Since FIFA started ranking women’s teams in 2003, the United States Women’s National Team has been ranked first or second in the world, and never worse. They’ve hoisted three of the seven Women’s World Cup trophies, and over the course of history have to be considered the best team in the world.
This is in stark contrast to the men’s side that has struggled to gain traction in our sports-fueled nation, despite the same or arguably better resources at its disposal. In Part 2 of this series we examined what features of a nation indicate success on the soccer pitch for the men. The results were in part what you’d expect: population size, financial well-being, and climate were key indicators. But so too was a metric that measures overall life satisfaction of the population as well as whether or not soccer was the favorite sport.
Turning attention to the women’s game could highlight what is driving that dramatic difference between the one letter in the middle of USNTs.
The data set for the model is the same as before with two exceptions. Because FIFA uses an Elo rating system for their ranking in the women’s game, the FIFA ranking is a perfectly fine choice and was the objective for the model. FIFA conducted a study of women’s soccer in 2006 which published the number of registered women soccer players. The numbers are old but a more recent study limited the results to the federation level and this data was the most recent available.
Variables like population, average height, climate, GDP, and the World Happiness Report metrics measuring social support, corruption, health expectancy, freedom to make choices and generosity were also included, as before.
Using a simple multivariate linear regression the following metrics proved to be significant in predicting how good a soccer team could be. The model is slightly better than the men’s and describes 72% of the variability of the FIFA ratings itself, which means that these metrics get most of the way to predicting who will be the best, but there is plenty left unknown.
In the women’s model the population size is the most significant variable. The women’s model has many of the same factors as the men’s with two key distinctions. The number of registered women’s soccer players in 2006 resonates more than soccer being the favorite sport of the nation. This makes logical sense as the metric highlights participation more than popularity for women. On the men’s side the lack of popularity may be driving the best athletes away from soccer, but that does not appear to happen on the women’s side. The financial motivation behind picking a sport is not as strong, so therefore sheer numbers are more important.
Height is also a statistically significant factor unlike in the men’s game. It’s plausible that as the game matures to the extent the men’s game has that physical stature will have less of an impact. It landed as the seventh most significant variable in the model.
The U.S. has more women registered to play soccer than anyone else in the world and that, along with the resources this country enjoys, makes them the favored nation in this sport.
Here is a chart looking at women’s soccer players in 2006 by population. You can see how how far ahead the U.S. is from the rest of the world. Also the top five countries in terms of registered players are all currently ranked in the top fourteen in the world.
As in the men’s model the women’s model rates Life Satisfaction as more significant than financial well-being, as measured in GDP per capita. Life Satisfaction ties more directly to a supportive culture that values more than just financial health. Overall Life Satisfaction likely does not lead to better women’s soccer but is indicative of nations that put emphasis on overall well-being for all populations.
Being apart of the Asia Football Confederation west of South Korea, Japan and Australia is not as significant a knock as on the men’s side, but it is still significant. High temperature climates are also detrimental to soccer success on the women’s side.
Here is a chart of the actual FIFA ranking compared to the model.
Despite being ranked first and third respectively the United States and Germany slightly underperform the model, meaning their resources and playing population suggest they could be even more dominant. But as you can see the U.S. have a strong advantage according to the model and should always be a world leader.
On a lighter note I became curious about that data point far away from all the others. The lowest-ranked team in FIFA is a small African island called Mauritius. I had never heard of the island before I started this research, but it appears they just recently played some official matches in 2016 for the first time in a very long time. That’s exciting to see and pictures of that island look amazing, so here’s to hoping that Mauritius continues their journey in women’s soccer and moves up the scale. Then maybe the U.S. team will play a match there and that’ll give me an excuse to go on vacation.
This concludes three articles on the importance of measurement and what drives success in world football. I’d like to end on the point I started with. Our increasingly measurable world is a great opportunity but what metrics we solve for is more critical than ever. That’s true in the world that really matters as well as the sport here in the United States. I hope the leaders of our soccer nation take measurement seriously and begin to publicly discuss goals beyond financial success and put the programs in place to steer our country toward those goals.