Georgoulis, Jim-Dimitris

Michalides, Marcos

Parpa, Koulla

Patras, Konstantinos

CLoK

ArticleVariability of sub2:30 Athens Classic marathon race performancesPatras, Konstantinos, Georgoulis, Jim-Dimitris, Parpa, Koulla and Michalides, MarcosAvailable at https://clok.uclan.ac.uk/46049/Patras, Konstantinos, Georgoulis, Jim-Dimitris, Parpa, Koulla orcid iconORCID: 0000-0002-1139-7731 and Michalides, Marcos orcid iconORCID: 0000-0002-9226-4657 (2023) Variability of sub2:30 Athens Classic marathon race performances. Sport Performance & Science Reports .  It is advisable to refer to the publisher’s version if you intend to cite from the work.For more information about UCLan’s research in this area go to http://www.uclan.ac.uk/researchgroups/ and search for <name of research Group>.For information about Research generally at UCLan please go to http://www.uclan.ac.uk/research/ All outputs in CLoK are protected by Intellectual Property Rights law, includingCopyright law.  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Terms and conditions for useof this material are defined in the policies page.CLoKCentral Lancashire online Knowledgewww.clok.uclan.ac.ukSWC of the Athens Classic marathonVariability of sub2:30 Athens Classic marathonrace performancesKonstantinos Patras, 1 2 Jim-Dimitris Georgoulis, 3 Koulla Parpa, 4 Marcos Michalides 41Laboratory of Microbiology, Department of Laboratory Medicine, University Hospital of Ioannina, Ioannina 45110, Greece2s11 Ltd, Nicosia 1096, Cyprus3First Department of Orthopaedic Surgery, National and Kapodistrian University of Athens, Athens, Greece4Sport and Exercise Science, UCLan University of Cyprus, Pyla 7080, CyprusBayesian modeling | Race to race variability | Performance enhancementHeadlineAnalysis of the smallest worthwhile performance enhance-ment provides the opportunity to evaluate the magnitudeand meaningfulness of different types of interventions (train-ing, tapering, ergogenic aids). The smallest worthwhile en-hancement is considered to be 0.3 x within athlete race-to-racevariability (1). However, unlike standardized tracks runningevents, virtually all marathon courses, inevitably display dif-ferences (flat vs. undulating vs. hilly courses and courses withturns vs. unidirectional courses). These course-specific dif-ferences may necessitate race-specific CV’s and subsequently,SWC’s (2, 3).AimTo estimate and evaluate the variability in overall race timefor the Athens Classic marathon.DesignAnalysis of official split times for ≤2:30 (hh:min) race timesfor male runners in the Classic Athens Marathon route during2002-2022.MethodsRace times were downloaded from the official site of the ClassicAthens Marathon (https://www.athensauthenticmarathon.gr) between December 2022 and January 2023. The final dataset consisted of 259 sub2:30 race-performances from 185 run-ners during 20 Athens Classic Marathon races held during2002-2022; the 2020 Athens Classic Marathon was canceleddue to Covid-19. The current datasets were freely availableon public domain; therefore we did not seek to obtain ethicalapproval. Race times analyses were performed in R (4) usingtidyverse for data wrangling and visualization (5) and brmsfor hierarchical Bayesian modeling (6). Hierarchical modelingaccounts for different sources of variation (7), and Bayesianmodeling incorporates background information in the form ofprior distributions to inform model estimation (7). These ap-proaches were well-suited to the present study because theyenabled estimation and evaluation within a multilevel frame-work. The population- (aka fixed-) effects included a lineartrend for calendar year, accounting for a general improvementof performance as result of better training and technology,an effect for nationality (domestic vs. foreign runners) andthe interaction between the linear trend for calendar year andnationality. The group- (aka random-) effects in the modelwere SD’s and included athlete ID, to estimate pure differ-ences between athletes’ mean ability, year ID to account forthe mean effect of environmental factors on performance timesas well as differences between competitions mean times not ac-counted for by the fixed effects and the model residual (within-athlete race-to-race variability). Performance times were log-transformed to yield the effects and SD’s in percent change ofthe mean. Within-athlete race-to-race, between-athletes andbetween-races CV’s (variability) were calculated as the squareroot of the model