Rick Kirby was the basketball coach at Parkway Central High in Chesterfield, Mo., for more than 20 years, one of those beloved local figures who influence all corners of America. He was one of those defense-first types, the gritty kind who makes you do things right.
Of all the kids Kirby coached, his most unlikely legacy might lie in one 6-foot-something forward who took to the whole thing, falling head over heels for the grit and the grind. That kid would risk any blow from an elbow or two for a chance to throw his own. He loved the chase, even if he wasn’t the most naturally talented kid in the race.
Max Scherzer always had an affinity for scratching and clawing.
“I loved the intensity of it,” Scherzer said years later, elbows on his knees in the Washington Nationals’ clubhouse, where he is the heart of the team, four years into his tenure. “I loved the physical demands of it, the conditioning, the scouting of it.”
What sort of summer has it been for Jack Wilshere? It rather depends, I suppose, on your perspective. In a parallel universe, Wilshere might have been one of the home-grown jewels in Unai Emery’s Arsenal revolution, a World Cup semi-finalist with England, enjoying a well-earned break after his month as a national hero. Instead here he is, fresh from pre-season training with his new club West Ham, reminiscing wistfully about watching England on television while promoting a new range of children’s laser toys. From the outside looking in, you’d be forgiven for wondering whether things had gone slightly awry.
Then again, Wilshere is nothing if not one of life’s glass-half-full guys, and if there’s a scintilla of melancholy to him, then he’s hiding it well. He talks with genuine enthusiasm about the prospect of a new start at West Ham, the club he supported as a boy. He expresses his excitement about working under new manager Manuel Pellegrini, “a gentleman”, as well as a manager whose style of play he’s always admired. He speaks with pride and affection about the latest addition to the Wilshere family: Siena, a little sister for Archie and Delilah. And he really does like the laser game, although he admits he’s not great at it. [videoa autoplays]
… Federer, who turns 37 during the week of the Toronto tournament, fell 13-11 in the fifth set of his Wimbledon epic against Kevin Anderson, who went on to reach the final.
“I had a fantastic time in Montreal last year and always enjoy playing in front of the Canadian fans, but unfortunately with scheduling being the key to my longevity moving forward, I have regrettably decided to withdraw from Toronto this year,” Federer said in a statement issued by organisers on Monday.
“I wish the tournament every success and am sorry to miss it.”
The purpose of this investigation was to examine differences between the peak running speed, acceleration, and metabolic power of elite youth soccer across a range of age levels by position. Ninety-six elite junior soccer players were assessed between 2015 and 2017. Ninety-six elite junior soccer players (at time of match: age, 15.8 ± 0.9 years; body mass, 69.1 ± 8.0 kg) were assessed during 61 games within the 2015, 2016, and 2017 season, for a total of 441 individual match observations (4.8 ± 3.3 matches per player, range 1-13). Participants were classified by age group: under 15 (U15, n = 121, 14.7 ± 0.3 years), under 16 (U16, n = 176, 15.8 ± 0.3 years), or under 17 (U17, n = 144, 16.7 ± 0.4 years), and according to their playing position: Attacker (ATT), Defender (DEF), Mid-Fielder (MID), or Wide (WIDE). Participants wore global positioning system units during each match, where speed (m·min), acceleration/deceleration (m·s), and metabolic power (Pmet) were established. A 1- to 10-minute moving average was applied to establish the intercept (c) and slope (n) of running intensity variables as a power law y = cx relationship. Linear mixed models were used to examine differences in the intercept and slope between age group and player position. There were no substantial differences in peak (intercept) or decline (slope) in running intensity between playing levels. Several differences were observed in the peak running speeds (m·min), particularly peak running speeds of ATT and DEF being substantially lower than the MID. Despite variability between positions, we suggest that the magnitude of these differences would not warrant the prescription of different running intensities across positions at the elite junior level. These findings describe the peak running intensities of elite junior soccer, useful in the monitoring and prescription of training to ensure that players are prepared for the most demanding periods of competition.
