# The Pivotal Point of 15-30

According to nearly every tennis commentator I’ve ever heard, 15-30 is a crucial point, especially in men’s tennis, where breaks of serve are particularly rare. One reasonable explanation I’ve heard is that, from 15-30, if the server loses either of the next two points, he’ll face break point.

Another way of looking at it is with a theoretical model. A player who wins 65% of service points (roughly average on the ATP tour) has a 62% chance of winning the game from 15-30. If he wins the next point, the probability rises to 78% at 30-all, but if he loses the next point, he will only have a 33% chance of saving the game from 15-40.

Either way, 15-30 points have a lot riding on them. In line with my analysis of the first point of each game earlier this week, let’s take a closer look at 15-30 points–the odds of getting there, the outcome of the next point, and the chances of digging out a hold, along with a look at which players are particularly good or bad in these situations.

Reaching 15-30

In general, 15-30 points come up about once every four games, and no more or less often than we’d expect. In other words, games aren’t particularly likely or unlikely to reach that score.

On the other hand, some particular players are quite a bit more or less likely.  Oddly enough, big servers show up at both extremes. John Isner is the player who–relative to expectations–ends up serving at 15-30 the most often: 13% more than he should. Given the very high rate at which he wins service points, he should get to 15-30 in only 17% of service games, but he actually reaches 15-30 in 19% of service games.

The list of players who serve at 15-30 more often than they should is a very mixed crew. I’ve extended this list to the top 13 in order to include another player in Isner’s category:

```Player                 Games  ExpW  ActW  Ratio
John Isner             3166    537   608   1.13
Joao Sousa             1390    384   432   1.12
Janko Tipsarevic       1984    444   486   1.09
Tommy Haas             1645    368   401   1.09
Lleyton Hewitt         1442    391   425   1.09
Tomas Berdych          3947    824   894   1.08
Vasek Pospisil         1541    361   390   1.08
Rafael Nadal           3209    661   713   1.08
Pablo Andujar          1922    563   605   1.08
Philipp Kohlschreiber  2948    652   698   1.07
Gael Monfils           2319    547   585   1.07
Lukasz Kubot           1360    381   405   1.06
Ivo Karlovic           1941    299   318   1.06```

(In all of these tables, “Games” is the number of service games for that player in the dataset, minimum 1,000 service games. “ExpW” is the expected number of occurences as predicted by the model, “ActW” is the actual number of times it happened, and “Ratio” is the ratio of actual occurences to expected occurences.)

While getting to 15-30 this often is a bit of a disadvantage, it’s one that many of these players are able to erase. Isner, for example, not only remains the favorite at 15-30–his average rate of service points won, 72%, implies that he’ll win 75% of games from 15-30–but from this score, he wins 11% more often than he should.

To varying extents, that’s true of every player on the list. Joao Sousa doesn’t entirely make up for the frequency with which he ends up at 15-30, but he does win 4% more often from 15-30 than he should. Rafael Nadal, Tomas Berdych, and Gael Monfils all win between 6% and 8% more often from 15-30 than the theoretical model suggests that they would. In Nadal’s case, it’s almost certainly related to his skill in the ad court, particularly in saving break points.

At the other extreme, we have players we might term “strong starters” who avoid 15-30 more often than we’d expect. Again, it’s a bit of a mixed bag:

```Player                 Games  ExpW  ActW  Ratio
Dustin Brown           1013    249   216   0.87
Victor Hanescu         1181    308   274   0.89
Milos Raonic           3050    514   462   0.90
Dudi Sela              1066    297   270   0.91
Richard Gasquet        2897    641   593   0.93
Juan Martin del Potro  2259    469   438   0.93
Ernests Gulbis         2308    534   500   0.94
Kevin Anderson         2946    610   571   0.94
Nikolay Davydenko      1488    412   388   0.94
Nicolas Mahut          1344    314   297   0.94```

With some exceptions, many of the players on this list are thought to be weak in the clutch. (The Dutch pair of Robin Haase and Igor Sijsling are 12th and 13th.) This makes sense, as the pressure is typically lowest early in games. A player who wins points more often at, say, 15-0 than at 40-30 isn’t going to get much of a reputation for coming through when it counts.

The same analysis for returners isn’t as interesting. Juan Martin del Potro comes up again as one of the players least likely to get to 15-30, and Isner–to my surprise–is one of the most likely. There’s not much of a pattern among the best returners: Novak Djokovic gets to 15-30 2% less often than expected; Nadal 1% less often, Andy Murray exactly as often as expected, and David Ferrer 3% more often.

Before moving on, one final note about reaching 15-30. Returners are much less likely to apply enough pressure to reach 15-30 when they are already in a strong position to win the set. At scores such as 0-4, 0-5, and 1-5, the score reaches 15-30 10% less often than usual. At the other extreme, two of the games in which a 15-30 score is most common are 5-6 and 6-5, when the score reaches 15-30 about 8% more often than usual.

The high-leverage next point

As we’ve seen, there’s a huge difference between winning and losing a 15-30 point. In the 290,000 matches I analyzed for this post, neither the server or returner has an advantage at 15-30. However, some players do perform better than others.

