Applied Sports Science newsletter – April 4, 2016

The hype train surrounding Moritz Boehringer was chugging along at a nice pace entering his pro day on Thursday. After an incredible workout in front of NFL scouts, that train is now in full runaway mode.

Officially at Florida Atlantic’s pro day, Boehringer ran a 4.43-second 40-yard dash, had a vertical of 39 inches, a 10-foot-11 broad jump, a 4.10-second short shuttle, 11.15-second 60-yard shuttle, and a 6.65-second three-cone drill that had scouts on the ground doing double-takes at their stopwatches, shaking their heads, and laughing. He also had 17 lifts on the bench press. All of these marks would have landed inside the top five among all wide receivers who tested at the NFL Scouting Combine in February. And his 40, which was run on grass, is about the equivalent of a 4.39 on turf — a time that would have tied for second among receivers at the combine. [video, 0:08]

The Pro-Agility test (I-Test) and 3-cone drill (3-CD) are widely used in football to assess quickness in change of direction. Likewise, the 10-yard (yd) sprint, a test of sprint acceleration, is gaining popularity for testing physical competency in football players. Despite their frequent use, little information exists on the relationship between agility and sprint tests as well the reliability and degree of change necessary to indicate meaningful improvement resulting from training. The purpose of this study was to determine the reliability and smallest worthwhile difference (SWD) of the I-Test and 3-CD and the relationship of sprint acceleration to their performance. Division-I football players (n = 64, age = 20.5 ± 1.2 years, height = 185.2 ± 6.1 cm, body mass = 107.8 ± 20.7 kg) performed duplicate trials in each test during 2 separate weeks at the conclusion of a winter conditioning period. The better time of the 2 trials for each week was used for comparison. The 10-yd sprint was timed electronically, whereas the I-Test and 3-CD were hand timed by experienced testers. Each trial was performed on an indoor synthetic turf, with players wearing multicleated turf shoes. There was no significant difference (p > 0.06) between test weeks for the I-Test (4.53 ± 0.35 vs. 4.54 ± 0.31 seconds), 3-CD (7.45 ± 0.06 vs. 7.49 ± 0.06 seconds), or 10-yd sprint (1.85 ± 0.12 vs. 1.84 ± 0.12 seconds). The intraclass correlation coefficients (ICC) for 3-CD (ICC = 0.962) and 10-yd sprint (ICC = 0.974) were slightly higher than for the I-Test (ICC = 0.914). These values lead to acceptable levels of the coefficient of variation for each test (1.2, 1.2, and 1.9%, respectively). The SWD% indicated that a meaningful improvement due to training would require players to decrease their times by 6.6% for I-Test, 3.7% for 3-CD, and 3.8% for 10-yd sprint. Performance in agility and short sprint tests are highly related and reliable in college football players, providing quantifiable parameters for judging true change in performance as opposed to random measurement variation in college football players.

When you’re in peak physical condition, you feel like a superhero—like you could go forever, outpace a cheetah, or lift a VW Bug. But your superpowers are ephemeral; the second you stop training, they start to fade. We asked sports physiologist Iñigo Mujika to give us a quick rundown of what’s behind the glory and the fall. The takeaway: you should never, ever stop training for more than two weeks if you can help it. Here’s why.

The hyperbolic form of the power-duration relationship is rigorous and highly conserved across species, forms of exercise and individual muscles/muscle groups. For modalities such as cycling, the relationship resolves to two parameters, the asymptote for power (critical power, CP) and the so-called W’ (work doable above CP), which together predict the tolerable duration of exercise above CP. Crucially, the CP concept integrates sentinel physiological profiles – respiratory, metabolic and contractile – within a coherent framework that has great scientific and practical utility. Rather than calibrating equivalent exercise intensities relative to metabolically distant parameters such as the lactate threshold or V[spacing dot above]O2 max, setting the exercise intensity relative to CP unifies the profile of systemic and intramuscular responses and, if greater than CP, predicts the tolerable duration of exercise until W’ is expended, V[spacing dot above]O2 max is attained, and intolerance is manifested. CP may be regarded as a ‘fatigue threshold’ in the sense that it separates exercise intensity domains within which the physiological responses to exercise can (CP) be stabilized. The CP concept therefore enables important insights into 1) the principal loci of fatigue development (central vs. peripheral) at different intensities of exercise, and 2) mechanisms of cardiovascular and metabolic control and their modulation by factors such as O2 delivery. Practically, the CP concept has great potential application in optimizing athletic training programs and performance as well as improving the life quality for individuals enduring chronic disease.