There are so many real-world physics problems involved in running. Lots of physicists have been inspired, for instance, by the crazy-fast speeds of Usain Bolt. Just take a look at this paper, “On the performance of Usain Bolt in the 100 m sprint” (European Journal of Physics), in which the authors examine the motion of Usain in one of his sprints.
But what if you want to look at more … unrealistic running? Or model running in situations you shouldn’t test in real life—like running at the pool? In order to explore these situations, you’ll need a physics model for running. Remember that science is all about building models, right?
Recently I tested a prototype of a wearable device that is intended to help runners monitor and control the intensity of their runs. During my back-and-forth email communications with the product’s lead developer, he sent me a link to a study titled “Intensity- and Duration-Based Options to Regulate Endurance Training.” The abstract began as follows: “The regulation of endurance training is usually based on the prescription of exercise intensity. Exercise duration, another important variable of training load, is rarely prescribed by individual measures and mostly set from experience.” Questioning the validity of experience as a guide to training prescriptions, the authors, a pair of Austrian exercise physiologists, went on to try to establish a more scientific method for determining how long individual athletes should train at different intensities.
… Aisha Schnellmann: Why is it important for students to learn collaboratively in the classroom, working as a team to achieve common goals?
Valentin Ruest: Students tend to perform better when they are in a learning environment that encourages them to work together with high-performing peers to reach a common goal. We also see this in families and sports teams: Positive teams reinforce themselves. When my co-founders and I started working on our ed-tech solution for classrooms, Go Pollock, we heard from many teachers that it would be helpful if we could come up with something that would encourage collaboration.
Classroom learning is a wonderful opportunity for children not only to master the curriculum, but also to develop the socioemotional skills they need to work together effectively and succeed as a team. Furthermore, teaching students collaboration instead of competition is absolutely essential in preparing them for the digital economy, where innovation is driven by partnerships and cooperation.
Runner's World, Budd Coates and Claire Kowalchik from
… My most frequent injury was to my left hip flexor. So I began to think: What if I could create a pattern that coordinated footstrike and breathing such that I would land alternately on my left foot and then right foot at the beginning of every exhale? Perhaps I could finally get healthy. It was worth a try.
I developed a pattern of rhythmic breathing and began using it between my junior and senior years of college. I also trained for and ran my first marathon the winter before graduating and finished in a respectable 2:52:45.
I continued to work on a rhythmic breathing method of running while pursuing my master’s degree in physical education and exercise physiology, during which time I trained for my second marathon. I honed in on a the three-step method for faster running during that second marathon and ran an incredibly even 2:33:29. Now I knew I could manage my effort through rhythmic breathing with a great deal of success. Since then, I’ve taught this method to the many runners I’ve coached over the years. It can work for you, too.
… Here’s the typical trainer’s concern; it’s believed that players outside of the top 100 have worries that a guy like Federer doesn’t – i.e. travel expenses, points to defend and so on. Many of these players perform a concentrated physical prep block for 4-6 weeks in Dec/Jan (just prior to the start of the new season) and then a number of 1-2 week blocks during the course of the year. These same trainers will also agree that this isn’t enough – performing one 4-6 week block along with 2-3 smaller blocks during the year is an insufficient amount of time to develop a quality like explosive power, for instance.
But is this true? Do players not have enough time in their schedules to plan 2-3 extended training blocks during the year? Blocks that focus on developing the underpinning physical qualities necessary for elite level play? And that allow players to stay healthier throughout the course of the season? And what about time for rest? Is that something we’re just going to put to the side for a rainy day?
In this post , I’ll attempt to uncover the so-called ‘realities’ of pro and semi-pro tennis. We’ll also briefly explore the benefits of focusing on an area we tend to take for granted – rest & recovery (something that top players meticulously schedule into their yearly plan).
