Dr. Wim Westera is a physicist and educational technologist. In his role as Head of Educational Implementation at the Educational Technology Expertise Center of the Open University of the Netherlands, he combines service, educational media development and innovation. He leads a group of 70 educational designers, media specialists and IT developers. He is also a master athlete and a racing cyclist.
ABSTRACT
The
author argues that the current IAAF decathlon scoring tables display
unacceptable bias as they favour some events over others. Performances in the
sprints benefit disproportionately to those in the throwing events and the
7500m. Moreover, the system is intrinsically unstable and tends to increase the
differences between disciplines over the course of time. This paper investigates
alternative scoring methods. It elaborates a well-grounded procedure to express
the performance scales of the events in a normalised form in order to allow
comparisons. Three alternative scoring models are developed as candidates for
replacing the existing model. These are based on 1) a power law description, 2)
a parabolic description and 3) an exponential description, respectively. The
proposed methods are uniform over the events and support self-stabilisation. They
combine practical evidence and sound principles. Calibration to the current
model is performed with existing data in order to enable a smooth transition
from current practice. Overall effects are limited, if not negligible. Under
each of the proposed models two of the current all time top 100 performers would
improve their ranking substantially and all three models indicate the current
number two in the ranking, Thomas Dvorak (CZE) should actually be the world
record holder.
It turns out that there are quite significant differences between the
disciplines. The athletes seem to profit disproportionately from the long jump,
the 110m hurdles and the 100m, while in contrast the 1500m, javelin, discus
throw and shot put are highly unfavorable. Apparently, top decathletes tended
to specialize in sprinting, which indeed may be regarded a common denominator of
the long jump, the 110m hurdles and the 100m. Throwing capabilities and
endurance, however, seem to be far less profitable and may even interfere with
sprint performance.
One might be tempted to infer from this pattern that performances in the
throwing events and 1500m are lagging behind and thus leave more room for
further improvements than the sprint-based events, but this conclusion is not
tenable. First, this would reflect an embarrassing disregard of the fact that
the top decathletes go to the limits of each discipline in any possible way. It
would be naive to assume that substantial improvements were possible, even if
radical changes in training were to be applied. It is not the athletes who
should be blamed for the apparently sub-optimal performance, but the scoring
method itself. In principle, the top 100 average score should be equally
distributed over the events. Indeed, decathletes who have achieved scores that
rank in the all time top 100 are the only candidates to set the empirical
standards for genuine all-round performances. Any anomalies in the performance
pattern of Figure 1 should thus be ascribed to imperfections of the scoring
method.
Secondly, the self-corrective nature of the performance pattern is refuted by
the numeric gradient in the scoring tables: a 1% increase of the long jump
performance yields 19 extra points, whereas the same increase in discus throw
yields only 9 points and javelin and shot put will bring only 10 points.
Improving the 100m performance 1% would produce 24 points! This pattern implies
a positive feedback loop for sprinting-based performances at the expense of
throwing skills and endurance. Therefore, the different scores in Figure 1
cannot be regarded as a temporary or coincidental deviation from equilibrium; on
the contrary, the pattern seems to be highly unstable and will probably show
increasing differences between disciplines in the course of time. Current
decathletes are excellent sprinters. Apparently, this is a self-establishing
fact, because further specialization in the sprints pays off. As such a tendency
conflicts with the premise that the decathlon champion should be the best
all-round athlete rather than a solid sprinter, modifications of the scoring
method are inescapable.
The current scoring method
Even though we have observed some problems with the current scoring method, we
want to emphasise that the method as such is quite sophisticated. It uses
objective, unambiguous, quantifiable performance data (i.e. time and distance)
and avoids the subjective
assessments of jurors (aesthetics, expression) that cause so many problems in
the rating of gymnastics, figure skating or dressage. It also avoids complicated
and probably unfair multistage accounting systems, like the system of rally
points, games and sets in tennis. Such systems are used for historical rather
than logical reasons. Furthermore, the scoring tables are progressive in kind,
as will be explained below. These are far better than the linear systems that
are still being used elsewhere, for instance in the combined events of speed
skating.
The current scoring tables were adopted in 1984 after
extensive discussions, negotiations and compromises. The process took into
account an abundant amount of empirical evidence. Basically, the current scoring
method for each discipline is covered by a mathematical expression of the type.
