23 research outputs found

    Effects of uphill treadmill gradient on running kinematic variables.

    No full text
    <p>Values represent mean and standard deviation for all subjects (<i>n</i> = 18). Percentage difference between slope conditions are presented: significant differences between conditions are highlighted in <b>bold</b>.</p

    R-R intervals whilst running at 0%, 2% and 7% slopes.

    No full text
    <p>The graph represents the R-R variation throughout the duration of each condition.</p

    Changes in kinematic and GRF variables when running uphill (on ground).

    No full text
    <p>Values represent mean and standard deviation for all subjects (<i>n</i> = 18). Percentage difference between slope conditions are presented: significant differences between conditions are highlighted in <b>bold</b>.</p

    Position of the force platforms with relation to the runway.

    No full text
    <p>Position of the force platforms with relation to the runway.</p

    Rectified and smoothed EMG curves indicate electrical activity of the lower limb muscle groups at different slopes 0%, 2% and 7% and during different phases of gait (stance and swing).

    No full text
    <p>Abbreviations: tibialis anterior (TA), vastus medialis (VM), rectus femoris (RF), gluteus major (MG), biceps femoris (BF) and gastrocnemius medialis (GM).</p

    Effects of uphill treadmill running on cardiovascular variables.

    No full text
    <p>Values are presented as mean and standard deviation. Abbreviations: heart rate (HR), R-R beat to beat interval (R-R), low-frequency heart rate variability (LF), high-frequency heart rate variability (HF).</p

    Relative age effect and second-tiers: No second chance for later-born players

    Get PDF
    <div><p>The main objective of this research was to determine the existence of relative age effect (RAE) in five European soccer leagues and their second-tier competitions. Even though RAE is a well-known phenomenon in professional sports environments it seems that the effect does not decline over the years. Moreover, additional information is required, especially when taking into account second-tier leagues. Birthdates from 1,332 first-tier domestic players from France, England, Spain, Germany and Italy and birthdates from 1,992 second-tier domestic players for the 2014/2015 season were taken for statistical analysis. In addition to standard statistical tests, the data were analyzed using econometric techniques for count data using Poisson and negative binomial regressions. The results obtained confirmed a biased distribution of birthdates in favor of players born earlier in the calendar year. For all of the five first-tier soccer leagues there was an unequal distribution of birthdates (France <i>χ</i><sup>2</sup> = 40.976, <i>P</i><0.001; England <i>χ</i><sup>2</sup> = 21.892, <i>P</i> = 0.025; Spain <i>χ</i><sup>2</sup> = 24.690, <i>P</i> = 0.010; Germany <i>χ</i><sup>2</sup> = 22.889, <i>P</i> = 0.018; Italy <i>χ</i><sup>2</sup> = 28.583, <i>P</i> = 0.003). The results for second-tier leagues were similar (France <i>χ</i><sup>2</sup> = 46.741, <i>P</i><0.001; England <i>χ</i><sup>2</sup> = 27.301, <i>P</i> = 0.004; Spain <i>χ</i><sup>2</sup> = 49.745, <i>P</i><0.001; Germany <i>χ</i><sup>2</sup> = 30.633, <i>P</i> = 0.001; Italy <i>χ</i><sup>2</sup> = 36.973, <i>P</i><0.001). Econometric techniques achieved similar results: estimated effect of month of birth, i.e., long-term RAE on players’ representativeness, is negative (statistically significant at the 1% level). On average, one month closer to the end of the year reduces the logs of expected counts of players by 6.9%. Assuming this effect as linear, being born in the month immediately before the cut-off date (i.e., December/August), reduces the logs of expected counts of players by approximately 75.9%. Further, I<sub>D</sub> (index of discrimination, that is, the ratio between the expected counts of players born in the middle of the first and the twelfth month of the selection year) is 2.13 and 2.22 for the first- and second-tier, respectively. In other words, in the top five European first-tier and second-tier leagues, one should expect the number of players born in the first month of the calendar year to be twice the number of those born in the last month. The RAE in the second-tiers is the same as in the first-tiers, so it appears that there is no second chance for later born players. This reduces the chances to recover talented players discarded in youth simply because of lower maturity.</p></div

    RAE on frequency of month of birth in the first- and second-tiers of the five most important European leagues.

    No full text
    <p>RAE on frequency of month of birth in the first- and second-tiers of the five most important European leagues.</p

    Counts and <i>χ</i><sup>2</sup> test of first- and second-tier players for the overall sample and for each country separately.

    No full text
    <p>Counts and <i>χ</i><sup>2</sup> test of first- and second-tier players for the overall sample and for each country separately.</p

    Soccer players’ birth dates monthly distributions for first- and second-tiers of the top five European leagues.

    No full text
    <p>Soccer players’ birth dates monthly distributions for first- and second-tiers of the top five European leagues.</p
    corecore