The Washington Post Is Way Off Base When it Comes to Big Data
The Washington Post Is Way Off Base When it Comes to Big Data
Sports and data collection have always gone hand in hand. It seems today’s sports fan must also moonlight as an amateur statistician. Batting average, yards per carry, field goal percentage—these terms are as common in sports circles as home run and touchdown. And this stats-sports collision is just as prevalent on the fan side as it is in the front office. The “moneyball” revolution, brought to life in a 2011 Brad Pitt film, turned baseball statisticians into accountant-recruiters who transformed meager budgets into capable teams by weighing a player’s statistical performance against his salary. The Oakland Athletics’ 2002 “moneyball” season is one of the most famous in franchise history and saw the team finish first in the AL West.
But this boon for the league, Washington Post columnist and professor Christopher J. Phillips argues, took sports down a slippery slope. All our data collection and number crunching only leads us to more questions, necessitating more data and so on ad infinitum—surely this is an unsustainable model. Big Data can’t teach us everything, Phillips argues, so we might as well stop trying to learn all we can.
While Phillips is correct that Big Data often poses many questions, he fails to acknowledge all the solutions it has given us. In the NBA, for example, league data showed that the three-point shot, despite its low chance of falling, led to higher scoring games than its two-point cousin. Because of this, teams have begun favor deep shooters like Steph Curry and Kevin Durant, sending the three-point hungry Golden State Warriors to every NBA final since 2015.
Even outside the world of sports, big data can provide solutions. Tesla, who treats its cars like computers on wheels, collects data from all of their vehicles. In 2014, an engine overheating problem was detected and a patch was sent to every Tesla with the issue, saving drivers a visit to the mechanic.
Netflix’s entire business is predicated on weaponizing user data for profit. In a book I recently co-authored called “The Bottomless Cloud,” futurist Tom Koulopoulos outlines how the streaming giant tailors a unique homepage to each user based on their interests, keeping them on the platform longer. Data is also a major factor in producing Netflix’s original content. Factors like actor popularity, genre prevalence, and thematic elements are considered before any series or film gets the green light.
Phillips and the Big Data naysayers’ argument, that more data won’t teach us anything or that it will only lead us to more questions, is maddeningly shortsighted. It overlooks the wealth of knowledge and solutions provided by big data analytics and curtails any future insight that may yet be gained by it. Sports and business alike have already proven, and will continue to prove, that big data still has plenty to offer.
the bucket
Sports and data collection have always gone hand in hand. It seems today’s sports fan must also moonlight as an amateur statistician. Batting average, yards per carry, field goal percentage—these terms are as common in sports circles as home run and touchdown. And this stats-sports collision is just as prevalent on the fan side as it is in the front office. The “moneyball” revolution, brought to life in a 2011 Brad Pitt film, turned baseball statisticians into accountant-recruiters who transformed meager budgets into capable teams by weighing a player’s statistical performance against his salary. The Oakland Athletics’ 2002 “moneyball” season is one of the most famous in franchise history and saw the team finish first in the AL West.
But this boon for the league, Washington Post columnist and professor Christopher J. Phillips argues, took sports down a slippery slope. All our data collection and number crunching only leads us to more questions, necessitating more data and so on ad infinitum—surely this is an unsustainable model. Big Data can’t teach us everything, Phillips argues, so we might as well stop trying to learn all we can.
While Phillips is correct that Big Data often poses many questions, he fails to acknowledge all the solutions it has given us. In the NBA, for example, league data showed that the three-point shot, despite its low chance of falling, led to higher scoring games than its two-point cousin. Because of this, teams have begun favor deep shooters like Steph Curry and Kevin Durant, sending the three-point hungry Golden State Warriors to every NBA final since 2015.
Even outside the world of sports, big data can provide solutions. Tesla, who treats its cars like computers on wheels, collects data from all of their vehicles. In 2014, an engine overheating problem was detected and a patch was sent to every Tesla with the issue, saving drivers a visit to the mechanic.
Netflix’s entire business is predicated on weaponizing user data for profit. In a book I recently co-authored called “The Bottomless Cloud,” futurist Tom Koulopoulos outlines how the streaming giant tailors a unique homepage to each user based on their interests, keeping them on the platform longer. Data is also a major factor in producing Netflix’s original content. Factors like actor popularity, genre prevalence, and thematic elements are considered before any series or film gets the green light.
Phillips and the Big Data naysayers’ argument, that more data won’t teach us anything or that it will only lead us to more questions, is maddeningly shortsighted. It overlooks the wealth of knowledge and solutions provided by big data analytics and curtails any future insight that may yet be gained by it. Sports and business alike have already proven, and will continue to prove, that big data still has plenty to offer.
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