Journal of Sports Medicine & Physical Fitness from
BACKGROUND:
The aim of the present study was to evaluate the changes in a panel of biomarkers including lactate, ferritin and uric acid in saliva of untrained and trained subjects after repeated explosive effort sequences, and to analyse the differences in interpretation of these biomarkers depending on the way the data is expressed (without any correction or corrected by protein content or flow). METHODS:
Eighteen volunteers (11 untrained and 7 trained) performed 8 consecutive explosive effort sequences (60 m sprints). Blood and saliva samples were collected before and after each sequence. Salivary data was analysed as absolute concentration and after correcting by their protein content and flow. RESULTS:
Lactate in saliva showed increases with acute exercise, being these increases of higher magnitude in non-trained subjects. In addition, when corrected by total protein, lactate in saliva correlates with blood lactate in non-trained subjects. Ferritin and uric acid in saliva did not show changes after the effort sequences. CONCLUSIONS:
From the biomarkers evaluated, the measurement of lactate in saliva corrected by total protein could be useful for the assessment of fatigue induced during repeated explosive effort sequences and could be potentially used as a non-invasive alternative to blood lactate. This report also points out that way of reporting the saliva analytes could greatly influence the interpretation of the results and that lactate in saliva has a different dynamic in trained and non-trained individuals that should be further explored.
… Inspiratory muscle fatigue (IMF) is most common in sports where high-intensity efforts are sustained for long periods of time e.g. cycling, running, swimming, and rowing. As the body approaches maximal ventilation during heavy exercise, the diaphragm’s force production plateaus yet the ventilation rates still rise as more and more accessory muscles are recruited to assist. During high-intensity efforts (above 90% effort), the respiratory system can demand up to 12% of blood flow for respiration. A classic study by Aaron et. al. showed that using pressure assisted ventilators to “unload the respiratory muscles” and make it easier to breathe increased performance(1).
With respect to swimming, Jakovljevic proved that breathing every four strokes during a 90% effort, 200-m swim produces more IMF than breathing every two strokes2. Mitch Lomax, an Australian sports science expert has extensively researched IMF for swimmers. She has found that race speed has an effect on IMF and such fatigue can affect stroke rate and length (3,4). Additionally, she has proved that IMF can occur in all four strokes, not just freestyle(5).
… a team of researchers at Stanford says that achievement is within reach. The group, led by materials science and engineering associate professor Alberto Salleo and postdoctoral research fellow Onur Parlak, announced in Science Advances that they’ve developed a wearable patch that can determine how much cortisol someone is producing in seconds, using sweat drawn from the skin under the patch.
The stretchy patch pulls in the sweat through perforations to a reservoir. A membrane on top of the reservoir allows charged ions, like sodium and potassium, to pass through. Cortisol, which has no charge, can’t pass, and instead blocks the charged ions. Signals sent from an electrical sensor in the patch can be used to detect these backups and determine how much cortisol is in the sweat.
One of the biggest pain points for triathletes is determining an optimal hydration and nutrition strategy both in training and on race day. It can be hard to know just how much (or how little) fluid your body needs in order to function over a long distance, especially in very warm or humid conditions.
In the latest episode of Triathlon Training Explained, Fell and Threlfall worked with Andy Blow of Precision Hydration to determine their own sweat rates and show you why testing not only your sweat level but the consistency of your sweat is an invaluable way to finally nail your race day hydration plan. [video, 11:11]
Many people feel devastated after their favorite team loses. Sometimes they have trouble sleeping. (Yes, I speak from personal experience.) That raises some legitimate questions: Why suffer? Is it even rational to be a sports fan?
Recent research suggests that it might not be. On average, soccer, the most popular sport on the planet, makes people a lot less happy. The lesson is that if you’re strongly attached to your local team, you might be better off if you decide to disengage — starting right now.
Peter Dolton and George MacKerron of the University of Sussex linked several large data sets. To measure people’s happiness, they used millions of reports from tens of thousands of people, mostly in the U.K., who recorded their levels of happiness at various times in the day, and who also reported on what they were doing during those times.