Applied Sports Science newsletter – February 6, 2016

The few inarguable facts about Ben Simmons are that he was born in Melbourne, Australia, 19 years and six months ago; he is a 6′ 10″ freshman at Louisiana State who averages 19.8 points, 12.7 rebounds and 4.9 assists per game; and he is the player most likely to be picked No. 1 in the 2016 NBA draft. After that, Simmons becomes more difficult to define. Is he a point-forward, a point-center, a giant floor general—or positionless? Is he lefthanded or (given all those righty finishes) ambidextrous? Should he be a primary ballhandler, to capitalize on his court vision and passing skills, or should he play in the paint, to exploit his advantage in athleticism over opposing big men? Is his game more Aussie-influenced or American-influenced, considering that he developed it in both countries and has one parent of each nationality? Can he responsibly be called the second coming of Magic or of King James? And can he be called the best player in the college game when his team is only 13–7?

… Two years ago, I asked you if you cared about those SportVU cameras in the rafters, and you said you weren’t really interested in motion-tracking stuff — that it might get in your head. Now you’re doing all this cool stuff in the Degree MotionSense, tracking the speed of your shot release and all that.

Have you changed your mind? Have you learned stuff about your body from all this technology?

Yeah, with all the different wearable technology, and the way we’re analyzing how players move — it’s valuable information when it’s used the right way. I try not to think about it when I’m on the court, like, “Oh, my release felt half-a-second slow that time.” Or: “I didn’t create enough force on that crossover.”

But when I’m working out in the summer, being able to analyze the different ways I move, and making it more efficient and explosive — that all has provided dividends, for sure. It helps. But when you’re on the court, you want to be free. You want to react to what’s in front of you, and not be all clogged up in your head.

The 50th Super Bowl is an irresistible-force-meets-immovable-object matchup between the National Football League’s highest-scoring offense and stingiest defense. It also raises a surprising question for a sport that has long featured some of the beefiest, bulkiest athletes on the planet: Where have all the fat guys gone?

When the Carolina Panthers meet the Denver Broncos at Levi’s Stadium in Santa Clara, Calif., there will certainly be plenty of extremely large humans. But these two teams comprise the most freakishly versatile collection of players ever to suit up for the big game. They will showcase a style of play, more than a decade in the making, that demands the kind of speed and all-round athleticism more associated with Olympic decathlon than smash-mouth football.

… The only hope of salvaging a respectable place in their last 14 games is the latest attempted return of the one player at their disposal who can turn defeats to draws, draws to victories, but it is a slim hope Daniel Sturridge will feature in the majority of games between now and May. It’s rather like waiting for Superman to come to the rescue, only to realise he is incapable of getting out of the phone box.

Sturridge is due to train with the rest of the team on Wednesday, but discussing the prolonged absenteeism of the England striker often feels like ballet dancing on eggshells.

Each update seems so carefully worded, as if a room of Harvard professors have convened to ensure each full-stop is in the right place for fear of misinterpretation or litigation.

Recent literature supports the importance of horizontal ground reaction force production for sprint acceleration performance. Modeling and clinical studies have shown that the hip extensors are very likely contributors to sprint acceleration performance.

Last month we conducted a survey on Sports Discovery looking into the views on Sport Science careers; questions included what fundamental skills are needed to succeed in High Performance Sport and what methods are used to further develop your career.

We had hundreds of respondents from all over the world; comprising Europe (69%), the Middle East (1%), Africa (2%), North America (11%), Latin America (1%) and Asia Pacific (16%). We would like to thank everyone who took the time to share their thoughts and hope some of the findings below will be of interest.

Whilst opportunities continue to be advertised; this month alone we have shared vacancies at Oklahoma City Thunder NBA, Orlando City FC, British Cycling, Manchester City FC, Tottenham Hotspur FC, Liverpool FC, Southampton FC, Leicester City FC, West Brom FC, Everton FC, Crewe Alex FC, Wasps Rugby, Saracens, Ulster Rugby as well as a number of University based roles, the competition within our industry continues to expand with more graduates and students fighting it out for internships and jobs. What makes the difference to those who have made it to the top?… What do those starting out think will make the difference for them?…

… Increasingly, the trend is that coaches in many school settings are often teachers, parents, or volunteers. In many if not most cases, these people are primarily motivated by the desire to help young athletes improve their skill sets. We have also seen an increase in ex-athletes moving directly from their competitive careers into the coaching realm. Yet are they always equipped to understand that not all the young athletes they will influence are capable, willing, or driven to achieve at the levels they once did?

