The Denver Broncos have four starters who once caught somebody’s eye during a preseason game as an undrafted rookie.
Running back Phillip Lindsay, linebacker Alexander Johnson, linebacker Todd Davis and cornerback A.J. Bouye each went from long shot to an NFL roster in their first year in the NFL. And had they entered the league this year — with no preseason games and practices curtailed because of the coronavirus pandemic — they would have had a much more difficult time making a roster.
“They have to show it; they have to earn it,” Broncos coach Vic Fangio said. “With the shortened training camp, obviously we have to get out the Ouija board and do some predicting. It’s a little bit of everything there. … I think they feel that urgency.”
Something special happened this past weekend in Major League Soccer. Something that points to the progress the league is making on one of its most celebrated fronts. Something that could pay off financially, in terms of real dollars and cents, and in reputation, bringing the league more attention from fans, the media, and soccer interests worldwide.
That something was 15 young American players, meaning players who qualify for the US Olympic U-23 team, scoring goals or assisting on them in league action. MLS itself rightfully trumpeted the stat in its Tuesday morning news roundup, calling it a “banner weekend for the player development system in North America.”
In a surprising twist, Jamie Newman will never take a snap at the University of Georgia.
Yahoo Sports’ Pete Thamel reported Wednesday that Newman, who transferred to Georgia back in January after beginning his career at Wake Forest, is opting out of the 2020 season and will begin preparing for the 2021 NFL draft. Newman later confirmed the news in a statement, saying the “uncertainties of this year amid a global pandemic” ultimately led to his decision.
The objective was to compare lower extremity inter-segment coordination between high-calibre and low-calibre ice hockey players during forward full stride skating. A 10-camera Vicon motion capture system collected kinematic data on male high-calibre (n = 8) and low-calibre (n = 8) participants. Continuous relative phase (CRP) was calculated for shank-sagittal/thigh-sagittal, shank-sagittal/thigh-frontal and foot-sagittal/shank-sagittal segment pairs. Principal component analysis (PCA) was used to extract features of greatest variability of the CRP and hierarchical linear model investigated relationships between principal components and skill level. High-calibre players demonstrated more out-of-phase coordination (higher CRP) of shank-sagittal/thigh-sagittal throughout glide/push-off (p = 0.011) as well as a delay in the transition to more in-phase coordination during early recovery phase (p = 0.014). For shank-sagittal/thigh-frontal (p = 0.013), high-calibre players had more out-of-phase coordination throughout the entire stride. High-calibre players were also associated with an earlier transition to more out-of-phase coordination of the foot-sagittal/shank-sagittal during push-off (p = 0.007) and a smaller difference in CRP between mid-glide/early recovery (p = 0.016). Utilising more out-of-phase modes of coordination may allow players to more easily adjust to optimal modes of coordination throughout skating strides. Skating drills incorporating varying speed, directionality and external stimuli may encourage the development of more optimal coordination during skating.
Background: The regular assessment of hormonal and mood state parameters in professional soccer are proposed as good indicators during periods of intense training and/or competition to avoid overtraining.
Objective: The aim of this study was to analyze hormonal, psychological, workload and physical fitness parameters in elite soccer players in relation to changes in training and match exposure during a congested period of match play.
Methods: Sixteen elite soccer players from a team playing in the first Tunisian soccer league were evaluated three times (T1, T2, and T3) over 12 weeks. The non-congested period of match play was from T1 to T2, when the players played 6 games over 6 weeks. The congested period was from T2 to T3, when the players played 10 games over 6 weeks. From T1 to T3, players performed the Yo-Yo intermittent recovery test level 1 (YYIR1), the repeated shuttle sprint ability test (RSSA), the countermovement jump test (CMJ), and the squat jump test (SJ). Plasma Cortisol (C), Testosterone (T), and the T/C ratio were analyzed at T1, T2, and T3. Players had their mood dimensions (tension, depression, anger, vigor, fatigue, confusion, and a Total Mood Disturbance) assessed through the Profile of Mood State questionnaire (POMS). Training session rating of perceived exertion (sRPE) was also recorded on a daily basis in order to quantify internal training load and elements of monotony and strain.
