Environment and Scheduling Effects on Sprint and
Middle Distance Running Performances
Amal Haı¨da1,2*, Fre´de´ric Dor1, Marion Guillaume1, Laurent Quinquis1, Andy Marc1,
Laurie-Anne Marquet1, Juliana Antero-Jacquemin1, Claire Tourny-Chollet2, Franc¸ois Desgorces1,3,
Geoffroy Berthelot1,3, Jean-Franc¸ois Toussaint1,3,4
1 IRMES (biomedical Research Institute of Sports Epidemiology, Paris, France), INSEP (Institut National du Sport de l’Expertise et de la Performance), Paris, France,
2 Universite´ de Rouen, Faculte´ des Sciences du Sport et de l’Education Physique CETAPS (Centre d’Etude Transformations des Activite´s Physiques et Sportives), Mont-
Saint-Aignan, France, 3 Paris Descartes University, Sorbonne, Paris Cite´, Paris, France, 4 Hoˆtel-Dieu Hospital, CIMS (Centre d’Investigations en Me´decine du Sport), AP-HP
(Assistance Publique-Hoˆpitaux de Paris), Paris, France
Abstract
Purpose: Achievement of athletes’ performances is related to several factors including physiological, environmental and
institutional cycles where physical characteristics are involved. The objective of this study is to analyse the performance
achieved in professional sprint and middle-distance running events (100 m to 1500 m) depending on the organization of
the annual calendar of track events and their environmental conditions.
Methods: From 2002 to 2008, all performances of the Top 50 international athletes in the 100 m to 1500 m races (men and
women) are collected. The historical series of world records and the 10 best annual performances in these events, amounted
to a total of 26,544 performances, are also included in the study.
Results: Two periods with a higher frequency of peak performances are observed. The first peak occurs around the 27.15th
60.21 week (first week of July) and the second peak around 34.75th 60.14 week (fourth week of August). The second peak
tends to be the time of major international competitions (Olympic Games, World Championships, and European
Championships) and could be characterized as an institutional moment. The first one, however, corresponds to an
environmental optimum as measured by the narrowing of the temperature range at the highest performance around
23.2563.26uC.
Conclusions: This is the first study to demonstrate that there are two performance peaks at a specific time of year (27th and
34th weeks) in sprint and middle distance. Both institutional and ecophysiological aspects contribute to performance in the
100 m to 1500 m best performances and define the contours of human possibilities. Sport institutions may take this into
account in order to provide ideal conditions to improve the next records.
Citation: Haı¨da A, Dor F, Guillaume M, Quinquis L, Marc A, et al. (2013) Environment and Scheduling Effects on Sprint and Middle Distance Running
Performances. PLoS ONE 8(11): e79548. doi:10.1371/journal.pone.0079548
Editor: Alejandro Lucia, Universidad Europea de Madrid, Spain
Received July 19, 2013; Accepted September 23, 2013; Published November 20, 2013
Copyright:  2013 Haı¨da 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.
Funding: The authors thank the Centre National de De´veloppement du sport and the Ministry of Health, Youth and Sports for financial assistance. The funders
had no role in study design, data gathering, data collection and analysis, decision to publish, or preparation of the manuscript. The corresponding author had full
access to all data in the study and had final responsibility for the decision to submit for publication.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: amal.haida@insep.fr
Introduction
Since the beginning of the modern Olympic era (1896), the best
performance (BP) are in a process of exponential growth which
now seems to have reached its limits [1]. Performance is often
understood as a very broad term which involves many components
such as : psychomotor abilities, flexibility and joint stiffness, muscle
strength and power [2]. Athletes, like any living organism, depend
on physiological regulations that respect the nycthemeral, seasonal
or vital cycles [3]. There are variations in physiological factors
such as maximum oxygen consumption (VO2 max) or concentra-
tions of melatonin on the basis of seasonal rhythms [4]. This
seasonal rhythmicity has been demonstrated for certain factors
such as mood [5], lung function [6] and the core body
temperature. It is also observed in the physical activity of the
general population, which tends to be higher during summer [7–
9]. Chan and colleagues (2006) [9] found a significant change in
physical activity in the general population, as number of steps
walked per day being related to temperature, precipitation and
wind speed.
There is a limited amount of research that has investigated
effects of seasonality in sports on sprint athletes’ performances.