Few of these scenarios include professional training in coaching the technical or psychological aspects of the given sport.

Want something that can give you an extra 10-20 days high intensity training a year?

Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100?Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5?s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79–92% classification accuracy) outperformed RF (32–43%) and SVM algorithms (27–40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5–30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.

PURPOSE:

To analyze the match running profile, distance travelled over successive 15 minutes of match-play, heart rates and effindex of professional soccer players with Global Positioning System (GPS) and heart rate (HR) in official competition.
METHODS:

Twenty-six professional players were investigated during full-matches in competitive club level matches (n=223). Time-motion data and HR were collected using GPS and HR technology.
RESULTS:

The relative total distance was 113±11 m·min-1 with substantial differences between halves. For all the playing positions, a substantial decrease in total distance and distance covered >13.0 km·h-1 was observed in the second half in comparison with the first. The decrease during the second half in distance covered >13.0 km·h-1 was substantially higher than in total distance. The average HR recorded was 86.0% HRmax and the relationship between external and internal load value (effindex) was 1.3, with substantial differences between halves in all playing positions, except strikers for effindex. Wide-midfielders reflected substantially the lowest mean HR and highest effindex, while CB showed substantially the lowest effindex than any other playing position.
CONCLUSION:

The current study confirmed the decrement in player’s performance toward the end of match in all playing positions. Wide-midfielders performed the highest and fittest levels of physical and physiological demands, respectively, whereas centre-backs performed the lowest and unfittest levels of physical and physiological demands, respectively. The relationship between external and internal load measures among position-specific confirms that players with more overall running performance during the full-match were the best in effindex.

… Six months after UA’s acquisition spree, one of these software-focused fitness tracking companies, Boston-based FitnessKeeper, makers of the app Runkeeper, restructured and laid off 30 percent of its workforce. For its part, the company said those lay-offs were not related to market conditions. But since then, CEO Jason Jacobs has reacted in a characteristically bold way, declaring his intentions to compete with Under Armour not only in the fitness space, but eventually as an apparel maker as well.

“It’s who can get there first,” Jacobs told MobiHealthNews. “Can the digital natives like Runkeeper, who are technology companies at the core, that have been cloud-based and personalized and mobile, that have these huge user bases and relationships with the consumer, can they figure out apparel more effectively than the apparel companies can figure out digital?”

If you know me, you know that for some time I’ve been placing a lot of emphasis on the “social fitness” phenomenon and the profound impact it’s having on the sports industry. From CrossFit to The Color Run, fitness has shifted from a private activity at the gym to a social interaction in the studio or on the street, driven by (you guessed it) the Millennials.

Many top fitness trackers have hidden security issues, according to a new study.

Canadian research nonprofit Open Effect, in partnership with the University of Toronto’s Citizen Lab, have been evaluating the privacy and security of popular wearables. A range of devices were evaluated (Apple Watch, Basis Peak, Fitbit Charge HR$137.98 at Amazon, Garmin Vivosmart, Jawbone Up2, Mio Fuse, Withings Pulse O2, Xiaomi MiBand) but only one passed the test: Apple Watch.

“The unique identifiers broadcast by all studied devices except for the Apple Watch are fixed,” the report found. “These static identifiers enable third parties, such as shopping malls, to persistently monitor where fitness wearables are located at a given point in time.

… The NFL panel on the injury data results conference call cited more screening, better detection, improved record keeping and more self-reporting of concussions as the reason for the 2015 increase. Crediting rules changes as the reason for the 2014 drop in concussions, the NFL said players were “changing the way they’re tackling” and “changing the way they play the game”. The contrasting explanations for this year’s increase versus last year’s decrease seem too simplistic.

Don’t get me wrong, I don’t think there is a continuing concussion conspiracy in the NFL. I would even applaud the NFL for being forthright with the bad news in pointing out the worst statistic of a 58% jump in concussions during games when they could have cited a much lower 32% overall increase in concussions. It is difficult to generalize injury data without a closer inspection of the information. I don’t doubt there is increased screening, detection, record keeping and self-reporting. I don’t doubt that some players have made a conscious effort to avoid helmet contact. It is also possible to just have “bad luck” year with more concussions