Results: Significant performance declines (T1 < T2 < T3) were found for SJ performance (p = 0.04, effect size [ES] ES1–2 = 0.15−0.06, ES2–3 = 0.24) from T1 to T3. YYIR1 performance improved significantly from T1 to T2 and declined significantly from T2 to T3 (p = 0.001, ES1–2 = 0.24, ES2–3 = −2.54). Mean RSSA performance was significantly higher (p = 0.019, ES1–2 = −0.47, ES2–3 = 1.15) in T3 compared with T2 and T1. Best RSSA performance was significantly higher in T3 when compared with T2 and T1 (p = 0.006, ES2–3 = 0.47, ES1–2 = −0.56), but significantly lower in T2 when compared with to T1. T and T/C were significantly lower in T3 when compared with T2 and T1 (T: p = 0.03, ES3–2 = −0.51, ES3–1 = −0.51, T/C: p = 0.017, ES3–2 = −1.1, ES3–1 = −1.07). Significant decreases were found for the vigor scores in T3 when compared to T2 and T1 (p = 0.002, ES1–2 = 0.31, ES3–2 = −1.25). A significant increase was found in fatigue scores in T3 as compared to T1 and T2 (p = 0.002, ES1–2 = 0.43, ES2–3 = 0.81). A significant increase was found from T1 < T2 < T3 intension score (p = 0.002, ES1–2 = 1.1, ES2–3 = 0.2) and anger score (p = 0.03, ES1–2 = 0.47, ES2–3 = 0.33) over the study period. Total mood disturbance increased significantly (p = 0.02, ES1–2 = 0.91, ES2–3 = 1.1) from T1 to T3. Between T1-T2, significant relationships were observed between workload and changes in T (r = 0.66, p = 0.003), and T/C ratio (r = 0.62, p = 0.01). There were significant relationships between performance in RSSAbest and training load parameters (workload: r = 0.52, p = 0.03; monotony: r = 0.62, p = 0.01; strain: r = 0.62, p = 0.009). Between T2-T3, there was a significant relationship between Δ% of total mood disturbance and Δ% of YYIR1 (r = −0.54; p = 0.04), RSSAbest (r = 0.58, p = 0.01), SJ (r = −0,55, p = 0.01), T (r = 0.53; p = 0.03), and T/C (r = 0.5; p = 0.04).
Conclusion: An intensive period of congested match play significantly compromised elite soccer players’ physical and mental fitness. These changes were related to psychological but not hormonal parameters; even though significant alterations were detected for selected measures. Mood monitoring could be a simple and useful tool to determine the degree of preparedness for match play during a congested period in professional soccer. [full text]
… Along with the drop in temperature and humidity, they signaled that summer was drawing to a close and it was now time to shift gears mentally, from relax-and-enjoy to get-it-together.
Research shows that human beings are built for fresh starts. We are sensitive to cues that prompt us to change our routines. And we capitalize on them by setting new goals, disrupting bad habits, and with optimism and energy, moving forward.
Going “back to school” in the midst of a pandemic is challenging for many reasons—among them the absence of cues that shake us out of our summer stupor.
… From the beginning, the founders knew their solutions needed to work with widely available data like health care claims, which include information on diagnoses, tests, prescriptions, and more. They also sought to build tools for cleaning up and processing raw data sets, so that their models would be part of what Guttag refers to as a “full machine-learning stack for health care.”
Finally, to deliver effective, personalized solutions, the founders knew their models needed to work with small numbers of encounters for individual physicians, clinics, and patients, which posed severe challenges for conventional AI and machine learning.
“The large companies getting into [the health care AI] space had it wrong in that they viewed it as a big data problem,” Guttag says. “They thought, ‘We’re the experts. No one’s better at crunching large amounts of data than us.’ We thought if you want to make the right decision for individuals, the problem was a small data problem: Each patient is different, and we didn’t want to recommend to patients what was best on average. We wanted what was best for each individual.”
Garmin has dipped its toes into the coaching platform realm with the release of a new app today, called Garmin Clipboard. This app allows coaches, teams, organizations, and other athletic groups of socially distant people to have both their workouts, as well as daily sleep/stress metrics, consolidated into a single view. A TrainingPeaks competitor today, this is not…at least for now.
Instead, this most targets a much simpler goal: High school and college athletics departments, where a given coach/sport will purchase a pile of identical units for a team to use. It’s an area that Polar has played in for decades (no kidding), notably both on software and hardware. And while Garmin has long had an educational group within the company that caters to that realm, they haven’t really had any cohesive app story to go along with it. Compared to Polar, which leads with their team app solution (though, you’ve likely never used it unless you were on a team with it).
Now, while I’m certainly no coach, I can play one on TV. And so in this case I got the app all installed, enrolled a few familiar faces as my athletes, and got to work. Here’s what things look like.
Modern fitness trackers often try to set themselves apart by adding more advanced hardware and sensors inside. Now, many high-end trackers and smart watches include ECG devices for tracking heart rhythms—one company, Withings, even promises sleep apnea detection in its upcoming wearable.
