Analysing Elite Running Performance with Historical Data
By: Matt Jerrard
Tags: Sports, Running, Data Science

1960 Olympic marathon (credit)

Introduction

Runners at every level strive to improve their performance. Whether it’s an amateur aiming to set a new personal best, or an elite athlete setting out to win a major competition, they all face the same question: how do I run faster?

This is a fascinating question, and intuitively we can expect the answer to be a complex mixture of factors. This analysis attempts to contribute to this discussion by examining historical data about the performance of elite runners. Specifically, it addresses the following questions:

How have elite runners’ performances changed through history? How have Olympic runners’ characteristics (height, weight, age, body mass index (BMI)) changed through history? Is there a relationship between Olympic runners’ characteristics and their performance?

Data

The data sets are available on Kaggle and the full process of data cleaning and analysis can be found on GitHub — see the References section below. It’s worth noting that a limitation of the data is gender imbalance — there is more data for men’s competition than women’s. This is because it was only relatively recently that women were allowed to compete at all modern Olympic distances. Nevertheless, there is often sufficient data to draw conclusions.

Analysis and Findings

Performances Have Improved

Plotting a graph of the best performances (meaning finish times in a race) in a particular event versus the date shows that performances have improved significantly through history. The graph in Figure 1 (from reference [4]) shows an example: performance in the men’s 200 metres improved by almost 20% over 117 years.

Figure 1: Graph showing the improvement in men’s 200 m performances over time

It’s not just 200 m performances that improved — all events for both genders show some improvement. It’s natural to wonder what factors led to this.

Athletes Have Adapted

One reason is that modern athletes are quite different from athletes who competed in the early days of the Olympics. Figure 2 [4] is an example, showing the change in the mean age of female athletes in several Olympic track events. It’s clear that mean age has increased, and male athletes have followed the same trend. This is probably one factor in the improved performances: athletes spend more years training before they reach their peak, and this makes their performances better.

Figure 2: Graph showing the increase in mean age of female track athletes over time

The change in age is just one of several changes in athletes’ characteristics. For example, the mean body mass index (BMI) of sprinters increased through history, while it decreased for long distance athletes. This is shown in Figure 3 [4]. This makes sense: short distance athletes rely on the explosive acceleration which comes from a high BMI. Long distance athletes don’t need much acceleration, but instead need the capability to work efficiently over an extended period — and a low BMI helps with this. The changes in athletes’ physical characteristics is evidence of adaptation to the requirements of each event, and that leads to better performance.

Athletes in a Particular Event Have Characteristics in Common

Figure 3 [4] shows a clear relationship between the distance of an event and mean BMI. For the short distance events, the mean BMIs are all relatively high; for the long distance events, mean BMI is low, with middle distance events falling between the two.

Figure 3: Graph showing body mass index of male track athletes

BMI is not the only characteristic that groups around a distinct mean value for each event: weight, height and age also show this effect. For example, the longer the event, the higher the mean age of the athlete.

Athletes with a Range of Characteristics Produce Top Performances

Each event seems to have a particular set of characteristic values, a “sweet spot” where athletes are most likely to perform well. Nevertheless, the top performances often come from athletes with characteristic values very different from the average. Figure 4 [4] shows the best (meaning lowest) women’s 400 m times are spread across a wide range of height values, and there is no particular trend in the data. This also shows that within the group of athletes for a particular Olympic event, as opposed to the general population of all athletes, there is no strong relationship between height and performance. The same is also true for age, weight and BMI.

Figure 4: Scatter plot showing variation in women’s 400 m times with athlete height

Athlete Weight is an Important Factor

Of all the characteristics considered (height, weight, age, BMI), the most important in determining performance is athlete weight. Figure 5 [4] shows that the mean athlete weights for each event are well separated. In particular, compare the mean weight of a distance runner to a sprinter: they are far apart. An athlete with a sprinter’s weight is unlikely to be a successful marathon runner and vice versa.

Figure 5: mean and standard deviation of male athlete’s weights in different events

BMI, being a function of weight, shows a similar pattern. Athlete’s mean heights in different events tend to overlap more than weight. This suggests that height has less of an influence on performance in a particular event. Mean age in different events have a large overlap. This suggests that age has a small influence, although caution is needed with this: the data tells us about top performing athletes, and they have a fairly narrow range of ages anyway. The data cannot tell us about the effect of age on veteran athletes who are significantly older.

Practical Application for Athletes and Coaches

This analysis leads to some useful insights.

For high performance in a particular event an athlete would ideally have a height, weight, age and BMI close to the mean values for the Olympic athletes in that event. Knowing this can influence training and the choice of events an athlete chooses to pursue.

Nevertheless, the range of characteristic values exhibited by top performing athletes is wide, and some extremely highly performing athletes have characteristics with values far from the mean. Usain Bolt is a great example of such an athlete — unusually tall and heavy for a 100 m sprinter, and yet the world record holder.

Of the four characteristics examined, weight has the biggest effect on performance. It’s also a characteristic which an athlete can change through training.

Conclusion

We have seen how elite athletes’ performances have changed, how athletes have changed, and gained some insight into how athlete characteristics are related to performance. This study examined just four characteristics — height, weight, age and BMI. The fact that many top performing athletes have far from average characteristic values shows that other factors must have a significant influence on performance. A good opportunity for a future study would be to examine the effect of factors such as training schedules (e.g., distance run per week, techniques such as weight training), diet, and athlete characteristics such as VO2 max.

References

The data sets used are:

The detailed data cleaning and analysis, including all the graphs in this article, can be found here:

Acknowledgements

Credit to those who created the three data sets (references [1], [2], [3]) and made them available on Kaggle.

Credit and source of picture from top of article: Wikimedia Commons, https://commons.wikimedia.org/wiki/File:Abebe_Bikila_1960_Olympics.jpg, from original source: https://roma.corriere.it/foto-gallery/cronaca/14_dicembre_15/olimpiadi-roma-1960-4e5bc10c-8491-11e4-b9cc-80d61e8956c5.shtml