Osaka had reset herself. She had found a way to win. It was, in a way, a breakthrough. She lost first sets twice in Melbourne—once against Hsieh, then in the next round against Anastasija Sevastova—but recovered both times. The road to hoisting the trophy ultimately ran through a series of wrenching, hold-your-breath three-set matches, including a dramatic final against current No. 3-ranked Petra Kvitova.
“It has been like a roller-coaster ride; every match has been like that,” says Osaka’s high-performance coach, Abdul Sillah, the day after Osaka wins in Melbourne. “Which is good, because more than the match itself, more than the victory itself, you’re trying to see if you have a champion on your hand, or if you have a winner. Winners are one-hit wonders, right? But a champion is like Serena, LeBron—can you do this consistently?”
Indeed, since winning the U.S. Open in September, Osaka has shown signs of a psychological growth spurt. It seems evident in her posture—she stands straighter in front of crowds instead of looking as if she’s willing herself to disappear. It shows in the way she fields questions. “She knows she’s funny,” says Osaka’s agent, Stuart Duguid. (Her deadpan rejoinder whenever a reporter asks why she has the surname of her mother instead of her father: “Everyone who was born in Osaka, their last name is Osaka.”)
… “You just try to maximize who you are. I’m not caught in up trying to be better than the guys I [was drafted] with,” he said. “I’m trying to be the best me I can be. So whatever that is, that is, and I’m OK with that. I was the 10th pick, people were thinking could be an All-Star, but I turned out to be a pretty damn good pro. I’ve been here seven years. I’m a solid [expletive] player. On any team I’ve played for, I’m going to play good minutes.
“Am I an All-Star? No. Doesn’t mean I can’t be in the future. You never know. There’s been guys like Kyle Lowry, guys who been in the league for six or seven years and they go somewhere and it clicks. No one thought D’Angelo Russell was going to be who he was this year, and then he goes to Brooklyn and turns into an All-Star.
“I’ll never count myself out, but in terms of accepting who you are, I’m fine with that.
… Kelly was found this past Friday evening in her Stanford apartment, having killed herself by inhaling toxic gas. The news sent shockwaves throughout the international cycling community. Kelly was a member of the Rally Pro Cycling professional road racing team and was known for her short hair, bright smile, and powerful time trial. She was also one of the world’s best riders on the velodrome and had won an Olympic silver medal in 2016 and three world titles with the U.S. women’s pursuit team. Teammates and friends described Kelly as a determined competitor who was sometimes shy, always focused on her training.
To the Catlin family, Kelly was someone else entirely. She was one of the triplets, with siblings Christine and Colin. She was a multi-talented child who excelled at sports, music, and even foreign languages. She read science fiction and marveled at stories of dragons and dinosaurs. She roared like a tiger when teased about her intense focus. She ate chocolate every day. She played classical violin, yet blared German heavy metal in her headphones. She could recite 400 digits of the mathematical constant Pi without hesitation. She handled intense pain with ease, and once declined anesthesia when doctors had to set her broken arm, fearing it might trigger a red flag with the U.S. anti-doping agency.
It might seem odd to think about training (which most of us love) as “physical stress,” but, as we talked about in the first part of the series, it’s the body’s reaction to stress which determines the all-important adaptation during recovery.
It’s only by balancing the amount of physical stress (training) with our ability to recover that athletes can reach optimal adaptation. Too little stress means we adapt slowly (or not at all), and too much will result in getting sick or injured sometimes for an extended period.
On the other hand, planned periods of overload followed by planned periods of recovery is termed overreaching.
… In the past 12 years, Japan has collected 24 medals from the world championships – including eight golds – in singles’ skating. How did a tiny island nation in Asia with a limited number of year-round ice rinks build such a strong team?
That is the question asked over and over.
Yoshiko Kobayashi from the Japanese Skating Federation agreed to give her thoughts on this in a phone interview:
… Sig Mejdal, one of the most successful analysts in baseball, understands this. Mejdal left a career as an aeronautical engineer to work for the St. Louis Cardinals in 2005, the dawn of the Moneyball era. Helping with the team’s player draft, he was there for a successful seven-year run, including two World Series championships. After moving to the Houston Astros with new general manager Jeff Luhnow in 2012, he helped rebuild that long-suffering franchise, culminating in yet another World Series in 2017.