S(P) = A.(P-B)^{C} (1)
● P is the performance (i.e. the achieved distance in the long jump).
● S is the score (the number of ascribed points).
● A, B & C are event-dependent parameters that define the nature of the scoring table.
For running events (P-B) should be replaced with (B-P) because of the descending
nature of performance with time. Note that the performance assessment method
comprises two
stages: first the performance P is measured (in units of time or distance), next
the performances are converted to a score S in order to allow addition.
Clearly, it is this second stage of assessment that is problematic.
Figure 2 shows the scoring curve for the long jump, according
to equation (1). It uses the following values: A=0.14354, B=220 em, C=1.40,
while P is expressed in cm.
Such scoring curves have the following characteristics. The parameter B
defines a threshold value (2.20m), below, which no score is assigned. This is
substantiated by the assumption that any athlete is assumed to reach such
distance without any effort whatsoever and therefore will not receive any points
for performances below B. Above this threshold value the performances are rated
through a slightly progressive curve, the nature of which is mainly determined
by parameter C. The underlying idea of this nonlinearity is that an improvement at low performance levels is much easier than an improvement at
high performance levels. Indeed, improving the long jump from 8.00m to 8.20m is
far more impressive than the same improvement from 4.00m to 4.20m as the scoring
table assigns 51 extra points against 33 extra points. The overall scaling of
the curve is determined by a parameter A. Thus, the current decathlon scoring
method comprises a set of 10 power laws that is specified
by 30 calibration parameters (A, B and C for each of the 10 events).
In due course, several inadequacies seem to have crept into the scoring method.
Moreover, the theoretical foundation of the formula is weak. The progressive
form is assumed to be associated with the kinetic energy that an athlete has
to develop during the event, irrespective of whether a run, jump or throw is
involved. This would suggest that performance is proportional to squared speed
(v'). which would indirectly suggest a progressive form with power 2.0. In
practice, however, it was necessary to apply power functions with exponents (parameter C) well below 2.0, with some variations over the disciplines (i.e. javelin
C=1.08; long jump C=1.40; 100m C= 1.81). Note that the progression of the curves
is partly determined by the threshold values B (i.e. javelin B= 7.0 m; long jump
B= 2.20m; 100m B= 18.0 see). The current tables are pragmatic in kind rather
than based on solid explanation. Consequently, some arbitrariness is involved.
Indeed, it is difficult to explain why the long jump scoring table should start
at 2.20m rather than at 2.40m, 1.80m or even at 0.00m. An additional weakness of
the current system is its inability to self correct in
due course: as indicated before, the current scoring system is intrinsically
unstable in that differences between disciplines tend to increase rather than
fade away. These observations amplify our call for a revision.
Premises for justified rating
Before we elaborate alternative scoring methods, we will list the basic
requirements they should meet. The envisioned methods should:
Allow a fair comparison between events;
Be uniform over all events (this follows from the starting point of the decathlon);
Use objective standards (distance and time measurements);
Be grounded in empirical evidence from the decathlon (practical significance);
Be based on sound principles and logic (consistent, transparent and substantiated);
Be stable over time and thus possess self-stabilising characteristics;
Allow smooth transitions from the existing model (acceptability).
Naturally, the method must be credible and acceptable in that it should not
degrade obvious top athletes to middle-of-the-road performers. This holds
even when it comprises the paradox that we reject the current method but still
demand the new system to yield more or less similar outcomes.
Next, we will explore new scoring models in two stages. First, we will develop
and discuss a procedure to express the diverse performance scales in a
normalised form in order to allow comparisons. Second, we will develop alternative expressions for the scoring function SIP).
Normalization of performance scales
Any scoring method for combined events is doomed to compare apples and oranges,
as it combines and reckons with different processes, different variables and
different types of performances. In order to enable a comparison of one type
of performance with another type of performance we need a way to transform each
performance scale into a normalised form. As will turn out below, such
normalization of decathlon performances can be achieved much easier than in the
case of apples and oranges.
In the current system, throwing and jumping performances are expressed in a
straightforward way by the achieved distance: larger distances correspond with better
performances. In running events, however, performances are expressed in the
length of time needed. Consequently, running performances and their quantification are inversely related, rather than linearly: the less time needed,
the higher the score. In order to achieve a sensible normalization procedure
we first have to align time measurement and distance measurement. Let the
performance in a certain event be quantified by a performance variable P. To
be consistent in terminology, high performances should correspond with large
values of P. Figure 3 displays such a performance axis.
