Applied Sports Science newsletter – October 8, 2018

… “Physically, I feel pretty good,” Hayward says. “But I don’t feel comfortable on the floor yet. It’s one thing to be physically able to do everything. It’s another to be a basketball player — the timing and the rhythms. When you’ve been playing a long time, you just know. But not after you take a huge break, and come back to a new system. The second surgery was such a setback. I was really looking forward to playing 5-on-5 the whole summer. What I’m going through now is what I wanted to do in the summer, but it didn’t work out that way.”

… “I would say Sarri’s philosophy has helped me a lot because over the years I haven’t been coached much and I am at an age now where I understand football a lot more,” Barkley said. “I know how important it is to take different tactics from different managers on board. I feel like I am ready to understand every aspect of the game.

“When I broke into the Everton side under Roberto Martínez I was playing in the No 10 role and I had never played there before, so I was getting used to that role as a first-team player. But usually I was a No 8. Before I broke my leg I was a deep-lying midfield player, so I have always been a centre midfielder, either in a two or a three. Now I am in a three and I feel really comfortable with that.”

It has gone beyond a streak, past a trend and into the realm of the bizarre.

David Price, a very good left-handed pitcher in the regular season, a five-time All-Star in fact, cannot win a start in the postseason. And he has had his chances — 10 of them, in fact — and whatever team he was pitching for at the time has lost all 10 of those games.

Saturday night was another fiasco to add to this list of the lost. Price, 33, who is in the middle of a seven-year, $217 million deal with the Red Sox, gave up three runs before being removed with two outs in the second inning, and he took still another postseason loss in a 6-2 Yankees victory that evened the American League division series at a game apiece.

It was the latest addition to a string of futility that thumbs its nose at probability.

The kid who almost quit football in high school is the breakout star of the NFL season, directing the Chiefs’ offense with a mixture of sublime confidence and did-you-see-that! spectacle. Mahomes’ style is no surprise for those who’ve known him—the guy’s always been that way

This one just popped up in BJSM as an Online First manuscript. Our own Evert Verhagen collaborated with Tim Gabbett to capture and illustrate the essence of what we pursue in performance enhancement and athlete health protection.

Relationships between load, load capacity, performance and health are topics of contemporary interest. At what intensity should an athlete train to achieve the best physiological response? How much (or little) can an athlete train without detri- mentally affecting health? Most studies addressing such questions have used a reductionist approach wherein factors were studied in isolation, thereby ignoring the complex inter-relationships and balance between factors. This editorial discusses the association between load and load capacity, and their relationship with athlete performance and health. It illustrates the practical use of a model for the management of athlete performance and health, and provide directions for future practice and research.

Leeds Trinity University is working with Premier League football club AFC Bournemouth to analyse the performance and physical responses of the club’s elite football goalkeepers.

Leeds Trinity PhD student Anthony White, who works at the club as their First Team Assistant Goalkeeper Coach and Goalkeeping Specific Sport Scientist, is researching the physiological demands of goalkeeping, which will then be used to inform training and development.

< Anthony's first-year research on the match play and performance test responses of goalkeepers has recently been published in the scientific journal, Sports Medicine, and is amongst the first to be published profiling the current evidence regarding football goalkeeping, match performance and training response.

Beliefs are a slippery concept. What actually are they? Philosophy has long struggled to define them.2 In this post-truth and ideologically polarized world, we need a better understanding of beliefs. As a psychiatrist, my job frequently involves identifying distorted beliefs, understanding how they formed, and helping people to learn to be more skeptical of their own beliefs.

Let’s consider a helpful evolutionary framework for making more coherent sense of what beliefs really are, and why mistaken beliefs can sometimes be so hard to change. Then we’ll talk about how to gain a more accurate grasp of reality, and how ultimately to advance society.

… a great deal of research shows that social support (from our family, friends, and significant others) can hold us to our health goals. Social support works by moving us to act in ways that we believe are popular or socially desirable, and we are motivated by words of encouragement — not only when we receive encouraging words, but also (actually, even more so) when we help others.

When we are supported, we experience better health outcomes. And there’s more than one type of social support. Both practical and emotional support are correlated with adherence to treatment recommendations, but in different ways. While practical social support (splitting the cost of a group tennis lesson, or sharing a ride to the soccer game) may be a more direct contributor to health, emotional social support may indirectly influence health factors by boosting mood and self-esteem and lowering stress (other people make us feel good, so we take better care of ourselves as a result). Social support may even alter our perception of how challenging a situation is. In one amusing study, for example, researchers found that people estimate hills to be less steep when they are with friends (as opposed to people judging hills alone or with non-friends).

Dexterous movements are at the pinnacle of motor control and are likely to be performed by a diverse set of neural circuits. In the following interview, Dr Adam Hantman outlines how his lab is attempting to identify and understand the neural elements responsible for dexterous motor control.

