The longest journeys in Super Rugby: 11 years of travel and performance indicators
Authors: Nicholas Gill
Andrew M Stewart
William G Hopkins
Michele Lo
Robert J Aughey
Journal: Journal of Sports Sciences (2019)
Discipline: CLINICAL MEDICINE
Keywords: Travel; away-match disadvantage; jet lag; match analysis; performance analysis.
Affiliations: a Institute for Health and Sport (iHeS) , Victoria University , Melbourne , Australia., b Adams Centre for High Performance , University of Waikato , Tauranga , New Zealand., c New Zealand Rugby Union , Wellington , New Zealand.

Abstract:  Regular air travel is common in sport. The aim of this study was to understand the extent to which travel has affected Super Rugby teams' performance from 2006, the first season with available Key Performance Indicators (KPIs), to 2016. Data were analysed with mixed linear models for the effects of number of time-zones crossed (east or west), travel duration, the away-match disadvantage, difference in ranking, a set of amendments to the laws of Rugby Union in 2008, a change in competition format (introduction of a conference system) in 2011, and a secular trend. In 2006, the predicted combined effects of travelling 24 h across 12 time-zones and playing away were trivial or small and negative but generally unclear for most of the KPIs in both directions of travel. In 2016 more effects were clear, small and negative for westward travel, while most effects for eastward travel were clear, small to moderate and negative. Most KPIs showed small to moderate increases over the 11 years, while difference in ranking, the introduction of new rules and game format led to mostly small changes. Changes in the physical demands of the game, and inadequate recovery time for long-haul travel can explain these effects. 

Retracted: No

PubPeer comments:
 In 2014, Alan Welch presented his work along with Emma Knight to the AIS where they looked into a method of statistics called Magnitude Based Inference (MBI). MBI has never been published in a statistical or mathematical journal but is heavily used by the sports science field. You can view the presentation here https: maths.anu.edu.au files AIS-slides.pdf . After presenting MBI was supposedly discontinued at the AIS after Alan and Emma showed the method to be fundamentally flawed. In 2018 a 538 article was written about MBI uptake within the sports science field. https: fivethirtyeight.com features how-shoddy-statistics-found-a-home-in-sports-research This article notes to further work was done by Kristin Sainani, showing that MBI has numerous issues. She gives reproducible mathematics to show this. https: www.ncbi.nlm.nih.gov pubmed 29683920 This article sneakily still uses MBI, while not saying it directly, I think this is a deliberate effort to avoid coming up with key searches https: twitter.com biomechstu status 1229403524695576579 , 
 Magnitude-based inference, or as it is now known, magnitude-based decisions (MBD), does not give  false results . It turns out to be equivalent to several hypothesis tests with well-defined and acceptable Type-2 error rates (failed discovery of substantial effects, 5% for the non-clinical version used in this study), and the Type-1 rates (false discovery of substantial effects) for the non-clinical version are only ever slightly more than 5% and usually a lot less. An article substantiating these assertions first-authored by a mathematics professor and intended for a statistical journal is in preparation. I am also publishing something at Sportscience shortly that will be more accessible to researchers without substantial training in statistics. Meantime, for some recent background, see my posting and other postings each side of it in the datamethods forum: https: discourse.datamethods.org t what-are-credible-priors-and-what-are-skeptical-priors 580 53 It is worth noting one of statistician Sander Greenland s postings to that conversation in the datamethods forum:  Earlier applications of MBI did not necessarily reach incorrect conclusions. Instead when a report that used MBI is seen as supplying data relevant to a topic, that report needs to be scrutinized to see if its conclusions hold up or fail in light of critical insights about what MBI was actually producing (as opposed to the original MBI descriptions). Note that this advice involves study-specific examinations, and thus is a far cry from simply discarding everything that was published using MBI. Being able to follow it in a particular case hinges on how well the report described its design, execution and data, and if the needed summary statistics were given or computable from the report; if not, the report authors would need to supply those items. You will find that we supply compatibility limits and identify smallest important effects for the main outcomes in all our papers using MBI MBD, so it is a simple matter for readers to evaluate for themselves whether precision of the estimates is adequate for a probabilistic conclusion, in the manner that epidemiologist Ken Rothman and psychologist Geoff Cumming evaluate outcomes. Rothman uses 90% confidence compatibility limits in his introductory text on epidemiology three times more frequently than 95% CIs. Cumming s default is 95%. MBI MBD uses 90% CIs for non-clinical effects, and 99% on the harm side and 50% on the benefit side for clinical effects, all for very good reasons. , 
 How long has MBI been out for, as yet there has not been any mathematical formulation in a peer reviewed statistics or mathematics journal. I believe Sander Greenlands comments mean well, however it doesn t represent what is underlying the spreadsheets which Kristin, Alan and Emma have all shown to be misleading at best. All critiques of MBI are done with reproducible simulations and show the underlying formula. No response has. ,