Speed Trends in Male Distance Running
Timothy N. Kruse1, Rickey E. Carter2, Jordan K. Rosedahl2, Michael J. Joyner3*
1 The University of Washington School of Medicine, 1959 N. E. Pacific Street, Seattle, WA, 98195, United States of America, 2 Department of Health Sciences Research,
Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, United States of America, 3 Department of Anesthesiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905,
United States of America
Abstract
The major cycling ‘‘Grand Tours’’ have shown an attenuation of performance over the last decade. This has been interpreted
as circumstantial evidence that newer anti-doping strategies have reduced the use of performance-enhancing drugs. To
examine this idea under more controlled conditions, speed trends for world class 5000 m, 10000 m, and marathon
performances by men from 1980 to 2013 were analyzed. We obtained comprehensive records from the International
Association of Athletics Federations, Association of Road Racing Statisticians, and the Track and Field All-time Performances
database webpages. The top 40 performances for each event and year were selected for regression analysis. For the three
distances, we noted cumulative performance improvements in the 1990s thru the mid-2000s. After the peak speed years of
the mid 2000 s, there has been limited improvement in the 5000 m and 10,000 m and world records set during that time
remain in place today, marking the longest period of time between new records since the early 1940s. By contrast marathon
speed continues to increase and the world record has been lowered four times since 2007, including in 2013. While the
speed trends for 5000 m and 10000 m track results parallel those seen in elite cycling, the marathon trends do not. We
discuss a number of explanations other than improved anti-doping strategies that might account for these divergent
findings.
Citation: Kruse TN, Carter RE, Rosedahl JK, Joyner MJ (2014) Speed Trends in Male Distance Running. PLoS ONE 9(11): e112978. doi:10.1371/journal.pone.0112978
Editor: Maria F. Piacentini, University of Rome, Italy
Received June 18, 2014; Accepted October 17, 2014; Published November 19, 2014
Copyright:  2014 Kruse et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. Ethical restrictions prevent
public deposition of data. Requests for data may be sent to Michael Joyner, joyner.michael@mayo.edu.
Funding: This work was supported by the National Institutes of Health grants R25 GM075148 and UL1 TR000135. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* Email: Joyner.Michael@Mayo.edu
Introduction
The use of performance-enhancing drugs (doping) can be dated
back to the ancient Olympics [1], [2]. Since then athletes have
used a wide range of substances including red wine, caffeine,
nitroglycerin, cocaine, opiates, amphetamines, growth hormone,
blood transfusions, anabolic steroids, and erythropoietin (EPO) in
an
effort
to
gain
a
physiological
advantage
[3].
Because
performance-enhancing drugs compromises the idealized princi-
ples of pure competition, the World Anti-Doping Agency (WADA)
was created in 1999 [4]. Since the formation of WADA, it has
developed widely applied policies and drug testing protocols
(including regular out of competition testing) in an attempt to stop
the apparently wide spread doping in elite sports competition [5].
If recent improvements in athletic performance have been
driven by doping, then improved doping control might be
reflected by a leveling off or declining performances in sports
where doping is thought to be ubiquitous. In recent analyses of
major cycling races including the Tour de France, Giro d’Italia,
Vuelta A Espan˜a, the average speed has been leveling off or
declining [6], [7] since the introduction of improved techniques to
detect use of exogenous EPO in 2005 [4]. However, the analysis of
cycling is confounded by varying race distances, yearly changes in
course, and weather. Endurance running eliminates many of these
confounding factors. The tracks and courses are identical from
year to year. For the two shorter distances, there are numerous
competitive opportunities per year and at least some would likely
have nearly ideal environmental conditions.
With this information as a background, we tested the hypothesis
that elite distance running times would show a pattern of leveling
in the middle 2000 s similar to that seen in cycling. Such a finding
could be explained by improved doping control. We also discuss
alternate explanations including that humans are reaching the
biological limits of performance and the potential role technical
innovations in training and equipment [8], [9]. Finally, any
potential explanation might also be confounded by changes in the
economic landscape associated with world class distance running.
