Applied Sports Science newsletter – September 30, 2015

Cleveland Cavaliers point guard Kyrie Irving is planning to alter his game in an attempt to reduce the injuries that so far have been a defining part of his career.

Recovering from a broken kneecap suffered during the NBA Finals, Irving said Monday that he spent part of the offseason reviewing film and looking for ways to avoid the hard falls that have contributed to a string of injuries over the first four years of his career.

… “It’s perfect for basketball. I saw Steve Nash a couple days ago and was telling him he should be proud of the facility here,” said Casey, name-dropping the two-time NBA MVP who sits on the Fortius athlete advisory board.

Few studies have investigated the effects of exercise training on sleep physiology in well-trained athletes. We investigated changes in sleep markers, mood state and exercise performance in well-trained cyclists undergoing short-term intensified training and carbohydrate nutritional intervention. Thirteen highly-trained male cyclists (age: 25 ± 6y, VO2max: 72 ± 5 ml/kg/min) participated in two 9-day periods of intensified training while undergoing a high (HCHO) or moderate (CON) carbohydrate nutritional intervention before, during and after training sessions. Sleep was measured each night via wristwatch actigraphy. Mood state questionnaires were completed daily. Performance was assessed with maximal oxygen uptake (VO2Max). Percentage sleep time fell during intensified training (87.9 ± 1.5 to 82.5 ± 2.3%; p < 0.05) despite an increase in time in bed (456 ± 50 to 509 ± 48 min; p = 0.02). Sleep efficiency decreased during intensified training (83.1 ± 5.3 to 77.8 ± 8.6%; p < 0.05). Actual sleep time was significantly higher in CON than HCHO throughout intensified training. Mood disturbance increased during intensified training and was higher in CON than HCHO (p < 0.05). Performance in the VO2Max exercise protocol fell significantly with intensified training. The main findings of this study were that 9-days of intensified training in highly-trained cyclists resulted in significant and progressive declines in sleep quality, mood state and maximal exercise performance.

Heart rate, time-motion, and body impacts when changing the number of teammates and opponents in soccer small-sided games. J Strength Cond Res 29(10): 2723-2730, 2015-The purpose of this study was to determine the internal (heart rate) and external load (body load, distance covered, and exertion index) during different types of unbalanced soccer small-sided games (SSGs) in professional (PRO) and amateur (AMA) players. In 2 separated sessions (PRO and AMA), participants played 3 SSG formats (4vs3, 4vs5, and 4v7). Data were analyzed from the fixed team’s perspective (4vsX) according to the number of opponents (3, 5 and 7) and from the variable team (3 + Xvs4) according to the teammates (without teammates, 2 and 4 teammates). The time-motion and body impact data were collected using a nondifferential global positioning system with integrated heart rate measurement. Differences in internal and external workload between the game formats were compared using Cohen’s dunb effect sizes with 95% confidence intervals. Results reveal that the higher the number of players involved in the task, the lower the internal and external workload. The analysis also showed different teammates and opposition-related trends that need to be considered when planning and monitoring training performance. Playing in low-inferiority (4vs3 and 4vs5) had higher physiologic impact to players than the other higher unbalanced situations. This evidence was similar to both PRO and AMA players; however, the PRO presented higher physical and lower physiological responses across games. Our results suggest that coaches should consider the usage of unbalanced SSG formats to simultaneously facilitate the emergence of defensive and offensive proficient scenarios also representing opportunities to increase the practice workload.

Following on from the success of the first ever youth development phase conference in July, The FA has successfully hosted another inaugural phase-specific conference event – this time dedicated to the 5-11 age-group.

The aim of this review and meta-analysis was to critically determine the possible effects of different cooling applications, compared to non-cooling, passive post-exercise strategies, on recovery characteristics after various, exhaustive exercise protocols up to 96 hours (hrs). … Pooled data from 27 articles revealed, that cooling and especially cold water immersions affected the symptoms of DOMS significantly, compared to the control conditions after 24 hrs recovery, with a standardized mean difference (Hedges’ g) of -0.75 with a 95% confidence interval (CI) of -1.20 to -0.30. This effect remained significant after 48 hrs (Hedges’ g: -0.73, 95% CI: -1.20 to -0.26) and 96 hrs (Hedges’ g: -0.71, 95% CI: -1.10 to -0.33). A significant difference in lowering the symptoms of RPE could only be observed after 24 hrs of recovery, favouring cooling compared to the control conditions (Hedges’ g: -0.95, 95% CI: -1.89 to -0.00). There was no evidence, that cooling affects any objective recovery variable in a significant way during a 96 hrs recovery period.

The weather in Iceland can be hugely unpredictable and changes the way training is conducted. The obvious drive in Iceland toward the building of better sporting facilities has altered that reality dramatically.

The changes to how football is played in Iceland have been enormous in the last 15-20 years. At the end of the 20th century players were used to training on gravel pitches in the off-season, all the way up to just before the season started in May. Two full-sized pitches with artificial turf were built in 1985 and 1992 with grass similar to the one used in the Premier League at the same time by Luton Town. This artificial turf was subsequently banned by the English Football Association in 1988 but was still used in Iceland. The Nordic nation’s facilities were primative at best.

