Applied Sports Science newsletter – April 24, 2019

… After two years spent carrying the weight of being baseball’s top-prospect, Guerrero will soon go to the majors, where everyone will expect him to instantly look like he belongs. And maybe he will. No level of affiliated baseball has challenged him yet. But plenty of ballplayers have enjoyed wild success climbing the minor-league mountain, only to find the demands, stresses, and level of competition at the summit too much to handle.

“It’s a different animal up there in the big leagues,” [Bobby] Meacham says. “It’s just so different.”

The last thing Meacham wants after Guerrero leaves his clubhouse for Toronto is to see him return. And yet, that would be a completely normal progression for the average professional ballplayer. Even Meacham, the No. 8 overall pick in the 1981 draft, went up and down between the majors and triple-A in four of his six big-league seasons with the Yankees. Mike Trout, the best player on the planet, went up and down in his age-19 season before he stuck.

Toronto FC star striker Jozy Altidore apologized Monday for a post-game outburst in which he accused team president Bill Manning of letting his ego get in the way of player welfare.

Altidore left Toronto’s 4-3 win over Minnesota in the 82nd minute Friday night, complaining of hamstring tightness. The U.S. international laid into Manning after the game, saying the president’s ego was hurting player welfare by banning trainer Giuseppe Gueli because of his affiliation with the now-departed Sebastian Giovinco.

On Monday, Manning met with Altidore and then talked to reporters on a conference call, saying Gueli was back in the fold. Manning said his only issue with the Italian trainer was that he wanted to formalize their relationship so he was available to all players and not just a few.

… What we’ve seen the Milwaukee Brewers slugger do since July 2018 isn’t necessarily unprecedented, but it is historic, because we’ve most likely just seen a player make the Hall of Fame.

A few years ago, I looked at how many WAR a player needed to produce by each age level to be more likely to make the Hall of Fame than not. In other words: Of all the players who had at least X WAR through each age, half made the Hall and half did not. We called “X” the 50 percent probability.

Through age 25, Christian Yelich was right on the bubble. The 50 percent probability mark for 25-year-olds was about 18 WAR, and Yelich had about 19. For 26-year-olds, the mark was 20 WAR. Through the All-Star break last year, Yelich had 21.

Why the top American in Monday’s Boston Marathon is all about training transparency

A new study led by researchers from the University of California, Irvine finds exercising in the morning, rather than at night, may yield better results.

This study, published today in Cell Metabolism, points to daily timing as a critical variable for metabolic benefits from exercise and implications in chronobiology-based exercise therapy for patients with metabolic disorders.

“Using mice, we compared the impact of exercise on the skeletal muscle metabolism at different times of day,” said Paolo Sassone-Corsi, PhD, Donald Bren Professor and director of the Center for Epigenetics and Metabolism at UCI’s School of Medicine. “We discovered that exercising at the correct time of day – around mid-morning – results in more oxygen in the cells and a more rejuvenating effect on the body.”

… Since the first Bluetooth headset sold in 2000, the wearable tech industry has been hard at work inserting itself into our daily lives. And by all accounts, it’s working: The number of connected wearable devices worldwide is expected to jump to 1.1 billion or more by 2022, and some experts predict that fitness trackers will generate more than $3 billion in global revenue by that same year.

But for many people, donning a health-focused device each day isn’t necessarily a good thing. Research suggests that even if you don’t ditch your tracker after the first few months, it can be difficult to develop a healthy, effective relationship with the device that’s monitoring your calories, steps, and minutes of sleep. In one study, published in the Journal of the American Medical Association, overweight participants who wore fitness trackers each day lost less weight than those who didn’t. In another, people who wore them for a full year were no healthier than they’d been at the start of the study. On the other end of the spectrum, users can become too obsessed with the data their devices are collecting, leading them to self-diagnose problems that don’t exist; they can get so invested in their stats that it drains any enjoyment from previously pleasant activities; and, in some cases, fitness trackers can even exacerbate disordered eating behaviors.

In nature, group decisions are often a matter of life or death. At first glance, the way certain groups of animals like minnows branch off into smaller sub-groups might seem counterproductive to their survival. After all, information about, say, where to find some tasty fish roe or which waters harbor more of their predators, would flow more freely and seem to benefit more minnows if the school of fish behaved as a whole. However, new research published in Philosophical Transactions of the Royal Society B sheds light on the complexity of collective decision-making and uncovers new insights into the benefits of the internal structure of animal groups.

In their paper, Albert Kao, SFI Baird Scholar and Omidyar Fellow, and Iain Couzin, Director of the Max Planck Institute for Ornithology and Chair of Biodiversity and Collective Behavior at the University of Konstanz, simulate the information-sharing patterns of animals that prefer to interact with certain individuals over others. The authors’ modeling of such animal groups upends previously held assumptions about internal group structure and improves upon our understanding of the influence of group organization and environment on both the collective decision-making process and its accuracy.

Modular — or cliquey — group structure isolates the flow of communication between individuals, so that only certain animals are privy to certain pieces of information. “A feature of modular structure is that there’s always information loss,” says Kao, “but the effect of that information loss on accuracy depends on the environment.”

Making better decisions is one of the best skills we can develop. Good decisions save time, money, and stress. Here, we break down what makes a good decision and what we can do to improve our decision-making processes.

