Are tennis players tougher to predict when they haven’t played an official match for almost half a year? Last week’s WTA return-to-(sort-of)-normal in Palermo gave us a glimpse into that question. In a post last week I speculated that results would be tougher than usual to forecast for awhile, necessitating some tweaks to my Elo algorithm. The 31 main draw matches from Sicily allow us to run some preliminary tests.
At first glance, the results look a bit surprising. Only two of the eight seeds reached the semifinals, and the ultimate champion was the unseeded Fiona Ferro. Two wild cards reached the quarters. Is that notably weird for a WTA International-level event? It doesn’t seem that strange, so let’s establish a baseline.
Palermo the unpredictable
My go-to metric for “predictability” is Brier Score, which measures the accuracy of percentage forecasts. It’s nice to pick the winner, but it’s more important to assign the right level of probability. If you say that 100 matches are all 60/40 propositions, your favorites should win 60 of the 100 matches. If they win 90, you weren’t nearly confident enough; if they win 50, you would’ve been better off flipping a coin. Brier Score encapsulates those notions into a single number, the lower the better. Roughly speaking, my Elo forecasts for ATP and WTA matches hover a bit above 0.2.
From 2017 through March 2020, the 975 completed matches at clay-court WTA International events had a collective Brier Score of 0.223. First round matches were a tiny bit more predictable, with R32’s scoring 0.219.
Palermo was a roller-coaster by comparison. The 31 main-draw matches combined for a Brier Score of 0.268. Of the 32 other events I considered, only last year’s Prague tourney was higher, generating a 0.277 mark.
The first round was more unpredictable still, at 0.295. On the other hand, the combination of a smaller per-event sample and the wide variety of first-round fields means that several tournaments were wilder for the first few days. 9 of the 32 others had a first-round Brier Score above 0.250, with four of them scoring higher–that is, worse–than Palermo did.
The Brier Score of shame
I mentioned the 0.250 mark because it is a sort of Brier Score of shame. Let’s say you’re predicting the outcome of a series of coin flips. The smart pick is 50/50 every time. It’s boring, but forecasting something more extreme just means you’re even more wrong half the time. If you set your forecast at 50% for a series of random events with a 50/50 chance of occurring, your Brier Score will be … 0.250.
Another way to put it is this: If your Brier Score is higher than 0.250, you would’ve been better off predicting that every match was 50/50. All the fancy forecasting went to waste.
In Palermo, 17 of the 31 matches went the way of the underdog, at least according to my Elo formula. The Brier Scores were on the shameful side of the line. My earlier post–which advocated moderating all forecasts, at least a bit–didn’t go far enough. At least so far, the best course would’ve been to scrap the algorithm entirely and start flipping that coin.
Moderating the moderation
All that said, I’m not quite ready to throw away my Elo ratings. (At the moment, they pick Simona Halep and Aryna Sabalenka, my two favorite players, to win in Prague in Lexington. So there’s that.) 31 matches is small sample, far from adequate to judge the accuracy of a system designed to predict the outcome of thousands of matches each year. As I mentioned above, Elo failed even worse at Prague last year, but because that tournament didn’t follow several months of global shutdowns, it wouldn’t have even occurred to me to treat it as more than a blip.
This time, a week full of forecast-busting surprises could well be more than a blip. Treating players as if they have exactly the abilities they had in March is probably the wrong way to do things, and it could be a very wrong way of doing things. We’ll triple the size our sample in the next week, and expand it even more over the next month. It won’t help us pick winners right now, but soon we’ll have a better idea of just how unpredictable the post-COVID-19 tennis world really is.