Measured by their success rate serving at 15-30 relative to their typical rate of service points won, here is the top 11, a list unsurprisingly dotted with lefties:

```Player             Games  ExpW  ActW  Ratio
Donald Young       1298    204   229   1.12
Robin Haase        2134    322   347   1.08
Steve Johnson      1194    181   195   1.08
Benoit Paire       1848    313   336   1.08
Fernando Verdasco  2571    395   423   1.07
Thomaz Bellucci    1906    300   321   1.07
John Isner         3166    421   449   1.07
Xavier Malisse     1125    175   186   1.06
Vasek Pospisil     1541    243   258   1.06
Rafael Nadal       3209    470   497   1.06
Bernard Tomic      2124    328   347   1.06```

There’s Isner again, making up for reaching 15-30 more often than he should.

And here are the players who win 15-30 points less often than other service points:

```Player                  Games  ExpW  ActW  Ratio
Carlos Berlocq          1867    303   273   0.90
Albert Montanes         1183    191   173   0.91
Kevin Anderson          2946    377   342   0.91
Guillermo Garcia-Lopez  2356    397   370   0.93
Roberto Bautista-Agut   1716    264   247   0.93
Juan Monaco             2326    360   338   0.94
Matthew Ebden           1088    186   176   0.94
Grigor Dimitrov         2647    360   341   0.95
Richard Gasquet         2897    380   360   0.95
Andy Murray             3416    473   449   0.95```

When we turn to return performance at 15-30, the extremes are less interesting. However, returning at this crucial score is something that is at least weakly correlated with overall success: Eight of the current top ten (all but Roger Federer and Milos Raonic) win more 15-30 points than expected. Djokovic wins 4% more than expected, while Nadal and Tomas Berdych win 3% more.

Again, breaking down 15-30 performance by situation is instructive. When the server has a substantial advantage in the set–at scores such as 5-1, 4-0, 3-2, and 3-0–he is less likely to win the 15-30 point. But when the server is trailing by a large margin–0-3, 1-4, 0-4, etc.–he is more likely to win the 15-30 point. This is a bit of evidence, though peripheral, of the difficulty of closing out a set–a subject for another day.

Winning the game from 15-30

For the server, getting to 15-30 isn’t a good idea. But compared to our theoretical model, it isn’t quite as bad as it seems. From 15-30, the server wins 2% more often than the model predicts. While it’s not a large effect, it is a persistent one.

Here are the players who play better than usual from 15-30, winning games much more often than the model predicts they would:

```Player             Games  ExpW  ActW  Ratio
Nikolay Davydenko  1488    194   228   1.17
Steve Johnson      1194    166   190   1.14
Donald Young       1298    163   185   1.13
John Isner         3166    423   470   1.11
Nicolas Mahut      1344    172   188   1.09
Benoit Paire       1848    266   288   1.08
Lukas Lacko        1162    164   177   1.08
Rafael Nadal       3209    450   484   1.08
Martin Klizan      1534    201   216   1.08
Feliciano Lopez    2598    341   367   1.07
Tomas Berdych      3947    556   597   1.07```

Naturally, this list has much in common with that of the players who excel on the 15-30 point itself, including many lefties. The big surprise is Nikolay Davydenko, a player who many regarded as weak in the clutch, and who showed up on one of the first lists among players with questionable reputations in pressure situations. Yet Davydenko–at least at the end of his career–was very effective at times like these.

Another note on Nadal: He is the only player on this list who is also near the top among men who overperform from 15-30 on return. Rafa exceeds expectations in that category by 7%, as well, better than any other player in the last few years.

And finally, here are the players who underperform from 15-30 on serve:

```Player               Games  ExpW  ActW  Ratio
Dustin Brown         1013    122   111   0.91
Tommy Robredo        2140    289   270   0.93
Alexandr Dolgopolov  2379    306   288   0.94
Federico Delbonis    1110    157   148   0.94
Juan Monaco          2326    304   289   0.95
Simone Bolelli       1015    132   126   0.96
Paul-Henri Mathieu   1083    155   148   0.96
Gilles Muller        1332    179   172   0.96
Carlos Berlocq       1867    256   246   0.96
Grigor Dimitrov      2647    333   320   0.96
Richard Gasquet      2897    352   339   0.96```

Tentative conclusions

This is one subject on which the conventional wisdom and statistical analysis agree, at least to a certain extent. 15-30 is a very important point, though in context, it’s no more important than some of the points that follow.

These numbers show that some players are better than others at certain stages within each game. In some cases, the strengths balance out with other weaknesses; in others, the stats may expose pressure situations where a player falters.

While many of the extremes I’ve listed here are significant, it’s important to keep them in context. For the average player, games reach 15-30 about one-quarter of the time, so performing 10% better or worse in these situations affects only one in forty games.

Over the course of a career, it adds up, but we’re rarely going to be able to spot these trends during a single match, or even within a tournament. While outperforming expectations on 15-30 points (or any other small subset) is helpful, it’s rarely something the best players rely on. If you play as well as Djokovic does, you don’t need to play even better in clutch situations. Simply meeting expectations is enough.

## 2 thoughts on “The Pivotal Point of 15-30”

1. Zeyes says:

Hi Jeff,

I couldn’t help but wonder about one additional facet: Does it make any difference (for individual players and/or overall) whether the 15-30 score was reached via 15-15 or 0-30?

And that sort of ties into another question that popped up while reading: The “strong starters” who bypass 15-30 relatively often, might that be including some players who do it partly by going 0-40 a lot?

1. Jeff says:

I tested a similar question (does it matter whether 30-40 is reached via 30-30 or 15-40) and there isn’t much of a difference:
http://www.tennisabstract.com/blog/2011/11/28/the-hot-hand-in-reverse-at-30-40/

Haven’t tested the latter, but I’d be really surprised if that’s a factor — an average male server ends up at 0-40 less than 5% of the time; good servers (who account for several of the strong starters) far less.