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Middle- and
long-distance races viewed from the perspective of complexity: Macroscopic
analysis based on behaviour as a power law
By Juan M. Garcia-Manso, Juan M. Martin-Gonzalez, Enrique Arriaza, Lucia
Quintero
AUTHORS
Dr. Juan M.
Garcia-Manso works in the Department of Physical Education at the University
of Las Palmas of Grand Canaria in Spain. He is an athletics coach and was formerly responsible for the national junior team of Spain.
Dr. Juan M. Martin-Gonzalez works in the Department of
Physics at the University of Las Palmas of Grand Can aria in Spain. He holds a
doctorate in Mathematics.
Prof Enrique Arriaza works at the University of Valparaiso, Chile. He is a
professor of Training Theory.
Lucia Quintero is a graduate student in Physical Activities and Sport in the
Department of Physical Education at the University of Las Palm as of Grand Canaria in Spain.
ABSTRACT
The term 'power law' describes the organising principle that very few nodes will
maintain a large percentage of links in a network or system. In a continuation
of an earlier work, the authors use this idea to characterise the different
performance levels into which top-class male athletes in the middle- and
long-distance races in athletics can be grouped. They assume that the total
system has a critical behaviour and that the performances in these races should
strictly follow a power law. Using the best times of the all-time top 550
ranked performers in the events from 1500m to marathon (excluding the steeplechase) on 30 October 2003 as basis for analysis, they attempt to detect those
values (performances) that show clearly abnormal behaviour within their performance level and thus compare the level of one event to the others. A
box-plot of the residuals from the regression model is used to analyse
exceptional performers or outliers, who act as targets, barriers and/or powerful
attractors that increase the level of performance in one event in
comparison to the other distances analysed.
Introduction
In an earlier paper (GARCIA-MANSO et aI., 2005), we verified that performances in the middle- and long-distance
athletics races behave as power laws when time (or average velocity) and distance are related, regardless of their
individual characteristics. This suggests the presence of critical
phenomena. We also emphasised the importance of the universe (the number of
people involved in the activity) but we limited the study to the world's best
athletes at each competition distance, referring particularly to the world
records for each distance.
In the present study, we used the top 550 positions in the all-time world
rankings for 1500m, 3000m, 5000m, 10,000m, half marathon and marathon events as
samples. These give only the best time for each of the athletes running the
different competition distances. The world records represent the times
officially recognised as such as of 30 October 2003 and the times used for the
analysis of the other positions correspond to those athletes occupying that
position in the all-time world ranking as of the same date.
The data we utilised showed that, in spite of the performance level (world
ranking position) of the individual concerned, when the average velocity of a
race is related to the distance covered, the same type
of scaling law is always found:
In Equation 1, r is the athlete's position in the all-time world ranking, C(r)
and a(r) are constants at each level of performance r, while d represents the
competition distances covered. The value log(C(r)) represents the intersection
point of the regression line at each level with the axis log(v) and, to some
extent, is an indicator of how average race velocity decreases as we go down the
world ranking. This fact led us to define C(r) as "Performance Index" (PI),
which also seems to roughly follow a scaling law.
A deeper analysis of the PI shows the existence of natural barriers, in the
evolution of the times, which seem to correspond to performance levels, or
significant times, in an athlete's evolution towards better results and world
records. Furthermore, the way in which PI times are distributed on different
scales seems to show an underlying multifractal structure.
The sporadic appearance of subjects able to deliver times that are clearly
better than those existing, would define new goals or targets that would act
as attractors or reference points for other athletes. At the same time, a new
elite group or area specific to the race in question would be established. As
this happens, Equation 1 would be defined more clearly as a power law to which
all the values would evolve in these resistance races.
The present study will analyse the current situation of three groups of
athletics running events (middle-distance, long-distance and marathon) in terms
of the corresponding power laws that characterise the different performance
levels that group together the athletes occupying the top 550 positions of the
all-time world ranking for the respective
distances. We will thus try to detect those values that show clearly abnormal
behaviour within their performance level. With this intention, we shall assume
that the total system has a critical behaviour and that the races should
strictly follow the power-law shown in the Equation 1. Or, in other words, the
differences in each average velocity value (or time taken) to the curve (or
logarithms to the regression line) should be or tend towards zero. To this end,
we have taken Equation 1
to a logarithmic form:
From this point onwards, we will use this equation as a reference base for each
position in the ranking. We calculated the differences (residuals) from the
actual data (time or velocity) to the value suggested by the regression model.
