A Numerical Guide to the 2017 IAAF World Cross Country Championships
With the 2017 World Cross Country Championships slated to pop off mid-day Sunday (East Africa Time Zone), the Citius Mag Stats Department scoured the internet for the finest publicly available XC figures, data and numbers, in order to prepare the following statistical dossier.
First off, we would like to thank the good folks at the IAAF for sending some excel files our way. The following charts could not have been made without them. Also, many thanks to Isaac Wood of BYU coaching fame for providing a lot of help with data collection. More on Isaac to come.
Let’s jump right in. The first chart we have showcases the average age of each team (with at least four racers) for the Senior Women’s 10,000m contest. There is a pretty wild range, from twenty-one years-old for the Japanese women’s team, all the way up to thirty-one years-old for the Spanish team.
For the Senior Men’s race, we a see a similar spread, although it is slightly more compressed. Burundi comes in as the youngest, with an average age of twenty-one years-old, and Kuwait rounding up the top-end of the range, with an average age of twenty-nine years-old.
Interestingly, both American teams are near the older end of the spectrum, with the Women’s team at an average age of twenty-seven, and the Men averaging twenty-eight.
Here is the same data, displayed geographically.
Senior women’s race, average age by country (mobile link):
Senior men’s race, average age by country (mobile link):
One interesting trend – it appears that the East Africa countries are younger than average, while the American and European teams appear slightly older.
Now to get into the meat of our analysis. The following two charts involve a lot of tables and aggregation in the background. Along with the help of Isaac and Justin Britton, we identified a 5,000m, 10,000m, half marathon and/or marathon time that they have run recently. From there, we indexed their time to the IAAF scoring tables, which approximate the strength of each performance, making it possible to draw comparisons across different events. Now, you may point out that this may not be the most precise way to calculate the final result. I would agree. But what this approach brings in is a objective approach that is applied evenly to the entire population. Which is better than blindly guessing.
Unsurprisingly, Kenya has the strongest team, based on past performances. They have multiple athletes who have run under 13:00 for 5,000m and under 27:00 for 10,000m. The following chart shows the rest of the field benchmarked against the Kenyan team. So, for example, Kenya’s top 5 athletes average 1,209 points on the IAAF tables. That is equivalent to 13:00 in the 5,000m, 27:11 in the 10,000m and 2:07:23 in the marathon. Pretty good! By comparison, the U.S. has an average score of 1,138, which is 94% of Kenya’s score. 1,138 points gets you 13:19 in the 5,000m, 27: 56 in the 10,000m and 2:11:21 in the marathon. Also pretty good!
For those asking what the heck is going on with Nigeria, they have several athletes with marathon PBs north of 2:40. It is possible that some of these athletes have run times slightly more commensurate with the rest of the field but I have yet to find anything on the world wide web that would indicate that. It could be a rough day for the Nigerian team.
Here are those same data points, displayed geographically:
It’s a little tough to discern the differences in Africa, so here is a zoomed view of the region:
As you can tell, it is going to be pretty tight up front, with 10 teams in the 90%-100% range. It’s sports. Anything could happen. That’s why we are racing.
For posterity, here are Isaac’s selections, based on a blended statistical/judgemental approach:
And here are mine, based on a pure Power Score approach: