… Back then, Brooks had just turned 18 and was coming off that 11-point campaign. He had one fewer point than the previous year as a league rookie – in five more games.
He was buried on the Pats depth chart. It looked like his career was stuck in neutral, or perhaps even rolling backwards.
“I needed to find a new gear if I was going to make the team (in Regina),” Brooks said. “When you’re a young kid and you make that jump (to the CHL), it’s frustrating when you go from a top guy in your bantam league to you see guys that you were ahead of playing lots and finding success in the league.”
The Brazilian, who has joined Lazio, was heavily criticised when he joined from Grêmio in 2007 but earned the respect of his team-mates and the 30-year-old will be sorely missed in the Anfield dressing room
… “I’m not a fan of locking in to one sport,” McCaffrey told Dan Patrick on Friday. “I just think it’s so important as a kid to venture off and do multiple things. And it’s so tough nowadays too because a lot of these baseball teams are all year-round and the only you’re going to get playing time is if you’re there all year-round so you can’t play any other sports. And to me, I look at what basketball and track and baseball did for my football career was more than anything you’re training different muscles, you’re training your mind, and just being a part of multiple teams you learn a lot of life lessons.
… A point of emphasis for Lynn during the offseason was creating a training regimen that would give his players a better chance of staying healthy during a long and grinding season. New head trainer John Lott focused on building more endurance and lower-body strength to help prevent soft-tissue injuries during the year. So we’ll see how that translates once training camp begins. Football is a collision sport, and injuries will happen, but the Chargers would like to avoid having the most players on injured reserve for the second straight year.
The Celtics are making a deeper plunge into the sports science realm, parting ways with longtime trainer Ed Lacerte, while strength coach Bryan Doo has chosen to move on.
The work the latter began with rookies Jayson Tatum and Ante Zizic will continue to have an effect.
“The team was making some changes and looking at some new and different things, and I think it’s really good that they’re doing that,” Doo said. “But when I looked at what was going to be needed from me, I had to make some decisions. They gave me options, but when I looked at everything, I decided to make a clean break.”
… “What makes this unique is that it’s a national program that involves high-level athletes at the high school level,” [Thayne] Munce explained. “We’re going to have access through these clinics and meets to the top high school athletes throughout the country. So rather than seeing an individual team or an athlete here and there, it’s really going to be the best of the best at that level in this particular sport.”
Munce’s other project, which involves researching and preventing heat illness, is more widespread in its focus and could carry a more immediate impact in the world of sport abroad.
The Morning Call (Allentown, PA), Nick Fierro from
As if the Philadelphia Eagles’ Doug Pederson isn’t under enough pressure just heading into the second year of his first NFL head coaching job, he’s also dealing with another hazardous situation: resisting his instincts.
In a wide-ranging conversation with the team’s beat reporters following mandatory minicamp last month, Pederson admitted that the use of analytical data, now known without question to be a job requirement, could have him going against his gut in some crucial situations.
The aim of this study was to describe and compare the distances and displacement speeds of U12 Soccer-7 athletes during four tournament Soccer-7 games (TG) played in less than 24-h (experimental condition) with those recorded during two league Soccer-7 games (LG) with 24-h of rest prior to the match (control condition). Ten participants (age = 10.3 +/- 0.5 years) were recruited for the study. Main data analyzed during games included distance completed relative to match duration (Drel), maximal velocity and distance completed at different running speeds (including acceleration, deceleration, standing, walking, jogging, medium-intensity running, high-intensity running and sprinting). For data collection during games, athletes wore a GPS unit. Different (p<0.05) mean playing time was recorded during TG and LG (15.1 and 31.8 minutes/match, respectively). Drel during the four TG was maintained between 85.7 +/- 8.5 m/min and 87.5 +/- 8.5 m/min (P>0.05) and during the two LG between 84.2 +/- 10.9 m/min and 87.5 +/- 9.9 m/min (P>0.05). Moreover, similar Drel was recorded during TG and LG (86.8 m/min and 85.9 m/min, respectively). Compared to LG, during TG maximal velocity was lower (23.0 km/h and 21.3 km/h, respectively; P<0.05). In addition, compared to the last game of the tournament, in the preceding games the distance covered at low speeds (3.1-8.0 km/h) was lower (37.7% and 32.4%, respectively; P<0.05) and at high speeds (>=18.1 km/h) tend to be higher (2.5% and 3.3%, respectively). Therefore, compared with the control condition, accumulated Soccer-7 games with less than 24-h of inter-day rest negatively affects displacement speeds distribution (but not overall relative distances) in U12 Soccer-7 athletes. These results may help to better plan training and competition schedules to youth players.
