The Power of One Point Per Thousand

Last week, I offered a method to rank smash-hitting skill. I measured the results in “points per 100”–the number of points a player could expect to gain or lose, relative to tour average, thanks to their ability hitting that one shot. The resulting figures were quite small: My calculations showed that Jo-Wilfried Tsonga has the game’s best smash, a shot worth 0.17 points per 100 above average, and 0.27 points per 100 above the weakest smash-hitting player I found, Pablo Cuevas.

That gap between best and worst of 0.27 per 100 gives us a rough maximum of how much difference a good or bad smash can make in a player’s game. The rate is roughly equivalent to one point out of 370. It sounds tiny, and since most players are closer to the average than they are to either of those extremes, the typical smash effect is even smaller still.

However, it’s difficult to have any intuitive sense of how much one point is worth. In any given match, a single point, or even five points, isn’t going to make the difference. On the other hand, plenty of matches are so close that one or two points would flip the result. If an average player could train really hard in the offseason and develop a smash just as good as Tsonga’s, what would that extra 0.17 points per 100 mean for him in the win column? What about in the rankings?

This is a relatively straightforward question to answer once we’ve posed it. Over the course of a season, the best players win more points than their peers–obviously. Yet the margin isn’t that great. In 2017, no man won points at a higher clip than Rafael Nadal, who came out on top 55.7% of the time. That’s less than seven percentage points higher than the worst player in the top 50, Paolo Lorenzi, who won 49.1% of points. Nearly half of top 50 players–22 of them–won between 49.0% and 51.0% of total points, and another 15% fell between 51.0% and 52.0%.

Fixing total points won

These numbers are slightly misleading, though only slightly. The total points won stat (TPW) tends to cluster very close to the 50% mark because competitors face what, in other sports, we would call unbalanced schedules. If you win, you usually have to play someone better in the next round; win again, and an even more superior opponent awaits. This means that the 6.6% gap between Nadal and Lorenzi is a bit wider than it sounds: Had the Italian faced the same set of opponents that Rafa did, he wouldn’t have managed to win 49.1% of points.

That problem, however, is possible to resolve. Earlier this year I shared an algorithm that analyzed return points won by controlling for opponent, by comparing how each pair of players fared in equivalent matchups. (That analysis hinted at the second-half breakthrough of return wizard Diego Schwartzman.) While we don’t know exactly what would happen if Lorenzi played Nadal’s exact schedule, we can use this common-opponent approach to approximate it. When we do so, we find that the 1st-to-50th, Nadal-to-Lorenzi spread is almost 10 percentage points; setting Rafa’s rate at a constant 55.7%, Lorenzi’s works out a less neutral-sounding 46.2%. Many players remain packed in the 49%-to-51% range, but the overall spread is wider, because we control for tennis’s natural tendency to cancel out player’s wins with subsequent losses.

Even when we widen the pool of players to 71–everyone who played at least 35 tour-level matches this season–the ten-percentage-point spread remains. Lorenzi remains close to the bottom, a few places above Mikhail Youzhny, whose competition-adjusted rate of points won is 45.7% ranks last, exactly ten points below Rafa.

Think about what that means: In a typical ATP match, for every hundred points played, only ten are really up for grabs. That isn’t literally true, of course: There are plenty of matches in which one player wins 60% or more of total points. But on average, you can expect even the weakest tour regular to win 45 out of 100 points. In team sports analytics, this is what we might call “replacement level”–the skill level of a freely available minor leaguer or bench player. I don’t like importing the concept of replacement level for tennis, because in an individual sport you’re never really replacing one player with another. But at the most general level, it’s a useful way of thinking about this subject–just as even a minor league batter could hit .230 in the major leagues (as opposed to .000), so a fringey ATP player will win 45% of points, not 0%.

Points to wins

In team sports analytics, it’s common to say that some number of runs, or goals, or points is equal to one win. Thinking in terms of wins is a good way to value players: If you can say that upgrading your goalkeeper is worth two wins over your current option, it makes very clear what he brings to the table. Again, the metaphor is a bit strained when we apply it to tennis, but we can start thinking about things in the same way.

Another oddity in tennis is that players not only face very unequal competition, they also play widely different numbers of matches. The year-end top 50 contested anywhere from 35 matches up to more than 80; part of the variation is due to injury, but much is structural: The more matches you win, the more you play. Rafa managed his schedule by entering only a handful of optional events, yet only David Goffin played more matches. So we have another quirk to handle: In this case, let’s adopt the fiction that a tennis season is exactly 50 matches long. Rafa’s actual record was 67-11; scaled to a 50-match season, that’s roughly 43-7.

