Tennis Note #8

Rafa in Paris: The Numbers

Nikita Taparia
The Tennis Notebook

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I looked at the king of clay: Rafael Nadal. Since then, Rafa lost in the Madrid final and Rome quarterfinals so the number of consecutive matches between losses continues to decrease. I neglected Roland Garros data on purpose because despite his previous losses in 3 set matches, Rafael Nadal has only lost ONCE in Paris. So even though a version of this visual on his decline became rather popular on social media, I really should point out the obvious…

There must be some logical explanation for why Rafael Nadal dominates at Paris other than he has super powers. I mean his previous matches are pretty impressive.

The major difference between these matches are the maximum number of sets played (Roland Garros is best of 5 sets and the rest are best of 3 sets) and the road to the final has more matches (7 in Paris). However, this cannot be the only driving force behind Nadal’s success in Paris. Thus, I will be looking at several matches*, especially after watching him lose against Andy Murray in the Madrid final, which was deconstructed beautifully by Craig O’Shannessy.

Side note: The number of charted matches is limited, which is why I would advocate tennis fans getting involved because this data is not readily available.

I want this to be an interactive note. I provide several detailed visualizations so please share your interpretations!

Every moment in tennis is important, from the first point to the last. This has always been the mentality of Rafael Nadal. Therefore, I want to deconstruct point distributions in two ways. The first graphic is a rather obscure way to present the complete point distribution, scaled for each match. Obviously, the most points played will be at 0–0 between the two players, hence the dark green.

Maximum to minimum goes from green to yellow to red for each match. To read this, let’s pick an example. Fore instance, 2nd row, 3rd column is 30–15 Nadal or 15–30 Djokovic. Note: Two tiebreaks, which were split, in 2009, were not mapped in this visual.

The top two are best of 3 set losses to Djokovic. The bottom are two Roland Garros matches, last year’s win and his only loss. Each loss tells a different story. For instance, this year in Monte Carlo, you can see that there were many opportunities at deuce for which Djokovic got the majority of the advantage. I will let you interpret the rest of the distributions. Tell me what you think!

Inspired by these game trees of Nadal’s matches in 2013 from Damien Saunder, the second set of graphics below illustrates the four matches when Rafael Nadal is on serve. Saunder analysed Rafael Nadal’s service against Novak Djokovic in the 2013 Roland Garros semifinal. Nadal was incredibly dominant on serve but as we know from the scoreline, Djokovic took advantage of the very few key moments he was given.

(1)2015 Monte Carlo SF (2) 2014 Rome Final
(3) 2014 Roland Garros Final (4) 2009 Roland Garros R16

From these particular matches it is clear Rafael Nadal needs to take the path of least resistance and win those points. By this I mean, if the farther right paths have thicker line widths, he has won a clean game. It is something I have noticed quite a bit but especially this year. He needs to capitalize quickly if he has four set point opportunities in a tiebreak (I still cannot get over his loss in Rome this year).

First serve frequency (darker regions = more frequent) and percentage of points won.

Serve placement is hardly a mystery when it comes to Rafael Nadal, especially to the best returner in the game but I wanted to look at the evolution of his first serve placement. I illustrated the frequency (color) and the percentage of points he won when he served in the different regions.

Now let’s focus on deuce out wide (it would go to the forehand of right-handed players). Back in 2013 Roland Garros semifinal, he won 44% of those points in less than 3 shots, with 4 aces. He had made the change in 2012, according to Craig O’Shannessy, who noted:

Nadal served much more to Djokovic’s forehand in this match and reaped the rewards of serving not where he prefers to serve but where Djokovic is not as deadly returning.

Of course, Nadal won Roland Garros in 2014 without this serve but the lack of it was apparent in his losses in Monte Carlo and Rome earlier that year as well as this year at Monte Carlo. The two times he did serve there in Monte Carlo, he won the point both times. The one good thing Nadal reintroduced is his serve down the line on the Ad (left) side, which would also hit Djokovic’s forehand, and he won a majority of those points. His frequent service spots may be beneficial against anyone with a less dominant backhand, but certainly not the likes of Djokovic, Murray, or Wawrinka. His service games have become more difficult for him to defend but if he can hit to the forehand, he can win.

When you are inside the baseline, you are more aggressive and attack the slower 2nd serve.

