Applied Sports Science newsletter – June 7, 2016

… Back in September, over at the much missed Grantland, Mike Goodman did a fine job of describing how Ronaldo‘s style of play had evolved under then coach Rafa Benitez. Part of the thrust of that article involved how Ronaldo’s game had changed as he had aged; no longer was he the marauding left forward that blew past players at will, no longer did he give and go like in his younger days. At least partially, this is because the two things he has maintained require him to be high up the pitch: shot and goal volume. Maybe he’s sticking around a while?

This is where I start to become perplexed at attempts to dismiss him or his impact.

As news broke that 18 year old Manchester United phenom Marcus Rashford would be joining the England squad for Euro 2016 in France, there were, as Ted Knutson identified on Statsbomb this past week, two schools of thought on the subject.

The first is fairly straightforward: how could Hodgson NOT take the young player, one of the most exciting England prospects in a generation(ish)?

The second comes from the analytics crowd: doubt. Not strong doubt, necessarily; there was no “What is Hodgson doing?” as far as I could tell from a cursory Twitter check. It was more, “This makes sense but, to be honest, there are some question marks over whether Rashford can continuing his scoring form over the summer and into next season for United.”

The reason for this doubt is understandable—Rashford’s conversion rate is very high, but his shot volume is not.

Perception of effort, also known as perceived exertion or sense of effort, can be described as a cognitive feeling of work associated with voluntary actions. The aim of the present review is to provide an overview of what is perception of effort in Exercise Science. Due to the addition of sensations other than effort in its definition, the neurophysiology of perceived exertion remains poorly understood. As humans have the ability to dissociate effort from other sensations related to physical exercise, the need to use a narrower definition is emphasised. Consequently, a definition and some brief guidelines for its measurement are provided. Finally, an overview of the models present in the literature aiming to explain its neurophysiology, and some perspectives for future research are offered.

Purpose: Non-rearfoot strike landing patterns (NRFS) have been associated with reduced injury risk in runners. The purpose of this ongoing study was: 1) assess foot strike patterns between two groups of rearfoot strike (RFS) runners attempting to transition to a NRFS pattern with and without the use of biofeedback technology, and 2) to prospectively compare injury incidence rates at 6 and 12 months between RFS and NRFS runners.
Methods: 108 habitual RFS runners (mean age= 27.1 ± 9.4, 75% male) participated in a 2 hour training session to transition to a NRFS pattern. Participants were then randomized into a Biofeedback Group (BFG, n= 52), which wore an ankle-based sensor providing feedback on their strike pattern, and a control group (CON, n=56), which received no feedback. Both groups were instructed in a foot and lower leg strengthening program in preparation for the transition. Foot strike pattern and cadence were assessed pre and post-training, and at 6 months. Injury and mileage data were collected through weekly email surveys over 6 months. Chi square analyses were used to assess differences between groups and between times.
Results: Eighty-one percent of runners demonstrated a NRFS pattern following the 2 hour training session (87/108, p < 0.001). The percentage of NRFS runners remaining at the six month follow-up decreased in both groups, but this was not significant (CON = 68%, p = 0.20; BFG = 75%, p = 0.34). There was no statistical difference in the number of participants that transitioned from RFS to NRFS pattern between groups at the six month follow-up (p = 0.41). Compliance in the BFG was only 14% due to technical limitations of the sensor. Injury rates were similar between RFS runners (29% injured) and NRFS runners (22% injured) after six months (p = 0.47). The relative risk for knee injury in RFS runners, regardless of group, was 9.93 (95% CI: 1.15, 85.4; p-value= 0.04).
Conclusion: A large percentage of runners were able to transition to a NRFS pattern with small, group based training without the use of a biofeedback device. Greater changes in the BFG may have been seen had compliance been better. There was no definitive difference in injury rates between RFS and NRFS runners at the 6 month follow-up. Injury rates and mechanics will be reassessed at 12 months.

By investing in leadership academies, abroad programs and career services, Jack Swarbrick believes a better-rounded Irish athlete will also be a winning one for Notre Dame.

