In a post last week, I presented some data that suggested that servers weaken a bit under the pressure of a tiebreak. It’s not a strong effect, but it’s a consistent one. A possible explanation–that all that time between points gives servers a chance to psych themselves out, yet may not affect returners the same way–would apply almost as much to games toward the business end of a set, such as at 5-5 or 5-6.
In other words, if players don’t serve as well (or they return better) when things get tight, we’d expect to see more breaks toward the end of a set–more breaks than expected at 5-5, but perhaps fewer breaks than expected at 2-2.
This also opens up a possible method for evaluating players, as Carl Bialik has suggested. If someone is losing more sets 5-7 than they are winning 7-5, it may be that they are wilting under the pressure of 5-5 more than the average player. It would make sense if the players who consistently exceed tiebreak expectations also regularly outperform 7-5 expectations as well.
Within the constraints of the ATP’s Matchstats, 7-5 sets are a great way to identify these patterns. While some 6-4 sets end with a break (or a break followed by a set-sealing hold), a 6-4 set doesn’t necessarily end that way. But a 7-5 set must have reached 5-5 before one player took control.
If the hypothesis is correct that players get tighter on serve as the end of the set approaches, we would expect more 7-5 sets in the real world than simulations would imply.
To estimate the number of sets that should end 7-5, we need to take each player’s service points won from each match. With that, we can calculate the probabilities that sets will end at any given score. Repeat the process for every match over a period of time and we get a general idea of how often we should see 7-5 sets.
As it turns out, 7-5 sets should make up about 7.8% of all sets. In fact, 8.8% of sets end 7-5. Not a huge difference, but one that is fairly consistent from year to year. Every year since 1991, where this dataset begins, there have always been more 7-5s than expected. It certainly adds more weight to the claim that the balance of power swings to the returner toward the end of a tight set.
(My set-prediction model doesn’t exactly replicate reality, since players win more games than their service winning percentages predict, in large part because almost all servers are better in either the deuce or ad court, and the variance between them makes it more likely that the player wins a given service game. When applying a crude adjustment for this, the crumbling-server hypothesis looks even better–the more games servers are predicted to win, the fewer predicted 7-5 sets.)
Identifying the unbreakable
This type of discussion must make you wonder: Which players are good as this stuff? If it is true that late-set pressure results in more breaks, it seems obvious that some players are more prone to that pressure, and that other players take advantage of that pressure.
In an ideal world, we’d be able to identify some great 7-5 records, point out some 5-7 records, and have some great new insights into players.
As it is … we might.
As we saw last week with tiebreak analysis, we can’t simply count up a player’s 7-5 sets and compare that total to his 5-7 set losses. Over the last three years, Andy Roddick won more than 55% of his 7-5 and 5-7 sets, but given the players he faced in those sets and their performances in those matches, he should have won 62%.
There are two ways to quantify player accomplishments in this department. The first evaluates how well a player avoids losing 5-7 when he reaches 5-5; the other compares his ability to break for 7-5 against his proneness to being broken for 5-7.
Let’s call the first stat Five-Seven AVoidance, or FSAV. For any player, we first add up the sets that reached 5-5, then count the sets that he won 7-5 or reached a tiebreak. Then we use the general method described above to estimate how many times the player should have reached 5-5, and how many of those times he should have avoided 5-7. Since the beginning of 2010, Kei Nishikori has avoided a 5-7 finish in about 92% of the sets in which he reached 5-5. My model would have expected him to avoid 5-7 only about 84% of the time. (The model expects that most players will avoid 5-7 about 82-90% of the time they reach 5-5.)
From those numbers, we discover that Nishikori lost 5-7 less than half as often as we would have expected him to. No other player comes close to that mark. In everyday language, FSAV approximates how often a player was able to hold serve at 5-5 or 5-6. Important skill, that.
The second stat is more narrowly focused on 5-5 sets that do not reach a tiebreak. Let’s call this one the Seven-Five Outperformance Rate, or SFOR, similar to the TBOR (TieBreak Outperformance Rate) I introduced last week.