The most striking image, at least for me, was from the 2015 Madrid final. Andy Murray’s win was so convincing and I am pretty certain 2nd serve return court positioning tells a major story.

I do not have information on his court positioning during all these other matches but I can answer a couple of other questions. For instance, where does Nadal return and what percentage of the points does he win when he returns in certain areas?

Return placement frequency (darker region = more frequent) and percentage of points won.

Recall, when Nadal loses, his 1st serve return ~ 33% and 2nd serve return ~ 45%. In his two wins, especially 2014 Roland Garros final, he won a majority of the 2nd serve points. It seems the other key thing is winning ~50% of the points when he returns the shot inside the service box. Typically, a deep return is better but if his return does fall short, especially if he is going to position himself outside the baseline, he must do whatever is possible to win those points and not rely on his opponents to make the mistakes.

The last thing I did was deconstruct the actual points and try to figure out what failed Nadal each time he lost and how it was different to his wins in Roland Garros. I know from the summary of his match against Murray, his biggest problem was his forehand and this is not a singular incident for 2015. The only positive from the Madrid final is his net play, in which he won a majority of the points. This is pretty common among many of these matches but he typically chooses to play these shots less frequently. The other major thing I know, especially from my last note, is his ability to save or convert break points has decreased. So let’s dig a little deeper. From this point onward, other than 2009, all other matches are against Djokovic for consistency.

Rally outcomes. Total number of points for different length rallies and percentage Nadal won. The color scheme is the difference between the percentage of winners and unforced errors. Basically, bang for buck: if you are winning over 50% of the points and you are in the green, you hit more winners than errors to win a majority of the rallies.

Here, I illustrated the distribution of points Nadal won for different length rallies. Over 50% means he won the majority of that particular rally. Of these different length rallies, I wanted to see if he had more winners (green/green border) or unforced errors (pink/pink border). I took the difference between the percentages so if he makes an equal number of winners and errors, he breaks even (white/black border). Make sense?

Now, there is one thing very clear in this visual. Rafael Nadal was absolutely dominant and made the most of every situation in the 2014 Roland Garros Final: I mean look at that green! In 2009 Roland Garros loss to Soderling, Nadal was only dominant for rallies over 10 shots and made plenty of errors for rallies between 4–9 shots. His loss this year may show that he is in the green for extended rallies but keep in mind, the number of shorter points played was much greater than longer points.

Shot Direction and Type. Let’s break this crazy animation down. 1) The line width is actually the difference between the number of winners and unforced errors. Only positive values are shown. 2)For every forehand and backhand played by Nadal, you can see what percentage of points he won. Of these points, you can see by how much percentage did he hit winners vs. errors.

I might have have gone a little crazy with the GIFs but I really wanted to know the distribution of forehand and backhands and the actual shot directions. Fact: Rafael Nadal’s most dominant shot is the forehand. Even if his backhand is not the best, it really is dependent on the forehand. So what happened in 2009 Roland Garros? Robin Soderling did a pretty good job to neutralize the inside out forehand. In fact, Nadal did not dominate on his forehand and his backhand was even worse. His 2015 Monte Carlo semifinal loss is comparable and you can see that he barely hit more forehand winners than errors and actually his backhand saved him in certain instances. In his victories at Roland Garros, both his inside out and down the line forehand were dominant and attacked Djokovic’s forehand.

Through this entire process, I find myself reaching very obvious conclusions for Rafael Nadal: take the simplest path to win the GSM(game-set-match), serve to the forehand if your opponent’s backhand is lethal, crush the 2nd serve return, win the shorter points, and kill it with that forehand. I look forward to this year’s Roland Garros and hope Rafael Nadal can win his 10th title in Paris. #VamosRafa

All data is from Tennis Abstract’s amazing database available on GitHub. I talked about it previously. Special thanks to those who took the time to chart the matches. If you enjoy reading these tennis notes, make sure to follow the publication, ‘Recommend’ and share! Check us out on Facebook! Made a cool observation? Interested in certain topics and writing? Are you a tennis photographer? Comment, add notes, and check out the submission guideline. Let me know which visuals are good and which are not so great. Cheers!

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Nikita Taparia
The Tennis Notebook

Engineer. Scientist. Data Nerd. Cookie/Coffee Addict. Educator. Tennis/WoSo. Photographer. Musician. Artist. Whiteboards. Writer.