Development of osteoarthritis commonly involves degeneration of epiphyseal trabecular bone. In previous studies, we observed 30-44% loss of epiphyseal trabecular bone (BV/TV) from the distal femur within one week following non-invasive knee injury in mice. Mechanical unloading (disuse) may contribute to this bone loss, however it is unclear to what extent the injured limb is unloaded following injury, and whether disuse can fully account for the observed magnitude of bone loss. In this study, we investigated the contribution of mechanical unloading to trabecular bone changes observed following non-invasive knee injury in mice (female C57BL/6N). We investigated changes in gait during treadmill walking, and changes in voluntary activity level using Open Field analysis at 4, 14, 28, and 42 days post-injury. We also quantified epiphyseal trabecular bone using µCT and weighed lower-limb muscles to quantify atrophy following knee injury in both ground control and hindlimb unloaded (HLU) mice. Gait analysis revealed a slightly altered stride pattern in the injured limb, with a decreased stance phase and increased swing phase. However, Open Field analysis revealed no differences in voluntary movement between injured and sham mice at any time point. Both knee injury and HLU resulted in comparable magnitudes of trabecular bone loss, however HLU resulted in considerably more muscle loss than knee injury, suggesting another mechanism contributing to bone loss following injury. Altogether, these data suggest that mechanical unloading likely contributes to trabecular bone loss following non-invasive knee injury, but the magnitude of this bone loss cannot be fully explained by disuse.

The purpose of this retrospective cohort study was to (a) describe the clinical presentation of femoroacetabular impingement (FAI) and hip labral pathology; (b) describe the accuracy of patient history and physical tests for FAI and labral pathology as confirmed by hip arthroscopy. Patients (18–65 years) were included if they were referred to a physical therapist to gather pre-operative data and were then diagnosed during arthroscopy. Results of pre-operative patient history and physical tests were collected and compared to arthroscopy. Data of 77 active patients (mean age: 37 years) were included. Groin as main location of pain, the Anterior Impingement test (AIT), Flexion-Abduction-External Rotation (FABER) test, and Fitzgerald test had a high sensitivity (range 0.72–0.91). Sensitivity increased when combining these tests (0.97) as either groin as main location of pain and a positive FABER test or a positive AIT and a positive FABER test were the shortest most sensitive combinations. The results of this study point out that in clinical practice absence of groin as main location of pain combined with a negative FABER test or the combination of a negative AIT and a negative FABER test are suggested to rule out the diagnosis of symptomatic FAI and/or labral pathology.

Objective: To determine whether high-level athletes with patellar tendinopathy have diminished knee proprioceptive acuity.

Design: Cross-sectional study.

Setting: University research laboratory (institutional).

Participants: Twenty-one basketball and volleyball players with patellar tendinopathy (13 men and 8 women; mean age 24.5 +/- 3.6; body mass index = 22.5 +/- 2.0 kg/m2) and an equal number of athletes without symptoms of patellar tendinopathy injury were included in this study.

Assessments: Participants underwent knee proprioception assessments on a single day. Furthermore, age, sex, height, weight, VISA-P (Victorian Institute of Sport Assessment) questionnaire sports participation, medical history, knee injuries, previous treatment, and medication were obtained.

Main Outcome Measures: Knee proprioception was evaluated by assessing sense of resistance, using a weight discrimination protocol, and joint position sense (JPS).

Results: No significant differences were observed in JPS at 30 and 60 degrees of knee flexion between groups (P = 0.165 and 0.481, respectively). In regard to the ability to discriminate weight, significant differences between the 2 groups were found with the tendinopathy group showing a higher percentage of error (P = 0.009), namely when the set of incremental weights varied by 10% from the standard weight.

Conclusions: Athletes with patellar tendinopathy have a diminished perception of force signals required for weight discrimination, whereas JPS remains unaffected in these athletes.

CONTEXT:

? Reported injury rates and services in sports injury surveillance may be influenced by the employment setting of the certified athletic trainers (ATs) reporting these data.
OBJECTIVE:

? To determine whether injury rates and the average number of AT services per injury in high school football varied by AT employment status.
DESIGN:

? Cross-sectional study.
SETTING:

? We used data from the National Athletic Treatment, Injury, and Outcomes Network (NATION) and surveyed ATs about their employment setting.
PATIENTS OR OTHER PARTICIPANTS:

? Forty-four responding ATs (37.9% of all National Athletic Treatment, Injury, and Outcomes Network participants) worked at high schools with football programs and were included in this study. Fourteen ATs were full-time employees of the high school, and 30 ATs were employed as outreach ATs (ie, full-time and part-time ATs from nearby clinics, hospitals, and graduate school programs).
MAIN OUTCOME MEASURES:

? We calculated injury rates per 1000 athlete-exposures and average number of AT services per injury.
RESULTS:

? Reported injury rates and services per injury were greater among full-time school employees compared with outreach ATs. However, injury rates did not differ when restricted to time-loss injuries only.
CONCLUSIONS:

? Our findings suggest that ATs who are full-time school employees may be able to identify and care for more patients with injuries.