How does Mejdal spend his time? In the summer of 2017 he was a coach in Troy, New York, deep in the Astros minor league system. This 51-year-old was wearing a uniform, coaching first base, warming up players, and eating with the team after games. The top analyst in the organization spent his summer evenings riding the team bus between small towns in upstate New York!
The Astros are considered a model for blending analytics with traditional expertise. They took this unusual approach with Mejdal because of their commitment to embedding analytics in the organizational DNA. They wanted to break down the barriers that typically exist between those who think in regressions and those who can hit 95-mile-per-hour fastballs. They wanted to create opportunities for players and coaches to ask “the analyst” questions and for the analyst to ask questions of them. It worked so well in 2017 that Mejdal did a second tour the next summer.
We live in an age of unprecedented access to information. To buy the right phone, find the best tacos, or hire the perfect employee, just hop online and do as much research as you need before choosing. Having so much information at our fingertips has made us more knowledgeable than ever before.
Or has it? The information age certainly has the potential to improve our understanding. But new evidence suggests that access to information may work better in theory than in practice. People think they will rationally assess all available information before forming conclusions, but then, with so much information at their disposal, they actually form conclusions nearly right away. Minds are made up long before we make it through the evidence.
Many of us suspect that the beautiful, often highly-edited images of people we see on social media make us feel worse about our own bodies. But what does the research say?
On the list of endangered retail species, the local running store is right up there with the independent bookshop. We want it to exist, but the internet has made it too easy to buy whatever we want, whenever we want, at the price we want instead.
One of the last great names in running shops has survived, however, thanks to a bit of high-tech jiujitsu. Fleet Feet, which first opened in 1976 in Sacramento, began adding 3D scanners at its 177 franchise locations in 2017, using runners’ love of data to entice them into its stores and then keep them there long enough to buy something. “People who run enjoy going to a store, because it acts as a bit of a clubhouse,” says Huub Valkenburg, chief executive officer of Karhu, which three years ago cut a deal with Fleet Feet to become its exclusive in-house brand. “They want to connect with other runners.”
The foot-scanning strategy was originally implemented as a way to make it easier for salespeople to help a customer narrow down options more quickly. Measuring 12 data points, the scans give runners hard data on their own bodies that’ll help them find the right fit, whether it’s to accommodate an abnormally high arch or a propensity for extensor tendinitis on the top of the foot. Either way, the scanning process has been a hit, serving as an entertaining activity even for nonshoppers.
… the goal of data science is not to execute. Rather, the goal is to learn and develop profound new business capabilities. Algorithmic products and services like recommendations systems, client engagement bandits, style preference classification, size matching, fashion design systems, logistics optimizers, seasonal trend detection, and more can’t be designed up-front. They need to be learned. There are no blueprints to follow; these are novel capabilities with inherent uncertainty. Coefficients, models, model types, hyper parameters, all the elements you’ll need must be learned through experimentation, trial and error, and iteration. With pins, the learning and design are done up-front, before you make it. With data science, you learn as you go, not before you go.
In the pin factory, when learning comes first, we neither expect nor want the workers to improvise on any aspect the product, except to produce it more efficiently. Organizing by function makes sense since task specialization leads to process efficiencies and production consistency (no variations in the end product).
But when the product is still evolving and the goal is to learn, specialization hinders our goals in several ways.
… When the math gets as complicated as it has, it presents a new set of challenges for the people doing the work. Part of the reason that football analytics coalesced around expected goals is that it fits comfortably within how people, both coaches and fans, traditionally think about the game. How many chances were created? How good were those chances? Would you rather have a lot of speculative efforts or a couple of golden chances?
It’s not only that the questions xG sought to answer were ones that people intimately familiar with the game were already asking, it’s that it’s methodology was fairly simple as well. Look at all the shots, factor all the things that went into them (where they were, what part of the body they were with, now increasingly where the defenders in front of them were) throw in just a little dash of math to figure out how best to weight the variables, and you have an answer that works well (but clearly not perfectly) for both descriptive and predictive purposes.
But, as analytics increasingly moves into the spaces behind the shots, into they “why” of it all, the chasm gets harder to bridge.
