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Friday, March 29, 2013

Recent studies with practical applications to elite football





 

Testing visual elements at Panathinaikos FC Performance Lab (March 2008)

 
Soichi (2013). Peripheral visual perception during exercise: why we cannot see. Exercise & Sport Sciences Reviews 41(2): 87-92

Faculty of Sports and Health Science, Fukuoka University,  Japan

Peripheral visual perception may be relevant to performance in sports. Peripheral visual perception seems to be impaired during strenuous exercise. The hypothesis proposed is that a decrease in cerebral oxygenation is associated with impairment in peripheral visual perception during strenuous exercise. Recent behavioral and physiological data are presented to support the hypothesis.

Free access
http://journals.lww.com/acsm-essr/Fulltext/2013/04000/Peripheral_Visual_Perception_During_Exercise___Why.4.aspx


Lehr et al (2013). Field-expedient screening and injury risk algorithm categories as predictors of noncontact lower extremity injury. Scand J Med Sci Sports March 20, [Epub ahead of print]

Department of Physical Therapy, Lebanon Valley College, Pennsylvania, USA.

In athletics, efficient screening tools are sought to curb the rising number of noncontact injuries and associated health care costs. The authors hypothesized that an injury prediction algorithm that incorporates movement screening performance, demographic information, and injury history can accurately categorize risk of noncontact lower extremity (LE) injury. One hundred eighty-three collegiate athletes were screened during the preseason. The test scores and demographic information were entered into an injury prediction algorithm that weighted the evidence-based risk factors. Athletes were then prospectively followed for noncontact LE injury. Subsequent analysis collapsed the groupings into two risk categories: Low (normal and slight) and High (moderate and substantial). Using these groups and noncontact LE injuries, relative risk (RR), sensitivity, specificity, and likelihood ratios were calculated. Forty-two subjects sustained a noncontact LE injury over the course of the study. Athletes identified as High Risk (n = 63) were at a greater risk of noncontact LE injury (27/63) during the season [RR: 3.4 95% confidence interval 2.0 to 6.0].

Conclusion
These results suggest that an injury prediction algorithm composed of performance on efficient, low-cost, field-ready tests can help identify individuals at elevated risk of noncontact LE injury.



Meister et al. (2013). Indicators for high physical strain and overload in elite football players. Scand J Med Sci Sports March 20, [Epub ahead of print]

Institute of Sports and Preventive Medicine (FIFA, Medical Centre of Excellence), Saarland University, Saarbrücken, Germany Institute of Sports Medicine, University Paderborn, Paderborn, GermanyUniversity of Basel, Institute of Exercise and Health Sciences, Basel, Switzerland.


Laboratory, psychological and performance parameters as possible indicators of physical strain and overload during highly demanding competition phases were evaluated in elite male football players. In two studies with the same objective, periods of high (HE: >270 min during 3 weeks before testing) and low (LE: <270 min) match exposure were compared over the course of an entire season. In study 1 (n=88 players of the first and second German leagues; age: 25.6±4.3 years; body mass index (BMI): 23.2±1.0 kg/m(2) ), blood count, CK, urea, uric acid, CRP and ferritin were determined. In study 2, 19 players of the third German league and the highest under-19 league (age: 19.7±2.8 years; BMI: 22.8±1.7 kg/m(2) ) were screened for individual vertical jump height, maximal velocity and by the Recovery-Stress-Questionnaire for Athletes (REST-Q Sport). The mean differences in exposure times were 180 min (study 1: quartiles: 105, 270 min) and 247 min (study 2: 180, 347 min), respectively. Significant differences were found neither in blood parameters (study 1; P>0.36) nor in physiological testing results or in REST-Q scores (study 2; P>0.20).

Conclusion
A 3-week period of high match exposure in elite football players does not affect laboratory, psychometric and performance parameters.


Casals and Martinez (2013). Modelling player performance in basketball through mixed models. Int J Perfom Anal Sport, 13(1): 64-82

University of Wales, Cardiff

The aims of this study were to identify variables which may potentially influence player performance, and to implement a statistical model to study their relative contribution in order to explain two outcomes: points and win score. We used all the possible variables affecting player performance creating a comprehensive database from two sources of statistical information about the NBA 2007 regular season: www.basketball-reference.com and www.nbastuffer.com. The data employed for the analysis were composed of 2187 cases (27 players * 81 games), having followed a filtering process. We dealt with a balanced study design with repeated measurements given that each player was observed the same number of games, and therefore the player was considered as a random effect. We carried out mixed models to quantify the variability in points and win score among players. Minutes played, the usage percentage and the difference of quality between teams were the main factors for variations in points made and win score. The interaction between player position and age was important in win score.