This allows us to compare the position or state of each race to the rest, as
well as to organise the times for each competition distance. We will thus have Dd(r)= ud(r)-ud(r) where ud(r) is the logarithm of the average velocity of
subject rth for competition distance d, and Qd(r) is the value given by the
regression line. The numerical values, for some of the positions, are given in
Table 1.
To interpret the results we shall use a box-plot of the matrix Dd(r], with r =
1 st, 2nd,...,550th for each value of d. The box-plot produces a box and whisker
plot for each d value. The box has lines at the lower quartile, median, and
upper quartile values. The whiskers are lines extending from each end of the box
to show the extent of the rest of the data. In our box-plot analysis, an outlier
corresponds to a time whose value is more than 1.5 times the inter-quartile
range away from the top or the bottom for the specific box for each race
distance.
Detection of atypical behaviour in the races
If we analyse the competition distances in Figure 2, we can see that in each
event there are extraordinary runners whose performance
levels do not conform to the norm of the compiled data. The incidence of this
situation is not the same, either in the way it occurs or in proportion, in the
different distances studied.
Thus, we can see (Figure 3) that this type of runner (known
as an outlier), capable of recording times that are significantly different to those of most
of the athletes analysed in the series used for each distance, is more frequent
in the 1500m (34 athletes) and 5000m (27 athletes). By contrast, the tendency
is something different for the 3000m (11
athletes), 10,000m (16 athletes), half marathon (7 athletes) and marathon (15 athletes) races.
We understand that outliers act as a kind of target, barrier and/or powerful
attractor leading the event towards a greater position in comparison with the
other distances analysed. The importance of the outliers and their effect on
the evolution of such a system depends on many factors. However, given the
practical experience that characterises these races, we can mention the most
relevant factors: the potential value of the time, the number of outliers in the
event and the circumstances in which the exceptional times are run. The quality
of the behaviour in this case is relevant given that, according to our initial
hypothesis, these attractors are responsible for leading the system (composed of
competition distances) to a critical state.
For example, the record and/or times for 10,000m and marathon races behave like
outliers in comparison with the rest of the times that appear in the ranking
used, although they do so in different ways. The outlier for the 10,000m
(difference 0.0063, in Table 1) shows the extraordinary merit of the world
record (26:22.75) recognised at the time this table was produced, not to mention
the subsequent performances by Bekele (ETH). Other athletes at this distance
will find the record extremely difficult to beat. Sometimes, although it is not
this case, experience shows us that extraordinary times can be attributed to the
effect of variables that are not connected with the athlete. These may include
particularly favourable competition conditions (excellent pace-marker, ideal
weather conditions, significant competition incentives, sea level, etc.).
Alternatively, factors that are difficult to control may come into play (these
might include doping). Besides these considerations, proximity to the fit and
the presence of an outlier, the 10,000m shows a relatively compact behaviour,
with a normal distribution of recorded times and low dispersion of times (Figure
3).
The overall behaviour of times in the marathon is very similar to that observed
in the 10,000m, with concentrated times and a low level of dispersion, showing
the internal normality of this race, particularly if we consider the top
positions. The previous world record (2:05:38) by Khannouchi (USA). seemed, at
the time, to differ significantly from the average behaviour of other values
analysed for this distance. However, at that time, its value could be considered
as low in comparison with the rest of the times recorded for other races and for
the same distance and, therefore, open to improvement in the short term by a
fair number of specialists. This was confirmed by the times recorded by
different athletes at the end of 2003, particularly by Tergat (KEN) and Korir
(KEN), who broke the existing world record during the Berlin marathon with their
respective times of 2:04:55 and 2:04:56. Despite this latest qualitative change
in the all-time records, an overall analysis of the event seems to indicate that
the ranking list will undergo significant changes over coming seasons.
This hypothesis of the future can also be accepted from a
physiological point of view. We understand that a runner with a low BMI (Body
Mass Index), a VO2max near to
80
ml/kg-1/m-1
and the ability to run the distance to an intensity close to 90% of this value
with a high economy (~0.185-0.190 ml.kg-1.m-1),
will be able to beat these marks or others of greater level.
Analysis by race groups
For this analysis, we organised the competition distances
into three categories (middle-distance, long-distance and marathon), which
represent similar distances in terms of the athletes who are successful in each
category. Obviously, each of these categories could be reorganized based on the
individual profile of its athletes, but we do not think this step is either
necessary or helpful in this overall race analysis. The middle-distance group
includes the 1500m, 3000m and 5000m; the long-distance group covers the 10,000m
and the half marathon while the third category includes only the marathon.