In accordance with the current system, we may define a threshold performance Po
that would correspond with the performance below which no score is assigned
(5=0). In the current system Po is given by parameter B in equation (1). The
value P=Q would correspond with the ultimate inactivity. Naturally, such
performance scale easily matches the distance scale of throwing events and
jumping events. For running events, the performances should no longer be
expressed in units of time, but rather in units of speed or, likewise, in units
of reciprocal time. If so, the value P=O would correspond with the ultimate
inactivity: indeed, it would take forever.
With such alignment of the throwing-jumping events and running events in mind,
the definition of a normalised performance scale can be formalized by a linear
transformation of the performance variable P. We would need two calibration
values. Po and Pl, to define the normalised performance PN(P) of a performance P
in a particular event:
Here,
Pl represents the high-end calibration value of the performance scale, whereas
the performance threshold Po is the low-end calibration value.
As for the high-end calibration value Pl, we would need a stable reference value
that represents high performances. While maximum performance is indefinite,
per se, we propose to equate P1 with the average of the all time top 100
performances that have been used before in Figure 1. This choice may seem
somewhat arbitrary, but as it being used for the relative alignment of the
performance scales of the various events, it is not critical. We might have
chosen the top 50 average as a reference as well, or even the world record data.
This would indeed produce different transformations (cf. Equation (2)). but it
would still preserve the idea of normalization. Actually, what matters is that
the data is representative. By using the all time top 100 average existing
peaks and exceptions are dimmed by the statistics. The current averages of the
performances in the all time top 100 decathlons' are listed in Table 1.
So, when we choose the values of P1 to correspond with the average
performances listed in Table 1, we conform to the idea that athletes who
achieve all time top 100 decathlon scores have the same normalised performance
(e.g. PN(P1)= 1) for each event. Consequently,
this means that 10.76s for the 100m is the same performance as a long jump of
7.66m and so on. In fact, starting from the principle of all-roundness, this is
the only sensible decision. It also means that the associated scores S(P1) (cf.
Figure 1) should be the same for each event. Note that this (arbitrary)
normalization of the performance P does not mean that PN has an upper limit of 1;
indeed, PN may become larger than 1 if P> P1, naturally when performances
exceed the top 100 average (which may occur regularly).
For the low-end calibration of value Po, the official threshold parameters B, as
defined in the current scoring method (cf. Equation (1)) may seem interesting
candidates. However, in contrast with the values of P1 (the all time top 100
averages), which represent exemplary, real and reliable data, the current values
of B are quite problematic, because they are the result of accumulated
modifications loaded with historical bias and lack logical foundation. The
origins of the existing values B are unclear and their fairness is questionable.
Therefore, the indiscriminate import of these existing threshold values,
which for their part may be an important cause of the unbalance in the current
scoring method, is not acceptable. This becomes manifest when we list the
current IAAF threshold values B relative to the high-end performances P1
(cf. the third column in Table 2).
It appears that the relative positions of the current IAAF threshold values B
are very different for the different disciplines. Relative positions spread over
a factor of 7, ranging from 0.085 for discus throw to 0.598 for the 100m.
Theoretically a different threshold for each event might be plausible, because,
indeed, each discipline requires different techniques, different muscles and
different procedures. Yet, the current thresholds seem to display quite a degree
arbitrariness and break through the uniformity of the disciplines without any
foundation. Our proposition here is that in the absence of any reasoning about
the physical parameters that would substantiate the necessity of different
thresholds, a uniform approach over the disciplines is indicated. Indeed, if uniformity over all disciplines is our starting point, Po should be at the same
position for each event. This means that we want P0/P1 to be a constant. A first
approximation of P0/P1 would be the average of B/P1, which yields a ratio of
0.340. Using this ratio produces a uniform estimate for the threshold values
for each discipline (cf., Table 2, fourth column). Note the substantial
differences between our uniform threshold values Po (fourth column) and the
current IAAF thresholds B (second column). especially in the running and
throwing events.
Current scoring method: comparison of events
The performance normalization procedure described above allows us to display the
current scoring curves (cf. Equation (1)) at normalised performance PN (cf.