residual, the square root of the athlete IDvariance, and the square root of the year ID variance, respec-tively. We modified the ROPE approach (8) to derive exactprobabilities as per the mass of the posterior distribution ofthe population-effects contained within thresholds of 0.3, 0.9,1.6, 2.5, and 4.0, respectively, of the within athlete race-to-race CV (residual); when 95% of the posterior distributionwas outside the above thresholds, the effects were deemed be-ing small, moderate, large, very large, and extremely large(1). We sampled the posterior distribution using HamiltonianMonte Carlo with four chains and 1500 postwarm-up samplesper chain. The model passed all diagnostic statistics (allˆRvalues ≤ 1.01, all effective sample sizes > 400, zero divergentiterations) (7).ResultsThe observed mean sub2:30 race performances were 145.75 ±2.73min for domestic and 139.51 ± 4.77min for foreign run-ners (Figure 1A), whilst mean sub2:30 race performance acrossyears ranged from 138.94±5.56min to 146.12±1.50min (Fig-ure 1B). Group-level effects as CV’s are presented in Table 1.The variability in total race to race time provided a smallestworthwhile enhancement of 0.75% (0.3 x 2.5%). The popula-tion mean expected race time at baseline was 144.88min fordomestic runners and -2.2% (-3.7%; -0.7%, small effect) lowerfor foreign runners (Table 2). The population mean expectedlinear trend was 0.9%(-1.3%; 3.3%, trivial effect) for domesticrunners and -4.5%(-6.7%; -2.4%, moderate effect) for foreignrunners (Table 2). The mean expected difference in popula-tion linear trends between domestic and foreign runners was-5.6%(-8.5%; -2.8%, moderate effect) (Figure 2). The expectedmean performance across years for the sub-population of do-mestic and the sub-population of foreign runners is presentedin Figure 3.DiscussionThere has been an emerging interest in estimating the variabil-ity of elite race performance (9). The substantial between-sports variation (0.3–7.0%) warrants estimation of sport-specific variability in order to derive the smallest worthwhileenhancement (9); however there may be also a need for course-sportperfsci.com 1 SPSR - 2023 | March | 184 | v1SWC of the Athens Classic marathonspecific SWC in marathon (3). The within-athlete race-to-racevariability in total race time for the Athens Classic marathonis in the upper range to what has been reported for most en-durance sports (0.3–2.4%) (9). It has been shown that profi-ciency influences within-athlete variability, with the best ath-letes showing lower variability than lower ranked athletes buthere we did not tested for a differential within-athlete vari-ability. Our simple model provided a clear indication for therelative performance progression (or lack thereof) in a nation’smost prestigious marathon race (10). Despite the growing in-terest and promotion of the historical marathon route as wellas advancements in sports science support and technology, thelast 20 years have been characterized by no apparent improve-ment in domestic mean race performance. In fact our analysisrevealed a ∼56% probability for a small deterioration. On theother hand mean race performance for the foreign runners hasproduced a moderate improvement (Table 2, Figure 2, andFigure 3); potentially the organizers have been able to attractbetter foreign runners over the years.Table 1. Within- and Between-Athletes Variability in Performance Times Expressed as CV (%).Within-athlete SD (%)Race to race Between-athletes SD (%) Between-races SD (%)(mean±95%CI) (mean±95%CI) (mean±95%CI)2.5(2.0;2.9) 1.3(0.2;2.2) 0.7(0.2;1.2)Table 2. Effects (back-transformed) provided by the Bayesian linear mixed model.Effect ROPE ROPE ROPE ROPE ROPEsmall moderate large Very large ExtremelylargeIntercept 144.88 n/a n/a n/a n/a n/aNationality.foreign -2.2% (-3.8%; -0.7%) 0.6% 51.3% 100% 100% 100%Linear year trend 0.9% (-1.3%; 3.3%) 38.8% 88.2% 100% 100% 100%Nationality∗linear year trend -5.6% (-8.5%; -2.8%) 0.0 % 0.0% 11.5% 72.6% 100%Intercept: mean expected performance at baseline race (2002) by the domestic runners; Nationality.foreign: the difference inmean expected performance at baseline race (2002) between domestic and foreign runners; Linear year trend: the (linear)change in mean expected performance between baseline race (2002) and last race (2022) for domestic runners;nationality*linear year trend: the difference in the (linear) change in mean expected performance between baseline race (2002)and last race (2022) between domestic and foreign runners. Percentages in ROPE represent the % of posterior distribution ofeach effect that lies within each limit. The magnitude of the effect is the one where coverage inside ROPE is <5%.Fig. 1. Central tendency and dispersion of observed mean performance for domestic and foreign runners.sportperfsci.com 2 SPSR - 2023 | March | 184 | v1SWC of the Athens Classic marathonFig. 