How the NFL Leveraged Data Integration at Super Bowl 50
In looking at the news, I discovered “Super Bowl 50 was the culmination of a season-long effort by SMT (SportsMEDIA Technology) to deliver a robust system that could truly take the visualization of player tracking data and other information to the next level.” The secret was data integration. Indeed, on Super Bowl Sunday CBS Sports took advantage of those efforts, and had more ways than ever to integrate data into the broadcast. 10 cameras were configured to display the virtual line and other virtual graphics related to player match-ups, situational success rates, etc..
Core to this is the DMX 2.0 football intelligence engine that is tied into the NFL’s player tracking data. The system interprets data by using events and the timecode of those events to marry it together and give context to the data, according to the article. “A current example of that is the ability to bring up a statistic and then simultaneously insert a graphic identifying the players live on the screen.”
As someone who has played football before, this kind of integration really fills in a gap that I always thought existed. They used to go by statistics alone to judge players, when there was really much more to it. This gets us closer to understanding what actually occurred, and thus brings context to the mounds of data that sports geeks cull through weekly.
What this means is that you can tell the whole story of a player’s ability to progress play, not only what numbers, such as yards gained, pass completions, etc., but how those things occurred against a particular type of defense and individual player, and how things looked at the time, as well as how the numbers looked.
This is not just for the NFL. Baseball can gain from these insights, as well as basketball, hockey, and soccer. The same issues exist within those sports, and, again, the numbers don’t tell the whole story.
Count on new insights to be gained from this technology, such as the real value of a player or a type of play. I suspect that the coaches will take advantage of this technology, as well as the fans, and we’ll have even more types of data analysis that will occur, now that we have better insights and data collection capabilities.
The use of data science is affecting most industries. The NFL use case is certainly impressive, given the popularity of the sport in the US, and the need to integrate more interesting layers of information around a game that’s over 100 years old.
However, the NFL is not done. Count on IoT-based systems, such as helmet sensors, to measure impact force, ball speed in real time, and even the ability to have systems determine the outcome of close calls, not instant replay. While some consider this taking the purity out of the game, it’s just another instance of using data effectively. That’s the name of the data integration game.