Objectives

To examine the influence of maturation and its interaction with playing position upon physical match performances in U15 footballers from a national federation.
Design

Observational Study
Methods

278 male outfield players competing in a national tournament were assessed for somatic maturity and match physical performances according to playing position. Stature, sitting height, and body mass were measured and entered into an algorithm to estimate the age at peak height velocity (APHV). Players match movements were recorded by Global Positioning System devices (10 Hz), to determine peak speed, and total- (TD), low-speed running (LSR; ≤ 13.0 km·h-1), high-speed running (HSR; 13.1 – 16.0 km·h-1), very high-speed running (VHSR; 16.1 – 20.0 km·h-1) and sprint distances (SPR; > 20.0 km·h-1) expressed relative to match exposure (m·min-1).
Results

Linear-mixed models using log transformed response variables revealed a significant contribution of estimated APHV upon TD (1.01; 95% CI: 0.99-1.02 m·min-1; p < 0.001), HSR (1.05; 95% CI: 0.98-1.13 m·min-1; p < 0.001) and VHSR (1.07; 95% CI: 1.00-1.14 m·min-1; p = 0.047). An increase by one year in APHV was associated with an increase of 0.6, 5.4 and 6.9% in TD, HSR and VHSR respectively. No effects of APHV were observed for LSR, SPR, and peak speed. Further, no APHV effects were observed relative to players’ field position. Conclusion

Later maturing players covered substantially more higher-intensity (HSR and VHSR) running in matches, irrespective of playing position. The greater match intensity of later maturing players may inform talent identification and athletic development processes within a national federation.

he vast majority of neuroscientific studies contain three elements: a person, a cognitive task and a high-tech machine capable of seeing inside the brain. That simple recipe can produce powerful science. Such studies now routinely yield images that a neuroscientist used to only dream about. They allow researchers to delineate the complex neural machinery that makes sense of sights and sounds, processes language and derives meaning from experience.

But something has been largely missing from these studies: other people. We humans are innately social, yet even social neuroscience, a field explicitly created to explore the neurobiology of human interaction, has not been as social as you would think. Just one example: no one has yet captured the rich complexity of two people’s brain activity as they talk together. “We spend our lives having conversation with each other and forging these bonds,” neuroscientist Thalia Wheatley of Dartmouth College says. “[Yet] we have very little understanding of how it is people actually connect. We know almost nothing about how minds couple.”

Ashburn-based Route Analytics Inc. quietly launched last year with a platform to improve the college football recruiting process — and it’s gaining yardage.

The data science startup, which uses algorithms and machine learning to help student athletes understand how they’d stack up at the college level, is building a new version of its platform and raising money to get it done.

Route emerged in a beta stage last summer and went live six months ago, already with “incredible traction,” said CEO Craig Ridley. It’s now in 44 states, with a 30% compound monthly growth rate for users. The company projects a five-year average of about 15% growth, “which will still have us exceeding goals if we continue this momentum,” he said.

Background

Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model’s predictions to clinical experts during interpretation.
Methods and findings

Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson’s chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts’ specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. Conclusions

Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

The New York Mets have selected Hospital for Special Surgery (HSS) as the Presenting Partner of the Mets Academy in the Dominican Republic.

In conjunction with this partnership, HSS will provide its expertise in connection with operation of the Mets Academy, including high quality preventative, educational, performance, and consulting services, for the benefit of the club’s Latin American prospects. Additionally, HSS will look to host educational workshops to medical professionals in the Dominican Republic as well as community and charitable events at the facility.

As the Mets Academy’s Presenting Partner, HSS will also receive branding within the training facility, which will be on display during the ribbon cutting ceremony on Monday, April 22 at 11:30am.

… Their top two and three of their top six position players this season were acquired from teams via trade during the last calendar year, and the 2.5 wins above replacement (WAR), according to FanGraphs’ measurement, of those three accounts for almost half of the Rays’ position player total. And their top pitcher to date, Tyler Glasnow, was acquired in the same July 31 trade with the Pittsburgh Pirates that brought them their best position player, Austin Meadows.

“We feel very strongly about our ability to get the best out of guys,” Chaim Bloom, Tampa’s vice president of baseball operations, told FiveThirtyEight last summer.

Two of those guys — third baseman Yandy Diaz, acquired in a December trade, and Glasnow — provide a glimpse into what the Rays might be doing right, and why this surprising start might be sustainable.

Say the name Jadon Sancho to any football fan and you will rightly hear exclamations of adulation for Borussia Dortmund’s shooting star, none more so than the man who signed the then 17-year-old in 2017 for just £7.5m from Manchester City, Borussia Dortmund’s technical director Michael Zorc: “I haven’t been surprised by his performances, but with how fast he managed to get to this senior level, because before that he only played on youth teams. He played nearly every game for us last autumn, always at a high level, but he could still play in our Under-19 team. That is unbelievable to me.”

In an February interview with the Uk’s Daily Telegraph, Zorc, who is regarded by many as one of the best technical directors in league football, went out of his way to emphasise that there is more to the English game’s success than Jadon Sancho.

Zorc more to the point believes there is a wider trend towards excellence in the English game which the Bundesliga can only benefit from, extolling the successes of the Three Lions since 2017. It is international tournament results that stood out, with the success of England’s U-17s and , both world champions in 2017. “You can recognise it in those results,” he said. “You know the English teams are reaching the finals of U-17 [World Cup], U-19 [European championships] compared to the German ones. It seems to me that they overtook us