Conclusions
We encourage managers and coaches of sports teams to choose appropriate methods according to their aims. Future research should take into consideration the use of models with random effects on players' characteristics.


Fradua et al (2013). Designing small-sided games for training tactical aspects in soccer: extrapolating pitch sizes from full-size professional matches. J Sport Sci 31(6): 573-581.

University of Granada, Physical Education and Sport , Granada , Spain.

The aims of this study were to examine the 1) individual playing area, 2) length and width of the rectangle encompassing the individual playing area and 3) distance between the goalkeepers and their nearest team-mates during professional soccer matches and compare these to previously reported pitch sizes for small-sided games (SSGs). Data were collected from four Spanish La Liga matches of the 2002-03 season, and notated post-event using the Amisco® system. The pitch sizes obtained from real matches were smaller and different from those used previously for SSGs. In addition, the current pitch sizes show significant (P < 0.001) effect of ball location in all variables examined. For example, overall individual playing area (F [5, 2562] = 19.99, P < 0.001, η(2 )= 0.04) varied significantly across six different zones of the pitch. Based on these empirical results, pitch sizes with individual playing areas ranging from 65 m(2) to 110 m(2) and length to width ratio of 1:1 and 1:1.3 are generally recommended for training tactical aspects according to different phases of play.

Conclusion
It is possible to design SSGs with a more valid representation of the tactical conditions experienced in full-size matches and their use may improve the training effect of tactical aspects of match performance in soccer.



Eynon et al. (2013). ACTN3 R577X polymorphism and team-sport performance: a study involving three European cohorts. J Sci Med Sport March 20 [Epub ahead of print]

School of Sport and Exercise Sciences, Victoria University, Australia; Institute of Sport, Exercise and Active Living (ISEAL), Victoria University, Australia.


We compared the genotype and allele frequencies of the ACTN3 R577X (rs1815739) polymorphisms between team-sport athletes (n=205), endurance athletes (n=305), sprint/power athletes (n=378), and non-athletic controls (n=568) from Poland, Russia and Spain; all participants were unrelated European men. Genomic DNA was extracted from either buccal epithelium or peripheral blood using a standard protocol. Genotyping was performed using several methods, and the results were replicated following recent recommendations for genotype-phenotype association studies. Genotype distributions of all control and athletic groups met Hardy-Weinberg equilibrium (all p>0.05). Team-sport athletes were less likely to have the 577RR genotype compared to the 577XX genotype than sprint/power athletes [odds ratio: 0.58, 95% confidence interval: 0.34-0.39, p=0.045]. However, the ACTN3 R577X polymorphism was not associated with team-sports athletic status, compared to endurance athletes and non-athletic controls. Furthermore, no association was observed for any of the genotypes with respect to the level of competition (elite vs. national level).

Conclusion
The ACTN3 R577X polymorphism was not associated with team-sport athletic status, compared to endurance athletes and non-athletic controls, and the observation that the 577RR genotype is overrepresented in power/sprint athletes compared with team-sport athletes needs to be confirmed in future studies.


Sparks and Close (2013). Validity of a portable urine refractometer: the effects of sample freezing. J Sports Sci 31(7): 745-749.

Department of Sport and Physical Activity , Edge Hill University, UK.

The use of portable urine osmometers is widespread, but no studies have assessed the validity of this measurement technique. Furthermore, it is unclear what effect freezing has on osmolality. One-hundred participants of mean (±SD) age 25.1 ± 7.6 years, height 1.77 ± 0.1 m and weight 77.1 ± 10.8 kg provided single urine samples that were analysed using freeze point depression (FPD) and refractometry (RI). Samples were then frozen at -80°C (n = 81) and thawed prior to re-analysis. Differences between methods and freezing were determined using Wilcoxon's signed rank test. Relationships between measurements were assessed using intraclass correlation coefficients (ICC) and typical error of estimate (TE). Osmolality was lower (P = 0.001) using RI (634.2 ± 339.8 mOsm · kgH2O(-1)) compared with FPD (656.7 ± 334.1 mOsm · kgH2O(-1)) but the TE was trivial (0.17). Freezing significantly reduced mean osmolality using FPD (656.7 ± 341.1 to 606.5 ± 333.4 mOsm · kgH2O(-1); P < 0.001), but samples were still highly related following freezing (ICC, r = 0.979, P < 0.001, CI = 0.993-0.997; TE = 0.15; and r=0.995, P < 0.001, CI = 0.967-0.986; TE = 0.07 for RI and FPD respectively). Despite mean differences between methods and as a result of freezing, such differences are physiologically trivial.

Conclusion
The use of RI appears to be a valid measurement tool to determine urine osmolality.


Source: Pubmed

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