No NFL team assigns too much importance to the combine’s athletic tests, but a performance for the ages could send a few players shooting up draft boards. For others, the weigh-in represents a chance to put some questions to bed. Which prospects will come out of this week as winners?
The Los Angeles Angels are wasting the greatness of Mike Trout. It’s not just that they are one of only seven teams not to win a playoff game since Trout debuted in 2011. It’s also because they have not supported him with players who get on base in front of him.
New Angels manager Brad Ausmus faces the same riddles that vexed his predecessor, Mike Scioscia: where should Trout hit? And can anybody on the roster get on base consistently in front of him?
Last year the Los Angeles analytics department recommended Trout hit in the second spot. It makes sense in a vacuum. Each spot in the lineup is worth about 18 plate appearances per year (so hitting second would give a hitter 36 more turns at bat than cleanup, for example). But because the Angels were so bad at getting hitters on in front of Trout—historically bad—Scioscia moved Trout to third in the second half of the season.
… Recovery from a torn ACL usually takes six to nine months. Tanielu appeared in only two minor league games in 2017, both with the Astros’ Gulf Coast League, once he recuperated.
The Astros nevertheless invited him to major league spring training prior to the 2018 season. Manager A.J. Hinch said Tanielu was “not fully recovered” and the team was guarded in how it utilized him.
“Now,” Hinch said Tuesday, “we’re just finding out a little bit about what he can do and how versatile he can be.”
It has been an interesting part of the Ole Gunnar Solskjær revolution at Manchester United – perhaps not the most high-profile but a component nonetheless and an element in the movement that appears to be sweeping the caretaker manager towards the job on a permanent basis.
Luke Shaw was outstanding in the club’s 3-1 win at Crystal Palace on Wednesday, having also starred in the 0-0 home draw with Liverpool on Sunday. According to Romelu Lukaku, the 23-year-old left-back has been United’s player of the season so far. And it has been difficult to ignore the evidence that his performances have gone up a level since Solskjær took over from José Mourinho in December.
The Norwegian has brought liberation to virtually every pore of the club and it has been clear to see how players such as Paul Pogba and Marcus Rashford have thrived under his charge. But Shaw, too, has grown, having been encouraged by Solskjær to play higher up the pitch and express himself more freely. Moreover, and this might be key, he has been able to do so without the fear of reprisals if he makes mistakes.
… More than a third of the 300-plus players invited to the combine last year didn’t get drafted, whereas 38 players who weren’t at the combine did.
Among them were P.J. Hall, a defensive tackle from Sam Houston State who was drafted by Oakland in the second round, and two players from Southern Miss: defensive back Tavarius Moore, a 49ers’ third-rounder, and running back Ito Smith, a fourth-round selection by Atlanta.
The biggest snub last year was Colorado running back Phillip Lindsay , the first undrafted offensive rookie ever selected to the Pro Bowl.
… The England manager is sticking with players already in the fold, yet to be swayed by the form of Chelsea’s top scorer, Beth England, or Birmingham’s defensive rock Aoife Mannion.
Neville’s decision not to include the 24-year-old England – who has signed a new deal at Chelsea that keeps her at Kingsmeadow until 2021 – in his plans could prove costly, despite his insistence that his team is picked on form and performance.
The Lionesses benefit from some of the finest attacking talent in the world, with the WSL’s all-time top scorer, Nikita Parris, and Barcelona’s Toni Duggan currently the squad’s in-form forwards. But is that enough?
… 100 days before the World Cup opens — a milestone that arrived on Wednesday — [Jill] Ellis and her players said this week that their biggest questions already had been asked and answered.
“You’ve kind of built the skeleton,” Ellis said. “Now we’re working on the nervous system.”
The process has not always been pleasant. Lloyd, the star of the 2015 World Cup victory, has been eased into a supporting role, and players like Lindsey Horan and Crystal Dunn — who were not part of the 2015 team — have become nearly irreplaceable. Blessed with a wealth of speed and attacking options, Ellis toyed with a three-defender system early in the process to see if a relentless focus on getting forward would enjoy adequate cover if the ball was turned over. Spoiler: It didn’t, so she adjusted again.
How do you get players at the elite level to keep on improving? For United States women’s national volleyball team head coach Karch Kiraly, video analysis is a vital tool that facilitates higher learning.
As a three-time Olympic gold medalist, coach Kiraly is a strong authority on what technology makes elite players tick. He explains the suitability of video to the success of his elite program.
“My biggest goal with this program for the other coaches, our staff and I, is trying to give our players every tool possible to improve and to learn at the fastest rate possible,” said Kiraly.
“The bottom line is we’re trying to figure out who might be able to come to the USA gym and continue to develop”.
The British Psychological Society, Research Digest, Emma Young from
Say you’re planning to run a marathon, and you have a target time in mind. Or you’re on a weight- loss diet, and your aim is to lose six kilos in six weeks. Or, there’s an exam coming up, and you want to score above 75 per cent. These are all individual goal pursuits. In theory, you’re not in direct competition with anybody else, though of course if you’re part of a running club, or a weight loss group, or an undergraduate class, you will be aware that others around you are striving to achieve their own goals.
There’s plenty of evidence that sharing your own goals and hearing about other people’s can be helpful – in providing mutual encouragement, emotional support and motivation. However, a paper in the Journal of Personality and Social Psychology shows how, in certain circumstances, we can’t help ourselves from competing with others – an effect that can be surprisingly counterproductive. The findings suggest that when we’re paired with an individual striving for a similar goal and who is similarly able, we begin to view this other person as an opponent – leading us to sabotage their efforts, ultimately to the detriment of our own performance.