Is that an image of a cat? It’s a simple question for human beings, but was a tough one for machines—until recently. Today, if you type “Siamese cats” into Google’s image search engine, voilà!, you’ll be presented with scores of Siamese cats, categorized by breed (“lilac point,” “totie point,” “chocolate point”), as well as other qualities, such as “kitten” or “furry.”
What’s key here is that while some of the images carry identifying, machine-readable text or meta information, many do not. Yet the search still found them. How? The answer is that the pictures— more accurately, a pattern in the pictures—was recognized as “Siamese cat” by a machine, without requiring a human to classify each instance.
This is machine learning. At its core, machine learning upends the programming model, forgoing the hard coded “if this, then that” instructions and explicit rules. Instead, it uses an artificial neural network (ANN)—a statistical model directly inspired by biological neural networks—that is “trained” on some data set (the bigger, the better) to accomplish some new task that uses similar but yet unknown data.
I’ve been wanting to write something about this subject for a while and when I saw a study last week that revealed the placement of activity trackers affected step tracking accuracy as being some kind of amazing breakthrough, I thought it was time I shared my two cents on these studies.
Now don’t get me wrong, these studies that aim to find out whether fitness-focused wearables are useful and accurate are important because they support a lot of what we already know at Wareable and that’s that most are not without their flaws. Things are getting better, sure, but we also know that there’s still a lot of work to do and tech companies need to keep striving to make things better.
My real problem is that it is really easy to pick out the problems in a lot of the studies we’ve covered. This raises a lot of alarm bells about whether they are actually telling us something that’s actually of great value or insight.
… what makes Apple so bullish on a technology that seems doomed to failure? Jay Blahnik, Apple’s director of fitness and health, spoke about the company’s design philosophy at a breifing with reporters last week attended by MobiHealthNews. He said the Apple Watch takes a distinct approach from other wearables, and that Apple hasn’t seen the sort of attrition suggested by the Endeavor study.
“One of the big notions of the activity app is that it automatically keeps track of something you’re already doing and makes it really easy to achieve it,” Blahnik said. “Those are the things that lead to rituals. People don’t get tired of brushing their teeth; they do it for most of their life. So there are things that don’t create fatigue — if it’s really simple to do and it starts to become a ritual. And that was the design philosophy behind this. Let’s not try to make this about the most you can do, let’s save that for the workout app. This is not about the most you can do, but the least you should do every day.”
Picture a rectangle of fabric cut from a standard grey t-shirt. It’s stretchier than most tees, because it’s made from a mix of nylon and spandex, not cotton. And it stands out in another way, too: If you flip back a corner of the cloth, one side has an unexpected metallic sheen.
This textile isn’t the creation of a sci-fi costume director. It’s called shieldex, and it was exactly what textile engineer Asli Atalay and her team at Harvard needed to develop a soft, stretchable, motion-measuring sensor. The metallic shine comes from silver coating the flexible fibers, so the fabric can stretch and conduct electrons at the same time. Rather than slapping silicon chips into bracelets, these electronics could give wearables more of the stretchability and comfort of the best sweatpants.
Virtually any modern information-capture device — such as a camera, audio recorder, or telephone — has an analog-to-digital converter in it, a circuit that converts the fluctuating voltages of analog signals into strings of ones and zeroes.
Almost all commercial analog-to-digital converters (ADCs), however, have voltage limits. If an incoming signal exceeds that limit, the ADC either cuts it off or flatlines at the maximum voltage. This phenomenon is familiar as the pops and skips of a “clipped” audio signal or as “saturation” in digital images — when, for instance, a sky that looks blue to the naked eye shows up on-camera as a sheet of white.
Last week, at the International Conference on Sampling Theory and Applications, researchers from MIT and the Technical University of Munich presented a technique that they call unlimited sampling, which can accurately digitize signals whose voltage peaks are far beyond an ADC’s voltage limit.
The Gold Cup break gives MLS teams a mid-season breather. For some, it couldn’t come at a better time. The season still has enough left in it to turn things around. It’s likely that trying to figure out how to win games will include the use of analytics and data to help the front office and coaches come to some conclusions.
Like all of America’s major professional sports, analytics are crucial for MLS. The data helps determine lineups, transfer strategy, and how to solve problems. For some, the use of all this data in soccer is a red herring. Unlike baseball and football, the sport doesn’t rely on too many statistics. A soccer game box score is often the lineups and who scored and assisted on the goal. Not much more to it.
In some cases, the overuse of statistics and analysis to measure success already comes with a backlash. Colorado Rapids coach Pablo Mastroeni lashed out against its use last month after his side defeated Sporting Kansas City 1-0. Sporting KC looked better across the board. The Rapids withstood an absolute onslaught, out-shot 24 to 6, out-passed 629 to 225, and out-possessed 73% to 27%.
“People have lost the plot,” Mastroeni told reporters adding that statistics “lose to the human spirit every day of the week.”