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Ric Lovell and Grant Abt

Purpose:

To report the intensity distribution of Premier League soccer players’ external loads during match play, according to recognized physiological thresholds. The authors also present a case in which individualized speed thresholds changed the interpretation of time–motion data.

Method:

Eight outfield players performed an incremental treadmill test to exhaustion to determine the running speeds associated with their ventilatory thresholds. The running speeds were then used to individualize time–motion data collected in 5 competitive fixtures and compared with commonly applied arbitrary speed zones.

Results:

Of the total distance covered, 26%, 57%, and 17% were performed at low, moderate, and high intensity, respectively. Individualized time– motion data identified a 41% difference in the high-intensity distance covered between 2 players of the same positional role, whereas the player-independent approach yielded negligible (5–7%) differences in total and high-speed distances covered.

Conclusions:

The authors recommend that individualized speed thresholds be applied to time–motion-analysis data in synergy with the traditional arbitrary approach.

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Jeremy Williams, Grant Abt and Andrew E. Kilding

Purpose:

To determine the effects of acute short-term creatine (Cr) supplementation on physical performance during a 90-min soccer-specific performance test.

Methods:

A double-blind, placebo-controlled experimental design was adopted during which 16 male amateur soccer players were required to consume 20 g/d Cr for 7 d or a placebo. A Ball-Sport Endurance and Speed Test (BEAST) comprising measures of aerobic (circuit time), speed (12- and 20-m sprint), and explosive-power (vertical jump) abilities performed over 90 min was performed presupplementation and postsupplementation.

Results:

Performance measures during the BEAST deteriorated during the second half relative to the first for both Cr (1.2–2.3%) and placebo (1.0–2.2%) groups, indicating a fatigue effect associated with the BEAST. However, no significant differences existed between groups, suggesting that Cr had no performance-enhancing effect or ability to offset fatigue. When effect sizes were considered, some measures (12-m sprint, –0.53 ± 0.69; 20-m sprint, –0.39 ± 0.59) showed a negative tendency, indicating chances of harm were greater than chances of benefit.

Conclusions:

Acute short-term Cr supplementation has no beneficial effect on physical measures obtained during a 90-min soccer-simulation test, thus bringing into question its potential as an effective ergogenic aid for soccer players.

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Ibrahim Akubat, Steve Barrett and Grant Abt

Purpose:

This study aimed to assess the relationships of fitness in soccer players with a novel integration of internal and external training load (TL).

Design:

Ten amateur soccer players performed a lactate threshold (LT) test followed by a soccer simulation (Ball-Sport Endurance and Sprint Test [BEAST90mod]).

Methods:

The results from the LT test were used to determine velocity at lactate threshold (vLT), velocity at onset of blood lactate accumulation (vOBLA), maximal oxygen uptake (VO2max), and the heart rate–blood lactate profile for calculation of internal TL (individualized training impulse, or iTRIMP). The total distance (TD) and high intensity distance (HID) covered during the BEAST90mod were measured using GPS technology that allowed measurement of performance and external TL. The internal TL was divided by the external TL to form TD:iTRIMP and HID:iTRIMP ratios. Correlation analyses assessed the relationships between fitness measures and the ratios to performance in the BEAST90mod.

Results:

vLT, vOBLA, and VO2max showed no significant relationship to TD or HID. HID:iTRIMP significantly correlated with vOBLA (r = .65, P = .04; large), and TD:iTRIMP showed a significant correlation with vLT (r = .69, P = .03; large).

Conclusions:

The results suggest that the integrated use of ratios may help in the assessment of fitness, as performance alone showed no significant relationships with fitness.

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Dan Weaving, Phil Marshall, Keith Earle, Alan Nevill and Grant Abt

Purpose:

This study investigated the effect of training mode on the relationships between measures of training load in professional rugby league players.

Methods:

Five measures of training load (internal: individualized training impulse, session rating of perceived exertion; external—body load, high-speed distance, total impacts) were collected from 17 professional male rugby league players over the course of two 12-wk preseason periods. Training was categorized by mode (small-sided games, conditioning, skills, speed, strongman, and wrestle) and subsequently subjected to a principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subjected to a varimax rotation.