Freelap USA – Your study with Dr. Michael Stone looked at explosive jumping and hormonal profiling of different athletes and found sprinters had the best androgen metrics for power. One interesting finding was the different levels between handball and soccer. What may be confusing is how genetics (fiber type) and volume of aerobic work in a sport affect free testosterone. Could too much aerobic work create interference at the receptor level? I am sure coaches would love to split training into group practice for tactical reasons and split volumes based on athlete DNA (fiber profile) since the preseason is so short and seasonal training is essential.

Marco Cardinale – Let me clarify the paper and correct some terminology. The study found sprinters had the highest testosterone levels when compared to other athletes. I am not sure what “androgen metrics for power” means. Because the correlation coefficient between T levels and jump was 0.61 in the study, we can say that 37% of the performance in CMJ (Counter-Movement Jump) can be explained by testosterone levels. This is it, nothing more. The differences between the groups considered are specific to those groups, not necessarily that football players will always be different from handball players in team average values and every part of the world.

Now to the other questions. There is no doubt that to some extent T levels are regulated genetically (recent work here or here) or by genetic aspects connected to SHBG. These influence the bioavailable free testosterone as well as being regulated by pathological conditions (e.g. polycystic ovarian syndrome, common in some female athletes).

Former Panther Shawn King’s career went up in smoke. Then he found another way to get high…

… I think Wade Phillips spoke on Friday on what every coach on every level of sports should hear. All sports, not just football. Phillips thinks coaches should tailor what they do to the talent they have, not the other way around. “I don’t understand the people that say, ‘Hey, this is our scheme and that guy can’t play in it,’” Phillips said Friday. “Well, to me, there’s something wrong with your scheme. You adapt the scheme to what the players can do, not what you can think of. We’ve always done it that way.

The Thunder trailed the Knicks by three after the third quarter and, with both Kevin Durant and Russell Westbrook on the bench, the Knicks’ lead went from three points to 11 in the first 2:29 of the fourth.

Thunder coach Billy Donovan took a timeout and got Westbrook back in the game. It wasn’t too late, the Thunder erased the 11-point deficit by the end of regulation, and won in overtime.

But the situation brought to the surface the question about whether the Thunder have to play any minutes with neither Durant nor Westbrook on the floor. The pair average 70 minutes per game between them and Donovan could stagger their playing time so that he never has to play a full bench unit.

Heart rate variability (HRV) is an important marker of an individual’s physiological stress level. Due to recent technological improvements in terms of computation power and accessibility to high quality wearable technology we are seeing all sorts of applications making use of HRV today, from optimizing performance in sports, to monitoring psychological stress in the workplace.

While HRV is a powerful tool and can be very helpful in better understanding physiological responses to both acute and chronic stressors, interpreting HRV data at the individual level is still challenging.

This post focuses on practical ways to acquire and interpret HRV data in the context of monitoring training load and optimizing performance.

We have heard a lot recently about the growing length of a baseball game and there has been some discussion in MLB about possibly moving towards a time clock where a pitcher has to pitch within a specific number of seconds. Pace is also an issue in tennis — in women’s tennis, a server is supposed to serve within 20 seconds of the end of the previous point. In this post, I’ll summarize some data I collected about server pace from the recent Australian Open and then explore pitcher pace data using the Pitch F/X data.

By now, everyone seems to agree, the only way to beat Golden State is to not let the Warriors control the pace, and rather play the game at your own speed.

This makes a great deal of intuitive sense. After all, the Warriors are experts at accelerating the speed of the game, capitalizing on opponents’ mistakes to rip off huge scoring jags. The Spurs played last Monday’s much anticipated1 Clash of Presumptive Titans at Golden State’s speed, letting bad offensive possessions reflect back at them with dizzying speed as the Warriors sprinted over, around, through and in-between them.

Speed of the game, its energy, tempo, is vitally important. Controlling that tempo is choosing the field on which the battle will be fought, a huge advantage to whichever team is able to grab it. But for all the understanding of the importance of pace, we do not have a very good way of describing it in statistical terms.