Equation (2)). Figure 4 displays the results for 5 of the events. Similar
curves result for the other events.
From Figure 4 we conclude that the unbalance of the scoring is not restricted
to high end performances as was inferred from Figure 1, but that it is present
at all performance levels. Note that the curves not only have different scoring
levels, but also very different curvatures and associated gradients. These
different gradients imply that equal (normalised) performance improvements are
rated differently in each discipline. The calculations confirm our
preliminary conclusion that these differences cause the scoring system to be
intrinsically unstable. We remark that the calculations indicate that throwing
events (shot, javelin and discus) have very similar curves, which differ only up
to 4%. Such resemblance might be expected with events that technically have many
points in common. Similarly, running events seem to display a common pattern
too: a steep rise at high performances. Yet, the differences in running scores
are much greater, as is the case for the jumping events.
Note that the curves in Figure 4 only represent an intermediate stage of our
analysis, because the normalisation affects only the horizontal scale, while the
vertical scale is kept unchanged. As a consequence, one may signal some
inconsistency while the horizontal scale uses the uniform threshold values of
PO, according to Table 2, whereas the vertical scale still uses the current IAAF
thresholds B, according to Equation (1). In the next section we will elaborate
alternative methods to redefine the vertical scale.
Towards alternative scoring methods
So far, the divergence of the scoring curves in Figure 4 is an embarrassing
confirmation of the inappropriateness of the current scoring method. If the
normalisation procedure according to Equation (2) is accepted to be valid, the
scoring curves of the various events should coincide rather than diverge. In
accordance with the principles of the decathlon, the scoring should be uniform
over all disciplines. This means that we have to redefine the scoring formula
S (P) of Equation (1) in a uniform way. As a further constraint, we refer to the
calibration values P0 and P1 that we have used to transform performance values
P into a normalised form. For the threshold value Po it follows that:
S(P0) = 0 (5)
It turns out that the average all time top 100 decathlete has a score of 8639
points. Because the scoring curve S is assumed to be uniform over all events, it
follows that for each event:
S(P1) = 863.9 (6)
Such empirical calibration ensures that the total scores of the all time top 100
decathletes stay in the same range as the current scores, in accordance with
our premise.
Naturally, when we want to rewrite the scoring function S as a function SN of
the normalised performances PN, according to Equations (2). (3) and (4), we
obtain:
SN(0) = S(P0) = 0
(7)
SN(1) = S(P1) = 863.9
(8)
While uniformity over all disciplines is assumed for SN, we have to find and
substantiate a progressive curve with two fixed points, given by Equations (7)
and (8). Below we will present three alternative approaches, the results of
which are presented in Figure 5. The three models will be explained below.
Model I: Power law
In accordance with the current scoring method, we assume that SN can be
described by a power law:
SN (PN) = A,(PN)^{C} (9)
From Equations (2) and (9) we find that the
regular scoring function S(P) can be written as:
S (P) = A,((P P0)/(P1-
P0) (10)
Note that this power law approach significantly differs from the current IAAF
power law in that performance in the running events is expressed in units of
reciprocal time, rather than in units of negative time (cf. Equation (1)). Also,
it follows from the uniformity of SN that A and C are constant over the events,
in contrast with the current IAAF scoring method which demands different values
of A and C for each discipline.
The constraint in Equation (6) gives:
A = 863.9 (11 )
The only remaining unknown in Equation (10) is the power C. Naturally, the value
of C determines the progressive form of the scoring curve, so it follows that
C> 1. A simple estimate of C can be obtained by conforming to the 10 IAAF power
parameters C that are used in the current scoring metho. When we equate C
with the average of these current powers we find:
C = 1.479 (12)
The resulting score curve is displayed in Figure 5. When we compare the
suggested power law curve of Figure 5 with the scoring curves in Figure 4, it
turns out that the new curve has an intermediate position. Coincidentally, the
new curve almost coincides with the high lump curve in Figure 4. Relevant data
for this suggested power law curve are summarised in Table 3.
Model II: Parabolic
It was mentioned above that the progressive form of the scoring curve may be
associated with the role of the kinetic energy that is developed by the athlete.
