Sysyphus
Talk Tennis Guru
[Trigger warning: this thread is directed primarily at those who are unduly interested in the nitpicky and trivial aspects of tennis stats. If that ain't you, this thread's probably not for you.]
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As many of you already know, dominance ratio (DR) is a nifty little stat to show how dominant a player has been in a given match, season, or similar. It has been shown to explain a lot of the variation in players' win %, and is thought to be a good predictor of future results. On the face of it, it's pretty similar to looking at what % of points a player wins, but not quite. How is it calculated? By dividing the percentage of points you win against your opponent's serve versus the percentage of points your opponent wins against your serve. Wouldn't that amount to pretty much exactly the same as just % of total points won? Not entirely – let's look at two hypothetical examples.
Say player A wins 70% of his serve points of his service points and 35% of his return points. Assuming an equal number of points played on serve and return, this should suggest that he wins 52.5% of points overall. His DR would be 35/30 ≈ 1.17
Player B wins 65% of service points and 40% of return points, which (assuming equal amount of serve and return points played) suggests the same % of total points won. However, played B would end up with a lower DR of 40/35 ≈ 1.14.
Therefore, it looks like ceteris paribus DR is slightly skewed toward players with better serve stats. Now, I wasn't sure whether this slight skew meant that DR is slightly inaccurate compared to just % of points won, or whether it's actually a clever way of weighting for an actual advantage of stronger serving. Perhaps the ratio of return points won vs. service points lost tells us something important beyond what just total % of points won tells us. That was my hunch, that DR would be a slightly stronger predictor.
To check this, I ran a quick regression analysis comparing first the connection between DR and match win %, then % of points won and match win %. Thanks to this excellent thread by Falstaff, where he shows that DR explains a lot of variance in match win %, there was already a data set to look at where half the data was already plotted in (the clay seasons of Nadal, Ferrer, Djokovic and Federer through 2012/2013), so I used that sample for convenience's sake.
What were the results? Dominance ratio explained 76.9% of the variation in winning percentage (like Falstaff found). The correlation between DR and match win percentage was .877.
Percentage of total points won explained 78.3% of the variation in match win percentage, and the correlation was .885.
As such, this seems to me to beg the question: does using dominance ratio really add anything useful compared to just using the simple nuts and bolts metric of % of points won? The explained variation and correlation is almost exactly the same. If anything, % of points won seems to do slightly better, which may give credence to the idea that DR has a slightly unnecessary skew in favor of better serving.
This was pretty spur of the moment, and I may have overlooked something obvious. Calling tennis stat enthusiasts @falstaff78 @Chanwan @TheFifthSet @Gary Duane @Meles and the rest.
———
As many of you already know, dominance ratio (DR) is a nifty little stat to show how dominant a player has been in a given match, season, or similar. It has been shown to explain a lot of the variation in players' win %, and is thought to be a good predictor of future results. On the face of it, it's pretty similar to looking at what % of points a player wins, but not quite. How is it calculated? By dividing the percentage of points you win against your opponent's serve versus the percentage of points your opponent wins against your serve. Wouldn't that amount to pretty much exactly the same as just % of total points won? Not entirely – let's look at two hypothetical examples.
Say player A wins 70% of his serve points of his service points and 35% of his return points. Assuming an equal number of points played on serve and return, this should suggest that he wins 52.5% of points overall. His DR would be 35/30 ≈ 1.17
Player B wins 65% of service points and 40% of return points, which (assuming equal amount of serve and return points played) suggests the same % of total points won. However, played B would end up with a lower DR of 40/35 ≈ 1.14.
Therefore, it looks like ceteris paribus DR is slightly skewed toward players with better serve stats. Now, I wasn't sure whether this slight skew meant that DR is slightly inaccurate compared to just % of points won, or whether it's actually a clever way of weighting for an actual advantage of stronger serving. Perhaps the ratio of return points won vs. service points lost tells us something important beyond what just total % of points won tells us. That was my hunch, that DR would be a slightly stronger predictor.
To check this, I ran a quick regression analysis comparing first the connection between DR and match win %, then % of points won and match win %. Thanks to this excellent thread by Falstaff, where he shows that DR explains a lot of variance in match win %, there was already a data set to look at where half the data was already plotted in (the clay seasons of Nadal, Ferrer, Djokovic and Federer through 2012/2013), so I used that sample for convenience's sake.
What were the results? Dominance ratio explained 76.9% of the variation in winning percentage (like Falstaff found). The correlation between DR and match win percentage was .877.
Percentage of total points won explained 78.3% of the variation in match win percentage, and the correlation was .885.
DR
% points won
% points won
As such, this seems to me to beg the question: does using dominance ratio really add anything useful compared to just using the simple nuts and bolts metric of % of points won? The explained variation and correlation is almost exactly the same. If anything, % of points won seems to do slightly better, which may give credence to the idea that DR has a slightly unnecessary skew in favor of better serving.
This was pretty spur of the moment, and I may have overlooked something obvious. Calling tennis stat enthusiasts @falstaff78 @Chanwan @TheFifthSet @Gary Duane @Meles and the rest.
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