These are questions facing every sport as we enter Super Bowl weekend. In soccer, hockey, cricket, baseball, basketball, tennis, football, etc etc, fans, coaches, owners and athletes seem to be clamoring for more certainty.
In part this may be because more money is involved. Those with a financial stake in the outcome want to be rewarded. And fans, with their attachments enhanced via social media and global audiences looped in through other forms of internet connectivity, want less randomness. In all sports, there is less tolerance for officiating mistakes.
The flip side may be that contests become more like video gaming: with boundaries more transparent and better defined. And given the overlap between gamers and sports fans, that is probably not a coincidence.
But as the following article points out, we may be giving up some of the essence of what drives our excitement and compulsive behavior. Data is great, but it is the emotional charge and our connection to it that is, as they say, why they play the game. JL
Tom Krazit reports in GigaOm:
Do we really need computers to make all of our decisions for us? I’d like to think that competitors would use data analysis as a tool, and not a crutch, when analyzing how best to proceed in games that are still subject to a huge degree of randomness.
It was early in the 2015 NFC championship football game last Sunday when Green Bay Packers coach Mike McCarthy was twice confronted with a tough decision: should the Packers, having put the Seattle Seahawks — owners of the National Football League’s best defense — on its heels in front of its raucous home crowd, go for a touchdown on fourth-and-short from near the goal line? Or should they settle for the easy field goal attempt?
McCarthy chose to take the three points on two separate occasions, despite the agonized screams of Structure Data event lead (and Wisconsin native) Derrick Harris into his television thousands of miles away, urging McCarthy to roll the dice. That decision did not work out for the Packers, who eventually succumbed to Seattle in overtime after the defending Super Bowl champs mounted a furious comeback in the last three minutes of the game.
Criticizing coaches with the benefit of hindsight is a time-honored sports media tradition. But we’re finally getting to the point where gut instinct is starting to look foolish given the reams of data available to coaches of all sports; especially in the NFL, contractually obligated to show off Microsoft’s Surface 3 tablets in as many ways as possible. I’m talking about stuff way beyond the New York Times’ Fourth-Down Bot.
What if McCarthy had access to real-time data that showed how Seattle’s defense was responding (or not) to a ferocious Green Bay drive that got them deep into enemy territory? What if he could seize upon that data to identify a weakness in the secondary within the 40-second play clock and and hurry-up offense (which makes it almost impossible for the defense to substitute) to call a play designed to exploit that weakness, one that he could feel much more confident about employing than the standard generic run up the middle on a goal-line situation?
The proliferation of data and mobile computing has made it quite possible for NFL coaches to start employing such tactics, and that’s something I want to explore on stage.
Coaches have always sought to exploit weaknesses in an opponent in their game planning. And ever since Bill James and Billy Beane woke up the stodgy baseball world to the power of statistics and data analysis, the science of player evaluation and scouting has been changed forever.
But I’m really curious about the impact of in-game data analysis. We explored this a little at Structure last year, when Booz-Allen Hamilton showed off some of the work it had done analyzing Major League Baseball pitchers and their tendencies to throw certain pitches in certain situations. But there is so much potential here, both for teams themselves and broadcasters like ESPN seeking to give its viewers more insight into the game.
Take the Packers-Seahawks game: What if McCarthy (or Fox, broadcasting the game) had access to real-time data about how the Seahawks defense was reacting to the Packers’ drive. Were the linebackers hesitating on a certain snap cadence? Did they tend to blitz in situations in which the offense deployed a certain formation? Were certain members of the defense more fatigued then others, something you could assess by seeing how fast they were running downfield on pass plays?
Football coaches are obsessed with preparation, and go over hours and hours of film in the days leading up to a game. They also review pictures of a given drive within a game once the offense or defense gets back to the sidelines. But imagine a situation in which McCarthy, Green Bay offensive coordinator Tom Clements, and quarterback Aaron Rodgers had access to a real-time heads-up database (projected onto Rodgers’ face mask, perhaps) that provided accurate information about the condition of the defense cross-referenced against the Packers playbook?
That might have changed McCarthy’s thinking in those crucial fourth-down situations, had he been able to ascertain weaknesses in Seattle’s defensive approach. And, of course, Seattle’s defense wouldn’t be standing still: if it could tell its linebackers that the right tackle is getting off the snap a half-second slower than the rest of the line, that could translate into a huge advantage.
I’m also interested in the ability of real-time data analysis to monitor the health of professional football players. Anyone who has grown up with the NFL has been witness to an uneasy transition in which players have gotten so much bigger and faster while playing basically the same game with basically the same equipment. Even Iron Mike Ditka isn’t sure he would allow his kids to play football at this point, knowing what we know now: data (especially real-time data) could give us a much better sense of what is actually happening to these athletes at all levels of the game and how we can best protect them.As a fan, I’m not entirely sure how real-time data analysis would affect the game. Do we really need computers to make all of our decisions for us? I’d like to think that NFL competitors would use data analysis as a tool, and not a crutch, when analyzing how best to proceed in games that are still subject to a huge degree of randomness.
But as a journalist, these are fascinating topics.
