Interpreting magnitude of change in strength and conditioning: Effect size selection, threshold values and Bayesian updating.

The magnitude of change following strength and conditioning (S&C) training can be evaluated comparing effect sizes to thresholds. This study conducted a series of meta-analyses and compiled results to identify thresholds specific to S&C, and create prior distributions for Bayesian updating. Pre- and post-training data from S&C interventions were translated into standardised mean difference (SMDpre) and percentage improvement (%Improve) effect sizes. Bayesian hierarchical meta-analysis models were conducted to compare effect sizes, develop prior distributions, and estimate 0.25-, 0.5-, and 0.75-quantiles to determine small, medium, and large thresholds, respectively. Data from 643 studies comprising 6574 effect sizes were included in the analyses. Large differences in distributions for both SMDpre and %Improve were identified across outcome domains (strength, power, jump and sprint performance), with analyses of the tails of the distributions indicating potential large overestimations of SMDpre values. Future evaluations of S&C training will be improved using Bayesian approaches featuring the information and priors developed in this study. To facilitate an uptake of Bayesian methods within S&C, an easily accessible tool employing intuitive Bayesian updating was created. It is recommended that the tool and specific thresholds be used instead of isolated effect size calculations and Cohen's generic values when evaluating S&C training.

Examination of differential ratings of perceived exertion (dRPE) during bio-banded small-sided games.

The aims of the current study were to investigate the use of dRPE with academy soccer players to: 1) examine the effect of bio-banded and non-bio-banded maturity groups within SSG on players dRPE; 2) describe the multivariate relationships between dRPE measures investigating the sources of intra and inter-individual variation, and the effects of maturation and bio-banding. Using 32 highly trained under (U) 12 to U14 soccer players (mean (SD) age 12.9 (0.9) years, body mass 46.4 (8.5) kg and stature 158.2 (14.9) cm) academy soccer players from two English professional male soccer academies. Players were categorised according to somatic maturity status using estimated percentage of adult stature attainment, with players randomly assigned into teams to play 4v4 SSG. The study used a repeated measures design, whereby the selected players participated within 6 bio-banded (maturity matched [pre-PHV Vs pre-PHV and post-PHV vs post PHV] and miss-matched [pre-PHV vs post-PHV] and 6 mixed maturity SSG at their respective clubs. Using mixed and fixed effect regression models, it was established hat pre-PHV players exhibited higher dRPE compared with their post-PHV counterparts. Mixed bio-banded games reported higher dRPE outputs overall. Variation in dRPE measures across a series of bio-banded games are caused by both between and within sources of variation in relatively equal amounts. Across a series of bio-banded games, the four dRPE measures do not provide unique information, and between variation is best expressed by one or two highly correlated components, with within variation best explained by a single equally loaded component. Using a bio-banding SSG design study, we have shown that pre-PHV players report higher subjective measures of exertion than post-PHV players during. Additionally, when evenly mixing players based on measures of maturation, higher measures of perceived exertion were generally reported.

The influence of season phase on multivariate load relationships in professional youth soccer.

The purpose of this research was to assess relationships between subjective and external measures of training load in professional youth footballers, whilst accounting for the effect of the stage of the season. Data for ratings of perceived exertion (RPE) and seven global positioning systems (GPS) derived measures were collected from 20 players (age = 17.4 ± 1.3 yrs, height = 178.0 ± 8.1 cm, mass = 71.8 ± 7.2 kg) across a 47-week season. The season was categorised by a pre-season phase, and two competitive phases (Comp1, Comp2). The structure of the data were investigated using principal component analysis. An extraction criterion of component with eigenvalues ≥1.0 was used. Two components were retained for the pre-season period explaining a cumulative variance of 77.1%. Single components were retained for both Comp1 and Comp2 explaining 73.3% and 74.3% of variance, respectively. Identification of single components may suggest that measures are related and can be used interchangeably, however these interpretations should be considered with caution. The identification of multiple components in the pre-season phase suggests that univariate measures may not be sufficient when considering load experienced. These results suggest that factoring load based on measures of volume and intensity should be considered.

Relationship Between Subjective and External Training Load Variables in Youth Soccer Players.

PURPOSE: To quantify and describe relationships between subjective and external measures of training load in professional youth soccer players. METHODS: Data from differential ratings of perceived exertion (dRPE) and 7 measures of external training load were collected from 20 professional youth soccer players over a 46-week season. Relationships were described by repeated-measures correlation, principal component analysis, and factor analysis with oblimin rotation. RESULTS: Significant positive (.44 ≤ r ≤ .99; P < .001) within-individual correlations were obtained across dRPE and all external training load measures. Correlation magnitudes were found to decrease when training load variables were expressed per minute. Principal component analysis provided 2 components, which described 83.3% of variance. The first component, which described 72.9% of variance, was heavily loaded by all measures of training load, while the second component, which described 10.4% of the variance, appeared to have a split between objective and subjective measures of volume and intensity. Exploratory factor analysis identified 4 theoretical factors, with correlations between factors ranging from .5 to .8. These factors could be theoretically described as objective volume, subjective volume, objective running, and objective high-intensity measures. Removing dRPE measures from the analysis altered the structure of the model, providing a 3-factor solution. CONCLUSIONS: The dRPE measures are significantly correlated with a range of external training load measures and with each other. More in-depth analysis showed that dRPE measures were highly related to each other, suggesting that, in this population, they would provide practitioners with similar information. Further analysis provided characteristic groupings of variables.

Relationships Between Training Load Variables in Professional Youth Football Players.

This study aims to investigate the relationship between subjective and external measures of load in professional youth football players whilst accounting for the effect of training theme or competition. Data from ratings of perceived exertion and global positioning system-derived measures of external training load were collected from 20 professional youth players (age=17.4±1.3 yrs) across a 46-week season. General characteristics of training sessions were categorised based on their proximity to match day. The underlying structure of the data was investigated with principal component analysis. An extraction criterion comprising eigenvalues >1 was used to identify which components to retain. Three components were retained for training performed three days prior to match day (80.2% of variance), with two components (72.9-89.7% of variance) retained for all other modes. Generally, the first component was represented by measures of volume (Total Distance, PlayerLoad and low intensity running) whilst the second and third components were characterised by measures of intensity. Identification of multiple components indicates that load monitoring should comprise multiple variables. Additionally, differences in the underlying structure across training days that reflected different goals suggest that effective monitoring should be specific to the demands of different session types.