Results:

Small-sided games and conditioning extracted 1 principal component, explaining 68% and 52% of the variance, respectively. Skills, wrestle, strongman, and speed extracted 2 principal components each explaining 68%, 71%, 72%, and 67% of the variance, respectively.

Conclusions:

In certain training modes the inclusion of both internal and external training-load measures explained a greater proportion of the variance than any 1 individual measure. This would suggest that in training modes where 2 principal components were identified, the use of only a single internal or external training-load measure could potentially lead to an underestimation of the training dose. Consequently, a combination of internal- and external-load measures is required during certain training modes.

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Richard J. Taylor, Dajo Sanders, Tony Myers, Grant Abt, Celia A. Taylor and Ibrahim Akubat

Purpose: To identify the dose-response relationship between measures of training load (TL) and changes in aerobic fitness in academy rugby union players. Method: Training data from 10 academy rugby union players were collected during a 6-wk in-season period. Participants completed a lactate-threshold test that was used to assess VO2max, velocity at VO2max, velocity at 2 mmol/L (lactate threshold), and velocity at 4 mmol/L (onset of lactate accumulation; vOBLA) as measures of aerobic fitness. Internal-TL measures calculated were Banister training impulse (bTRIMP), Edwards TRIMP, Lucia TRIMP, individualized TRIMP (iTRIMP), and session RPE (sRPE). External-TL measures calculated were total distance, PlayerLoad™, high-speed distance >15 km/h, very-high-speed distance >18 km/h, and individualized high-speed distance based on each player’s vOBLA. Results: A second-order-regression (quadratic) analysis found that bTRIMP (R 2 = .78, P = .005) explained 78% of the variance and iTRIMP (R 2 = .55, P = .063) explained 55% of the variance in changes in VO2max. All other HR-based internal-TL measures and sRPE explained less than 40% of variance with fitness changes. External TL explained less than 42% of variance with fitness changes. Conclusions: In rugby players, bTRIMP and iTRIMP display a curvilinear dose-response relationship with changes in maximal aerobic fitness.

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Dajo Sanders, Grant Abt, Matthijs K.C. Hesselink, Tony Myers and Ibrahim Akubat

Purpose:

To assess the dose-response relationships between different training-load methods and aerobic fitness and performance in competitive road cyclists.

Methods:

Training data from 15 well-trained competitive cyclists were collected during a 10-wk (December–March) preseason training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas-exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister TRIMP, Edwards TRIMP, individualized TRIMP (iTRIMP), Lucia TRIMP (luTRIMP), and session rating of perceived exertion (sRPE). External load was measured using Training Stress Score (TSS).

Results:

Large to very large relationships (r = .54–.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol/L) were observed for all training-load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = .81 [95% CI .51–.93, r = .77 [95% CI .43–.92]) and TSS (r = .75 [95% CI .31–.93], r = .79 [95% CI .40–.94]). The strongest dose-response relationships with changes in the 8MT test were observed for iTRIMP (r = .63 [95% CI .17–.86]) and luTRIMP (r = .70 [95% CI .29–.89).

Conclusions:

Training-load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling.

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Dan Weaving, Clive Beggs, Nicholas Dalton-Barron, Ben Jones and Grant Abt

Purpose: To discuss the use of principal-component analysis (PCA) as a dimension-reduction and visualization tool to assist in decision making and communication when analyzing complex multivariate data sets associated with the training of athletes. Conclusions: Using PCA, it is possible to transform a data matrix into a set of orthogonal composite variables called principal components (PCs), with each PC being a linear weighted combination of the observed variables and with all PCs uncorrelated to each other. The benefit of transforming the data using PCA is that the first few PCs generally capture the majority of the information (ie, variance) contained in the observed data, with the first PC accounting for the highest amount of variance and each subsequent PC capturing less of the total information. Consequently, through PCA, it is possible to visualize complex data sets containing multiple variables on simple 2D scatterplots without any great loss of information, thereby making it much easier to convey complex information to coaches. In the future, athlete-monitoring companies should integrate PCA into their client packages to better support practitioners trying to overcome the challenges associated with multivariate data analysis and interpretation. In the interim, the authors present here an overview of PCA and associated R code to assist practitioners working in the field to integrate PCA into their athlete-monitoring process.