Materials and Methods
We obtained the top male performances from 1980–2013, by
year, in major endurance running races (5000 m, 10000 m on the
track; marathon on road courses) from the International Associ-
ation of Athletics Federations (IAAF: http://www.iaaf.org/home),
Association of Road Racing Statisticians (ARRS: http://www.
arrs.net), and the Track and Field all-time Performances data-
base websites (http://www.alltime-athletics.com/index.html) [10],
[11], [12].
The 1980–2013 epoch was selected because besides new
performance-enhancing drugs (PEDs), a case can be made that
potentially transformative changes in training or equipment has
not occurred. For example, by 1980 high volume and high
intensity training had been widely adopted by top competitors for
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several
decades
and
athletes
from
East
Africa
had
been
participating at the international level since the early 1960s. High
quality synthetic tracks were also widely available by 1980 and
carbohydrate loading was widely practiced in the marathon and it
is unclear if technical changes in shoes have had a measureable
impact on performance. While ideas about training have been
refined the extent to which these have been uniformly adopted by
elite athletes, especially the East Africans, is not known [13], [14],
[15].
Beyond
these
training
and
globalization
related
factors,
professionalism also emerged during the 1980s. We also restricted
our analysis to men because women were not routinely permitted
to participate in long distance racing until the 1970s and
performances
dropped
dramatically
in
the
early
years
of
widespread competition by women. While the gap in world
records has been steady since that time women still lag in
competitive depth in many events [16], [17].
Finally, the first synthetic EPO (Epogen) was approved by the
FDA in 1989 and within a few years it was clear that EPO can
have profound effects on maximal oxygen transport (VO2 max)
[18] in humans and was being used to enhance athletic
performance by the early 1990s. Additionally, because EPO and
related analogues are injectable the logistical challenges of
traditional blood doping (autologous red cell transfusions) are
eliminated [19].
The abstracted data consisted of the total number of perfor-
mances
below
2012’s
Olympic
A
standard:
5000 m-13:20,
10,000 m-27:45, and also performances under 2:10:00 for the
marathon (the Olympic A standard was 2:15 in 2012, a time that
equals an estimated 29.22 10,000 m [20]). Performances below
these standards were considered ‘elite’. To study the speed tends
more formally, the 40 fastest athlete performances (the fastest
performance of the 40 fastest athletes) were recorded per year and
event for regression modeling (described below). Age and country
of residence at time of race were also abstracted.
Initial data summaries included the frequency tabulation elite
performances by event and distance. To analyze the changes in
speed trends over the study period, we used regression techniques
consisting of quadratic splines (cubic B-splines with 3 equally
spaced interior knots) against our years of interest using the top 40
athlete performances per year. These generalized regression
models allowed for flexibility of estimating the change in
performance over time while providing traditional measures of
model fit (e.g., R-square value). It was hypothesized that different
regression profiles would be observed between the top 40 finishing
times and the fastest yearly performances, so the percentage
difference in speed of the fastest performance relative to the 40th
fastest time per year was also modeled using regression splines and
locally weighted smoothers (LOESS). When reporting measures of
model fit for the cubic B-splines regression models (i.e., change in
speed as a function of year), the omnibus F test for the regression
model and R-square were reported. The LOESS curves, which
were used for illustrative purposes of changes in pacing based on
the relative placing, were not summarized using traditional
regression summaries such as R-square on account of their
intended visual utilization.
Cubic B-spli ne regression analyses were conducted using The
SAS System (v9.3, Cary, NC) using PROC ORTHOREG.
LOESS smoothing was conducted using PROC SGPLOT using
default parameters.
Results
The number of performances below the 2012 Olympic A
qualifying standard plus sub 2:10:00 for each distance increased
over the 1980–2013 (Figure 1a–c). The world record for the
5000 m was set in 2004 while the 10000 m world record was set in
2005; these records stand today, which is the longest gap between
world records since the 1940s. The number of performances below
the 2012 Olympic A qualifying standard for the 5000 m and
10,000 m also appears to have leveled off since the middle 2000 s.