Yet the annual schedule of events seems to be a contributing
factor to performance. Comparison of track and field world
records (WR) shows that performance prevails in summertime.
The
influence
of
environmental
parameters
on
physiology
(ecophysiology)
partly
determines
the
evolution
of
human
performance [10,11]. Marathon optimal performances are set
at a temperature around 10u. This performance dependency on
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temperature occurs not only for elite-standard athletes but for
all participants also [12–14].
The objective of this study is to compare the date and
temperature of the BP in sprint and middle distance races
(100 m to 1500 m) for men and women during the annual
calendar of international competitions, and observe their evolution
over the Olympic era in order to assess the environment and
scheduling
effects
on
sprint
and
middle
distance
running
performances.
Methods
Data Collection
From
2002
to
2008,
all
performances
of
the
Top
50
international athletes in running events ranging from 100 m to
1500 m races for men and women were collected from the official
website of the International Association of Athletics Federations
(IAAF) [15]. For each event, data collection includes: full name of
the athlete, the completion date and place of the competition:
23,746 performances are collected, 11,813 from males and 11,933
from females. Performances are divided into five categories
defining the performance as a percentage in relation to the BP
obtained at the event. The percent categories (PC) used were: [95–
96%], [96–97%[, [97–98%[, [98–99%], [99–100%]. Within each
group, performances were collected according to the competition
type: (i) major competitions (the Olympic Games (OG), World
Championships (WC), European Championships (EC) and Amer-
ican selections (US)), (ii) the international circuit represented by
the Golden League and (iii) other meetings.
Date, place and name of the athlete when WR were set for the
same distances between 1952 and 2010 are collected (181 WR)
and the performances of the top 10 male and female 100 m race
are gathered from 1891 to 2008, representing 2,617 performances.
Temperatures for each city, at the time of the competition, are
recorded from 97 to 100 PC in 100 m, 200 m, 400 m, 800 m and
1500 m. In order to improve resolution, half PC are defined to
study temperature density: [97–97.5%], [97.5–98%], [98–98.5%],
[98.5–99%], [99–99.5%], [99.5–100%]. Temperature data are
collected from the weather underground website [16].
The total number of performances collected for this study is
26,544.
Statistical Analysis
Distribution of performance by PC.
The performance
data from 2002 to 2008 is based on the distribution of
performance per week of the year depending on the PC and the
type of competition: mathematical analysis and modeling are done
using Matlab. To estimate the two dates when the greatest
numbers of performances occur, two functions are adjusted using
the least squares method: the double Gaussian and double
Lorentzian functions. For each PC, the best-fitted function is
selected on the basis of adjusted R2 and the mean square error
(RMSE) (See Materials and Methods S1, Figure S1, Tables S1 and
S2). The two dates of peak performance are estimated using the
elected model for each PC (Inert Figure 1). The proportion of
performances in the two peaks is estimated by computing the area
under the curve (proportion of performances) of each elected
model and for each PC (See Materials and Methods S1, Figure S2,
Table S2).
Distribution of WR
Effect size for One-Way ANOVA is Cohen’s d and is evaluated
with Cohen’s conventional criteria [17]. It is used to study the
stability of the WR mean date by decades (1952–1959, 1960–
1969, 1970–1979, 1980–1989, 1990–1999, 2000–2010). Statistical
significance is considered at p,0.05.
Temperature
For the temperature, the statistical analyses are done on R,
Version 3.0.0 (R Core Team, Vienna, Austria, 2013) and results
are expressed as a mean 6 standard deviation. Fisher test is used
to compare the dispersions between the different PC with a value
of p,0.05 considered significant.
We estimate the density of temperature degrees for each of the
PC over a homogeneous mesh of 5*6 nodes. The resolution used is
of 7.5uC in the x-axis (temperature) and 0.59 percent in the y-axis
PC.
Results
Distribution of Performance by PC and Competition
The distribution of weekly performances for each PC (95 to
100 PC) over the competition calendar shows two high frequency
periods (Figure 1). The estimated dates of the two peak
performances are constant within all PC (on mean 27.15th
60.21 week for peak 1 and 34.75th 60.14 for peak 2) (Figure 1,
Inset).
Reaching the highest level, the areas under the curves of both
peaks converge toward the same 50% value (See Figure S2).
The number of performances during major competitions (OG/
WC/US/EC) increases from 16.7% for the 95 PC to 25.7% for
the 99 PC. The performances recorded during the Golden League
increase from 7.7% for the 95 PC to 29.1% for the 99 PC.
