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Victor H. de Freitas, Lucas A. Pereira, Eberton A. de Souza, Anthony S. Leicht, Maurizio Bertollo, and Fábio Y. Nakamura


This study examined the sensitivity of maximal (Yo-Yo Intermittent Recovery [IR] 1 and 2) and submaximal (5’-5’) tests to identify training adaptations in futsal players along with the suitability of heart-rate (HR) and HR-variability (HRV) measures to identify these adaptations.


Eleven male professional futsal players were assessed before (pretraining) and after (posttraining) a 5-wk period. Assessments included 5’-5’ and Yo-Yo IR1 and IR2 performances and HR and HRV at rest and during the IR and 5’-5’ tests. Magnitude-based-inference analyses examined the differences between pre- and posttraining, while relationships between changes in variables were determined via correlation.


Posttraining, Yo-Yo IR1 performance likely increased while Yo-Yo IR2 performance almost certainly increased. Submaximal HR during the Yo-Yo IR1 and Yo-Yo IR2 almost certainly and likely, respectively, decreased with training. HR during the 5’-5’ was very likely decreased, while HRV at rest and during the 5’-5’ was likely increased after training. Changes in both Yo-Yo IR performances were negatively correlated with changes in HR during the Yo-Yo IR1 test and positively correlated with the change in HRV during the 5’-5’.


The current study has identified the Yo-Yo IR2 as more responsive for monitoring training-induced changes of futsal players than the Yo-Yo IR1. Changes in submaximal HR during the Yo-Yo IR and HRV during the 5’-5’ were highly sensitive to changes in maximal performance and are recommended for monitoring training. The 5’-5’ was recommended as a time-efficient method to assess training adaptations for futsal players.

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Kathryn E. Phillips and Will G. Hopkins

Purpose: To further the understanding of elite athlete performance in complex race environments by examining the changes in cyclists’ performance between solo time trials and head-to-head racing in match-sprint tournaments. Methods: Analyses were derived from official results of cyclists in 61 elite international sprint tournaments (2000–2016), incorporating the results of 2060 male and 1969 female head-to-head match races. Linear mixed modeling of log-transformed qualification and finish ranks was used to determine estimates of performance predictability as intraclass correlation coefficients. Correlations between qualifying performance and final tournament rank were also calculated. Chances of winning head-to-head races were estimated adjusting for the difference in the cyclists’ qualifying times. All effects were evaluated using magnitude-based inference. Results: Minor differences in predictability between qualification time trial and final tournament rank were suggestive of more competitiveness among men in the overall tournament. Performance in the qualification time trial was strongly correlated with, but not fully indicative of, performance in the overall tournament. Correspondingly, being the faster qualifier had a large positive effect on the chances of winning a head-to-head race, but small substantial differences between riders remained after adjustment for time-trial differentials. Conclusions: The present study provides further insight into how real-world competition data can be used to investigate elite athlete performance in sports where athletes must directly interact with their opponents. For elite match-sprint cyclists, qualifying time-trial performance largely determines success in the overall tournament, but there is evidence of a consistent match-race ability that modifies the chances of winning head-to-head races.

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Rodrigo Rodrigues Gomes Costa, Jefferson Rodrigues Dorneles, Guilherme Henrique Lopes, José Irineu Gorla, and Frederico Ribeiro Neto

-hypothesis significant statistic often struggles with a sample size issue to achieve a significant P value when assessing a performance outcome. 6 , 7 The magnitude-based inference model seems to be a better statistic proposition compared with inferential statistics to analyze sports performance outcomes. 6 , 7 The

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Alister McCormick, Carla Meijen, and Samuele Marcora

—particularly considering the small sample size and posttest-only design—the probabilities that the true effect size is beneficial (Cohen’s d  > 0.20), trivial (between ±0.20), or harmful (< −0.20) were calculated using a magnitude-based inferences spreadsheet ( http

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Jorge Carlos-Vivas, Jorge Perez-Gomez, Ola Eriksrud, Tomás T. Freitas, Elena Marín-Cascales, and Pedro E. Alcaraz

21.0; IBM SPSS Inc, Chicago, IL). Data are presented as mean (SD). All data were log-transformed for intragroup pre–post differences analysis to reduce bias arising from nonuniformity errors of the data and then analyzed for practical significance using magnitude-based inferences (MBIs). 25 The

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Daniel J. Peart, Michael Graham, Callum Blades, and Ian H. Walshe

magnitude-based inferences. Reproducibility data from pilot work identified that 6 people would be suitable to detect a difference of 7 lights between rounds with 80% power, based on within- and between-subject SDs of 6 and 10, respectively. Both the magnitude-based inferences analysis and sample size

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Irineu Loturco, Timothy Suchomel, Chris Bishop, Ronaldo Kobal, Lucas A. Pereira, and Michael R. McGuigan

. Athletes were divided, using a median split analysis, into 2 groups according to their bar-power outputs in the BHT (eg, higher and lower MP, MPP, and PP). Magnitude-based inferences 12 were used to analyze the differences between groups in the power and velocity outcomes. The magnitudes of the

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Sharna A. Naidu, Maurizio Fanchini, Adam Cox, Joshua Smeaton, Will G. Hopkins, and Fabio R. Serpiello

classified as follows: r  < .1 = trivial, .1 to .3 = small, .3 to .5 = moderate, .5 to .7 = large, .7 to .9 = very large, >.9 = nearly perfect, and 1 = perfect. The differences between the 2 correlations (ie, sRPE/Edwards vs sRPE/TRIMP) within individuals were assessed via magnitude-based inference

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Niall Casserly, Ross Neville, Massimiliano Ditroilo, and Adam Grainger

differences between groups). Mixed modelling 25 was adopted to account for the possibility of unequal variances between groups. A reference Bayesian approach with a dispersed uniform prior was used to estimate the effects. 26 This has most commonly been referred to as magnitude-based inference in sport

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Mitchell J. Henderson, Bryna C.R. Chrismas, Christopher J. Stevens, Aaron J. Coutts, and Lee Taylor

(SPSS) (version 25; IBM, SPSS Inc, Chicago, IL) and magnitude-based inferences customizable spreadsheets, using the raw data. 19 Initially, descriptive statistics were generated, and normality checked using quantile–quantile plots. 20 Descriptive statistics are reported as median and range (minimum