… “They showed me that they wanted me. They showed me that they believed in me,” said Montero. “For me, it was about the loyalty that I felt when they were talking to me. That’s all I was looking for. That, and stability for my family and a long-term contract. The decision was easy for me and my family.
“It wasn’t about money. I had some (more lucrative) opportunities to go to the Middle East. I wasn’t thinking about that. My family is the most important thing in this time of my life and career.
… Fresh off the best campaign of his career — one that included a storybook no-hitter — the 30-year-old left-hander is tying up loose ends after an off-season trade sent him to the Yankees from the only MLB organization he’s ever known. [James] Paxton is headed across the country to the centre of the baseball universe, where every pitch on the mound, and every step off of it, will be closely monitored by fans and media alike. Expectations will be lofty and that’s even before considering that, by virtue of his presence in the contender’s rotation, he’s poised to become the most prominent Canadian in the sport. It’s a daunting change and challenge, yes, but those who know Paxton say he’s well-equipped for it. And from the pitcher’s perspective, this is a grand opportunity — one that’s arriving at precisely the right time. “I feel like I’m ready for this next chapter,” he says. “I’m just excited to be on the big stage.”
… “I could make a snide comment,” Snyder joked. After all, Ingles isn’t exactly known for the speed at which he runs up and down the court, nor for his wild danger-inducing athleticism. He’s, well, the man Jazz play-by-play commentator Craig Bolerjack calls “Slow-Mo Joe.”
“Take care of them knees,” teammate Derrick Favors laughed. “He’s not dunking every play, not running superfast.”
… “O.K., boys,” Cooper says, setting down his coffee at 9:21 a.m. “Let’s get our s— organized.”
Bent over their laptops, the boys—assistants Todd Richards, Jeff Halpern and Derek Lalonde, video coach Nigel Kirwan, video coordinator Brian Garlock and goalie coach Frantz Jean—are preparing for tonight’s matchup with Atlantic Division–rival Toronto. Over the next nine hours they will plod through the usual game-day routine: morning skate, film-study sessions, team meetings (two), a meeting with each power-play unit, a meeting with the penalty kill…not to mention several discussions about scheduling more meetings.
Don’t get the wrong idea, though. This isn’t one of those 24/7 operations, all-nighters pulled on air mattresses beneath a projection screen’s glow. Tacked onto a cork board behind Richards’s desk is the schedule for La Liga—not the Spanish fútbol association but the coaches’ monthly nine-hole golf tournament. They play tennis on road trips, race laps in hotel pools and recently held a board game night at which Richards taught everyone the rules to Settlers of Catan. “We also have a chess ladder,” Lalonde notes from his desk. “The Princeton guy is not at the top.”
Objectives We examined the relation between global positioning system (GPS)-derived workloads and injury in English Premier League football players (n=33) over three seasons.
Methods Workload and injury data were collected over three consecutive seasons. Cumulative (1-weekly, 2-weekly, 3-weekly and 4-weekly) loads in addition to acute:chronic workload ratios (ACWR) (acute workload (1-week workload)) divided by chronic workload (previous 4-week average acute workload) were classified into discrete ranges by z-scores. Relative risk (RR) for each range was then calculated between injured and non-injured players using specific GPS variables: total distance, low-intensity distance, high-speed running distance, sprint distance, accelerations and decelerations.
Results The greatest non-contact injury risk was when the chronic exposure to decelerations was low (<1731) and the ACWR was >2.0 (RR=6.7). Non-contact injury risk was also 5–6 times higher for accelerations and low-intensity distance when the chronic workloads were categorised as low and the ACWR was >2.0 (RR=5.4–6.6), compared with ACWRs below this. When all chronic workloads were included, an ACWR >2.0 was associated with a significant but lesser injury risk for the same metrics, plus total distance (RR=3.7–3.9).
Conclusions We recommend that practitioners involved in planning training for performance and injury prevention monitor the ACWR, increase chronic exposure to load and avoid spikes that approach or exceed 2.0. [full text]
Retired NFL lineman Geoff Schwartz explains what it’s like to be measured in front of a room full of scouts and to get asked questions from coaches who want to make you cry.
What’s an s-shaped animal with scales and no legs? What has big ears, a trunk and tusks? What goes ‘woof’ and chases cats? The brain’s ability to reconstruct facts – ‘a snake’, ‘an elephant’ and ‘a dog’ – from clues has been observed using brain scanning by researchers at Aalto university. Their study was published today in Nature Communications.
In the research, test subjects were given three clues to help them guess what familiar objects the clues described. In addition to well-known animals, the clues depicted vegetables, fruits, tools and vehicles. The familiar objects and concepts described in the clues were never presented directly to the test subjects.
The researchers at Aalto University demonstrated that brain activation patterns contained more information about the features of the concept than had been presented as clues. The researchers concluded that the brain uses environmental clues in an agile way to activate a whole range of the target concept’s properties that have been learned during life.
… Apple Director of Fitness and Health Technologies Jay Blahnik moderated the panel, which consisted of Apple VP of Health Dr. Sumbul Desai; Dr. Harlan Krumholz, director of the Center for Outcomes Research and Evaluation at Yale-New Haven Hospital; Dr. John Rumsfeld, chief innovation officer for the American College of Cardiology; and Jenkins.
The doctors on the panel spoke broadly about how patient-generated healthcare data is starting to revolutionize how they do their work.