Conversely, the number of performances in the other competitions
decreases from 75.6% to 45.1% (Figure 1) (See Table S3).
Distribution of WR
The mean distribution of WR date by decade from 1952 to
2010 is concentrated at the 206.09th day 646.17. The variability
of WR date decreases considerably. In the first period (1952–
1959), SD is 64.34, in the last period (2000–2010), SD is 35.09.
However the mean day remains stable throughout the period
(p = 0.29) (Figure 2), with a large effect size (d = 0.98).
Influence of Temperature on Performance
The analysis of the distribution of PC according to temperature
shows a restriction in the thermal interval when reaching the
highest performance level. This interval narrows from 10–32uC at
97 PC of the BP to 20–27uC for the 100 PC with a mean
temperature of 23.2364.75uC. Subdividing the data into PC, the
mean temperature is 23.1364.80uC for the PC [97 to 97.5[,
23.4964.88uC for the PC [97.5 to 98[, 23.2364.92uC for the PC
[98
to
98.5[,
22.8964.56uC
for
the
PC
[98.5
to
99[,
22.6363.72uC for the PC [99 to 99.5[and 23.2563.26uC for
the BP [99.5 to 100]. Figure 3 highlights the narrowing of the
temperature range at the BP interval. The peak value of the
density mesh is 362 temperature values at 23uC and at 97.59%.
The density decreases in both dimensions (temperature and PC)
from this point confirming the mean temperature value stated
above (Inset, Figure 3).
Top 10 Sprinters from 1891 to 2008
There is no evolutionary trend in the completion date on the
100 m performances throughout the modern Olympic era. Since
1891, men accomplish their best performance around July 10th
(650 days) id est during the 28th week and since 1921, women
perform best around July 20th (644 days) id est during the 29th
week (Figure 4).
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Discussion
Our study is the first to our knowledge to analyze the
exhaustiveness of the best performers in sprint and middle distance
races in relation to temperature.
Previous studies have mostly analysed seasonality in the rhythms
of daily life [2,18] or in marathon runners [12] but no studies have
demonstrated effects of seasonality through environmental or
institutional conditions on performance in sprint.
Two yearly performance peaks are observed for all levels in this
study. The first peak corresponds to the 27th week of the year (first
week of July) suggesting an environmental optimum for sprint
events. The second peak occurs at the 34th week (fourth week of
August), which is related to the main sporting events such as:
Olympic Games, European and World Championships. As seen in
Figure 1, both peak dates are stable throughout all performance
categories.
Cultural Peak at the 34th Week
The impact of major international competitions corresponds to
the performance peak in August. The calendar scheduling for
world championships or Olympic Games can be considered as an
institutional attractor. IAAF hosts competitions taking place
outdoor between February and October. Major competitions
such as the World Championships, European championships and
Olympic Games are usually scheduled in August whereas the
international circuit of the Golden League covers the whole period
between June and September. National federations plan their own
schedules proposing competitions that allow their athletes to
qualify for the major competitions.
This study highlights the existence of a cultural peak (second
peak) occurring at the same times as the major international
events. Globally, top athletes focus on the same goal: to be the
most physically and mentally fit for this time of the year (Figure 1).
This second peak corresponds to the athlete’s own planning for
major competitions, which is a result of long term training,
Figure 1. Number of performances per week and per percent category (PC) by (i) major competitions (Olympic Games (OG), World
Championships (WC), American selections (US), European championships (EC)), (ii) Golden League and the others meetings (iii).
INSET: Dates (week) of peaks performance modeling by PC.
doi:10.1371/journal.pone.0079548.g001
Figure 2. A. Distribution of world records (WR) date (day) in 100 m, 200 m, 400 m, 800 m and 1500 m running events from 1952 to
2010. B. Mean distribution of world records (WR) date (day) in 100 m, 200 m, 400 m, 800 m and 1500 m running events by decade from 1952 to
2010. The mean date is: 206,09 th day.
doi:10.1371/journal.pone.0079548.g002
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technical analysis, strategic choice, awareness of physical and
psychological limits [19–21].
Although, training and preparation are essential to reach a BP
at a specific moment, environmental factors will allow the
achievement of the highest level of performance.
