing by a touchdown with less than two minutes on the clock, Washington wide receiver Pierre Garçon caught a pass from quarterback Robert Griffin III, and scampered toward a first down before being tackled about a foot shy of the mark. It was clear to viewers at home that Garçon had come down short because of the well-placed, digital yellow first down line projected across the screen. The referees on the field, however, first signaled that he had made the mark, let a play run, then reversed the call after the fact, effectively ending Washington’s comeback—a confusion born of lack of access to technology. We tend to forget the time before the yellow line—before its debut in 1998—when TV viewers had no more idea than fans in the football stadium whether a receiver actually made the elusive first down. It conveyed a bare minimum of information, yet the innovation demystified football with a simple stroke of color. Since then, sports data has broken the dam, with complex analytics spilling over from first down visualizations to Moneyball-era baseball, revealing a need for context that can channel the information. It’s given rise to a mashup of statistics, predictive analytics and visualization that can unlock the game for fans and help athletes find an edge. Think of it as a quest for the next yellow line. “You can think of any sport and say, ‘How can we use visualization to enhance it?’” said Kirk Goldsberry, a visiting scholar at Harvard and writer at the sports site Grantland. “It can show the audience things, remove the opinion aspect for a more empirical evaluation.” Goldsberry is a data polymath for the Internet era, equally adept at using spatial data to map the availability of fresh fruit in the inner city and the likelihood of sinking an NBA three-pointer from beyond the arc. He was among the first to create shot charts – a collision between art and data that can variously show a player’s shooting percentages or all their makes and misses from everywhere on the floor for a game, a season and even a career. . You Can’t Unsee This: When Data Changes the Score

With a mean of error of 3.6mm, compared to the tracking of standard high-speed cameras, Hawk-Eye’s accuracy has put a lid on tennis court antics. On average 30 percent of calls scanned by Hawk-Eye end up overturned. Developed by Chicago-based SportVision, the yellow line has been an integral part of football viewing since 1998. SportVision’s graphic technology across all sports has been used across more than 20,000 live events. The human eye can accurately track a soccer ball at a speed of 12 kilometers an hour but some balls have been recorded traveling at speeds of 120 kilometers an hour. Referees have had trouble seeing whether a ball moving that fast has crossed the line so FIFA implemented GoalControl in all 2014 World Cup stadiums. Meant as a technological pièce de résistance of Japan’s failed bid for the 2022 FIFA World Cup (it went to Qatar), the bidding committee envisioned streaming holographic broadcasts of each World Cup game, filmed by 200 high definition cameras into local stadiums of FIFA member states. . .

Share this Feature

When he showed his work to LeBron James a few years ago, “He was floored by it.” The greatest player in the world had never seen his game so nakedly exposed. “It’s giving their games an MRI and exposing the special structures inherent in their talent,” he said. But as sophisticated as sports teams have become at understanding data, Goldsberry said that the tech industry is still attracting the best talent. And sport borrows a lot from that industry. Goldberry’s heat maps of makes and misses are fundamentally the same as maps that track your clicks, scrolls and hovers on this article. . Stats You Can Share 41 million

tennis match data points analyzed by IBM SlamTracker over last eight years

SOURCE 1,686,949

tweets about Rafael Nadal during the 2014 Australian Open

SOURCE Share this Feature

Elizabeth O’Brien, sports marketing manager for IBM, said that’s because the data challenges are the same for sports as for industry: What insights can you glean from a year’s worth of web traffic? How can you use past events to predict future performance? “Data is data until you put it in context and unlock it for people," O’Brien said. This is particularly true for data heavy sports like tennis, which featured 19,000 matches, 400,000 games and 2.5 million points in ATP and WTA events in 2010, the last year for which data is publicly available, according to sports statistics company Enetpulse. In an effort to organize this information, IBM took 41 million data points from past matches and put them in the SlamTracker app, which visualizes match data and predicts what each player needs to do to win. Using past data, for example, the app might say that Rafael Nadal needs to win 50 percent of the rallies between four and nine shots to prevail. Here, the company borrows insights from the predictive analytics that can make predictions based on past events. Since data is data, as O’Brien said, the company can make the same predictions about the conditions needed for Nadal to win as it does for allocating web resources for a web traffic spike and do it in a way that can be easily consumed. The ultimate goal, according to John Kent, program manager of Worldwide Sponsorship Marketing for IBM, is to “make something that is so visually intuitive that it needs no explanation.” SlamTracker gives viewers the information they need at the moment they need it. “If somebody hits an ace, a visualization will come up that says, ‘this person has had an average of four aces per set through the tournament,” IBM’s O’Brien said. Increasingly, as big data and visualizations evolve, it isn’t about flashing one yellow line, but about providing the right line at the right moment.

