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To find out who has, I compared actual and expected pace for each match 10 :. Nadal is vexed that time has become such a pressing topic lately. Murray knows sometimes he is too slow. And, yeah, sometimes you can go too slow. Australian Bernard Tomic said he plays fast deliberately. He told me that mixing up pace between points is another way to throw off opponents, like changing the speed or spin of the ball.
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Lukas Rosol, a Czech player, and American Sam Querrey both said they like to get into a rhythm when serving. Waiting too long between points can break that up. Querrey gets annoyed when he has to wait too long to return, and he figures spectators do, too. Some players told The Washington Post they want the game sped up, such as by forcing servers to hit their first ball toss, no matter how errant. He was a bit off, but the match was still pretty fast for a five-setter, taking two hours and 50 minutes.
Ivanisevic has to watch a lot of tennis these days, as coach of defending U. Open champ Marin Cilic, and he wishes players would move faster. Michael Chang, the French Open champ, correctly guessed that he was somewhere in between Ivanisevic and Nadal in pace.
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So did Pat Cash, the Wimbledon champ. He also often ended points at the net, so he needed time to walk back to the baseline. Nonetheless, he used to rush as a young player, until he was taught to slow down — so slow, he was one of the slowest players, relative to his era.
And the men notice. Just after the ATP became stricter, in early , men and women both played an Australian Open warmup event in Brisbane. The database was updated through April 27 when I downloaded it. Tennis data is imperfect, including some errors in the historical record. I scrapped all matches with no time info, plus other oddballs including matches with stats that looked wrong, like a handful of matches with points that averaged less than 15 seconds or more than six and a half minutes.
I used R. The dependent variable was time divided by points. The variables I included started with the breakdown of points in the match, by type. More precisely, these variables were the percentage of points that were followed by: changeovers after the first game of a set, when there is no prolonged break; changeovers later in sets, with longer breaks; ends of sets; end of matches by one player winning the requisite number of sets; end of matches by retirement; end of matches by default; all other ends of games; tiebreaker changeovers, when players switch side in tiebreakers, after each six points; tiebreaker changes of serve, after an odd number of points; and all other tiebreaker points.
Because of the nature of the regression, I had to choose one event to serve as the benchmark for all others. So I went with the first one listed when the events were lined up in alphabetical order.
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Also note that some of the names were formatted inconsistently, so a few tournaments appear more than once, covering different time periods. All the player data is relative to retired American Aaron Krickstein, again chosen because he came first in alphabetical order. Since he played slightly faster than average for his time, the regression output shows the average player as contributing time to matches, so is best used to compare players to each other, and not as an absolute number.
I measured time before of their serves between Thursday and Saturday.
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They were playing on courts at the same facility, in roughly the same weather conditions. The r-squared for the correlation was 0. The serve speed data also is on GitHub.
By inserting the regression coefficients, so I was controlling for opponent, surface, year and other factors. To find out who has, I compared actual and expected pace for each match 10 : Nadal is vexed that time has become such a pressing topic lately.
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