Business, like basketball, features many variables that complicate predicting outcomes. Source: Getty Images
The chances of filling out a perfect bracket for college basketball’s March tournament is calculated to be one in 2.4 trillion. That means you’re 24,000 times more likely to win the lottery. In fact, it’s so unlikely that anyone would pick all 63 games of the tournament correctly that Warren Buffet has offered $1 billion to anyone that pulls it off.
Needless to say, the Oracle of Omaha is in no danger of having to pay up.
And yet every year I do hours of research, analyze stats and matchups, carefully consider the opinions of experts, and weigh the importance of everything from strength of schedule to tip-off times. I consult with colleagues and college buddies. I study the spreads.
And every year, my bracket is busted by the end of the first day of play.
Because basketball—like business—has too many variables. There is, of course, a great deal of data that we can easily measure— like shot percentages, blocks, steals and turnovers. But there is also data that is more difficult to capture and quantify, like physical fitness, team chemistry, or the impact of a hostile crowd. These less structured forms of information greatly affect the outcome of games. And yet they are so vast and varied, so anecdotal in nature, they are nearly impossible to account for. They are immeasurable.
Or are they?
Peter Drucker famously said, “What gets measured, gets managed.” That’s true of basketball and business. A good coach, like any good manager, will use all of the information at their disposal to guide their strategy and get the most out of their team. The more information—assuming it’s accurate —you have, the better your decisions will be.
But with so many variables at play, so much information to absorb and weigh, it’s too much for any one person to handle. And besides, human emotion colors all of our decisions, in good ways and bad. Especially in sports. And that’s what got me thinking about IBM’s Watson, a project I worked on years ago, but has evolved beyond anything I could have imagined back then.
Watson is the first cognitive computing system. Cognitive computing refers to systems that understand, reason and learn through their interactions with humans and the information we generate. Most people know Watson from its world-beating turn on Jeopardy!, five years ago. But since then, the system has evolved to do much more than answer complex questions in natural language. Today it can adapt and make sense of many kinds of unstructured information. It can “read” text, “see” images, “watch” video and “hear” sounds. And it can interpret that information, organize it, offer explanations of what it means, and suggest courses of action.
So does this mean Watson could one day be coaching your favorite college basketball team? Of course not. But I could envision a scenario in which a coach uses Watson as a strategic advisor; a tool to analyze the reams of data—both structured and unstructured ¬that affect the outcome of games. The combination of a coach’s experience and instinct with the powerful analytical capabilities of Watson could provide a significant competitive edge, in game planning, player evaluation, and team operations.
After Watson’s chess-playing predecessor defeated World Chess Champion Garry Kasparov in 1997, the crestfallen champ explored the relationship between men and machines in games of strategy. Here is what he had to say:
“Teams of human plus machine dominated even the strongest computers. Human strategic guidance combined with the tactical acuity of a computer was overwhelming. We [people] could concentrate on strategic planning instead of spending so much time on calculations. Human creativity was even more paramount under these conditions.”
We have seen the potential of this man and machine partnership in the success of sabermetrics programs (applying analytics of players’ statistics to make coaching decisions) in baseball. Of course, data has always played an important role in sports. But cognitive computing takes these analyses to a new level, fundamentally changing the input to include information that doesn’t fit neatly into a table or a spreadsheet. It also alters the output by offering up actual recommendations.
I have no doubt that Watson would be a masterful analyst of college basketball during the tournament, from evaluating talent to picking winners and losers. But frankly, even if I could have Watson inform my bracket, I’m not sure I would.
Because part of the fun of sports is not knowing. That’s why we watch the games. That’s why it’s so dramatic. And we like being surprised by the unexpected upsets. But in matters of greater import, where the success or failure of a business acquisition is on the line, or lives are at stake, not knowing is not as much fun.