# Are Green Number Runners More Likely to Bail?

**Exegetic Analytics » R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Comrades Marathon runners are awarded a permanent green race number once they have completed 10 journeys between Durban and Pietermaritzburg. For many runners, once they have completed the race a few times, achieving a green number becomes a possibility. And once the idea takes hold, it can become something of a compulsion. I can testify to this: I am thoroughly compelled! For runners with this goal in mind, every finish is one step closer to a green number. They are slowly chipping away, year after year and the idea of bailing is anathema. However, once the green number is in the bag, does the imperative to complete the race fade?

I am going to explore the hypothesis that runners with green numbers are more likely to bail.

Let’s start by looking at the proportions of runners who finish the race as opposed to those who do not finish (DNF) and those who enter but do not start (DNS). As can be seen from the plot below, the proportion of runners who finish the race seems to increase with the number of medals that the runners in question have. So, for example, of the runners with one medal, 68.6% finished while only 21.7% were DNF. For runners with ten medals, 87.1% finished and only 9.5% were DNF.

On the face of it, this seems to make sense: there is a natural selection effect. Runners who have more medals are probably a little more hard core and thus less likely to bail. Less experienced runners might be more likely to jump on the bus when the going gets really tough.

But, unfortunately, it is not quite that simple.

The analysis above has a serious problem: consider those runners with one medal. We are comparing the number of finishers (those that have just received that medal) to non-finishers (who already have a medal!). So we are not really comparing apples with apples! What we really should be working with are the number of finishers who had *i-1* medals before the race and the number of non-finishers who had *i* medals.

Compiling these data takes a little work, but nothing too taxing. Let’s consider an anonymous (but real) runner whose Comrades Marathon history looks like this:

year status medal.count 1985 Finished 1 1986 Finished 2 1987 Finished 3 1988 Finished 4 1989 Finished 5 1990 Finished 6 1991 Finished 7 1992 DNF 7 1993 DNF 7 1998 DNF 7 1999 Finished 8 2000 Finished 9 2001 DNF 9 2002 DNF 9 2003 DNF 9 2009 DNS 9 2010 DNS 9 2011 DNS 9 2012 DNS 9 2013 DNS 9

What we want is a table that shows how many times he ran with a given number of medals. So, for our anonymous hero, this would be:

0 1 2 3 4 5 6 7 8 9 Finished 1 1 1 1 1 1 1 1 1 0 DNF 0 0 0 0 0 0 0 3 0 3 DNS 0 0 0 0 0 0 0 0 0 5

Things went well for the first seven years. On the first year he had no medal (column 0) but he finished (so there is a 1 in the first row). The same applies for columns 1 to 6. Then on year 7 he finished, gaining his seventh medal (hence the 1 in the first row of column 6: he already had 6 medals when he ran this time!). However, for the next three years (when he already had 7 medals) he got a DNF (hence the 3 in the second row of column 7). On his fourth attempt he got medal number 8 (giving the 1 in the first row of column 7: he already had 7 medals when he ran this time!). And the following year he got medal number 9. Then he suffered a string of 3 DNFs (the 3 in the second row of column 9), followed by a series of 5 DNSs (the 5 in the third row of column 9). To illustrate the proportions, when he had 7 medals he got DNS 0% (0/4) of the time, DNF 75% (3/4) of the time and finished 25% (1/4) of the time.

