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Source Tennis Abstract? Tennis Abstract's own stats disagree. I've extracted the M1000 in order from fastest to slowest:

Code:
Tournament        Surface  2019 Ace%  2019  2018  2017 
Shanghai             Hard      13.0%  1.24  1.17  1.53 
Paris Bercy          Hard      11.9%  1.19  1.06  1.03
Cincinnati           Hard      11.6%  1.04  0.98  1.22  
Canada               Hard      10.8%  1.03  1.17  0.97  
Miami Masters        Hard       9.5%  0.86  0.78  0.84  
Indian Wells         Hard       7.6%  0.76  0.84  1.03  
Madrid               Clay       7.0%  0.71  0.84  0.89  
Rome                 Clay       7.0%  0.69  0.69  0.85  
Monte Carlo          Clay       4.7%  0.50  0.56  0.50
 

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Source Tennis Abstract? Tennis Abstract's own stats disagree. I've extracted the M1000 in order from fastest to slowest:

Code:
Tournament        Surface  2019 Ace%  2019  2018  2017
Shanghai             Hard      13.0%  1.24  1.17  1.53
Paris Bercy          Hard      11.9%  1.19  1.06  1.03
Cincinnati           Hard      11.6%  1.04  0.98  1.22
Canada               Hard      10.8%  1.03  1.17  0.97
Miami Masters        Hard       9.5%  0.86  0.78  0.84
Indian Wells         Hard       7.6%  0.76  0.84  1.03
Madrid               Clay       7.0%  0.71  0.84  0.89
Rome                 Clay       7.0%  0.69  0.69  0.85
Monte Carlo          Clay       4.7%  0.50  0.56  0.50
Interestingly, DC finals pretty fast too.
Davis Cup Finals Hard 11.9% 1.20

But I would take this Ace measure with a grain of salt, read the fine print (from the link you provided):

"The main advantages of this approach are that it is simple to understand (more aces = higher rating!), and that we can calculate it with limited information–data that is available for ATP matches back to the early 1990s. Court Pace Index and other Hawkeye-based metrics surely have a lot more to add, but they require much more sophisticated tools–tools that federations and tours aren’t about to share with lowly fans like us."

So I will trust the CPI measure more.
 
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