Finally, we can look at the relationship between points and wins. Points, here, means the rate of total points won adjusted for competition. And wins is the number of victories in our hypothetical 50-match season. The relationship between points and wins is quite strong (r^2 = 0.75), though of course not exact. Roger Federer won matches at a higher rate than Nadal did, but by competition-adjusted total points won, Rafa trounced him, 55.7% to 53.5%. And as we’ve seen, Lorenzi is close to the bottom of our 71-player sample, despite hanging on to a ranking in the mid-40s. Luck, clutch play, and a host of other factors make the points-to-wins relationship imperfect, but it is nonetheless a healthy one.

It doesn’t take many points to boost one’s win total. An increase of only 0.367 points per 100 translates into one more win in a 50-match season. The average player contests 8,000 points per season, so we’re talking about only 29 more points per year. This puts my smash-skill conclusions in a new light: The spread between the best and the worst of 0.27 points per 100 seemed tiny, but now we see it’s worth almost a full win over the course of a 50-match season.

Wins to ranking places

Unless you’re nearing a round number and have a hankering for cake, even wins aren’t the currency that really matters in tennis. What counts is position on the ranking table. The relationship between wins and ranking position is another strong but imperfect one (r^2 = 0.63).

As we’ve seen, the middle of the ATP pack is tightly grouped together in total points won, with so many players hovering around the 50% mark, even when adjusted for competition. There’s not much to distinguish between these men in the win column, either: On average, an increase of 0.26 wins per 50 matches translates into a one-spot jump on the ranking computer. Put another way: If you win one more match, your ranking will improve by four places. Again, these are not iron laws–in reality, it depends when and where that extra win occurs, and the corresponding ranking improvement could be anywhere from zero spots to 30. Still, knowing the typical result allows us to understand better the impact of each marginal win and, by extention, the value of winning a few more points.

One point per thousand

Combine these two relationships, and we get a new, conveniently round-numbered rule of thumb. If an increase in one ranking place requires 0.26 additional wins per 50 matches, and one additional win requires 0.367 extra points per 100, a little tapping at the calculator demonstrates that one ranking place is equal to about 0.095 points per 100. Round up a bit to 0.1 per 100, and we’re looking at one point per thousand.

One extra point per thousand is a miniscule amount, the sort of difference we could never dream of spotting with the naked eye. Players regularly win entire tournaments without contesting so many points; even for Goffin, who served or returned more than 12,000 times this year, we’re talking about a dozen points. Yet think back to all of those players clustered between 49% and 52% of total points won; even when adjusted for competition, three men ended the 2017 season tied at exactly 50.4%, with less than one point per thousand separating the three of them.

The one part of the ranking table where one point per thousand is no more than a rounding error is the very top. Usually one player separates himself from the pack, and the top few distance themselves from the rest. This year is no different: The competition-adjusted gap between Nadal and Federer is a whopping 2.2% (22 points per thousand), while the next 2.2% takes us all the way from Fed through the entire top 10. The 2.2% after that, extending from 51.1% to 48.9%, covers another 20 players: spaced, on average, one point per thousand apart. For a player seeking to improve from 30th to 20th, the path is largely linear; from 5th to 3rd it is much less predictable–and probably steeper.

If this all sounds unnecessarily abstruse, I can only mention once again the example of my smash-skill findings. Now we know that the range of overhead-hitting ability among the game’s regulars is worth close to three places in the rankings. Imagine a similar type of conclusion for forehands, backhands, net approaches… it’s exciting stuff. While plenty of work lies ahead, this framework allows us to measure the impact of individual shots–perhaps even tactics–and translate that impact into ranking places, the ultimate currency of tennis.

Overperforming in Davis Cup

This is a guest post by Peter Wetz.

With the help of weighted surface specific Elo ratings we have a powerful new tool to measure player performance. The traditional conclusion of the tennis season, the Davis Cup final, provides us with an opportunity once again to examine which players thrive when competing for their nation and which players seem to suffer from the pressure. While we are at it, I don’t like the sound of the word offseason. After all, there are still ITF tournaments, not to mention the Australian Open Asia-Pacific Wildcard Play-offs.

As already hinted, Elo ratings have proven to represent a better picture of player quality than traditional ATP rankings. Hence, comparing expected wins based on Elo with actual wins provides us with a clearer picture of who outperforms expectations and who does not.