PURPOSE:

Cortisol (C) and testosterone (T) are impacted significantly by prolonged endurance exercise with inverse responses. Increases in C are witnessed concurrently with decrements in T, possibly impacting recovery. This study was conducted to assess the dynamics of C and free T (fT) concentration and recovery time following an exhaustive endurance exercise session (EES).
METHODS:

12 endurance-trained males (X ± SD: VO2max 66.3±4.8 ml/kg/min, age 22.8 ± 3.1 years, body fat 11.0 ± 1.4 %, training 7.1 ± 3.2 years) completed a treadmill EES at ventilatory threshold (74.7 ± 4.6 % of VO2max; 96.9 ± 10.8 min). Basal blood C and fT were collected at baseline: -48, -24 h, and immediately before (0 h) the EES as well as immediately (+0 h), +24 h, +48 h, and +72 h after the EES. Blood glucose (G) was measured to confirm no undue influence on C. Statistically data were analyzed with repeated measures ANOVA (LSD post hoc).
RESULTS:

C (nmol/L) increased significantly from -48 h (321 ± 59) to +0 h (701 ± 178) (p < 0.001), and displayed a baseline overshoot with +24 h (209 ± 67) being significantly lower than -48 and +0 h (p < 0.03). fT (pmol/L) decreased significantly from -48 h (161 ± 40) to +0 h (106 ± 38) (p < 0.01) and remained lower at +24 h (110 ± 33) and +48 h (129 ± 30) (p < 0.001). G remained stable throughout. A moderately negative correlation (r = -0.636, p < 0.026) was found between C and fT at +0 h.
CONCLUSIONS:

EES recovery may require 48 h for C and 72 h for fT to return to baseline values. Furthermore, C and fT were only correlated immediately post-exercise. Future research should perform more frequent measurements throughout time course.

The optimal scheduling of Nordic Hamstring exercises (NHEs) relative to football training sessions is unknown. We examined the acute neuromuscular and performance responses to NHE undertaken either before (BT) or after (AT) simulated football training. Twelve amateur players performed six sets of five repetitions of the NHE either before or after 60 min of standardised football-specific exercise (SAFT60). Surface electromyography signals (EMG) of the hamstring muscles were recorded during both the NHE, and maximum eccentric actions of the knee flexors (0.52 rad · s–1) performed before and after the NHE programme, and at 15 min intervals during SAFT60. Ten-metre sprint times were recorded on three occasions during each 15 min SAFT60 segment. Greater eccentric hamstring fatigue following the NHE programme was observed in BT versus AT (19.8 %; very likely small effect), which was particularly apparent in the latter range of knee flexion (0–15°; 39.6%; likely moderate effect), and synonymous with hamstring EMG declines (likely small–likely moderate effects). Performing NHE BT attenuated sprint performance declines (2.0–3.2%; likely small effects), but decreased eccentric hamstring peak torque (–14.1 to –18.9%; likely small effects) during football-specific exercise. Performing NHE prior to football training reduces eccentric hamstring strength and may exacerbate hamstring injury risk.

U.S. track cyclists training for the 2016 Olympic Games are equipped with a new high-tech wearable: the Solos smart cycling glasses. The shades display in real-time performance metrics culled from the athletes’ bike sensors, heart rate monitors, and other self-tracking devices, enabling riders to view their critical stats without taking their eyes off the road.

“It’s a new way of experiencing the sport,” says Ernesto Martinez, director of sports performance wearables at Westborough, Mass.-based Kopin Corporation, which is developing Solos.

Many professional cyclists use bike computers to track metrics such as how many watts of power they are exerting and the cadence of their pedal stroke. Many cyclists also use heart rate monitors strapped around their chests or wrists, and are eyeing some of the newer, more in-depth wearables, such as lactate threshold monitors. The data helps the athletes gain insights on the training and maximize their energy expenditure.

Background: Female soccer players have a well-known risk for anterior cruciate ligament (ACL) injury, but few studies have reported on second ACL injuries in this population.

Purpose: To (1) report the rates of subsequent ACL injury (ipsilateral graft rupture or contralateral tear) in competitive female soccer players, (2) compare these rates with those of other female athletes of similar competitive level, (3) determine risk factors for second ACL injury, and (4) report clinical outcome scores in this population.

Study Design: Cohort study; Level of evidence, 3.

