Both the ATP and WTA have adjusted their official rankings algorithms because of the pandemic. Because many events were cancelled last year (and at least a few more are getting canned this year), and because the tours don’t want to overly penalize players for limiting their travel, they have adopted what is essentially a two-year ranking system. For today’s purposes, the details don’t really matter–the point is that the rankings are based on a longer time frame than usual.
The adjustment is good for people like Roger Federer, who missed 14 months and is still ranked #6. Same for Ashleigh Barty, who didn’t play for 11 months yet returned to action in Australia as the top seed at a major. It’s bad for young players and others who have won a lot of matches lately. Their victories still result in rankings improvements, but they’re stuck behind a lot of players who haven’t done much lately.
The tweaked algorithms reflect the dual purposes of the ranking system. On the one hand, they aim to list the best players, in order. On the other hand, they try to maintain other kinds of “fairness” and serve the purposes of the tours and certain events. The ATP and WTA computers are pretty good at properly ranking players, even if other algorithms are better. Because the pandemic has forced a bunch of adjustments, it stands to reason that the formulas aren’t as good as they usually are at that fundamental task.
Hypothesis
We can test this!
Imagine that we have a definitive list, handed down from God (or Martina Navratilova), that ranks the top 100 players according to their ability right now. No “fairness,” no catering to the what tournament owners want, and no debates–this list is the final word.
The closer a ranking table matches this definite list, the better, right? There are statistics for this kind of thing, and I’ll be using one called the Kendall rank correlation coefficient, or Kendall’s tau. (That’s the Greek letter τ, as in Τσιτσιπάς.) It compares lists of rankings, and if two lists are identical, tau = 1. If there is no correlation whatsoever, tau = 0. Higher tau, stronger relationship between the lists.
My hypothesis is that the official rankings have gotten worse, in the sense that the pandemic-related algorithm adjustments result in a list that is less closely related to that authoritative, handed-down-from-Martina list. In other words, tau has decreased.
We don’t have a definitive list, but we do have Elo. Elo ratings are designed for only one purpose, and my version of the algorithm does that job pretty well. For the most part, my Elo formula has not changed due to the pandemic*, so it serves as a constant reference point against which we can compare the official rankings.
* This isn’t quite true, because my algorithm usually has an injury/absence penalty that kicks in after a player is out of action for about two months. Because the pandemic caused all sorts of absences for all sorts of reasons, I’ve suspended that penalty until things are a bit more normal.
Tau meets the rankings
Here is the current ATP top ten, including Elo rankings:
Player ATP Elo Djokovic 1 1 Nadal 2 2 Medvedev 3 3 Thiem 4 5 Tsitsipas 5 6 Federer 6 - Zverev 7 7 Rublev 8 4 Schwartzman 9 10 Berrettini 10 8
I’m treating Federer as if he doesn’t have an Elo rating right now, because he hasn’t played for more than a year. If we take the ordering of the other nine players and plug them into the formula for Kendall’s tau, we get 0.778. The exact value doesn’t really tell you anything without context, but it gives you an idea of where we’re starting. While the two lists are fairly similar, with many players ranked identically, there are a couple of differences, like Elo’s higher estimate of Andrey Rublev and its swapping of Diego Schwartzman and Matteo Berrettini.
Let’s do the same exercise with a bigger group of players. I’ll take the top 100 players in the ATP rankings who met the modest playing time minimum to also have a current Elo rating. Plug in those lists to the formula, and we get 0.705.
This is where my hypothesis falls apart. I ran the same numbers on year-end ATP rankings and year-end Elo ratings all the way back to 1990. The average tau over those 30-plus years is about 0.68. In other words, if we accept that Elo ratings are doing their job (and they are indeed about as predictive as usual), it looks like the pandemic-adjusted official rankings are better than usual, not worse.
Here’s the year-by-year tau values, with a tau value based on current rankings as the right-most data point:
And the same for the WTA, to confirm that the result isn’t just a quirk of the makeup of the men’s tour:
The 30-year average for women’s rankings is 0.723, and the current tau value is 0.764.
What about…
You might wonder if the pandemic is wreaking some hidden havoc with the data set. Remember, I said that I’m only considering players who meet the playing time minimum to have an Elo rating. For this purpose, that’s 20 matches over 52 weeks, which excludes about one-third of top-100 ranked men and closer to half of top-100 women. The above calculations still consider 100 players for year-end 2020 and today, but I had to go deeper in the rankings to find them. Thus, the definition of “top 100” shifts a bit from year-end 2019 to year-end 2020 to the present.
We can’t entirely address this problem, because the pandemic has messed with things in many dimensions. It isn’t anything close to a true natural experiment. But we can look only at “true” top-100 players, even if the length of the list is smaller than usual for current rankings. So instead of taking the top 100 qualifying players (those who meet a playing time minimum and thus have an Elo ranking), we take a smaller number of players, all of whom have top-100 rankings on the official list.
The results are the same. For men, the tau based on today’s rankings and today’s Elo ratings is 0.694 versus the historical average of 0.678. For women, it’s 0.721 versus 0.719.
Still, the rankings feel awfully stale. The key issue is one that Elo can’t help us solve. So far, we’ve been looking at players who are keeping active. But the really out-of-date names on the official lists are the ones who have stayed home. Should Federer still be #6? Heck if I know! In the past, if an elite player missed 14 months, Elo would knock him down a couple hundred points, and if that adjustment were applied to Fed now, it would push down tau. But there’s no straightforward answer for how the inactive (or mostly inactive) players should be rated.
What we’ve learned today
This is the part of the post where I’m supposed to explain why this finding makes sense and why we should have suspected it all along. I don’t think I can manage that.
A good way to think about this might be that there is a sort of tour-within-a-tour that is continuing to play regularly. Federer, Barty, and many others haven’t usually been part of it, while several dozen players are competing as often as they can. The relative rankings of that second group are pretty good.
It doesn’t seem quite fair that Clara Tauson is stuck just inside the top 100 while her Elo is already top-50, or that Rublev remains behind Federer despite an eye-popping six months of results while Roger sat at home. And for some historical considerations–say, weeks inside the top 50 for Tauson or the top 5 for Rublev–maybe it isn’t fair that they’re stuck behind peers who are choosing not to play, or who are resting on the laurels of 18-month-old wins.
But in other important ways, the absolute rankings often don’t matter. Rublev has been a top-five seed at every event he’s played since late September except for Roland Garros, the Tour Finals, and the Australian Open, despite never being ranked above #8. When the tour-within-a-tour plays, he is a top-five guy. The likes of Rublev and Tauson will continue to have the deck slightly stacked against them at the majors, but even that disadvantage will steadily erode if they continue to play at their current levels.
Believing in science as I do, I will take these findings to heart. That means I’ll continue to complain about the problems with the official rankings–but no more than I did before the pandemic.