Playoff time in any sport is where success is truly measured, and the point in time where every game matters. Perhaps nothing draws as much attention for as long of a window of time as NCAA’s March Madness. What is often overlooked at this point is that playoffs are when a year’s worth of work comes together … It is now commonplace to hear of athletes being rested to avoid injury, and frequent articles talk of the development of injury prediction technologies. The rising interest is shifting public opinion away from the idea that sporting success is simply the product of years of hard work, towards a greater understanding that it’s the result of years of hard work performed intelligently – athletes performing the optimal amounts and types of work, at the optimal times over long periods. Ultimately, this is the puzzle that sports scientists and coaches look to solve every day across the globe with the help of technologies, logic, and science.

I discussed the pros and cons of the different tracking technologies, meaningfulness of some selected variables, limitations of metabolic power and future areas of interest including accelerometer-related data. [video, 43:38]

BACKGROUND:

A hamstring strain injury (HSI) has become the most common noncontact injury in soccer. Isokinetic muscle strength deficits are considered a risk factor for HSIs. However, underpowered studies with small sample sizes unable to determine small associations have led to inconclusive results regarding the role of isokinetic strength and strength testing in HSIs.
PURPOSE:

To examine whether differences in isokinetic strength measures of knee flexion and extension represent risk factors for hamstring injuries in a large cohort of professional soccer players in an adequately powered study design.
STUDY DESIGN:

Cohort study; Level of evidence, 2.
METHODS:

A total of 614 professional soccer players from 14 teams underwent isokinetic strength testing during preseason screening. Testing consisted of concentric knee flexion and extension at 60 deg/s and 300 deg/s and eccentric knee extension at 60 deg/s. A clustered multiple logistic regression analysis was used to identify variables associated with the risk of HSIs. Receiver operating characteristic (ROC) curves were calculated to determine sensitivity and specificity.
RESULTS:

Of the 614 players, 190 suffered an HSI during the 4 seasons. Quadriceps concentric strength at 60 deg/s (odds ratio [OR], 1.41; 95% CI, 1.03-1.92; P = .03) and hamstring eccentric strength at 60 deg/s (OR, 1.37; 95% CI, 1.01-1.85; P = .04) adjusted for bodyweight were independently associated with the risk of injuries. The absolute differences between the injured and uninjured players were 6.9 N·m and 9.1 N·m, with small effect sizes (d < 0.2). The ROC analyses showed an area under the curve of 0.54 and 0.56 for quadriceps concentric strength and hamstring eccentric strength, respectively, indicating a failed combined sensitivity and specificity of the 2 strength variables identified in the logistic regression models.
CONCLUSION:

This study identified small absolute strength differences and a wide overlap of the absolute strength measurements at the group level. The small associations between lower hamstring eccentric strength and lower quadriceps concentric strength with HSIs can only be considered as weak risk factors. The identification of these risk factors still does not allow the identification of individual players at risk. The use of isokinetic testing to determine the association between strength differences and HSIs is not supported.

Background: Multivariate analysis that identifies the combination of risk factors associated with anterior cruciate ligament (ACL) trauma is important because it provides insight into whether a variable has a direct causal effect on risk or an indirect effect that is mediated by other variables. It can also reveal risk factors that might not be evident in univariate analyses; if a variable’s effect is moderated by other variables, its association with risk may be apparent only after adjustment for the other variables. Most important, multivariate analyses can identify combinations of risk factors that are more predictive of risk than individual risk factors.

Hypothesis: A diverse combination of risk factors predispose athletes to first-time noncontact ACL injury, and these relationships are different for male and female athletes.

Study Design: Case-control study; Level of evidence, 3.

Methods: Athletes competing in organized sports at the high school and college levels participated in this study. Data from injured subjects (109 suffering an ACL injury) and matched controls (227 subjects) from the same athletic team were analyzed with multivariate conditional logistic regression to examine the effects of combinations of variables (demographic characteristics, joint laxity, lower extremity alignment, strength, and personality traits) on the risk of suffering their first ACL injury and to construct risk models.

Results: For male athletes, increases in anterior-posterior displacement of the tibia relative to the femur (knee laxity), posterior knee stiffness, navicular drop, and a decrease in standing quadriceps angle were jointly predictive of suffering an ACL injury. For female athletes the combined effects of having a parent who had suffered an ACL injury and increases in anterior-posterior knee laxity and body mass index were predictive of ACL injury.

Conclusion: Multivariate models provided more information about ACL injury risk than individual risk factors. Both male and female risk models included increased anterior-posterior knee laxity as a predictor of ACL injury but were otherwise dissimilar.

