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.

Developments in data visualization will help us spend less time disputing calls and tell us exactly who won the game.


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.

“As big data and visualizations evolve, it isn’t about flashing one yellow line, but about providing the right line at the right moment.”

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’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 has been used over more than 20,000 live events across all sports.

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.

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“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.

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.

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.