Thermal Peak at the 27th Week
The analysis of WR (Figure 2) and the top 10 BP in 100 m
sprint (men and women) illustrates this first peak (Figure 4).
Numerous studies have demonstrated effects of environmental
conditions on the performance of marathon runners [22,23].
Marathon requires a number of appropriate environmental
conditions for thermoregulation of any runner, elite or amateur.
The humidity, barometric pressure, dew point, and temperature
are all essential in the quest of achieving optimal performance
[24].
A recent study analysed the impact of environmental param-
eters on the performance of marathon running. It established a
distribution of performances depending on temperature, observed
regardless of the athlete’s level. This distribution function defines
the field limits of the human possibilities [12]. The impact of
temperature and season on biological parameters is largely
documented in the literature [24–26].
In this present study, the results show a distribution for top
performance in sprint and middle-sprint where the effective
temperature range decreases with performance level (10–32uC at
the bottom (97 PC of the BP); 20–27uC at the top (100 PC of the
BP)) (Figure 3). Competitions are mainly organized in the northern
hemisphere. The range of temperatures collected from the
different host cities was large: ranging from 10 to 38uC but the
mean temperature when achieving the BP is 23.2364.75uC.
The standard deviations decrease progressively with increasing
level, but all categories remain centered on the 23.23uC value.
This suggests a very regulated process at all performance levels.
The effects of temperature on biological parameters.
All
biological structures and processes (human or not) are affected
by temperature in thermodynamical regulations [27]. Perfor-
mance depends on physiological responses to exercise perfor-
mance
in
an
interaction
between
body
temperature
and
environmental temperature [26]. Performance decreases pro-
gressively as the environmental heat stress increases [25]. As
with other biological rate processes, muscle function is strongly
influenced by temperature. Specifically, muscle contraction rates
(the
rates
of
both
force
development
and
relaxation)
are
accelerated by an increase in temperature in both invertebrates
and vertebrates [28,29]. Fundamental biological functions like
metabolic activity synchronize with the rhythmic phases of
environmental
change
such
as
temperature.
For
gradually
intensity increasing aerobic exercise the plasma concentration
of certain ions (K+, Ca2+) and lactic acids appear differently
when muscular exercise takes place at thermal neutrality (21uC)
in comparison to exercise performed at 0uC [30].
At the favorable season, body temperature and metabolic rates
increase and so does growth rate. Mammal growth depends on
Figure 3. Percent category (PC) depending on temperature: comparison of temperature at different level from 97 PC to 100 PC in
100 m, 200 m, 400 m, 800 m and 1500 m. Respectively, in each half PC the mean are 23.12uC, 23.49uC, 23.23uC, 22.89uC, 22.63uC and 23.25uC
and the median are 23.00uC, 23.00uC, 22.00uC, 22.00uC, 21.00uC and 22.50uC. INSET: Temperature density (ie. number of recorded temperatures) per
PC computed over a mesh. The maximal density is computed at 23uC and 97.59% and progressively decreases as PC increase (due to the decrease in
performance number). The density decreases as temperature increases or decreases from the maximal density (due to the effect of temperature on
performance).
doi:10.1371/journal.pone.0079548.g003
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Figure 4. Relation between day of the performance and year in men and women. A. Average day of the achievement of the performance
in the top 10 at the 100 m men since 1891. For all years combined, the average day is the 192.76th 649.77. B. Average day of the achievement of the
performance in the top 10 at the 100 m women since 1921. For all years combined, the average day is the 202.52th644.0.
doi:10.1371/journal.pone.0079548.g004
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seasonal variation even for their bones structure [31]. Climates
and seasons have a marked influence on human biology [18]
including mental abilities [32], sexual activity [33] or territorial
conflicts [34].
Temperature and mortality.
Many chronobiological health
aspects depend on season and temperature cycles. Affective
disorders show a predictable onset in the fall/winter months
and, reversely, a reduction in the spring/summer period [32].
Large-scale population studies have shown seasonal variations in
mortality rates in different parts of the world peaking during the
cold winter months [35,36]. Relations between mortality and
cardiovascular disease (CVD) in the winter months have been
reported for many countries and might be partly explained by
seasonal changes of risk factors. Cardiac death also depend on the
season even after adjustment for age, cholesterol, blood pressure,
and body mass index [36,37]. Several studies have reported the
existence of optimal ranges of air temperatures [38,39]. Specifi-
cally, cold weather has been reported to be associated with
increased risk of death from cardiovascular causes and respiratory
infections [39–41]. The mortality rate is lower on days in which
the maximum temperatures range between 20–25uC [38]. This
means that survival rate is highest at this temperature range.