Those are the data for a single athlete. To make a compelling case it is necessary to compile the same statistics for many, many runners. So I generated the analogous table for all athletes who ran the race between 1984 and 2013. A melted and abridged version of the resulting data look like this:

status medal.count number proportion 1 Finished 0 78051 0.83386039 2 DNF 0 11102 0.11860858 3 DNS 0 4449 0.04753104 4 Finished 1 52186 0.83512298 5 DNF 1 7336 0.11739666 6 DNS 1 2967 0.04748036 7 Finished 2 37478 0.83605863 8 DNF 2 5332 0.11894617 9 DNS 2 2017 0.04499520 10 Finished 3 28506 0.83472914 11 DNF 3 4072 0.11923865 12 DNS 3 1572 0.04603221 13 Finished 4 22814 0.83326637 14 DNF 4 3256 0.11892326 15 DNS 4 1309 0.04781037 16 Finished 5 18576 0.83630470 17 DNF 5 2585 0.11637853 18 DNS 5 1051 0.04731677 19 Finished 6 15538 0.83794424 20 DNF 6 2156 0.11627029 21 DNS 6 849 0.04578547 22 Finished 7 13300 0.84503463 23 DNF 7 1706 0.10839316 24 DNS 7 733 0.04657221 25 Finished 8 11809 0.86165633 26 DNF 8 1339 0.09770157 27 DNS 8 557 0.04064210 28 Finished 9 10852 0.81215387 29 DNF 9 1463 0.10948960 30 DNS 9 1047 0.07835653 31 Finished 10 7381 0.82047577 32 DNF 10 974 0.10827034 33 DNS 10 641 0.07125389 61 Finished 20 784 0.80575540 62 DNF 20 98 0.10071942 63 DNS 20 91 0.09352518 91 Finished 30 59 0.83098592 92 DNF 30 9 0.12676056 93 DNS 30 3 0.04225352

The important information here is the proportion of DNF entries for each medal count. We can see that 11.8% (0.11860858) of runners DNF on the first time that they ran. Similarly, of those runners who had already completed the race once (so they had one medal in the bag), 11.7% (0.11739666) did not finish. Of those who ran again after just achieving a green number, 10.8% (0.10827034) were DNF. It will be easier to make sense of all this in a plot.

Wow! Now that is interesting. Just to be sure that everything is clear about this plot: every column reflects the proportions of finishers, DNFs and DNSs who **already had** a given number of medals. There are a number of intriguing things about these data:

- all three proportions remain almost identical for runners who already had between 0 and 6 medals;
- the proportion of finishers then starts to ramp up for those with 7 and 8 medals (the DNS proportion remains unchanged, the DNFs decrease);
- there is a decrease in the proportion of finishers who already have 9 medals and a corresponding increase in the proportion of DNSs, while the DNFs remain unchanged;
- the proportion of finishers then increases slightly for those who already have 10 medals.

What conclusions can we draw from this? The second point seems to indicate a growing level of determination: these athletes are really close to their green number and they are less likely to sacrifice their medal. The third point is interesting too: the proportion of DNFs stays roughly the same but the DNS percentage grows from 4.1% for those with 8 medals to 7.8% for those with 9 medals. Why would this be? Well, I am really not sure and I would welcome suggestions. One possibility is that these runners are determined to have a good race so they might overtrain and end up injured or ill.

Are the differences in the proportion of DNFs statistically significant?

31-sample test for equality of proportions without continuity correction data: medal.table[2, 1:31] out of colSums(medal.table[, 1:31]) X-squared = 139.4798, df = 30, p-value = 4.744e-16 alternative hypothesis: two.sided sample estimates: prop 1 prop 2 prop 3 prop 4 prop 5 prop 6 prop 7 prop 8 prop 9 prop 10 0.11860858 0.11739666 0.11894617 0.11923865 0.11892326 0.11637853 0.11627029 0.10839316 0.09770157 0.10948960 prop 11 prop 12 prop 13 prop 14 prop 15 prop 16 prop 17 prop 18 prop 19 prop 20 0.10827034 0.10204696 0.10013936 0.10500000 0.11237335 0.10784314 0.11079137 0.10659026 0.09327846 0.11298606 prop 21 prop 22 prop 23 prop 24 prop 25 prop 26 prop 27 prop 28 prop 29 prop 30 0.10071942 0.10404624 0.09890110 0.09684685 0.14473684 0.10833333 0.14358974 0.07284768 0.14285714 0.16379310 prop 31 0.12676056

The miniscule p-value from the proportion test indicates that there definitely is a significant difference in the proportion of DNFs across the entire data set (for those with between 0 and 30 medals). But it does not tell us anything about which of the proportions are responsible for this difference. We can get some information about this from a pairwise proportion test. Here is the abridged output.

