Applied Sports Science newsletter – November 29, 2016

In a sense we’re all amateur psychologists – we’ve got our own first-hand experience at being human, and we’ve spent years observing how we and others behave in different situations. This intuition fuels a “folk psychology” that sometimes overlaps with findings from scientific psychology, but often does not. Some erroneous psychological intuitions are particularly widely believed among the public and are stubbornly persistent. This post is about 10 of these myths or misconceptions. It’s important to challenge these myths, not just to set the record straight, but also because their existence can contribute to stigma and stereotypes and to misinformed public policies in areas like education and policing.

1. We learn more effectively when taught via our preferred “learning style”

… When you say that a new experience “reminds” you of something, that’s an indication of how your memories thread together. Shohamy says that her memories of Election Night aren’t just tied to other second Tuesdays of November, but “disastrous political events” that she’s lived through, like when she was a student in Israel and Yitzhak Rabin, a prime minister pushing the Middle East toward peace, was assassinated. New York felt like it did on the days after 9/11, or so I am told. “We connect the memories we have on a lot of different associative levels,” she says. “It’s not a folder saying, ‘Here are the Election Nights.’ But there are a lot of common features and feelings and concepts that we use to connect across memories.”

Relatedly, one of the best ways to learn a new fact is through “elaboration”: new thing X is like old thing Y. “The more you can explain about the way your new learning relates to prior knowledge,” write Peter Brown, Henry Roediger, and Mark McDaniel, authors of Make It Stick: The Science of Successful Learning, “the stronger your grasp of the new learning will be, and the more connections you create that will help you remember it later.”

Purpose

There are several approaches to quantifying physical load in team sports using positional data. Distances in different speed zones are most commonly used. Recent studies have used acceleration data in addition in order to take short intense actions into account. However, the fact that acceleration decreases with increasing initial running speed is ignored and therefore introduces a bias. The aim of our study was to develop a new methodological approach that removes this bias. For this purpose, percentage acceleration was calculated as the ratio of the maximal acceleration of the action (amax,action) and the maximal voluntary acceleration (amax) that can be achieved for a particular initial running speed (percentage acceleration [%] = amax,action / amax * 100).
Methods

To define amax, seventy-two highly trained junior male soccer players (17.1 ± 0.6 years) completed maximal sprints from standing and three different constant initial running speeds (vinit; trotting: ~6.0 km·h–1; jogging: ~10.8 km·h–1; running: ~15.0 km·h–1).
Results

The amax was 6.01 ± 0.55 from a standing start, 4.33 ± 0.40 from trotting, 3.20 ± 0.49 from jogging and 2.29 ± 0.34 m·s–2 from running. The amax correlated significantly with vinit (r = –0.98) and the linear regression equation of highly-trained junior soccer players was: amax = –0.23 * vinit + 5.99.
Conclusion

Using linear regression analysis, we propose to classify high-intensity actions as accelerations >75% of the amax, corresponding to acceleration values for our population of >4.51 initiated from standing, >3.25 from trotting, >2.40 from jogging, and >1.72 m·s–2 from running. The use of percentage acceleration avoids the bias of underestimating actions with high and overestimating actions with low initial running speed. Furthermore, percentage acceleration allows determining individual intensity thresholds that are specific for one population or one single player.

… the depth jump is probably the most powerful exercise an athlete can utilize in terms of specific force overload. From Russian high jumping to cult sprint training methodology and commercial basketball performance programming, the depth jump is widely used.

The problem is that it is also the most misrepresented and misperformed exercise among many athletic populations. Much of this problem is due to a lack of understanding of the theory behind the depth jump, and what athletes are trying to accomplish in its performance!

