croschicago.blogg.se

For each tournament, I look at the ace rates in every match, and control for the servers and returners in those matches. Ace rate doesn’t tell the whole story, of course, but as you’ll see, it’s a pretty good first- or second-order approximation. I rate the playing speed of every ATP surface using ace rate as a proxy for surface characteristics. I’ll give a brief overview here, as well: If you’re interested in the long-form explanation of how these numbers work, what their limitations are, and so on, check out my post from three years ago.

Posted on JAuthor Jeff Categories Forecasting, French Open, Surface speed The Speed of Every Surface, 2019 Editionįans are constantly talking about surface speed … I have written a lot about surface speed … yet somehow, I haven’t published complete surface speed numbers for three years. The best players–based on their demonstrated clay-court prowess–tended to win, about as often as they always do at the French. While adjustments were surely necessary, most players were able to make them, and to similar degrees. However unusual the conditions at Roland Garros felt to the players, the weirdness didn’t cause the results to be any more random than usual. But the metrics tell us that the outcomes were closer to the average than to the extremes. The results this year were a bit more unpredictable than the typical major since 2000. For both men and women, the 2023 French was no more upset-ridden than the 2021 edition, and it ran considerably closer to script than the zany Covid tournament in autumn 2020. (“Min RG” and “Max RG” show the lowest and highest tournament Brier scores for each gender at the French since 2000.)Īgain, lower = more predictable. Here are the Brier scores for men’s and women’s completed main draw matches, along with several other measures for context: Tourney(s) Men Women Or put another way, for our purposes today: The lower the score, the more predictable the outcomes.Ĭonclusion: This year’s French wasn’t that weird. Brier score tells us the calibration of a group of predictions: Were they correct? Did they have the right level of confidence? The lower the score, the better the forecast. Elo isn’t perfect, but it’s pretty good, and the algorithm allows us to compare seasons and tournaments against each other. We measure predictability by taking the Brier score of my Elo-based pre-match forecasts. While unusually heavy balls don’t rank up there with my hypotheticals, the idea is the same: The more you deviate from typical conditions, the less predictable the results. Holding a top ranking in “normal” tennis doesn’t mean as much in “dark” tennis or “broken string” tennis. At the very least, there will be more upsets. In those situations, the factors that determine the winner of a match are so different than usual that they will probably seem random. If you don’t get the connection, think about really strange conditions, like playing in mud, or in the dark, or with rackets that have broken strings. The weirder the conditions, the more unpredictable the results should be. Were the conditions weirder? To put it another way, were they outside the normal range of variation on tour? We could be talking about anything that impacts play, including surface, balls, weather, you name it. We could compare ace rate, rally length, or a few other metrics to see whether the French played slower this year. Maybe the courts really did play differently. The balls were heavier than usual, especially when they had been in play for a little while and the clay began to stick to them. The clay, apparently, was slower and heavily watered, at least on some courts. Many players didn’t like the conditions at Roland Garros this year.