Pairwise comparisons using Pairwise comparison of proportions data: medal.table[2, 1:31] out of colSums(medal.table[, 1:31]) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 1.000 - - - - - - - - - - - - - - - 2 1.000 1.000 - - - - - - - - - - - - - - 3 1.000 1.000 1.000 - - - - - - - - - - - - - 4 1.000 1.000 1.000 1.000 - - - - - - - - - - - - 5 1.000 1.000 1.000 1.000 1.000 - - - - - - - - - - - 6 1.000 1.000 1.000 1.000 1.000 1.000 - - - - - - - - - - 7 0.107 0.734 0.179 0.205 0.457 1.000 1.000 - - - - - - - - - 8 4.8e-10 2.5e-08 3.8e-09 9.0e-09 6.4e-08 1.8e-05 5.8e-05 1.000 - - - - - - - - 9 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.689 - - - - - - - 10 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 - - - - - - 11 0.025 0.099 0.031 0.032 0.056 0.579 0.780 1.000 1.000 1.000 1.000 - - - - - 12 0.038 0.117 0.042 0.042 0.066 0.506 0.651 1.000 1.000 1.000 1.000 1.000 - - - - 13 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 - - - 14 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 - - 15 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 - 16 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

For between 0 and 6 medals there is no significant difference (p-value is roughly 1). The DNF proportion for those with 7 medals does start to differ from those with 4 medals or fewer, but the p-values are not significant. When we get to athletes who have 8 medals there is a significant difference in the proportion of DNFs all the way from those with 0 medals to those with 6 medals. However, the proportion of DNFs for those with 9 medals is not significantly different from any of the other categories. Finally, the DNF proportion for those athletes who already have 10 medals does not differ significantly from the athletes with any number of fewer medals.

So, no, it does not seem that runners with green numbers are more likely to bail (a conclusion that makes me personally very happy!). And good luck to the anonymous runner: I hope that you will be back in 2014 and that you will crack your green number!

Oh, and one last thing: as I mentioned before, the analysis above is based on the period 1984 to 2013. There are some serious issues with the data in the earlier years. Here is a breakdown of the number of runners in each of the categories across the years:

Finished DNF DNS 1984 7105 2 0 1985 8192 1907 1 1986 9654 1793 0 1987 8376 2458 0 1988 10363 1934 0 1989 10505 3065 2 1990 10272 1351 2 1991 12082 2936 1 1992 10695 2533 5 1993 11322 2270 2 1994 10274 2428 3 1995 10541 2990 1 1996 11269 2277 2 1997 11365 2467 3 1998 10496 2874 5 1999 11291 2835 3 2000 20030 4508 7 2001 11090 4270 1 2002 9027 2276 863 2003 11416 1065 892 2004 10123 1925 9 2005 11729 2163 7 2006 9846 1194 1025 2007 10052 1084 868 2008 8631 1745 813 2009 10008 1501 1441 2010 14339 2226 7000 2011 11058 2023 6506 2012 11889 1739 5916 2013 10278 3643 5986

Certainly something is deeply wrong in 1984! In the early years it does not make any sense to discriminate between DNF and DNS since there were no independent records kept: we simply know whether or not an athlete finished. The introduction of the ChampionChip timing devices improved the quality of the data dramatically. These chips have been used by all Comrades Marathon runners since 1997 although there is a delayed effect on the quality of the data.

Despite these issues, the conclusions of the analysis above remain essentially unchanged if you simply lump the DNF and DNS data together (because we cannot always make a meaningful divide between them!).

**leave a comment**for the author, please follow the link and comment on their blog:

**Exegetic Analytics » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.