In this evaluation, I consider completed World Group and Group 1 Davis Cup live rubbers played since 1980. The data set contains around 5000 matches through this year’s World Group Quarterfinals, and I’ve limited my focus to players having played 15 or more matches.

Let’s first take a glance at the obvious stat, win-loss percentage. The following table shows the top ten win-loss records of all players under consideration. (The Active column denotes if the player is still an active player).

Name	        W	L	Perc	Active
Rafael Nadal	20	1	95%	1
Boris Becker	31	2	94%	0
Andy Murray	25	3	90%	1
Balazs Taroczy	23	3	89%	0
David Ferrer	20	3	87%	1
Andre Agassi	23	4	85%	0
Roger Federer	40	7	85%	1
Novak Djokovic	27	5	84%	1
Guillermo Vilas	16	3	84%	0
Andrei Medvedev	16	3	84%	0

As one would expect, the big four and other all time greats are included. However, this obviously does not tell the whole story. Rafael Nadal is expected to win most of the time and that is what he does. For such a player, it is hard to outperform expectations.

If we compute how much a player outperforms his expectations, we get a clearer picture, given we want to know who does especially well in Davis Cup. Expected wins are calculated based on a half-and-half mix of surface specific Elo and overall Elo as this, in general, provides close to the best results, as pointed out in a previous article.

The tables below show the top and bottom five among all (first table) and active (second table) players in terms of over and underperforming expected wins. It shows actual wins (W), expected wins (eW), the percentage of over or underperformance (+/-), and if a player is still active.

Name	         W	eW	+/-	active
Francisco Maciel 11	6	72%	0
Slobodan Zi'vic  20	11	72%	0
Vasek Pospisil	 9	5	71%	1
Adrian Ungur	 6	3	56%	1
Mahesh Bhupathi	 5	3	55%	0
Wally Masur	 7	10     -31%	0
Sebastien Lareau 7	10     -31%	0
James Blake	 7	10     -36%	0
Nicolas Kiefer	 6	10     -40%	0
Aqeel Khan	 2	4      -57%	0
Name	        W	eW	+/-	Active
Vasek Pospisil	9	5	71%	1
Adrian Ungur	6	3	56%	1
Andrey Golubev	13	8	46%	1
Di Wu	        14	9	45%	1
Steve Darcis	15	11	35%	1
Florian Mayer	7	8      -14%	1
Gilles Muller	9	10     -15%	1
Alejandro Falla	8	9      -17%	1
John Isner	9	11     -19%	1
Jurgen Melzer	20	25     -22%	1

The tables seem to overlap with some conventional wisdom floating through the tennis sphere. Namely, that Steve Darcis, despite his recent losses at the Davis Cup final, plays above expectations. Also, Jurgen Melzer is known for regularly disappointing Austrian Davis Cup fans. (In his defense, he created several moments of joy, too).

If we were to pick a Davis Cup hero for the active and inactive group of players, Slobodan Zivojinovic and Andrey Golubev seem to be good choices. Golubev has a record of 13-6 (68%) and outperforms expected wins by 46%. He provides a good combination of consistently beating players he should beat and scoring more than his share of exceptional upsets (Wawrinka 2014, Goffin 2014, Melzer 2013 and Berdych 2011).

Zivojinovic provides a similar pattern with a record of 20-8 (71%), 72% better than expected. He tallied six wins out of ten matches in which Elo assigned him a win probability of less than 25%. Further, he only lost one match in when his pre-match odds of winning were greater than 35%.

This post provides insight into how Elo ratings help in quantifying a player’s performance. We identified players who have (not) shown great improvement on what the algorithm expected based on results from the regular tour. For future research it would be interesting to delve into Davis Cup doubles heroes: Where there are no dead rubbers, stakes are always high.

Peter Wetz is a computer scientist interested in racket sports and data analytics based in Vienna, Austria.

Measuring the Best Smashes in Tennis

How can we identify the best shots in tennis? At first glance, it seems like a simple problem. Thanks to the shot-by-shot data collected for over 3,500 matches by the Match Charting Project, we can look at every instance of the shot in question and see what happened. If a player hits a lot of winners, or wins most of the ensuing points, he or she is probably pretty good at that shot. Lots of unforced errors would lead us to conclude the opposite.

A friend recently posed a more specific question: Who has the best smash in the men’s game? Compared to other shots such as, say, slice backhands, smashes should be pretty easy to evaluate. A large percentage of them end the point–in the contemporary men’s game (I discuss the women’s game later on), 69% are winners or induce forced errors–which reduces the problem to a straightforward one.