Along this line of thought the resulting scoring curve should be parabolic,
because the performance P is always expressed in units of
distance or units of (reciprocal) time. This argumentation, however, is not
very specific, and it omits the effects of the different techniques and
constraints of the disciplines. Yet, there is another reasoning that underpins
the likelihood of a parabolic scoring curve. To find a solid basis for the
progressive behaviour we should return to the basic idea that progression
reflects that the gradient of the scoring curve increases with performance. In
mathematical terms we state the premise that the extra score dSN(PN) that
follows a performance improvement dPN is proportional with the performance PN:
dSN(PN) ~ PN . dPN
(13)
Indeed, achieving a performance increment dPN at a high performance level PN
produces more points dSN than the same increment at a lower level. Integrating
Equation (13) gives a parabolic dependence:
SN(PN) = A . (PN)^{2
}(14)
Note that this parabolic curve is a special case of the power law of Equation
(9), i.e. C=2. The scaling constant A is given by Equation (11).
As can be seen from Figure 5, the parabolic curve (C=2) is slightly more
progressive than the power law curve, which uses only a power of C= 1.479.
Differences between the two scoring curves are up to a few percent (0 40
points) in the high performance area (PN>0.9) and rise up to 100 points at low
performances (PN=O.55). Relevant data for this suggested parabolic curve are
summarised in Table 3.
Model III: Exponential
The progression of the scoring curve can also be approached with statistics.
Indeed, progression may be assumed to reflect the reduced chance of success at
increased performances. To define progression statistically we state that the
extra score dSN(PN) that follows a performance improvement dPN is inversely
proportional with the occurrence or frequency f(PN) of performance PN in the
population of decathletes:
dSN(PN) ~
1/f(PN) . dPN (15)
While the frequency f(PN) may be assumed to descend monotonously indeed fewer
and fewer athletes will be able to achieve better performances -, a performance
improvement dPN is more greatly rewarded at high performance levels.
The next question would be: what evidence is available about the frequency f(PN)?
The standard approach to sort out f(PN) would be to take a
random sample to represent the population of all decathletes. This, however,
introduces two severe conceptual problems. First, while gathering results from
decathlon competitions, national and international ranking lists and so on, we
would be taking biased samples that only represent the local top 10 or top 50
participants and disregard large groups of modal athletes who make up the
majority of the decathlon population. Secondly, the combination of data in
different performance intervals, e.g. the combination of international data
and sets of regional data, is not straightforward but should be linked with the
relative occurrences in the performance intervals. Obviously, combining
the results of the World Championships with the data of some unimportant event
would not produce a representative sample for the decathlon population. This
problem is circular in kind and thus irresolvable: to derive the performance
distribution f(PN) from combined results in various intervals we would need to
know the relative occurrences, which are given by f(PN) itself. Therefore,
empirical occurrence data will not be of any help here.
A second approach would be to suggest a theoretical probability distribution, by
investigating the conditions of the probability process. Although various
well-known distribution functions like the Poisson distribution or the normal
distribution may be interesting candidates, we would still need good empirical
estimates of the distribution's mean and variance. Also, we have to consider
that only (part of) the descending tail of such distribution is of relevance,
because only the descending tail reflects increasing failure; in contrast, the
ascending tail at low performances represents the fact that most athletes
easily exceed these low performances.
These observations indicate severe difficulties in a straightforward and
successful application of a statistical analysis. However, we have some
indications that the performance distribution function might be approximated by
the negative exponential distribution:
f(PN) ~ e^{-λPN} (16)
where λ is a constant.
This choice is underpinned by the following arguments:
The exponential distribution is often associated with the survival of species in biology or similar processes that account for failures and drop-outs, for instance the reliability of technical components. The process of survival has many things in common with sports events. Consider, for example, the high jump and pole vault, where the requested performance of athletes is incremented in steps, until eventually all competitors have dropped out. Theoretically all decathlon events can be mapped on to this approach and thus match a regular survival pattern.
As will be demonstrated below, the premise of Equation (16) provides a monotonous progressive scoring curve and thus fits our objectives.
Clearly, the probability function f in Equation (16) can be regarded the solution of the following differential equation: df(PN) ~ f(PN). dPN (17)
This equation establishes the sensible premise that a performance increment dPN causes a frequency change df(PN) that is linearly proportional with f(PN).
The exponential distribution is simple in its form, it has only one parameter (λ) and it can be integrated analytically.