“I see this as an emerging trend where people aren’t just coming in and saying ‘Tell me what to do,’ but people are wanting to be part of that partnership. It’s their lives, it’s their health, and I’m seeing engagement at a level I’ve never seen before,” Krumholz said.
Today, STATS, the worldwide leader in sports data and intelligence, and the Orlando Magic announced an exclusive deal that secures the Magic as the only NBA team with access to AutoSTATS, a revolutionary new artificial intelligence (AI) technology.
The Magic will use tracking data produced by AutoSTATS to analyze collegiate players and improve evaluation and decisions for the NBA draft. AutoSTATS delivers comprehensive player-tracking data directly from video through patented AI and computer vision technology. The new technology gives the Magic exclusive access to the college tracking data currently unavailable at this scale due to the scarce use of in-venue tracking systems.
Sensors have become an essential tool of everyday life. Think for example about the blood pressure monitor inside a smartwatch or the step counter on a smartphone. Sensors are accurate, which is why Statistics Netherlands (CBS) is exploring various ways to implement them – e.g. as a substitute for survey questionnaires. A number of experiments to this end were conducted at the second Sensor Data Challenge event in January 2019. The event was co-hosted by Statistics Netherlands, The Hague University of Applied Sciences, the National Institute for Public Health and the Environment (RIVM) and Utrecht University.
… Asked about that comment and whether there is a correlation between spending and winning, Henry replied, “I think the Baltimore payroll was double what the Tampa payroll was last year. Close to double. There is a correlation, I’m sure there is a correlation, but it’s not as perfect. It’s very difficult to predict things in baseball, to predict player performance. Spending more money helps.”
Many believe the luxury tax is working as a de facto salary cap, driving down spending, especially in free agency. Henry didn’t completely agree with that assertion, however.
“It has an influence, but the biggest influence is trying not to lose money,” he said. “I was talking to an owner of a big team at the owners meeting and he had the same viewpoint. It’s not the luxury tax he worries about. … Even though people are frustrated and the players are frustrated with free agency, we’re finding ways to spend money on other areas in baseball.”
… there are many factors that influence a quarterback’s ability to throw over, or through, the line – height, arm length and release point all play a role. Indeed, for Orlovsky, Murray’s biggest physical question mark is his weight, not his height. Even in the 2019 version of the NFL, with considerable protection in place for quarterbacks, physics are against a 195lbs quarterback against a 280lbs defensive end. “Quarterbacks needed to be bigger 15 to 20 years ago, they had to look like Big Ben [Roethlisberger] because they took a beating every game” says Orlovsky. “Still I think it would help if Murray came to the combine at 205 or 210lbs.”
Other than that, there aren’t many important physical or performance metrics that come out of the combine. There is little evidence that the attributes measured at the combine – speed, vertical jump, strength – translate to performance on the field, for any position. An article in the Journal of Strength and Conditioning Research found that combine measures had “zero correlation with quarterback success.” Even to the point that rookie quarterback ratings and 40-yard sprint time correlation went in the “wrong” direction: ie a higher quarterback rating was associated with a slower 40-yard sprint times.
Carnege Mellon Sports Analytics, Aashai Avadhani from
Missed the big basketball game last night? Or have you ever thought about if NBA teams use the court efficiently enough? These are the questions I aimed to answer from my NBA Visualizer Analysis Simulation. For the final term project for my introductory computer science course “Fundamentals of Programming and Computer Science,” I built an NBA visualizer that reads tracking data, visually displays the tracking data, and uses a social analysis model to determine how efficient NBA teams are using the spacing of the court when playing defense or offense. The tracking data was made available from a user’s GitHub profile where he stored the SportVU data for some NBA games of the 2015-2016 NBA season. The NBA released multiple data sets contain tracking play-by-play data (from the company SportVU) regarding occurrences of what occurred during a regulation NBA game. The data set contained the x and y coordinates of all players on the court as well as the location of the ball on the court. Iterating through the dataset, I aimed to link it to another data set in order to make the visualization of the game more realistic and immersive for the user.
The study was aimed to explore distinct players’ groups according to their anthropometric and individual features, and to identify the key performance indicators that discriminate player groups. Match statistics, anthropometric and personal features of 1188 male players competing during 2015–2017 main draw Grand Slam singles events were collected. Height, weight, experience, handedness and backhand style were used to automatically classify players into different clusters through unsupervised learning model. Afterwards, 29 match variables were analysed through MANOVA and discriminant analysis in order to evaluate the different match performance among player groups and to identify the key performance indicators that best differentiate player clusters in each Grand Slam. The analysis revealed the existence of four clusters, they were classified as Big-sized Right Two-handed Players (n = 387), Medium-sized Right One-handed Players (n = 265), Small-sized Right Two-handed Players (n = 414), and Left Two-handed Players (n = 122). Serve, winner, net and physical performance-related indicators (Structure Coefficient ≥ |0.30|) were showed to be the maximum contributors to the group separation. Left-handed players were the most homogenous group in performance. Taller players outperformed their peers in all Slams except for Roland Garros, where left-handed players demonstrated certain advantage playing on slow-pace surface. In Wimbledon and US Open, Medium-sized Right One-handed Players showed better net and physical performance. The advantage of left-handed player is over-represented at elite level. Current findings promote a better understanding of match-play from distinct player groups and offer information on evaluating contextual variability for achieving better performances.