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  • If you're a basketball fan,

  • you know your team's big stars, head coach,

  • maybe even the rookie bench warmers.

  • But you probably know very little

  • about the Ph.D. mathematicians like this woman.

  • Her job is to crunch the numbers of the game.

  • We're all in sort of an arm's race to acquire information.

  • And she's turning basketball

  • into a hard, cold science.

  • My name is Ivana Seric and I'm a data scientist.

  • For many years, Philly was a tough place

  • to be a basketball fan.

  • But having reached the top eight

  • in the playoffs for a second season in a row,

  • the 76ers right now are in a renaissance.

  • Embiid fakes, Embiid down the lane.

  • Just across the river

  • is where the team's been hard at work

  • and it's where I went to go meet Ivana in April.

  • This 125,000 square foot facility

  • comes with all the features you'd expect

  • from an NBA training complex.

  • It also features the office of people like Ivana

  • who support the team behind the scenes.

  • My job involves analyzing the data

  • that we have from NBA games.

  • My focus is mostly on the coaching strategy.

  • Coaches have a lot of intuition.

  • They know the game, they know the players,

  • but we try to complete their picture of the players

  • with data, which is hopefully unbiased.

  • Ivana is part of the Sixers

  • 10-person analytics team.

  • The work of these statisticians has already forced

  • a profound change in the game of basketball.

  • Teams are taking more three point shots.

  • Embiid for three.

  • Even though we make them at lower percentage

  • than the shots closer to the basket,

  • but they're worth more points

  • and the trade off actually pays off.

  • Take a look at this clip from the '80's.

  • See how all the players are bunched up right by the hoop?

  • Compare that with today.

  • More and more of the action is

  • taking place at the three point line.

  • Of course, it's not just basketball

  • that's been reshaped by analytics.

  • Businesses of all kinds are tracking a ton of information

  • they never tracked before thanks to new sensors,

  • improvements in computing power,

  • implementing cost and data storage.

  • Data scientists are the people

  • making sense of that information

  • with a combination of statistics and computer programming.

  • The profession's expanded rapidly in recent years

  • and comes with an average salary of $130,000 a year.

  • Much of the data Ivana works with comes from

  • technology the NBA adopted in 2013.

  • Every NBA arena has cameras that record the games

  • and then from those cameras, they can

  • extract player locations on a court.

  • These cameras record 25 frames per second,

  • so for each basketball game,

  • there's a million of throes of data.

  • And that allows Ivana to analyze plays

  • that were previously difficult to track.

  • For example, take the pick and roll.

  • It's one of the most important plays in basketball.

  • It involves one player setting up a human shield

  • to help a teammate shake off an opponent.

  • So we can look at each player,

  • how much they run pick and rolls

  • and how good they are at that

  • and then we can select a player and see

  • how often he's going to pass out of the pick and roll,

  • how often he's going to shoot.

  • And how would that help you

  • develop a strategy for the coaches?

  • So we could, for example this player,

  • he's going to pass pretty frequently.

  • We can say this to the coaches

  • and they're going to decide on the

  • defensive strategy for the player.

  • But this makes Ivana's job

  • look simpler than it actually is

  • because she spends a lot of her time

  • coding to extract the information she needs.

  • And I tried really hard to get her to talk about

  • what exactly she looks for.

  • And there's some stuff that you do

  • that's beyond just these interfaces

  • that you showed me today, right?

  • Yep.

  • Can you tell me more about that?

  • Um, not really.

  • So that's like a trade secret?

  • Yes.

  • NBA's a very competitive league,

  • so whatever can give us that advantage, we try to keep it.

  • When advising the coaches with her analysis,

  • Ivana has a decided advantage over many of her peers.

  • I started playing when I was seven years old

  • and I just loved it from the first day.

  • I think 'cause the game is so dynamic

  • and there's so many different skills.

  • It's what I wanted to do since

  • I was seven years old, really.

  • I wanted to be a professional basketball player.

  • Yeah.

  • At 19, Ivana moved from her home in Croatia

  • to New Jersey to go to college on a basketball scholarship.

  • And while she was a star player on her Division I team,

  • she was also an exceptional math student.

  • Much of her life has been like that,

  • balancing her love of basketball with her love of math.

  • I always thought I'm going to have to

  • choose between the two.

  • I went to graduate school and that's when

  • I really thought that, okay, I'm really choosing

  • one or the other this time.

  • So I thought I really chose just math.

  • The plan was to become a researcher or a professor.

  • Then, three years into pursuing her Ph.D. in math,

  • she heard that NBA teams were starting to hire

  • data scientists, but only 26% of data scientists are women

  • and Ivana didn't like her odds.

  • When I saw the job posting,

  • I didn't think I would actually get the job.

  • I thought because being a woman

  • in such a male dominated field,

  • they would never really consider me or give me a fair shot.

  • But this prediction turned out to be wrong.

  • She's got both the technical and basketball backgrounds,

  • which is sort of the ideal mix.

  • Her ability to capture these complex insights

  • and then share them with players or coaches,

  • with executives in a way that makes sense to us

  • is super valuable and frankly, not that common.

  • The next evening,

  • I attended my very first NBA game.

  • It was the last game of the regular season

  • before the playoffs and my chance to catch up

  • with the ultimate beneficiary of Ivana's work,

  • the Sixers head coach, Brett Brown.

  • The team has had a great season so far.

  • What role do you think analytics has played in that success?

  • I think it's played a significant role

  • in our success and many, many others.

  • The NBA playoffs are going to start in three or four days

  • and immediately, we'll get an analytical assessment

  • on the strengths or weaknesses of an opponent.

  • You can assess play calls, good or bad ones,

  • ones you should do more, ones you should avoid.

  • Really, I think it's going to continue to grow

  • and play a significant role in the design of organizations

  • and coaching staff's beliefs.

  • That night, even though none of the starting players

  • played in the game, the Sixers ended up crushing the Bulls.

  • A month later, the Sixers ended up advancing

  • to the conference semi-finals and suffered a

  • tough defeat at the hands of the Toronto Raptors.

  • Is this the tie breaker?

  • Ivana and her colleagues

  • will keep looking for ways to help their team

  • do better in the seasons ahead.

  • But the Sixers victories on the court

  • won't be the only way she's measuring her success.

  • It's really exciting to be able to

  • show young girls that they can actually have careers,

  • but it also feels like a big responsibility

  • because if I don't do well,

  • it's going to seem like

  • a woman cannot do this job,

  • because there are not so many of us.

  • It's a big part of what motivates me every day

  • to be able to show young girls

  • that they can succeed in STEM fields and in sports.

If you're a basketball fan,

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B1 中級

NBAデータサイエンティスト (The NBA Data Scientist)

  • 11 0
    林宜悉 に公開 2021 年 01 月 14 日
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