The purpose of this study was twofold: (a) to examine the relationship between heart rate variability (HRV) and acute:chronic workload ratio (ACWR)-based training load (TL) metrics and (b) to examine relationships across various A:C ratio-based TL metrics. Heart rate variability in 23 male college soccer players (mean ± SD; age, 21 ± 1 years; body mass, 80.3 ± 5.8 kg; height, 181.9 ± 6.5 cm; %body fat, 11.9 ± 2.0%; and V[Combining Dot Above]O2max, 51.9 ± 5.0 ml·kg·min) was measured at 5 time points: week(W)1, W3, W7, W12, and W14 during the 2015 NCAA men’s soccer season. Heart rate variability was calculated from beat to beat intervals using a heart rate monitor. Players donned a global position satellite-enabled device that measured the following TL metrics: session time (ST), Player Load (PL), PL·min, and total distance (TD). Acute:chronic workload ratio was calculated for each TL metric: ACWR-based ST (ACWRST), ACWR-based PL (ACWRPL), ACWR-based PL·min (ACWRPLM), and ACWR-based TD (ACWRTD): ACWR = week average TLs/mo average (30 ± 1 days) TLs. Relationships between HRV and ACWR-based each TL metric were evaluated using mixed effects models. Tukey pairwise comparisons were used to examine differences between types of ACWR-based TL metrics. An increase in ACWRST significantly reduced HRV throughout a season (-7.4 ± 3.6 m·s; p = 0.04). There were significant differences between ACWRPLM and ACWRST, ACWRPL and ACWRTD at W1, ACWRPLM and ACWRST at W3 (p < 0.05). In conclusion, ACWRST, ACWRPL, and ACWRTD were significantly different from ACWRPLM. ACWRST was found to significantly predict HRV; higher ACWRST was significantly associated with lower HRV. Therefore, tracking of the ACWR using ST may help to optimize athlete's physiological state throughout a season.

Global thresholds are typically used to band acceleration dependent on intensity. However, global thresholds do not account for variation in individual capacities, failing to quantify true intensity of acceleration. Previous research has investigated discrepancies in high-speed distance produced using global and individual speed thresholds, not yet investigated for acceleration. The current aim was to investigate discrepancies between global and individual thresholds when quantifying acceleration tasks. Acceleration data were recorded for 31 professional soccer players, using 10-Hz Global positioning systems devices. Distances traveled performing low-, moderate-, and high-intensity acceleration were calculated for athletes using global and individual thresholds. Global acceleration thresholds for low-, moderate-, and high-intensity acceleration were classified as 1-2, 2-3, and >3 m·s, respectively, with individual thresholds classified as 25-50%, 50-75%, and >75% of maximum acceleration, respectively. Athletes were grouped low (LO), medium (ME), or high (HI) maximum accelerative capacity, determined using 3 maximal 40-m linear sprints. Two-way mixed-design analyses of variance were used to analyze differences in acceleration distances produced between analysis methods and athlete groups. No significant differences were identified between analysis methods for LO. For ME, no significant differences were demonstrated for low intensity. Moderate- and high-intensity acceleration distances were significantly higher for global compared with individual analysis method (p < 0.01). For HI, significantly higher acceleration distances were produced for all acceleration intensities using global thresholds (p < 0.01). Significant differences identified between analysis methods suggest practitioners must apply caution when using global thresholds. Global thresholds do not account for individual capacities and may provide an inaccurate representation of relative intensity of acceleration tasks.

… The Apple Watch, a new version of which was announced at the event, was initially pitched, in 2014, as a salve for the excesses of your iPhone. Just as Apple had lost control of its iPhone to the invasive, distracting internet companies, those customers had lost control over their own experiences. The Apple Watch — with its smaller screen and emphasis on checking rather than interacting — was pitched with marketing that evoked adventure, activity and, above all, escape, as much from work or home as from the iPhone itself.

Four years and millions of sales later, the Apple-Watch-as-iPhone-antidote pitch remains, as do its use cases as fitness and health devices. More important is what hasn’t yet happened. Whether by accident or by design, the watch has so far been immune to the runaway success that redefined the iPhone. Apple is still in the process — and still in charge — of guessing what most people could get out of an Apple Watch. It’s a device that is neither a simple reflection of pre-emptive market research nor a product reshaped by its own popularity. What future it may have still belongs to Apple. And this appears to be making the company anxious.

… The gym selfie, experts say, is more than just a visual brag or photo-driven pep talk. Social media is fundamentally changing the way we work out—and the way we see ourselves in the mirror. In a recent study, professors Tricia Burke and Stephen Rains found that individuals who saw more workout posts in their feeds were more likely to feel concerned about their own bodies, especially if the posts came from a person they felt looked similar to them. This means that even a passive scroll through Instagram can be more about stoking self-consciousness, in oneself and in others, than providing motivation—and that we internalize these lessons more easily than we think. “If people become preoccupied with their weight, that could manifest itself in less healthy ways,” Burke told me.

Brunel University professor Tara Marshall was among a team of researchers who examined the how the “big five” personality traits, self-esteem, and narcissism all interact; you may remember their results going viral in 2016 under variations of the title “People who Post About Fitness Are Narcissists.” They found that narcissists were more likely to post online updates about their accomplishments, and, assuming that vanity is among the reasons they exercise, Marshall explains that “discussing their diet or exercise routine on Facebook seems to be one way they broadcast a core aspect of their identity”—here, their physical appearance.

Josh McCown knows the stomach-plunging feeling of approaching the huddle, anticipating a play call to come in through the two orange earphones inside his helmet, and hearing … nothing.

“You’re waiting, and waiting, and waiting,” McCown, the veteran Jets quarterback, said. “Then, after a certain amount of time, you’re like, wait a minute — something’s wrong.”

Even in today’s N.F.L., with its pylon cameras and sidelines stocked with tablet computers, technical glitches can still hamper one of the game’s most straightforward networks — a one-way radio connecting the offensive coordinator to the quarterback and the defensive coordinator to a designated defensive player.

In a sport as chaotic and violent as the NFL, the goal of predicting which players are going to get injured is both a Holy Grail and, to date, wholly unattainable with public data. While we have written many articles that identify risk factors for injuries — weight, prior injury history, position and age, to name a few — we have not written about a model that allows us to successfully predict which individual players will stay healthy or get hurt. That’s partly because if we had such a model it would be too valuable to share freely, but also because our efforts to date have come up short.