Our results show a mean temperature of 23.2364.75uC for the
BP which is converging with the temperature of the lowest
mortality rates. Therefore, both survival capability and physiolog-
ical capacities of the human are optimal at 20–25uC.
The two peaks of performance change its distribution in
function of the performance level (See Figures S1 and S2).
However, the first peak which corresponds to a ‘‘thermal peak’’
persists even at the highest level of competition. This demonstrates
that despite the presence of an institutional attractor, represented
by the major competitions, the environmental attractor remains
omnipresent with an ideal temperature period for maximal
performance. The adequacy of the thermal peak is as important
as the cultural peak at the highest level.
Conclusion
The range of possible combinations of environmental and
institutional components is narrowed for the top performers. For
sprint and middle-sprint races, when progressing toward the
highest levels of performance, the importance of the institutional
component regularly increases with a balanced effect for the top
performers.
The novelty of this study is that, environmental conditions must
be taken into account in order to achieve maximal speed. This
field of possibilities reveals an ideal temperature to achieve optimal
performance.
Calendars for major competitions should take this into account
in order to increase the probability of breaking the next records.
Supporting Information
Figure S1
Date of peak performance modeling for 95%
to 99% categories. The Double Lorentzian (continuous line)
and Double Gaussian functions (broken line) are adjusted to each
percent category. Although the two models differ in the estimate of
the tails, they roughly provide the same estimates of x01 and x02
(maximum difference is around 0.5 week).
(EPS)
Figure S2
Area under the curve (AUC) for the elected
functions and both peaks in each PC. Equations (10) and
(11) are used to estimate p1 and p2. The AUC of both peaks
converge toward a unique value as the PC increase (50%). The
proportion of performances: The estimation of the area
under the curve for the two peaks shows that, when increasing the
PC, the proportion of performances in each peak progressively
converge to the same value, from 93.67% (peak 1) vs. 6.33% (peak
2) for PC = 95% to 50.65% (peak1) vs. 49.35% (peak2) with
PC = 99% (Figure S2).
(EPS)
Table S1
Statistics of the two models. For each percent
category, the adjusted R2, rMSE and sse are given. Statistics of the
elected function are mentioned in bold.
(DOC)
Table S2
Results of the two models. For percent category
and model, the estimated date of peak (x01, x02), value of peak
(f(x01), f(x02)), the total proportion of performance (area under the
curve), and p1, p2 are given. Results of elected function are given
in bold.
(DOC)
Table S3
Number of performances per depending on
the type of competition and the percent category. N is the
number for different percent category (PC) and its equivalent
percentage. On the overall performance analyzed, 2,347 were
conducted during major competitions and 2,093 during the
Golden League. The other 8,079 performances were done in other
competitions (OG: Olympic Games; WC: World championships;
US: American selections; EC: European Championships).
(DOC)
Materials and Methods S1
The two models (double
Gaussian and double Lorentzian functions) are present-
ed. The methods for estimating the dates of the two peaks and the
area under the curve are described.
(PDF)
Acknowledgments
We thank the Centre National de De´veloppement du sport and the
Ministry of Health, Youth and Sports for financial assistance. We thank the
National Institute of Sports Expertise and Performance teams for their full
support. We thank Mrs Katrine Okholm Kryger and Ms Maya Dorsey for
their helpful comments for their critical reading of the Article.
Author Contributions
Conceived and designed the experiments: JFT AH F. Dor GB LQ F.
Desgorces. Analyzed the data: F. Dor MG GB AH AM. Wrote the paper:
AH F. Dor GB JFT. Reviewed the paper: KOK MD LAM JAJ CTC JFT.
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Environment and Scheduling in Track Races
PLOS ONE | www.plosone.org
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November 2013 | Volume 8 | Issue 11 | e79548

Environment and scheduling effects on sprint and middle distance running performances.
Haïda, Amal,Dor, Frédéric,Guillaume, Marion,Quinquis, Laurent,Marc, Andy,Marquet, Laurie-Anne,Antero-Jacquemin, Juliana,Tourny-Chollet, Claire,Desgorces, François,Berthelot, Geoffroy,Toussaint, Jean-François