“Pursuing individual goals together with others can at times lead to counter-productive behaviours that not only harm others but also harm oneself,” report Szu-chi Huang at Stanford University, and her colleagues.
Medicine & Science in Sports & Exercise journal from
The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users’ behaviors, including sleep. These devices are now able to capture different bio-signals for determining, for example, heart rate and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large datasets (Big Data), with the potential of offering an unprecedented window on users’ health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health, however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine.
Advancements in wearable technology have provided practitioners and researchers with the ability to conveniently measure various health and/or fitness indices. Specifically, portable devices have been devised for convenient recordings of heart rate variability (HRV). Yet, their accuracies remain questionable. Objective
The aim was to quantify the accuracy of portable devices compared to electrocardiography (ECG) for measuring a multitude of HRV metrics and to identify potential moderators of this effect. Methods
This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Articles published before July 29, 2017 were located via four electronic databases using a combination of the terms related to HRV and validity. Separate effect sizes (ESs), defined as the absolute standardized difference between the HRV value recorded using the portable device compared to ECG, were generated for each HRV metric (ten metrics analyzed in total). A multivariate, multi-level model, incorporating random-effects assumptions, was utilized to quantify the mean ES and 95% confidence interval (CI) and explore potential moderators. Results
Twenty-three studies yielded 301 effects and revealed that HRV measurements acquired from portable devices differed from those obtained from ECG (ES = 0.23, 95% CI 0.05–0.42), although this effect was small and highly heterogeneous (I2 = 78.6%, 95% CI 76.2–80.7). Moderator analysis revealed that HRV metric (p <0.001), position (p = 0.033), and biological sex (β = 0.45, 95% CI 0.30–0.61; p <0.001), but not portable device, modulated the degree of absolute error. Within metric, absolute error was significantly higher when expressed as standard deviation of all normal–normal (R–R) intervals (SDNN) (ES = 0.44) compared to any other metric, but was no longer significantly different after a sensitivity analysis removed outliers. Likewise, the error associated with the tilt/recovery position was significantly higher than any other position and remained significantly different without outliers in the model.
Conclusions
Our results suggest that HRV measurements acquired using portable devices demonstrate a small amount of absolute error when compared to ECG. However, this small error is acceptable when considering the improved practicality and compliance of HRV measurements acquired through portable devices in the field setting. Practitioners and researchers should consider the cost–benefit along with the simplicity of the measurement when attempting to increase compliance in acquiring HRV measurements.
Many major advances in medicine, especially in neurology, have been sparked by recent advances in electronic systems that can acquire, process, and interact with biological substrates. These bioelectronic systems, which are increasingly used to understand dynamic living organisms and to treat human disease, require devices that can record body signals, process them, detect patterns, and deliver electrical or chemical stimulation to address problems.
Transistors, the devices that amplify or switch electronic signals on circuits, form the backbone of these systems. However, they must meet numerous criteria to operate efficiently and safely in biological environments such as the human body. To date, researchers have not been able to build transistors that have all the features needed for safe, reliable, and fast operation in these environments over extended periods of time.
A team led by Dion Khodagholy, assistant professor of electrical engineering at Columbia Engineering, and Jennifer N. Gelinas, Columbia University Medical Center, Department of Neurology, and the Institute for Genomic Medicine, has developed the first biocompatible ion-driven transistor that is fast enough to enable real-time signal sensing and stimulation of brain signals.
This study investigated the effects of removing athletes’ electronic devices in the evening on sleep and performance during training camps. Water polo athletes (n = 26) attending a 7-night training camp (study 1) and triathletes (n = 23) attending a 4-night training camp (study 2) were randomly allocated to a no-device group (no electronic devices could be used after dinner or overnight; ND) or control group (unrestricted electronic device use; CON). Sleep was monitored through wrist actigraphy. The ND group completed a questionnaire measuring anxiety related to being unable to use electronic devices (“nomophobia”). Triathletes also completed a psychomotor vigilance test (PVT) at the start and end of camp. Water polo ND athletes went to bed earlier and spent longer time in bed than CON on the first night, but not on other nights. In triathletes, sleep quantity was not different between groups on any night. No statistically significant differences were observed for changes in nomophobia from the first to the last night of camp. No differences in PVT performance were observed between ND and CON triathletes. In conclusion, removal of evening electronic devices does not improve sleep quantity or cognitive performance in athletes during short-duration (4-7 nights) training camps.
University of Michigan, Michigan Health Lab blog, Kylie Urban from
New research explores stem cells in the rotator cuff in hopes of understanding why fatty accumulation happens at the tear site, instead of proper muscle healing.
The Berkeley Artificial Intelligence Research Blog, Tijana Zrnic from
… Much of today’s large-scale investigations still uses tools and techniques which, although powerful and supported by beautiful theory, do not take into account that each test might be just a little piece of a much bigger puzzle of exploratory research. Many disciplines have yet to acquire novel methodology for testing, one that promotes valid inferences at scale and thus limits grandiose publications comprised of irreplicable mirages.
Let us analyze why classical hypothesis testing might lead to many spurious conclusions when the number of tests is large. We do so by elaborating the three main steps of a test: “hypothesize”, “collect data” and “validate”.