Middle-distances
The current situation of races traditionally known as
middle-distance events constitutes a very interesting case (Figure 3). From a
metabolic point of view, runners of 3000m and 5000m races have the maximum
requirements for aerobic power, which correspond to times of about 420sec
(PERONNET and THIBAULT, 1989), 450-550sec (ALVAREZ-RAMIREZ, 2002) or 600sec
(MORTON and BILLAT, 1999). By contrast, the 1500m is the frontier between the
resistance and speed events (power and/or speed endurance). When we analyse the
position of these events on the box-plot, we realise the low relative value of
the times for the 3000m and 5000m. This tendency increases as the performance of
the athletes decreases. This type of behaviour, although it clearly exists, is
less acute in the longer distance (5000m), though we have to bear in mind that
this distance has been included in the official programme of the Olympic Games,
the World Championships in Athletics and the continental championships since
these competitions began. On the other hand, the 3000m has only been included in
indoor competitions or some outdoor invitational meetings over the last few
decades. This leads us to think that the all-time ranking of these two
distances, particularly the 3000m, could undergo a significant change and
improvement if the following variables were changed: an increase in the universe
of athletes with the current training characteristics and/or incorporation of
the 3000m in the programmes of the major outdoor championships. We believe that
the current world records for these two distances could be improved, and that
the new records would drag the other competing athletes with them to a higher
level of performance. The potential record times could lie around 7:16.55
(3000m) and 12:36.55 (5000m). similar to the 5000m world record of 12:39.36 used
in our analysis and Bekele's subsequent mark of 12:37.35.
The 3000m shows relatively homogeneous times with a small
inter-quartile range and low dispersion, and the times show a certain tendency
to approach the outliers.
The times for the 5000m are similar, but their behaviour is
different, given that in this case they form a compact block around a
concentrated average, with the obvious influence of a group of specialists who
powerfully drag the race towards its natural position. This suggests to us the
existence of a specific specialist profile (with times of <13:00) clearly
differentiated from other world-class runners (whose times range between 13:15
and 13:30). Athletes from North Africa (Morocco and Algeria) and East Africa
(Kenya and Ethiopia) have played an important part in shaping the dynamics of
the 5000m.
The 1500m behaves in a very different way to the two races we
have just discussed. This race has two significant characteristics: first, it is
a key event in athletics (its popularity giving it a greater universe), and
second, it is located close to the border between the endurance running
(>1000m), determined mainly by aerobic metabolism, and the speed events
(<1000m), which rely on anaerobic metabolism.
The results obtained in the series of times used demonstrates
the high average value of the times for the 1500m in relation to the tendencies
found in the other events analysed. There are three aspects that might affect
the position occupied by this distance: energy dependence, it's universe and the
profile of the athlete currently running the best times in the world.
Aerobic metabolism plays a very important role at this
distance, although the metabolic contribution is significantly different in the
case of each athlete according to his functional profile, muscular structure and
physical fitness. In this race, aerobic metabolism would appear to act as an
important attractor in the organism of current world-class athletes. The
anaerobic metabolism is also a determining factor, although in a different
proportion. According to WARD-SMITH (1999), the full potential of the anaerobic
capacity is available for conversion during extended periods of running, but
other authors say that the anaerobic energy contribution declines with race
duration (GOLLNICK and HERMANSEN, 1973); PERONNET and THIBAULT,1989).
According to most studies, aerobic metabolism is attributed
as contributing between 60% and 90% of the total energy contribution (SPENCER et
aI., 1996; WEYAND et al., 1993. These figures include a wide degree of
variability between individual athletes, which gives cause for thought. Clearly,
part of this difference could be explained by the characteristics of the samples
used in the different studies undertaken (whether they were physically very
trained, quite trained or sedentary], but we need to consider other parameters
as well. Experience has shown us that there are three prototypes for athletes
running the 1500m: those that run this distance as well as shorter distances
(800m and 1000m); those that run the 1500m well, but also perform well at longer
distances (3000m and 5000m) and genuine specialists in the 1500m. This practical
reality could explain some of the causes underlying this very wide range of
energetic behaviour, although we should also bear in mind the possible effect of
the procedures used in metabolic assessment.