CHAPTER 2 Protecting the World’s Most Valuable Human Real Estate Sports injuries don’t always just happen.

“This is some of the most expensive human real estate in the world," said Leslie Saxon, a cardiologist and the executive director of USC's Center for Body Computing. She was referring to athletes like $72 million-man LeBron James, who famously cramped up in Game 1 of the NBA finals this year. Saxon believes that if James had been training with sensors that could detect his biometrics, he might have predicted the cramping and avoided it (though it would have been tough to predict the failure of the AT&T Center's air conditioner). "There are early warning systems when you're about to cramp up," Saxon said. "The more you know about your training, the better you'll be." Biometrics and sensors are quietly making inroads into many sports to detect vital signs while athletes train and even play. Saxon originally set out to prevent dangerous heart conditions from felling elite athletes by predicting when these events would happen. But the study of biometrics is evolving into a tool that can maximize performance, extend careers and even become a revenue stream for athletes. Stats like shooting percentages and RBIs aren't enough — now we're looking inside athletes' bodies, at respiration levels and heart rate BPMs. Professional and college teams across the U.S. and around the world, including the World Cup winning German soccer team, the Pittsburgh Pirates and dozens of others, are using biometric tracking devices. It goes beyond the "Moneyball" obsession with complex sports analytics to "bio sports stats" that give managers and athletes more insight than ever into performance. And its impact is felt off the field too, letting fans know that, for example, when Pirates outfielder Travis Snider steps up to the plate, his heart rate can climb up to 180 beats. . Field of Ideas:

Quotes on How Data is Changing the Game

Share this Feature

The biometric trackers, which run the gamut from small electronic devices that fit in compression shirts to something resembling a stick-on tattoo, can monitor heart rate, breathing, perspiration, lactic acid and other vital signs. They can contain some combination of accelerometer, radio, GPS unit, magnetometer and gyroscope. With enough data, trainers can predict what will happen to an athlete based on previous events. Trainers of the German national soccer team can tell if a player is getting sick or fatigued if their heart rate remains elevated compared to what it was when they did the same drill previously. . Stats You Can Share 34

miles run by U.S. Mens National Team player Michael Bradley in the 2014 World Cup

SOURCE 2.15

Average number of games missed due to player injury for NSW Waratahs Rugby team

SOURCE Share this Feature

Trainers have also embraced biometrics in sports with high injury rates like rugby, which loses each player for an average of 2 games per season. The New South Wales Waratahs rugby team in Australia, for example, suffered separated shoulders and torn knees that can leave eight players on a 35-player roster on the bench for a typical game. In 2013, 18 players suffered 24 injuries, which cost the club roughly $2.7 million dollars and contributed to a ninth place finish. Desperate to keep its roster healthy, the team turned to IBM as a technology partner to bring the lessons of cloud analytics to the sweaty struggle of the rugby field. The company used its data expertise to track the Waratahs players' biometrics on the field using tracking units beneath their uniforms for practice and games, and their diet and sleep regimens off it. From each of the 119 data points that measure everything from force of tackles to calorie counts, IBM then uses predictive analytics to help trainers better understand what's injuring their players. Anecdotal evidence is promising; the team has dominated opposition this season, topping Super Rugby's Australia Conference for the first time in team history with a point differential of more than 200. Moreover, only six players had suffered nine injuries as of playoff time. "We thought the majority of injuries just happened," said NSW Waratahs Athletic Development Manager Haydn Masters. "Now we know we can prevent them and predict them." The same biometrics data that can prevent injuries is also some of the most personal data imaginable—a record of an athlete's every heartbeat, their speed, their ability to withstand blows. USC's Saxon sees enormous possibilities in that data, both for people who want to study it and for the athletes. "A lot of the issues with athletes is that they become these cultural figures and then when they're done, they're done and they're discarded," Saxon said. "Biometrics is an additional way to compensate the athlete." The emerging adoption of biometrics promises to not only enhance performance and lengthen careers, but also promises to be an immortal record of bodies in motion in the form of data, giving fans a look at how their favorite athletes' bodies work—and a way to understand how they play the game.