When we combine Equations (15) and (16) and integrate and make use of Equations
(7) and (8), we obtain the following progressive expression:
SN(PN) = A. (e^{λPN}-l)/ (e^{λ}-l)
(18)
Again A is given by Equation (11). To decide on the value of I we set the
pragmatic requirement that the exponential curve has an intermediate position
between the power curve and the parabolic curve. By minimising the total
squared differences between the curves at the interval [0.1] we find λ=1.602.
The resulting exponential curve is shown in Figure 5. Although our fitting
procedure implies an intermediate curve, the exponential relationship creates a
relatively strong progression at high-end performances (PN> 1). Differences with
the power law curve are up to 15 per cent in the midrange (up to 60 points).
Note that the inverse value of I represents that expect value of the performance PN.
This would indicate an average performance of <PN>=1/λ =0.62422. Relevant data for this suggested exponential
curve are summarised in Table 3.
Conclusion
All three suggested models meet the requirements for a justified rating for
which we have expressed a need. The normalisation procedure allows a fair
comparison between events. The proposed scoring methods are uniform over the
events and support self-stabilisation. They combine practical evidence and
sound principles. Various calibrations to the existing model would allow smooth
transitions from the current method. As a consequence, overall effects are
limited if not negligible.
In the all time top 100 ranking the average change is 10 positions for each of
the models, which corresponds with relative improvement (or degradation) of 30%.
The biggest leap is observed for the number 59 athlete in the current ranking
(Mike Smith (CAN)), who may be assumed to be greatly underrated and put at a
disadvantage by the current system because of relatively poor sprinting (100m in
11.23; 110m hurdles in 14.77). Both the parabolic method and the power method
allocate Smith a rank of 8th; the exponential yields a rank of 4th. Likewise
number 21 in the IAAF ranking (Uwe Freimuth (GER): 11.03, 14.66) enters the all
time top 10: 6th (parabolic), 7th (power) or 5th (exponential).
From this we see that the alternative models seem to counteract the sprint
bias of the current model. Remarkably, all three models indicate a new world
record holder, or rather a reinstatement of the old record holder, as Thomas
Dvorak's (CZE) 1999 performance in Prague outstrips Roman Sebrle's (CZE) subsequent mark from 2001 in Gotzis, which is unanimously ascribed to 2nd. Dvorak's
record would read 9232 points using the power law, 9469 with the parabolic or
9777 for the exponential curve. Note that these scores greatly exceed Sebrle's
currently recognised mark of 9026, especially in the case of parabolic and
exponential scoring due to the relatively high progression of the curves at
world level performances. The medallists at the 2005 World Championships in
Athletics' would remain unchanged under the three alternative methods, although
Brian Clay's (USA) winning margin would be even more pronounced, due to the same
effect.
In this paper we have shown that the current decathlon scoring method suffers
from severe bias and produces unfair outcomes. It would need a revision to
become more balanced and stable. We have demonstrated that it is possible to
devise alternative scoring methods that are uniform, balanced and substantiated
and that avoid the negative effects of the current method. On several occasion
we have chosen to estimate or calibrate data by falling back on
existing habits or data (e.g. performance thresholds P_{0}, power C) in order to
connect to existing practice. One may wonder about the exact value of the power
parameter C. or one may question the necessity to define thresholds P_{0} at all.
Indeed, other choices are possible and arguable, possibly with different outcomes and consequences, but the quintessence of this paper is to present a proof
of concept of appropriate alternatives.
The presented models not only have greater plausibility, they also are much
simpler and need fewer parameters. Instead of 30 parameters in the current
model, the power law method uses only 22 (magnitude A. power C and 10 times P_{0}
and P_{1}). as does the exponential model (magnitude A, rate A and 10 times P_{0}
and P_{1}); the parabolic method uses 21 (magnitude A and 10 times P_{0} and P_{1}).
This reduction is an improvement as, according to "Ockham's Law of Parsimony" or
"Principle of Economy" (called "Ockham's razor") one should make no more
assumptions than needed to explain ascertained facts. It supposes that the same
principle of simplicity prevails in the physical cosmos, since the laws of
nature are governed by the tendency towards minimum energy and a minimum number
of degrees of freedom.
Such a principle of economy would indeed fairly suit the efforts of decathletes
who seek to challenge the limits of performance, equally in all events.
FROM: IAAF/NSA 1-06