Similarly the number of athletes breaking 2:10:00 for the
marathon has also leveled off. 2:10:00 was chosen as a comparable
standard for the marathon because this time is considered
generally similar to or slightly slower than the A standards for
shorter distances based on various empirical point tables, scoring
systems and time conversion programs [20].
All three regression splines presented in Figures 2a–c were
statistically significant (p,0.0001 for each). Furthermore, year
alone explained a large percentage of the variation in the speed
trends (R-square: 53%, 37% and 69% for 5000 m, 10000 m and
marathon, respectively). Consistent with these overall model
estimates, the 5000 m and 10000 m had significant increases in
speed during the 1990s whereas the marathon showed an increase
over the entire three plus decades (Figures 2a–c). In particular, the
5000 m the speed trend levels off starting around 2000. The
marathon and 10000 m times do not show this as a pronounced
tendency.
Figures 3a–c explores the speed trends using an alternative
classification approach to provide additional insights into the
temporal effects. The fastest performance of the year is plotted
alone and summarized using a LOESS smoother. Speed trends of
the mean top 10, mean top 20 and mean top 40 athlete
performances are superimposed in these same figures. With the
5000 m and 10000 m, there is a pronounced ‘outlying’ effect of
the top performance from 1995 to late 2010. The marathon,
however, displays no attenuation of the increased speed over the
epoch sampled and the relative speed of the fastest annual time
does not appear to be an outlier (i.e., the figure lines appear as
roughly parallel).
To better quantify the observations made from Figure 3, the
percentage changes in speeds over time were examined and found
to be consistent with the differential
findings of the top
performance vs. the 40th fastest athlete performance. The degree
to which the fastest times were relatively fast (compared to other
years) was observed during the 2000s in the 5000 m and 10000 m
distances. The regression spline analyses supported these findings
that the fastest relative times for the 5000 m and 10000 m varied
over the epoch (5000 m: p = 0.048, R-square = 32%; 10000 m:
p = 0.0007, R-square = 51%). As illu strated in Figure 3c, trends
for the marathon distance were not clearly identified in the data
(p = 0.29, R-square = 19%).
Discussion
The speed and performance trends for top 5000 m and
10000 m distance running performers on the track show a period
of increased speed among the fastest runners to the mid-2000 s
with an attenuation of speed in either all (5000 m) or the fastest
performances (10000 m) after this period of time. For the
marathon, all indices of speed show a nearly linear increase in
speed with an increased number of elite performances over the
three plus decades we sampled. We believe there are a number of
possible explanations for these findings.
First, the findings for the 5000 m can be interpreted as
consistent with the hypothesis that improved drug testing has
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limited the ability of elite athletes to manipulate their oxygen
transport systems with EPO (or other techniques to improve
oxygen transport during exercise) since the middle 2000 s. These
observations are also broadly consistent with recent speed trends in
elite cycling races [6], [7]. This interpretation can also be applied
to the 10000 m results, but only when considering the fastest
times. By contrast, the data for the marathon shows continued
improvements in running speed during the same time period along
with more total elite performances and world records. These
observations challenge the idea that the speed leveling seen in the
5000 m on the track and in the so-called ‘‘Grand Tours’’ of
cycling is due primarily to better drug testing and the reduced use
of performance enhancing drugs.
A second possible interpretation is that world class performanc-
es are leveling off and reaching a physiological upper limit as has
been postulated for equine and canine athletes [8], [9,], [21]. In
the case of the marathon a number of empirical estimates and
physiological modeling suggest the record is relatively slow in
comparison to the 5,000 m and 10,000 m times and is merely
catching up by comparison [20], [22], [23]. In this context, it is
Figure 1. Total number of elite performances by year. Times under 13:20 for the 5000 m, 27:45 for the 10000 m, and 2:10 for the marathon.
doi:10.1371/journal.pone.0112978.g001
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interesting to note that top speeds have not fallen for the shorter
races but only leveled off.