CHAPTER 3 How Apps Might Change Your Swing Digital sports scoring is making the game more data driven at all levels

In October of 1984, the historically great 104-game winning Detroit Tigers took on the San Diego Padres and their historically dreadful brown and orange uniforms in the World Series. It was a brutal mismatch that the Tigers exploited as ace Jack Morris pitched two complete games and star outfielder Kirk Gibson pounded two home runs for five RBIs in the decisive Game 5. If you took the box scores from that game and every single major league game that year and every game in the three decades since then, you’d be looking at roughly 70,000 games and 10.5 million data points (at a fairly conservative 150 stats per game). That cache of strikeouts, walks, hits and other stats used to fill huge tomes, but these days it’s only a drop in the vast ocean of sports data. Just look at the scorekeeping platform GameChanger, a free app used by amateur and youth teams. “This past weekend we scored the equivalent of 30 years of Major League Baseball in two days,” said Ted Sullivan, co-founder of GameChanger, which has scored more than 5.3 million games since its 2010 launch. With more than 20 million kids participating in team sports, the sheer volume of this sports data at both the amateur and professional level has the potential to arm a new generation of athletes, coaches and scouts with player insights and even predict the future based on reams of information. People gather data on amateur matches, at least initially, to keep their friends and family informed. “My intention with GameChanger was not originally about leveraging Moneyball analytics,” said Sullivan, a former baseball player at Duke who played in the Cleveland Indians’ system before going to Harvard Business School. “That was almost a side benefit. There were core efficiency problems at the amateur level that needed to be solved.” . Stats You Can Share 700,000

approximate number of field goal attempts in the NBA between 2006-2011

SOURCE .9298

Rafael Nadal’s winning percentage on clay, the best in the Open Era

SOURCE Share this Feature

Sullivan found that parents who couldn’t attend games had no idea what was going on. The best they could hope for were anecdotal reports on the games, since most coaches were still using archaic scoring tools. By contrast, the app uses a video game-like interface that scorekeepers use to enter game play. From that, the app generates an animated play-by-play for viewers following the game remotely (GameChanger is also rolling out a basketball app this fall). At the end of the game, the app generates 150 different statistics and a recap story, something that used to take weeks or months for a single tournament. Coaches then use the statistics for player development and scouts use the database to track prospects for college and professional programs. Golf, the other major sport that still relies on pencils for scoring, is also benefiting from an injection of digital data gathering and analytics. Broadcasters are engaging viewers of the major tournaments by using data to show what will happen next, according to John Kent, program manager of worldwide sponsorship marketing for IBM. After struggling for years to cull new data that would be meaningful to viewers, IBM created a new feature called Hole Insights, which correlates statistics to outcomes. It borrows from the company’s data modeling that predicts what happens to sales of products like coffee when the weather heats up (it generally drops, telling retailers to target consumers with promotions during warm spells.) In the golf context, the Hole Insights provided detailed data-driven insights about what was likely to happen when a player hit the fairway on the 10th Hole of Pinehurst Resort’s No. 2 course, the location of the 2014 U.S. Open. “Did they get a par? Did they get a bogey or worse? Or a birdie?” Kent said. “What we find is that the things that are simpler in nature tend to resonate more with the audience.” A simple graphic told viewers exactly how much missing the fairway on that hole would hurt, since 75 percent of players who had gone into the rough in earlier rounds ended up with a bogey or worse. For now, those predictions are only for the pros, but with apps like GameChanger revolutionizing the amateur game, it’s easy to see how they will soon show weekend duffers how woefully their games are lacking.

CHAPTER 4 The Extinction of Spectator Sports How virtual and augmented reality are redefining sports fandom