The ability of the human body to generate maximal power is linked to a host of performance outcomes and sporting success. Power-force-velocity relationships characterize limits of the neuromuscular system to produce power, and their measurement has been a common topic in research for the past century. Unfortunately, the narrative of the available literature is complex, with development occurring across a variety of methods and technology. This review focuses on the different equipment and methods used to determine mechanical characteristics of maximal exertion human sprinting. Stationary cycle ergometers have been the most common mode of assessment to date, followed by specialized treadmills used to profile the mechanical outputs of the limbs during sprint running. The most recent methods use complex multiple-force plate lengths in-ground to create a composite profile of over-ground sprint running kinetics across repeated sprints, and macroscopic inverse dynamic approaches to model mechanical variables during over-ground sprinting from simple time-distance measures during a single sprint. This review outlines these approaches chronologically, with particular emphasis on the computational theory developed and how this has shaped subsequent methodological approaches. Furthermore, training applications are presented, with emphasis on the theory underlying the assessment of optimal loading conditions for power production during resisted sprinting. Future implications for research, based on past and present methodological limitations, are also presented. It is our aim that this review will assist in the understanding of the convoluted literature surrounding mechanical sprint profiling, and consequently improve the implementation of such methods in future research and practice.

The purpose of this study was to investigate fear of failure in highly competitive junior athletes and the association with psychological stress and burnout. In total 258 athletes (152 males and 108 females) ranged in age from 15 to 19 years (M = 17.4 years, SD = 1.08) participated. Athletes competed in variety of sports including both team and individual sports. Results showed in a variable-oriented approach using regression analyses that one dimension, fear of experiencing shame and embarrassment had a statistically significant effect on perceived psychological stress and one dimension of burnout, reduced sense of accomplishment. However, adopting a person-oriented approach using latent class analysis, we found that athletes with high levels of fear failure on all dimensions scored high on burnout. We also found another class with high scores on burnout. These athletes had high scores on the individual-oriented dimensions of fear of failure and low scores on the other oriented fear of failure dimensions. The findings indicate that fear of failure is related to burnout and psychological stress in athletes and that this association is mainly associated with the individual-oriented dimensions of fear of failure.

With nearly 70 percent of Internet data projected to be in video format by next year, it’s clear that the task of extracting textural data from video will be critical for data engineers, and that the process will have to be automated. BlueChasm’s CTO Ryan VanAlstine, and software developer Robert Rios demonstrated how to turn raw video into tagged data during the recent Watson Developer Conference in San Francisco.

Using a variety of open source tools and a simple algorithm, they are able to extract enough meaning from videos to summarize its content. The program is able to automatically start when a new video is submitted, leaving the entire process out of human hands. The code is available on their blog.

Video is just a sequence of images, but sending all of the images through the visual recognition is prohibitive both in cost and time. The key is sending through a representative sample from the video. Picking one frame out of 30, the code sends the images to Watson’s visual recognition program which returns the images tagged. The program adds up all the tags to determine what the video is about.

Youth club Del Mark Sharks are taking a new approach for collecting player performance data as they partner with SportsBoard based out of the Bay Area. Director Shannon MacMillan has provided an opportunity to ditch paper assignments and embrace the technology revolution that will allow coaches to remain focused on field.

PIQ, a French start-up in sports wearables, has introduced a genuine Artificial Intelligence interface (GAIA) for sports activities

GAIA is the first AI system that autonomously understand and analyze sports movement, according to the company, and it does so through specific motion-capture algorithms.

The multi-algorithm machine-learning intelligence is a result of fundamental and applied research that has allowed people to understand and analyze microscopic variations in sports movement.

The present study examined the ability of an interpersonal construct called athletic connectedness to mediate the relationship between task and ego goal orientations and well-being. We operationalised athletic social connectedness as a sense of social belonging and sense of connection with teammates. We hypothesised that athletic social connectedness would be positively associated with task goals, negatively associated with ego goals, and would at least partially mediate the relationship between achievement goals and well-being. We administered questionnaires to female (N = 106; mean age = 20.47, SD = 1.12) and male (N = 100; mean age = 20.95, SD = 1.21) NCAA Division III college athletes. We tested our hypothesised model using structural equation modelling, which included testing a measurement model that specified four latent variables and then comparing the estimates generated by our hypothesised model with our data. We also tested three alternative models and found our hypothesised model to fit best. As predicted, there were significant indirect effects of task and ego motivation on well-being through athletic connectedness, demonstrating formal evidence of mediation. The r2 coefficient indicated that the model explained 30% of the variance in well-being, a moderate effect size (Cohen, 1988). Discussion focuses on the importance of considering interpersonal constructs as a way to improve our understanding of relationship between task and ego goal orientations to well-being in athletes.