The third element of any interpretation focuses on the changing
financial incentives in professional distance running. Prize money
for top marathon performances has increased. Specifically, in 1980
the highest total payout for any marathon was $50,000; just over
two decades later the first million dollar race was run [24]. These
incentives could be attracting a stronger pool of competitors to
‘‘move up’’ and focus on the marathon and forgo record setting
attempts at shorter distances. This could lead to more competitive
races among top runners at the major marathons. Second the
Figure 2. Top 40 athlete performances of the 5000 m, 10000 m,
and marathon (circles). The solid line is a quadratic spline fit with
three equally spaced knots (points of inflection).
doi:10.1371/journal.pone.0112978.g002
Figure 3. LOESS smoothers fit through the fastest yearly
performance by year for the 5000 m (A), 10000 m (B) and
marathon (C). In addition, the mean speed for the top 10, top 20 and
top 40 athlete performances are also plotted for reference.
doi:10.1371/journal.pone.0112978.g003
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highest profile marathon races are now being staged in a way
designed for world record attempts that include the use of pacers.
Along these lines, the use of pacers has been wide spread for races
on the track for many years, and many top athletes have bonus
plans and other financial incentives from sponsors that reward fast
times at the shorter distances. There are a number strengths and
limitations to this study. A major strength of our data set and
analysis is that it includes standardized distances and courses with
numerous competitive opportunities at the shorter two distances
when environmental conditions are likely to be optimal. By
contrast, a limitation to our analysis is that we have no idea if
improved approaches to training or equipment (shoes and tracks)
might have contributed to the trends we report. However, we
favor the interpretation that the entire epoch we have analyzed has
been relatively stable from a technical perspective. This includes
widespread use of high volume and high intensity training,
widespread availability of synthetic tracks, and adequate footwear.
Additionally, while ideas about training have been refined it is not
known if how uniformly these have been adopted by elite athletes,
especially the East Africans [13], [14], [15]. This perspective
contrasts to the major improvements in equipment for cycling that
includes use of advanced materials and improved aerodynamic
designs to construct faster bikes.
A final concern whenever the topic of doping is raised is
discussed relates to what might be called the continuous ‘‘cat and
mouse’’ game between those trying to enforce the rules with
improved testing and those trying to circumvent them. This has
engendered speculation that micro-doses of EPO can be titrated
by athletes in a way to achieve high levels of performance and yet
avoid a positive drug test [19], [25], [26]. There is also widespread
speculation about the use less or undetectable compounds and so-
called designer performance enhancing drugs. Advocates of these
points of view have argued that while doping is considered
widespread the number of positive tests in major competitions is
quite low [5]. The counter argument is that the low number of
positive results demonstrates that the testing is working and
deterring doping. The lack of hard data on the true incidence of
doping and how it has or has not been influenced by improved
testing is unknown and a major limitation to any discussion on this
topic. However, it is clear that anonymous questionnaire based
surveys suggest the true incidence of doping it is much higher (14–
39%) than ,2% rate of positive tests suggests [27]. This is clearly
an area of sports sociology that requires increased attention.
It should also be noted that the sociology surrounding the
doping phenomenon along with the ongoing incentives to dope
are complex. In this context, strategies beyond testing alone will be
required to improve the efficacy of doping control. A compre-
hensive discussion of this complex topic is beyond the scope of our
analysis, but there has been much thoughtful discussion of related
topics [28], [29], [30], [31], [32], [33], [34].
Conclusion
In summary, our analysis demonstrates that speed trends for
elite distance running are divergent depending on distance and
have event specific patterns. Thus, any generalizations about
performances in world class competition providing evidence that
drug testing is or is or is not ‘‘working’’ need to be viewed with
caution. Further caution is required given the many caveats and
potential factors that could explain our findings.
Acknowledgments
We would like to thank IAAF, Peter Larsson, and Ken Young for
compiling the data. This work was supported by the National Institutes of
Health grants R25 GM075148 and UL1 TR000135.
Author Contributions
Conceived and designed the experiments: TNK MJJ REC. Performed the
experiments: TNK MJJ. Analyzed the data: TNK MJJ REC JKR.
Contributed reagents/materials/analysis tools: TNK MJJ REC JKR.
Contributed to the writing of the manuscript: TNK MJJ REC.
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Speed trends in male distance running.
Kruse, Timothy N,Carter, Rickey E,Rosedahl, Jordan K,Joyner, Michael J