Here, instead of comparing 5-7s to all 5-5 sets, we compare 5-7s to 7-5s. In other words: Is the player more likely to break for 7-5 or be broken for 5-7? As with the previous stat, after calculating the simple rate (that is, number of 7-5 sets divided by total number of 7-5 and 5-7 sets), we compare that to the results that the model would have expected the player to post.
Bizarrely enough, our three-year leader in SFOR is Ernests Gulbis, who has won about 73% of his 7-5 and 5-7 sets, compared to the 50% the model expects of him. (It’s even more impressive when compared to the 7% that I personally would have expected from him.)
As the highlighting of Gulbis suggests, these stats probably don’t yet belong in our everyday toolbox. There simply aren’t very many 7-5 sets, even if–as I established above–there are a few more than we would expect. For reference, there are almost twice as many tiebreaks as 7-5s.
And to keep Gulbis in the spotlight, it may be that winning 7-5 sets is more a function of getting to 5-5 when you shouldn’t. Perhaps many of those 7-5s racked up by the Latvian came when he should have put the set away 6-2. Once 5-5 came along, he finally decided to get serious. As Gulbis himself might tell you, it’s anybody’s guess.
Follow the jump for FSAV and SFOR on about 50 or so of the most active players (including all tour-level matches (but excluding Davis Cup) since the beginning of 2010, sorted by FSAV) and decide for yourself.
player FSAV SFOR Kei Nishikori 2.05 1.23 Feliciano Lopez 1.92 1.11 Ernests Gulbis 1.66 1.46 Juan Martin Del Potro 1.59 1.26 Janko Tipsarevic 1.48 1.13 Potito Starace 1.47 1.07 Sergiy Stakhovsky 1.36 1.06 Nicolas Almagro 1.35 1.24 Gael Monfils 1.34 1.26 Thomaz Bellucci 1.32 1.30 Stanislas Wawrinka 1.30 1.20 Gilles Simon 1.26 1.15 Andy Murray 1.22 1.13 Milos Raonic 1.19 1.02 Rafael Nadal 1.06 1.13 Juan Monaco 1.06 1.15 Alexandr Dolgopolov 1.05 1.17 Radek Stepanek 1.04 1.01 John Isner 1.02 1.15 Andreas Seppi 1.00 1.22 Marcos Baghdatis 1.00 1.17 Mikhail Youzhny 0.99 0.97 Jo Wilfried Tsonga 0.99 1.12 Marin Cilic 0.97 1.00 Nikolay Davydenko 0.97 1.15 Albert Montanes 0.96 1.02 Marcel Granollers 0.96 1.16 Florian Mayer 0.93 1.03 Jurgen Melzer 0.92 1.03 Jeremy Chardy 0.91 0.93 Robin Haase 0.91 0.53 Guillermo Garcia Lopez 0.91 0.79 Robin Soderling 0.89 1.15 Denis Istomin 0.89 1.04 Viktor Troicki 0.88 0.94 Pablo Andujar 0.87 0.81 Tomas Berdych 0.87 1.08 Jarkko Nieminen 0.87 1.07 Santiago Giraldo 0.84 0.98 Philipp Petzschner 0.82 1.04 Mardy Fish 0.82 0.90 Victor Hanescu 0.81 0.52 Fabio Fognini 0.80 1.00 Philipp Kohlschreiber 0.77 0.85 Andy Roddick 0.77 0.90 Fernando Verdasco 0.76 0.92 Juan Ignacio Chela 0.76 0.84 Kevin Anderson 0.73 0.84 Roger Federer 0.72 0.98 Xavier Malisse 0.71 0.93 Julien Benneteau 0.71 0.79 David Ferrer 0.70 0.92 Lukasz Kubot 0.70 0.82 Sam Querrey 0.68 0.98 Novak Djokovic 0.66 0.88 Richard Gasquet 0.55 0.69