… “I never, ever, ever get upset when my daughter tries something and it doesn’t go well. I explain to her that’s a learning experience, and that’s how we get better.
“It’s just about empowering her to understand that it’s OK to be uncomfortable when you’re learning, because that’s when you’re improving. That’s been my message to her: She can do anything she puts her mind to, it just might not happen right now. You’ve got to work toward it.”
Two hours on the training table. An electronic balance board. Ubering to arenas before the team bus. Inside the great lengths the NBA’s only 40-year-olds take to prepare for game action.
“I feel like maybe I embraced more of a coaching role at Stanford. There were many fans that sat above our bench who were telling me they could hear me screaming — whether it be just yelling at my teammates to do well or yelling for a good tackle, or actually giving different instructions,” she said. “It was hard for me not actively to be on the field, but doing as much as I could was a good experience to have.”
Now well on the road to recovery, the 5-foot-10 defender wants to show she’s worthy of a spot on the World Cup roster.
Unless you spend a lot of time in and around FC Dallas or the national elite youth soccer scene, odds are you’re not too familiar with Luchi Gonzalez, the man the North Texas club promoted from academy director to become their new head coach this week.
His new post is his first at the senior professional level, and FCD is the only pro club he’s ever coached at. He knows that very well, and so do the rest of Dallas’ brain trust.
“Absolutely,” Gonzalez told MLSsoccer.com in a phone conversation over the weekend. “I’m inexperienced, I’m unproven – that’s life. That’s for any one of our players that are going to have a new experience, any one of our staff. In anything in this life, we’re going to be out of our comfort zones doing something different, doing something new. I can’t control that. I can only focus on what I can control and what I can influence.”
… The evidence is clear: Academic excellence is not a strong predictor of career excellence. Across industries, research shows that the correlation between grades and job performance is modest in the first year after college and trivial within a handful of years. For example, at Google, once employees are two or three years out of college, their grades have no bearing on their performance. (Of course, it must be said that if you got D’s, you probably didn’t end up at Google.)
The British Psychological Society, Research Digest, Christian Jarrett from
It is better to ask a student to see if they can explain something to themselves, than for a teacher or book to always explain it to them. That’s according to a new meta-analysis of the findings from 64 prior studies involving nearly 6000 participants that compared learning outcomes from prompted self-explanation compared to instructor explanation, or compared to time spent using other study techniques such as taking notes, summarising, thinking out loud (without the reflection and elaboration involved in self-explanation), or solving more problems.
The authors of the meta-analysis, published recently in Educational Psychology Review, say that self-explanation is a powerful learning strategy because learners “generate inferences about causal connections and conceptual relationships that enhance understanding”. The process of self-explanation also helps the learner realise what they don’t know, “to fill in missing information, monitor understanding, and modify fusions of new information with prior knowledge when discrepancies or deficiencies are detected”.
Acting prosocially can be quite challenging in one of the most salient intergroup contexts in contemporary society: Soccer. When winning is the ultimate goal, balancing self-interest with helping a fellow player in distress can be a tough decision; yet it happens. To date, we know little about what motivates soccer players to offer such help in the heat of the game. We propose that sex and what is at stake will matter in such prosocial dilemma situations. A pilot study (N = 107) indicated that female players may be more likely to help than male players, but this difference was only observed when the players are close to scoring position rather than far away from the goal (midfield). The main study (N = 366) finds that young soccer players show elevated inclinations to help in low-stakes situations, for example when their team is winning or when the outcome of the game seems pretty much decided. Contrariwise, helping intentions decline in high-stakes situations, for example when one’s own team is losing, when one is close to a scoring position in the offense (rather than at the midfield), or when the outcome of the game is still uncertain. Furthermore, female players show somewhat greater inclinations to help than their male counterparts. The current data point at some differences for male and female soccer players, albeit small in effect size. In contrast, we conclude that especially quick cost-benefit judgments regarding the stakes can play a major role in decisions to help or not to help another player on the soccer field.
Just beside the sprawling media work room where hundreds of writers tapped out the day’s news at this past week’s baseball winter meetings in Las Vegas, baseball executives across a makeshift hallway had an eye on writing the game’s future.
Because of the collection of data and the fast pace of technological advances, front-office executives requested Major League Baseball gather as many companies as they could in the fields of motion-capture technology, data management, wearable health tracking and even virtual reality, to — in a sense — meet them where they are.
The event’s purpose was twofold for the Orioles. They’ve made clear that the first step is building an analytics infrastructure that mirrors what worked for executive vice president Mike Elias and assistant general manager Sig Mejdal with the Houston Astros, and many of the vendors at Monday’s event knew the Astros well.
Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects’ demographics, baseline psychological information, and five consecutive days of free-living physiological and contextual measurements, collected through wearable devices and smartphones. This dataset represents a healthy population, showing associations between wearable physiological signals and self-reported daily-life stress. Using a data-driven approach, we identified digital phenotypes characterized by self-reported poor health indicators and high depression, anxiety and stress scores that are associated with blunted physiological responses to stress. These results emphasize the need for large-scale collections of multi-sensor data, to build personalized stress models for precision medicine. [full text]
No matter the product, eventually, for every product review I post, sooner or later someone has a power related accuracy issue with their unit. In today’s post, I’ll be focusing on power meter and trainer accuracy issues. But perhaps down the road I’ll expand it to other categories. Most of this post is aimed at helping you figure out if there’s an issue in the first place, and if so – whether or not you’re able to fix it. I also cover how I go about validating accuracy and the tools I use.
All in all, there honestly isn’t a ton of complexity in any of this. It’s more about process of elimination and step by step troubleshooting than some magical wizardry. And sometimes, the answer is simply that the unit in question is inaccurate. Either systematically (all units by that company), or simply as a one-off (your specific unit).
Microfluidic flow rate sensors have constraints in both detection limits and dynamic range, and are not often easily integrated into lab-on-chip or wearable sensing systems. We constructed a flow rate sensor that easily couples to the outlet of a microfluidic channel, and measures the flow rate by temporarily shorting periodic droplets generated between two electrodes. The device was tested in a dynamic range as low as 25 nL min-1 and as high as 900 000 nL min-1 (36 000× range). It was tested to continuously operate up to ∼200 hours. The device is also simple to fabricate, requiring inexpensive parts, and is small enough to be integrated into wearable devices. The required input pressure is as low as 370 Pascals. An ultra-low flow rate application was demonstrated for wearable sweat biosensing where sweat generation rates (nL min-1 per gland) were accurately measured in human subjects. The digital nanoliter device provides real-time flow rates for sweat rates and may have other applications for low flow rates in microfluidic devices.
A typical weekly shopping list for the Cleveland Browns looks something like this: 700 pounds of chicken, 300 pounds of salmon, 420 pounds of broccoli, 400 pounds of strawberries, 35 pounds of creamy peanut butter, and 195 gallons of water. You might want to grab an extra cart.
Each member of the 53-man roster burns between 3,000 and 6,000 calories on a typical practice day. It’s Katy Meassick’s job to make sure those calories are replenished in a way that boosts player performance on the field. As the team’s performance dietician, Meassick attends to the very specific nutritional needs of every player on the team, paying particular attention to height, weight and position.
… “These days there’s such an emphasis on the 3 because it’s proven to be analytically correct,” Popovich offered Monday with what appeared to be a sneer. “Now you look at a stat sheet after a game and the first thing you look at is the 3s. If you made 3s and the other team didn’t, you win. You don’t even look at the rebounds or the turnovers or how much transition (defense) was involved. You don’t even care. That’s how much an impact the 3 shot has and it’s evidenced by how everybody plays.”
Pop wasn’t done.
“I hate it, but I always have,” Popovich said even as he’s adjusted over the years. “I’ve hated the 3 for 20 years. That’s why I make a joke all the time (and say) if we’re going to make it a different game, let’s have a four-point play. Because if everybody likes the 3, they’ll really like the 4. People will jump out of their seats if you have a five-point play. It will be great. There’s no basketball anymore, there’s no beauty in it. It’s pretty boring. But it is what it is and you need to work with it.”
… As the NCAA grapples with a debate over whether it should pay student-athletes, a growing set of new professional minor leagues and alternative development leagues are shaping up as legitimate challengers for talent. The startup development league template was set by the NBA, where the G League has proved both innovative and effective, with 40 percent of current NBA players having spent time in the 17-year-old minor league. In Europe, soccer and other sports have for years had developmental leagues that have nurtured young players, independent of the higher education system. Football in America is finally joining the party.
Launched in 2017, the Spring League serves as a showcase for NFL and Canadian Football League scouts. Matchups are televised via Bleacher Report Live, Facebook Live and FloFootball; through two seasons, 33 participants have signed NFL or CFL contracts. But competition is brewing.
… Perhaps the most sophisticated passing model to appear in the public domain to date is Will Spearman’s physics-based model, which uses equations of motion to predict ball motion and player intercept trajectories. This approach requires tracking data, because it needs to know the position of every player at the moment the pass is attempted. Other recent models, such as those developed by Will Gurpinar-Morgan and StatsBomb use event data to predict pass completion probabilities.
In this post I will describe a model that uses deep learning to predict pass probabilities from event data. Deep learning is a powerful machine learning technique for finding patterns in data. A deep learning model consists of several layers of nodes connected to form an ‘artificial neural network’. Nodes in the first (input) layer of the network receive inputs from features in the data and transform it, passing the results onto nodes in the next layer. This layer in turn transforms its inputs and passes the results on to the next layer and so on. Learning by example, the network adjusts the strengths of the connections between nodes to give some desired response in the final or output layer of the network. In a binary classification task for example the output layer would be a single node that yields a high number when the input is a member of the positive class and a low number when it is a member of the negative class.