Methods: The medical records at a single institution were reviewed for female patients who were injured during a competitive athletic event and treated with primary ACL reconstruction (ACLR) between 1998 and 2013. Patients were followed for a mean of 68.8 months postoperatively (range, 24-115.2 months). Clinical outcome was obtained via Lysholm and International Knee Documentation Committee (IKDC) scores. Soccer players were matched 1:1 to non-soccer athletes for age, activity level, and graft type.

Results: A total of 180 female ACLR patients with a mean ± SD age of 19.6 ± 6.9 years met the study inclusion and exclusion criteria (90 soccer players and 90 non-soccer players). Soccer players sustained more second ACL injuries, including both graft failures (11% vs 1%; P < .01) and contralateral ACL tears (17% vs 4%; P < .01), compared with non-soccer players. Of the 67 patients who returned to soccer after ACLR (mean age, 17.5 years; range, 13-27 years), significantly more had graft tears compared with those who did not return to soccer (15% vs 0%, respectively; P = .04); however, the difference in contralateral ACL tears (19% for returning players vs 9% for those who did not return; P = .34) was not significant. Relatively older age (odds ratio, 1.5 per year; P = .03) was a significant risk factor for ACL graft tear but not for contralateral ACL injury. Both groups had similar mean Lysholm (96 vs 95) and IKDC scores (95 vs 96) at final follow-up.

Conclusion: Twenty-eight percent of all female soccer players and 34% of those players who returned to soccer had a second ACL tear. Soccer players had an increased rate of both graft tear and contralateral ACL injury compared with similar non-soccer athletes. Older age and return to soccer were significant risk factors for graft rupture.

Take Home Message: Only 39% of respondents indicated that they had a written mental health screening plan. There is a wide variability between mental health screening practices among NCAA institutions.

… For a player like Curry, the MCL is a particularly important ligament, and one of the treatments he sought for his injury remains, well, scientifically unproven. … the lateral movements that allow a twitchy dribbler like Curry to get past the guy guarding him and go to the rim or to juke a defender to open up a shot are going to put pressure on the ligament.

“His injury was mild on the sprain scale, but, because of what he does, it ends up having a huge impact on what he does and what he can do,” said Rob Landel, a professor of clinical physical therapy at the University of Southern California.

While opining on her latest protein-rich breakfast experiment, Jane Plain mentioned ghrelin as an important mediator of the circadian component (more on this below).

T.S. Wiley wrote a lot about the protein-rich breakfast; here’s my understanding of her take on it.

… Analysing data in relation to games and minutes played in club football this season, intensity of play and injuries suffered, we compared the current England squad to the major sides at the European Championship and the other home nations.

Even before a ball is kicked, do England already have an excuse?

… The NFL could have the technological capabilities to make a sideline look like a Blade Runner reboot. But it already has a mountain of data?—?it’s just that the mountain is largely inaccessible. In an effort to facilitate progress, league officials in Boca Raton pitched the NFL’s latest data technology: a system that would allow franchises to view player-tracking data for all 32 teams. If implemented, the technology would enable clubs to monitor every movement on the field for the first time, yielding raw data on player performance. For example: A team concerned about a slow cornerback could actually find out how much slower he is than Antonio Brown, who, according to data shared on a 2015 Thursday Night Football broadcast, posted a maximum speed of 21.8 mph during the season.

The proposal for teams to have access to all raw player-tracking data did not make it past the league’s Competition Committee, a group of team executives, owners, and coaches, according to an NFL official. Certain coaches griped about what might happen if other teams or the public had access to this data, and the committee told team representatives that it was too much, too soon, preventing the matter from reaching the teams for a vote at the March meeting.

“In other industries it is crazy to think you are going to limit innovation just to protect the people who aren’t ready,” said Brian Kopp, president of North America for Catapult Sports, which says it has deals with 19 NFL teams to provide practice data, but not game data.

Why do some people work hard while others falter?

Why does Silicon Valley produce new technologies and billion-dollar companies while efforts in other cities fail to foster entrepreneurship and brisker economies?

You could spend a career studying this question, and depending on whom you ask, you may receive contradictory answers.

But we suggest starting with a number of experiments run by Stanford psychologists in the sixties and seventies involving children and promises of marshmallows. Because the way that toddlers struggle to resist the temptation of a tasty treat, and the conclusions researchers drew from it, provides insight into what motivates individuals to work hard, Silicon Valley’s secret sauce, and the roots of global poverty.