Pantelis Theodoros Nikolaidis, Beat Knechtle, Filipe Clemente and Gema Torres-Luque
Study aim: The aim of the present study was twofold: firstly, to examine the effect of age on a 20 m sprint performance; and secondly, to establish normative data for the 20 m sprint performance by age in football players.
Material and methods: The anthropometric characteristics of 474 football players (aged 16.81 ± 5.35 yrs, range 9.02–35.41 yrs) were examined and their 20 m sprint performance (with 0–10 and 10–20 m splits) was monitored by a photocell system (Brower Timing Systems, Utah, USA).
Results: A one-way analysis of variance revealed significant differences between the yearly age groups with regards to the sprint time (p < 0.001, η2 = 0.584), as well as the 0–10 m (p < 0.001, η2 = 0.361) and 10–20 m split times (p < 0.001, η2 = 0.635). The older groups scored better than the younger groups. The time attained in the 20 m sprint, and the 0–10 m and 10–20 m splits correlated moderately to largely with the athlete’s age (r = –0.53, –0.40 and –0.57, respectively, p < 0.001).
Conclusions: In summary, the speed ability of the football players improved with age until 15 years old, where it reached its peak. On the other hand, the other age groups U16 to U35 revealed no major differences in the speed over a 20 m sprint. The reference values presented in this study might help football coaches and fitness trainers in monitoring training and in the selection of players. Moreover, since this is the first study of this kind to compare adult age groups, sport scientists focusing on relevant topics might use it as a reference in future studies.
Filipe Manuel Clemente, Fernando Manuel Lourenço Martins, Pantelis Theodoros Nikolaidis and Rui Sousa Mendes
Study aim: The aim of this study was to evaluate the association between objectively measured daily physical activity (PA) and body fat mass (BF) and body mass index (BMI). A further aim was to analyse the variance of PA between quartiles of BF and BMI.
Material and methods: A cross-sectional, observational study of 126 university students (53 males aged 20.46 ± 2.04 years and 73 female aged 19.69 ± 1.32 years) was conducted.
Results: The female participants and PA characteristics explain 57.10% of BF variance and the model was statistically significant (F(6, 875) = 196.38; p = 0.001). BMI was also included in the model. Standard binary logistic regression was used to test the hypothesis that female sex and PA characteristics can influence overweight. The full model containing all variables was statistically significant (G2(6) = 58.598, p-value = 0.001). Analysis of variance between BF quartiles revealed statistically significant differences in male participants in light PA (p = 0.001; ES = 0.09), moderate PA (p = 0.001; ES = 0.042) and vigorous PA (p = 0.001; ES = 0.130).
Conclusions: The statistical model in the regression analysis suggests that low and vigorous levels of PA explain 57% of BF variance in female participants.
Beat Knechtle, Caio Victor de Sousa, Herbert Gustavo Simões, Thomas Rosemann and Pantelis Theodoros Nikolaidis
The aim of this study was to examine the effects of the performance level and race distance on pacing in ultra-triathlons (Double, Triple, Quintuple and Deca), wherein pacing is defined as the relative time (%) spent in each discipline (swimming, cycling and running). All finishers (n = 3,622) of Double, Triple, Quintuple and Deca Iron ultra-triathlons between 1985 and 2016 were analysed and classified into quartile groups (Q1, Q2, Q3 and Q4) with Q1 being the fastest and Q4 the slowest. Performance of all non-finishers (n = 1,000) during the same period was also examined. Triple and Quintuple triathlons (24.4%) produced the highest rate of non-finishers, and Deca Iron ultra-triathlons produced the lowest rate (18.0%) (χ2 = 12.1, p = 0.007, φC = 0.05). For the relative swimming and cycling times (%), Deca triathletes (6.7 ± 1.5% and 48.8 ± 4.9%, respectively) proved the fastest and Double (9.2 ± 1.6% and 49.6 ± 3.6%) Iron ultra-triathletes were the slowest (p < 0.008) with Q4 being the fastest group (8.3 ± 1.6% and 48.8 ± 4.3%) and Q1 the slowest one (9.5 ± 1.5% and 50.9 ± 3.0%) (p < 0.001). In running, Double triathletes were relatively the fastest (41.2 ± 4.0%) and Deca (44.5 ± 5.4%) Iron ultra-triathletes the slowest (p < 0.001) with Q1 being the fastest (39.6 ± 3.3%) and Q4 the slowest group (42.9 ± 4.7%) (p < 0.001). Based on these findings, it was concluded that the fastest ultra-triathletes spent relatively more time swimming and cycling and less time running, highlighting the importance of the role of the latter discipline for the overall ultra-triathlon performance. Furthermore, coaches and ultra-triathletes should be aware of differences in pacing between Double, Triple, Quintuple and Deca Iron triathlons.
Filipe Manuel Clemente, Adam Owen, Jaime Serra-Olivares, Pantelis Theodoros Nikolaidis, Cornelis M. I. van der Linden and Bruno Mendes
The purpose of this study was to analyze the day-to-day variance of a typical weekly external training workload of two professional soccer teams from different countries. Twenty-nine players from two professional teams from Portugal and the Netherlands participated in this study. The players’ external load was monitored for 7 weeks, by means of portable GPS devices (10 Hz, JOHAN, Noordwijk, Netherlands). Results revealed that match day -1 (MD-1), i.e. the training day before a match, had significantly (p = 0.001) less training volume (4584.50 m) than the other days. MD-5 (training five days before a match), MD-4 (four days before a match) and MD-3 (three days before a match) were the most intense (390.83, 176.90 and 247.32 m of sprinting distance, respectively) and with large volume (7062.66, 6077.30 and 6919.49 m, respectively). Interestingly, significant differences were found between clubs of different countries (p < 0.05) with the Portuguese team showing significantly higher intensity (sprinting distance) and volume (total distance) in all days with exception of MD-1 than the Dutch team. The results of this study possibly allow for the identification of different training workloads and tapering strategies between countries in relation to volume and intensity. It should be noted, however, that both clubs used a significant tapering phase in the last two days before the competition in an attempt to reduce residual fatigue accumulation.