Generally speaking, the main functional factors restricting
performance in the 1500m could be taken as the elevated depletion of the
muscular CrP, the high metabolic acidosis produced by a significant activation
of the glucolitic metabolism and the insufficient capacity of the aerobic
metabolism to produce enough energy. The importance of each of these aspects to
the result of a race varies in each athlete, his functional profile and the type
of training employed. The current world record holder at 1500m, EI Guerrouj
(MAR) is a particularly interesting case, because of his exceptionally good time
(3:26.89). His times appear closer to the regression line (distance: 0.0064, in
Table 1) than the rest of the times used in the study (up to the all-time 550th
position). This led us to think that this athlete could be included in the group
of runners who also run well at longer distances (2000m, 3000m and even 5000m).
This opinion was confirmed when we checked h is best times over 3000m (7:23.09)
and 5000m (12:50.24).
If we add to the mixed metabolic dependence, the enormous
historical importance of this event and the high number of athletes who have
competed at this distance over the last century (universe), we should expect a
different behaviour to that shown in the other events used to determine the
scaling law proposed in this paper. If we look at Figure 2, we can see that at
all levels (from position 1st to 550th), this distance shows a much higher level
of behaviour than the other distances, particularly in comparison with the next
two events (3000m and 5000m). This could correspond to the two points analysed
above.
Long-distances
The 10,000m provides the stable model around which the other
distances analysed in this study vary. However, there are some details relating
to the best times for this distance that we feel should be mentioned. The world
record at this distance when the figures were produced, as well as the times
recorded by the other top athletes over this distance, were all recorded at the
end of the last century, when they broke the barrier of 27 minutes quite easily.
Initially, these times produced a significant effect on the event. A subsequent
period of stagnation appears to have been overcome now. This leads us to think
that, all things being equal, this event is unlikely to see any spectacular
surprises over the next few years.
The half marathon is a particularly interesting event. From
here on, the scaling law, and its possible explanation, is somewhat more
complex, but not for that reason less interesting. If we look at Figure 2, we
see that, regardless of the level we look at (positions 1st,...,500th) the
results of the half marathon are always clearly situated above the fit and,
therefore, above those of the adjacent events. The box-plot (Figure 3) shows
that half marathon times do not show many outliers, and that all levels present
analogue distances (0.0030 - 0.0060, in Table 1). This behaviour is interesting
if we bear in mind that this distance is not included in the Olympic Games,
World Championships in Athletics or the continental
championships. It has only recently become widely popular (in the last three
decades), although the level of participation is very significant. Given the
results obtained, we could posit that performances by top class athletes in the
half marathon benefit from the fact that this distance falls within the range of
two types of specialists (10,000m and marathon). This produces better
performance and bodes well for the event's evolution.
Marathon
The marathon is clearly different from the long-distance
races considered above, as top-level success calls for a highly specialised type
of runner whose energy needs (and therefore training needs) are clearly
differentiated from the runners of shorter distances. There is no other
sufficiently developed race with similar characteristics, in terms of length,
functional or physical dependence, that can be considered in this category.
From the metabolic point of view we know that races over two
hours in duration significantly increase the participation of fats in the
aerobic metabolism: through l3-oxidation replacing the reserves of glycogen,
which is the major source of energy in races lasting around an hour (for example
the half marathon) (BERGSTRÖM and HULTMAN, 1967; SALTIN and LARLSSON, 1971;
COSTILL et al, 1971 and 1973; SHERMAN et al, 1981; MADSEN et ai, 1990; SAHLIN et
al, 1990; WELTMAN, 1995; TSINTZAS et al, 1996; HAWLEY et al, 1997). In the
marathon, the runner's energy dependence on fats is around 20%, although the
importance of this substrate increases with the length or duration of the race,
and can reach 60-70% in 100km races (NEWSHOLME et al, 1992; LEIBA and TERRADOS,
1996).
The change in energy dependence in races lasting 90-120
minutes could possibly be expressed in a new scaling law, which would allow us
to find the decisive point between long-distance and marathon. This cut-off
point could define the limits between the two aerobic metabolisms described
above (carbohydrates and fats). However, in order to do this we would need
enough information about other long-distance races (for example, 50km or 100km
races). Unfortunately, this type of race is not sufficiently developed for our
purposes, and potential top athletes for these races normally opt for those
events that are currently more popular, such as the triathlon. The only distance
in athletics that has an established competitive tradition is the 100
kilometres, but the number of participating athletes is low and there are very
few genuine specialists at this distance. This fact limits the effective
universe, gives a biased behaviour pattern over this distance and pre- vents us
from broadening the scope of our work.
FROM: IAAF/NSA 1-06
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