It was not until the start of the 21st century that new full-sized pitches were built, with newer models of artificial turf. The first full sized hall was built in 2000 and the first outdoor, all-weather pitch in 2001. Today, Iceland boasts seven full sized halls, four smaller ones and 22 pitches outdoors. Furthermore, there are close to 150 smaller pitches around the country.

They were just unpacking the GPS devices from the boxes in Penn’s basketball offices. For their cars? No, for practice. Every Quakers men’s hoops player is going to get a tracking device for the back of his jersey. It weighs about an ounce.

“I don’t know if we’re allowed to wear them in games or not,” said men’s basketball coach Steve Donahue. “We’re trying to figure that out.”

Miles toiled will be added to a database that closely monitors sleep and nutrition and – here’s where things really get state-of-the-art – individual traits such as load and explode and drive abilities. All Penn athletes are now tracked for all that.

… How is Big Data helping your team’s strategy?

Big Data is helping on both the business and the basketball side. On the business side, we are able to identify behaviours before they even happen. You could be sitting at a game and tweet that you had cold pizza and we’re able to pick that up. If you’re coming to a game with your family and you have no parking, there’s excess VIP parking. It’s the end of the third quarter and your kids love hot dogs but they’ll go stale at the end of the game, we have excess hot dogs. We can tell if you’re walking to a franchise store or away from a store. On the basketball side, we have six overhead cameras at every game. That’s creating 30-40 GB of information and you can look at time and space like never before.

For example, we have a player called Rudy Gay. If you look at one data point, it says he averages 23 points per game and then you’d say give him a contract. If you looked at his shooting accuracy of 30%, you’d say don’t give him a contract. What we did was look at spatial data. We came to the conclusion that with the right spacing, he’d be the second or third option on the court but not the first.

In the fast-paced tech world, there is a race to develop the best software that collects more and more data.

In athletics, this manifests itself through tracking systems like those that athletes wear to gauge even the exact steps they take. However, those devices don’t measure anything about how an athlete actually feels.

Enter John Cone, who wants to offer athletic teams a way to track subjective measures alongside objective ones.

This project is based in the Football Exchange in the world-leading Research Institute for Sport and Exercise Sciences in collaboration with a leading Premier League Football Club. In a recent research assessment 97% of our research activity was rated as world-leading or internationally excellent and of the 1,911 submissions to the assessment in all areas from all Universities in the UK, we were ranked 11th nationally for overall research quality. The academic supervisors are a multi-disciplinary team of experts in biomechanics (Dr Mark Robinson), mathematics (Prof. Paulo Lisboa) and exercise physiology (Prof. Warren Gregson) who all have considerable expertise in football research.

Project background

A precise combination of training load and volume is essential to provide a beneficial training effect whilst avoiding overload injuries. In many sports including soccer, GPS and accelerometer technology is used to quantify the external load placed on players and this has been successfully used to monitor metabolic loads and predict soft-tissue injury. The mechanical load experienced, which represents the cumulative stresses experienced by bones, cartilage, muscles, tendons and ligaments, is, in contrast to the metabolic load, comparatively unknown. The ability to use wearable sensors to estimate cumulative mechanical load has significant implications for training monitoring and prescription in football. The aim of this project is to critically evaluate the means of quantifying cumulative soft-tissue loading.

Louis van Gaal’s philosophy appears to be paying off as new figures reveal that only Bournemouth have covered more ground.

Sitting in the pub the other night reflecting on the pool stages of the Rugby World Cup the conversation turned to nerds. An acquaintance was telling the story of his computer-savvy mate who had worked out the seating plan for his wedding via something called an optimisation algorithm.

Basically he tapped out the guest list, separated the drinkers from the teetotallers, the gigglers from the serious-minded, the sporty from the unathletic and the single from the married, pressed a button and – hey presto! – the algorithm did the rest.

Apparently the party was a triumph. Everyone enjoyed themselves and were suitably amused when the groom confessed in his speech how he had rigged the table plans for the greater good. Which begs the obvious next question: is there not a rugby-loving nerd out there who can iron out the flawed World Cup fixture schedule?

Physical and psychological differences related to birthdate amongst athletes of the same selection year have been characterised as the “relative age effects” (RAEs). RAEs have been identified in a variety of sports, both at youth and adult level, and are linked with dropout of athletes and a reduction of the talent pool. This study examined the existence, mechanisms and possible solutions to RAEs using qualitative methodology. Seven experts in the field of talent identification and development were interviewed. Inductive analysis of the data showed that, while there was mixed evidence for the existence of RAEs across sports, the eradication of RAEs was attributed to controllable features of the development environment. The factors reported included the structure of “categories” used to group athletes within the sport (e.g. age, weight, size, skills), recognition and prioritisation of long-term development over “short term win focus.” Education of relevant parties (e.g. coaches, scouts, clubs) about RAEs and the nature of “talent” within a long-term context was suggested, along with careful consideration of the structure of the development environment (e.g. delayed selection, provision for late developers, focus on skills not results, use of challenge). Implications for research and practice are discussed.