2. Posterior distribution of plausible values for the difference between the linear trend for domestic and foreignrunners. The solid black line is mean of the posterior distribution of the effect, the red dashed line denotes zero andthe black dashed lines denote small, moderate and large thresholds.Fig. 3. Posterior distribution of the expected year-to-year mean 2:30 performance for the domestic and foreign groups.The dashed lines denote the observed mean for each group respectively.sportperfsci.com 3 SPSR - 2023 | March | 184 | v1SWC of the Athens Classic marathonPractical applications• The typical variation in sub2:30 Athens Classic marathonperformances is ∼2.5%; physiological tests suitable fortracking SWC in performance need typical errors of mea-surement ≤2.5%.• Sub2:30 Athens Classic male marathoners should focus ontraining interventions, ergogenic aids equipment that thatenhance performance by at least 0.75% for a substantialeffect on race time.• Greek athletic governing bodies could scrutinize physiolog-ical characteristics of contemporary and past national levelrunners as well as current and past selection processes andtraining and support practices in an attempt to increasethe pool of 2:30 marathon runners (10).Limitations• The limited number of races for many foreign athletes mayhave impacted the results.• The effect of age on athlete performance could not berecorded properly, thus it was not included in the model(3).• The reported within-athlete variability accounts specifi-cally for the race to race performance for consecutiveAMA’s and cannot be generalized to other races or courses.Twitter: Konstantinos Patras (@KostasPatras_MD)Conflicts of interestThe authors declare no financial, institutional, and/or per-sonal conflicts of interest that might inappropriately influenceactions or statements of the present study.Accompanying datasetData and code will be made available to all interested re-searchers upon request from the corresponding author (KP).References1. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Pro-gressive statistics for studies in sports medicine and exercisescience. Med Sci Sports Exerc, 2009; 41(1):3-13.2. Hopkins WG, Hewson DJ. Variability of competitive per-formance of distance runners. Med Sci Sports Exerc, 2001;33(9):1588-15923. Maffetone PB, Malcata R, Rivera I, Laursen PB. TheBoston Marathon versus the World Marathon Majors. PLoSOne, 2017; 12(9):e01840244. R Core Team. R: A language and environment for statis-tical computing, (2021).5. Wickham, H. et al. Welcome to the Tidyverse. J. OpenSource Softw, 2019; 4: 1686.6. Bürkner PC. brms: An R package for Bayesian multilevelmodels using Stan. J Stat Softw, 2017; 80(1):1-28.7. Johnson AA, Ott MQ, Dogucu M. Bayes Rules! Anintroduction to applied Bayesian modeling. Chapman andHall/CRC, 2022.8. Kruschke JK. Doing Bayesian data analysis: A tutorialwith R and BUGS. (2nd edition). Elsevier Academic Press,2014.9. Malcata RM, Hopkins WG. Variability of competitive per-formance of elite athletes: a systematic review. Sports Med,2014; 44(12):1763-177410. Allen SV, Vandenbogaerde TJ, Hopkins WG. The perfor-mance effect of centralizing a nation’s elite swim program. IntJ Sports Physiol Perform, 2015; 10(2):198-203.Copyright: The article published on Science Performanceand Science Reports are distributed under the terms ofthe Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits un-restricted use, distribution and reproduction in any medium, pro-vided you give appropriate credit to the original author(s) and thesource, provide a link to the Creative Commons license, and indicateif changes were made. The Creative Commons Public Domain Dedi-cation waiver (http://creativecommons.org/publicdomain/zero/1.0/)applies to the data made available in this article, unless otherwisestated.sportperfsci.com 4 SPSR - 2023 | March | 184 | v1

Variability of sub2:30 Athens Classic marathon race performances

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Variability of sub2:30 Athens Classic marathon race performances

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