Background Studies investigating the development of ACL injuries over time in football are scarce and more data on what happens before and after return to play (RTP) are needed.

Aim To investigate (1) time trends in ACL injury rates, (2) complication rates before return to match play following ACL reconstruction, and (3) the influence of ACL injury on the subsequent playing career in male professional football players.

Methods 78 clubs were followed between 2001 and 2015. Time trend in ACL injury rate was analysed using linear regression. ACL-injured players were monitored until RTP and tracked for 3?years after RTP.

Results We recorded 157 ACL injuries, 140 total and 17 partial ruptures, with a non-significant average annual increase in the ACL injury rate by 6% (R2=0.13, b=0.059, 95% CI ?0.04 to 0.15, p=0.20). The match ACL injury rate was 20-fold higher than the training injury rate (0.340 vs 0.017 per 1000?h). 138 players (98.6%) with a total rupture underwent ACL reconstruction; all 134 players with RTP data (4 players still under rehabilitation) were able to return to training, but 9 of them (6.7%) suffered complications before their first match appearance (5 reruptures and 4 other knee surgeries). The median layoff after ACL reconstruction was 6.6?months to training and 7.4?months to match play. We report 3-year follow-up data for 106 players in total; 91 players (85.8%) were still playing football and 60 of 93 players (65%) with ACL reconstruction for a total rupture played at the same level.

Conclusions The ACL injury rate has not declined during the 2000s and the rerupture rate before return to match play was 4%. The RTP rate within a year after ACL reconstruction was very high, but only two-thirds competed at the highest level 3?years later.

… there is no perfect answer. … if you’ve been heading out for runs on an empty stomach to lose body fat, not seeing results and not seeing progress then it’s time to realize that method isn’t working! If you’ve been loading up prior to every long run, bonking mid-way through or having digestive issues…again stop torturing yourself by trying to follow a rule and test out other options.

It is great to see the much needed new and revised position statement by the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine! … The statement discusses the evidence that the performance of, and recovery from, sporting activities are enhanced by well-chosen nutrition strategies. The guidelines go into a fair amount of detail, specifying amounts, timing and in many cases the type of nutrients that have to be ingested in order to get these benefits. The guidelines span across a range of sports and are suitable for both training and competition.

Americans are obsessed with protein. Protein powders make up 70 percent of the country’s $6.7 billion sports nutrition market, according to market research firm Euromonitor International, and the high-protein diet has officially become the nation’s favorite. But while athletes need more protein than the general population, experts say, there is a point of diminishing returns.

“Athletes are better off consuming 1.6 grams of protein per kilogram of bodyweight per day [over the government’s recommendation of 0.8 grams],” says Stuart Phillips, director of McMaster University’s Centre for Nutrition, Exercise, and Health Research in Ontario, Canada. For a 150-pound athlete, 1.6 grams per kilo works out to about 109 grams of protein daily, or 2.5 chicken breasts. “That’s not to say you can’t consume more,” Phillips says, counter to theories that eating too much protein can cause kidney failure or bone loss. “But after about 1.8 grams per kilogram per day, the benefits start to level off.”

… I suspect Arsenal are probably furthest along in football research and they should be, as StatDNA had the biggest head start (outside of the Bolton group that dissipated). I have met a number of Arsenal’s top level people on the analysis side and they are wicked smaht. It is annoying when your favorite team is also the team that would need your skill set the least, but thems the breaks. … All this stuff is supposed to be secret. If you are developing edges inside a club, you should NOT be talking about it. That makes it a whole lot of guess work on my part to say who is doing what well.

… “I have a lot of admiration for those who build new tactical systems, but I always thought the most important thing a good coach must do is build the team around the characteristics of his players. So I told the players that I trusted them and would speak very little of tactics.” In short, he has let the players get on with it.

It might be surprising that his initial approach was quite so hands-off, but it really shouldn’t be. At 64, Ranieri has shown he’s modern enough to appreciate what football has become. For while the cult of the manager exists in the mind’s eye, it’s little more than an illusion – a conjurer’s trick in the era of player power.

… Unless you are directly in the data business (e.g., credit scores or audience measurement), chances are data is not part of your business model, at least in terms of value received by the customer or revenue generated from the customer. Companies use data to optimize their own operations, but they rarely share any of that data with the customer.

That’s going to change, big time. As data generation and capture becomes both cheaper and more pervasive, new business models will emerge where more and more of the value delivered to the customer resides in the data rather than the product or service.