watch the documentary

this documentary explores the athlete as a symbolic figure through five chapters: nation, sex, class, race and body

launch the Equaliser

choose your own lineup and use the equaliser tool to create a level playing field by eliminating the influence of nation, sex, class, race and body


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Select one of the Olympic finals to equalise


about 51 Sprints

Het Nieuwe Instituut presents 51 Sprints - The Human Race

More than just a race for gold, silver or bronze, the 100-metre sprint tells multiple stories through the athletes who compete in it. Stories about their country, their people, and their own bodies. Stories about the human race.

This interactive documentary recounts the history of one of the great events of the mod­ern Olympic Games: the 100-metre sprint. A story of record-breaking achievement, 51 Sprints explores the athlete as a symbolic figure, a representative of nation, race or gender. While pro­moting the ideal of equality for all, the Olympics has increasingly become a projection screen for national status, the superiority of some systems over others, the ultimate in physical preparation, and the gradual emancipation of minorities.

A specially developed tool called the Equaliser allows you to create a level playing field by removing the influence of five different factors: nation, sex, class, race and body. This will adjust each athlete’s running time, resulting in an outcome in which the chosen factors play no role.

An online magazine gives all the background stories, research material and data behind 51 Sprints.

read the making of 51 sprints

An idea of: Klaas Kuitenbrouwer (Het Nieuwe Instituut)
Concept developer: Yuri Veerman
Design: Random Studio & Yuri Veerman

Concept: Random Studio
(developed in collaboration with Yuri Veerman)
Technical realisation: Random Studio
Data analysis: Mikhail S. Spektor and Dr. Gilles Dutilh (University of Basel)
Research assistant: Marc van der Valk

Concept: Yuri Veerman
Art Direction, edit and production: Random Studio
Script: Yuri Veerman
Proofread and script edit: Jane Szita
Voiceover: In-Casting

Film Footage
International Paralympic Committee (IPC)
Olympic Television Archive Bureau (OTAB)
UCLA Film & Television Archive

All Olympic Footage available in this production is copyright of and reproduced only with the consent of the International Olympic Committee

51 Sprints: The Equaliser

How do you use the Equaliser?

You can use the equaliser tool to create a level playing field by removing the influence of 5 different factors (sex, class, nation, body, race). This will adjust each athlete’s running time, resulting in an outcome in which the chosen factors play no role.

For example: statistically speaking a finalist’s running time is influenced by the sex of the runner. Women run relatively slower than the average end time of all sprinters and men run averagely faster. Using the Equaliser, you can add 0.431 seconds to the running times of men and subtract 0.529 of the running times of women. This equalises the statistical difference between men and women.

How were the concepts selected?

We statistically analysed these five concepts and tried to quantify how these may be factors that influence the running times of the sprinters.

Evidently, this experiment has some limitations and ambiguities. The chosen concepts are highly contested within themselves and often politically charged. Difficult to define, their meaning changes over time. In addition, other important factors are not accounted for, and the available information on some factors is very sparse. However, bearing these and other limitations in mind, we worked in accordance with rigorous standards of statistical analysis.

We believe our analysis to be insightful, precisely because it uses politically problematic simplifications that play a big role in public perception. Although our statistical analysis can not claim to make any statement on the absolute influence of these categories, it still reveals relative inequalities between groups of athletes that cannot be denied.

How are these five factors defined?

Initially, we investigated what available data might be able to give quantifiable measurements (so called proxy-variables) to our five fuzzy categories of interest. This led to a set of about 35 variables. After checks on data availability, statistical assumptions and content validity, we were left with 20 variables for which we had information on most of the 385 sprint finalists in the modern Olympics since 1896.

nation This factor consists of the following variables: nationality, gross domestic product of the count that the runner represents, population level at the time of performance, type of government (democratic, socialist, authoritarian), and the confirmed presence of state-sponsored doping programmes.
sex Athletes are categorised as male, female or intersex. Since there was only one confirmed intersex participant (Stella Walsh, gold in Los Angeles 1932), we could statistically not structure this category. Other complexities of current and past gender concepts escaped our quantification.
class This factor consists of the following variables: profession, college educated (yes/no), education funding (private, public or none), privileged upbringing, and sponsorship (present/not present).
race The concept of race presents certain dilemma’s. It is hugely charged in political and social terms and a deeply layered issue. For some it is a question of identity, for others it is a social category. Yet others state it should be ignored altogether. Although the notion cannot be delineated, it can also not be denied.
It is a prominent factor in the politics and perception of sports, it appears to play a role in running times of sprinters. With this in mind, we decided to radically oversimplify the notion of race to a difference of three types of skin colour: black, yellow and white. Focusing on these few visual positions, in an otherwise complex spectrum of racial identity allows us to highlight some relative tendencies.
body This factor consists of the following variables: standard or non-standard body plan, (the latter with specifications of lower-leg amputation or blindness), body height, weight, age at the time of performance and use of doping if this was ever officially recorded during the runner’s career.

What initial conclusions were drawn from the Equaliser?

Once the data table was complete, we were interested in identifying the variables which contribute the most to the finishing time of the athlete. We used the multiple linear regression (MLR) statistical method. In this case, MLR has enough explanatory potential for a thought experiment: it can foreground relative differences among groups of athletes, but it does not give any explanation of the causes for these differences.

During the analysis, it became apparent that the year in which the respective Olympic Games took place indicated most of the variance in finishing times. Runners have become faster over the last 100+ years. Because we are interested in the story of the individual athlete embedded in a specific context of our five factors of interest (and taking into account a number of statistical limitations), we excluded chronology as an explicit factor. In other words: we accept without explicit statistical correction the fact that later runners are faster then earlier ones.

Running times were used as the dependent variable and a full model of the factors was fitted. This model was then used to calculate, per runner and per variable, the running time offset (gain or loss) in seconds.


The data collection and analysis methods applied here have been tailored to the demands of credibly enabling a thought experiment. The emergent display of differences does not explain causes. They are no more but also no less than relative differences among groups of athletes that can be made visible through statistical analysis of a dataset. Having said that, we did our utmost to conduct the most proper statistical investigation of the data possible.

If you have any questions, please contact either Random Studio or, for analysis related inquiries, Mikhail S. Spektor and Dr. Gilles Dutilh from the University of Basel.

Bearing in mind the limitations of our analysis in scope and ambition, we invite you to analyse the data yourself. It can be downloaded here. Numerous interesting stories are hidden in this table, and we sincerely hope that more of them will be uncovered and shared.