“I don’t want to be vegan,” David Carter once said to his wife. “That’s for weaklings.”

At the time, Carter was an NFL defensive end, weighing in at more than 300 pounds. Growing up in Los Angeles, he was raised on barbecue at his family’s restaurant and felt that meat was his source of strength, so there was no way that he’d ever adopt his wife’s vegan diet. That is, until he started to experience health issues that affected his career, which ended in 2015.

“I had been big and strong,” Carter says. “But . . . I was taking medication for high blood pressure and suffering from nerve damage” and as a result, he had a hard time doing bench presses and pushups. On Feb. 14, 2014, his attitude suddenly changed: “I was drinking a milkshake and watching the [animal rights documentary] “Forks Over Knives.” And I just thought to myself, ‘That’s it, I’m done.’ I got up, threw out the milkshake and went vegan.”

Recently my friends Nick Dika, Joshua Howsam, and Greg Wisniewski were discussing the third time through the order effect on the season recap episode of the Artificial Turf Wars Podcast.

While Nick and I typically keep our conversations to what beer we’re going to order next, and how can we split four pounds of wings without people judging us, occasionally I like to bug him with my science talk. It appeared to work on Nick, because he started talking about the fact Marcus Stroman has the second fastest pace of all MLB pitchers. Pace, as I have written about before, has a strong relationship with predicted fatigue in pitchers (Figure 1). This was also the rationale I used to rally against the MLB implementation of pitch clocks. Nick hypothesized that it isn’t the fact that Stroman’s opponents adjust to his style on the third time through the order that causes his late game struggles, but that he becomes more fatigued due to his fast pace.

… It’s apparent now that a market correction has arrived in college football. The explosion in salaries for head coaches and top assistants has had a two-pronged effect on the coaching search industry.

First, whereas it may have cost a school $3 million or $4 million to get rid of its coaching staff five years ago, it’s now often a $10 million-or-more proposition, which is enough to make boosters and administrators balk.

Second, with the gold-plated contracts coaches are now enjoying, it is simply quite difficult for any school to put together a package attractive enough to get an established, successful coach to move. In the last five hiring cycles, only eight Power Five schools have been able to poach from another Power Five program, with the most notable examples being Arkansas’ hire of Bret Bielema from Wisconsin and Nebraska luring Mike Riley from Oregon State.

In other words, throwing gobs of money at a great coach no longer works because, in most cases, that coach already makes gobs of money.

Bob Bradley – long a leading light of American coaching – being appointed Swansea City manager caught some of our media’s typically slack-jawed suspects by surprise. “Does he know the league?” asked those who would rather have seen Ryan Giggs appointed.

But things haven’t gone well so far for Bob, so you know those critics are already circling like vultures around a wounded animal. Bradley is one of the bookies’ favourites to get his P45, even though his side did provide a magnificent game on Saturday, albeit one of massive defensive ineptitude from both sides.

Swansea seem to have lost their way as a club and the playing staff isn’t exactly choked with talent. Had he been Ryan, this would have been the excuse given for his poor start. But he’s not British, he’s an American who ‘doesn’t know the league’. And as Dean Saunders helpfully reminded us on Saturday: “His accent isn’t helping him.”

Err…what?

Big data and data science is so much in vogue that we often forget there were plenty of professionals analyzing data before it became fashionable. This can be thought of as a divide between Analytics 1.0, practiced by those in traditional roles like data analysts, quants, statisticians, and actuaries, and Analytics 2.0, characterized by data scientists and big data. Many companies scrambling to hire data science talent have begun to realize the wealth of latent analytics talent right at their fingertips — talent capable of becoming data scientists with a little bit of training. In other words, the divide between Analytics 1.0 and 2.0 is not as wide as you might believe.

Analytics 1.0 professionals come from many industries, including finance, health care, government, and technology. But they all share the same core technical skill sets around computation and statistics that make them ideal candidates for training to get them caught up on data science. In addition to possessing the foundational data science skills, these employees already understand the industry needs — often being corporate veterans. Of course, these advantages are not without their challenges, and from my experience, the three main challenges revolve around learning new computational techniques, new statistical techniques, and a new mindset. Let’s walk through each one.