Amazon, however, has decided to set its new fitness device apart from the rest by relying heavily on AI algorithms and listening to your voice. The $99 device is called Halo and it has a heart rate monitor inside, as well as an accelerometer, temperature sensor, and no screen all. It tracks the typical fitness stuff you’d expect like steps and pulse trends. But, It also has a pair of microphones, which are important for the device’s most interesting—and so far, controversial—feature, called Tone.
When you first set up Halo, it asks you to read a few quotes from classic literature. It uses that information to build a voice profile of how you typically speak. The device will then listen to you as you talk during the day and use an algorithm to give you a positivity score regarding your speech. If it determines that you don’t have enough positivity when you talk, it will give you suggestions about how to cheer up your speech pattern.
MDPI, Sensors journal; Hymalai Bello, Bo Zhou and Paul Lukowicz from
Many human activities and states are related to the facial muscles’ actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers’ expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ≥60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class). [full text]
… Founded by Ryan Watson, a British engineer who works for Brave Software, MuscleWiki is a database dedicated to making fitness simple. It accomplishes that, in part, by presenting as an extremely simple website. The crux of the user experience always comes back to the musclebound figurine you see pictured above. After you select a male or female frame, you can scroll around the body, and muscle groups light up red one by one. Once you’ve found one you’re interested in — say, shoulders — you can choose between Exercises, Stretches, Bodyweight or Kettlebells. Each category has around four or five moves, with corresponding GIFs on endless loops and bullet explainers on how to pull them off without hurting yourself.
Science Advances, Research Article, Jared Zitany et al. from
Overuse injuries to dense collagenous tissues are common, but their etiology is poorly understood. The predominant hypothesis that micro-damage accumulation exceeds the rate of biological repair is missing a mechanistic explanation. Here, we used collagen hybridizing peptides to measure collagen molecular damage during tendon cyclic fatigue loading and computational simulations to identify potential explanations for our findings. Our results revealed that triple-helical collagen denaturation accumulates with increasing cycles of fatigue loading, and damage is correlated with creep strain independent of the cyclic strain rate. Finite-element simulations demonstrated that biphasic fluid flow is a possible fascicle-level mechanism to explain the rate dependence of the number of cycles and time to failure. Molecular dynamics simulations demonstrated that triple-helical unfolding is rate dependent, revealing rate-dependent mechanisms at multiple length scales in the tissue. The accumulation of collagen molecular denaturation during cyclic loading provides a long-sought “micro-damage” mechanism for the development of overuse injuries. [full text]
Infection and malnutrition in this age group hinder development and increase risk of mortality, but little is known about the global burden of and long-term significance of malnutrition in middle childhood. Further, in this time of COVID-19, middle childhood nutrition is particularly a concern because school-age children across the globe are excluded from school meals.
… Within the past five years, baseball executives found themselves obsessed by a single phrase: attention span. Overall attendance has dropped for four straight years. The last time the game added fans over the previous year was 2015; the last time it enjoyed consecutive attendance increases was 2011 and 2012, and the last time it saw at least four consecutive years of gains was 2004 to 2007. America’s screen addiction has convinced the people who run the sport that the easy, pastoral pace of baseball is in special danger.
To the unbothered, fans don’t come to the ballpark to speed-date. They come to see baseball. When Rob Manfred took over as commissioner in 2015, however, length of game was a specific point of emphasis. When that seemed intractable, what with the Yankees and Red Sox and Astros taking pitches for sport and the owners unwilling to ease up on the commercial breaks, the sport focused on pace of play: too much dead time for a generation of screen swipers. The incremental changes — automatic intentional walks and mound visit reductions — proved insufficient, leading baseball to get more radical. The league forced pitchers to face at least three batters as a way to undo the Tony La Russa legacy of incessant matchup-based pitching changes that made the final three innings take nearly as much time to play as the first six. A nine-inning game has averaged three hours every year since and including 2016; in 2019, the last full season, the average time was 3 hours, 8 minutes. The last time a baseball game averaged less than 2 hours, 40 minutes was 1984 (2:39). In 2003 and 2005, the 2:49 average time was the lowest since 1988.
The pandemic has allowed a theory that existed only in private to be expressed in public: Baseball itself is the problem.
The 2020 NFL season is just over a week away, so it’s time for our annual ranking of the league’s top 100 players. What can we expect from the game’s best players?
We asked a panel of 46 ESPN NFL experts to rate players based on how good they will be in the 2020 season in comparison to their peers. Emphasis was entirely on expectations for the upcoming season — that means injured safety Derwin James won’t make the list — and predicting potential greatness, rather than past performance, career résumé or positional value. From those ratings, we were able to rank the best of the best, 1 to 100.