Factors Affecting Match Outcome in Elite Australian Football: A 14-Year Analysis
Authors: Andrew M Stewart
Brendan H Lazarus
William G Hopkins
Robert J Aughey
Journal: International journal of sports physiology and performance (2018)
Discipline: CLINICAL MEDICINE
Keywords: fixture; mixed modelling; performance; team characteristics; team sport.

Abstract:  Effects of fixture and team characteristics on match outcome in elite Australian football were quantified using data accessed at AFLtables.com for 5109 matches for seasons 2000 to 2013. Aspects of each match included number of days' break between matches (≤7 d vs ≥8 d), location (home vs away), travel status (travel vs no travel), and differences between opposing teams' mean age, body mass, and height (expressed as quintiles). A logistic-regression version of the generalized mixed linear model estimated each effect, which was assessed with magnitude-based inference using 1 extra win or loss in every 10 matches as the smallest important change. For every 10 matches played, the effects were days' break, 0.1 ± 0.3 (90% CL) wins; playing away, 1.5 ± 0.6 losses; traveling, 0.7 ± 0.6 losses; and being in the oldest, heaviest, or shortest, quintile, 1.9 ± 0.4, 1.3 ± 0.4, and 0.4 ± 0.4 wins, respectively. The effects of age and body-mass difference were not reduced substantially when adjusted for each other. All effects were clear, mostly at the 99% level. The effects of playing away, travel, and age difference were not unexpected, but the trivial effect of days' break and the advantage of a heavier team will challenge current notions about balancing training with recovery and about team selection. 

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 In 2019 there was an article written in The Age talking about how still MBI is being used. https: www.smh.com.au national cold-water-poured-on-scientific-studies-based-on-statistical-cult-20191108-p538t6.html Until the excel sheet has version control and this analysis becomes reproducible, given the changing nature of MBI to MBD I suggest this study be flagged. , 
 Not sure what you mean by  flagged . 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. , 
 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, serious academic misconduct at worst. All critiques of MBI are done with reproducible simulations and show the underlying formula. No response has. , 
 Again this method has existed and been used for well over a decade. A decade of researchers using a method where there exists no underlying mathematics in a statistics journal. A method where there is no version control n the excel spreadsheets. ,