The simplest algorithm to answer my friend’s question is to determine how often each player ends the point in his favor when hitting a smash–that is, with a winner or by inducing a forced error. Call the resulting ratio “W/SM.” The Match Charting Project (MCP) dataset has at least 10 tour-level matches for 80 different men, and the W/SM ratio for those players ranges from 84% (Jeremy Chardy) all the way down to 30% (Paolo Lorenzi). Both of those extremes are represented by players with relatively small samples; if we limit our scope to men with at least 90 recorded smashes, the range isn’t quite as wide. The best of the bunch is Jo-Wilfried Tsonga, at 79%, and the “worst” is Ivan Lendl, at 57%. That isn’t quite fair to Lendl, since smash success rates have improved quite a bit over the years, and Lendl’s rate is only a couple percentage points below the average for the 1980s. Among active players with at least 90 smashes in the books, Stan Wawrinka brings up the rear, with a W/SM of 65%.

We can look at the longer-term effects of a player’s smashes without adding much complexity. It’s ideal to end the point with a smash, but most players would settle for winning the point. When hitting a smash, ATPers these days end up winning the point 81% of the time, ranging from 97% (Chardy again) down to 45% (Lorenzi again). Once again, Tsonga leads the pack of the bigger-sample-size players, winning the point 90% of the time after hitting a smash, and among active players, Wawrinka is still at the bottom of that subset, at 77%.

Here is a list of all players with at least 90 smashes in the MCP dataset, with their winners (and induced forced errors) per smash (W/SM), errors per smash (E/SM), and points won per smash (PTS/SM):

PLAYER              W/SM  E/SM  PTS/SM  
Jo-Wilfried Tsonga   78%    6%     90%  
Tomas Berdych        76%    6%     88%  
Pete Sampras         75%    7%     86%  
Roger Federer        73%    7%     86%  
Rafael Nadal         69%    7%     84%  
Milos Raonic         73%    9%     82%  
Andy Murray          67%    6%     82%  
Kei Nishikori        68%   11%     81%  
David Ferrer         71%    9%     81%  
Andre Agassi         67%    8%     80%  
Novak Djokovic       66%    9%     80%  
Stefan Edberg        62%   12%     78%  
Stan Wawrinka        65%   10%     77%  
Ivan Lendl           57%   13%     71%

These numbers give us a pretty good idea of who you should back if the ATP ever hosts the smash-hitting equivalent of baseball’s Home Run Derby. Best of all, it doesn’t commit any egregious offenses against common sense: We’d expect to see Tsonga and Roger Federer near the top, and we’d know something was wrong if Novak Djokovic were too far from the bottom.

Smash opportunities

Still, we need to do better. Almost every shot made in a tennis match represents a decision made by the player hitting it: topspin or slice? backhand or run-around forehand? approach or stay back? Many smashes are obvious choices, but a large number are not. Different players make different choices, and to evaluate any particular shot, we need to subtly reframe the question. Instead of vaguely asking for “the best,” we’d be better served looking for the player who gets the most value out of his smash. While the two questions are similar, they are not the same.

Let’s expand our view to what we might call “smash opportunities.” Once again, smashes make our task relatively straightforward: We can define a smash opportunity simply as a lob hit by the opponent.* In the contemporary ATP, roughly 72% of lobs result in smashes–the rest either go for winners or are handled with a different shot. Different players have very different strategies: Federer, Pete Sampras, and Milos Raonic all hit smashes in more than 84% of opportunities, while a few other men come in under 50%. Nick Kyrgios, for instance, tried a smash in only 20 of 49 (41%) of recorded opportunities. Of those players with more available data, Juan Martin Del Potro elected to go for the overhead in 61 of 114 (54%) of chances, and Andy Murray in 271 of 433 (62.6%).

* Using an imperfect dataset, it’s a bit more complicated; sometimes the shots that precede smashes are coded as topspin or slice groundstrokes. I’ve counted those as smash opportunities as well.

Not all lobs are created equal, of course. With a large number of points, we would expect them to even out, but even then, a player’s overall style may effect the smash opportunities he sees. That’s a more difficult issue for another day; for now, it’s easiest to assume that each player’s mix of smash opportunities are roughly equal, though we’ll keep in mind the likelihood that we’ve swept some complexity under the rug.

With such a wide range of smashes per smash opportunities (SM/SMO), it’s clear that some players’ average smashes are more difficult than others. Federer hits about half again as many smashes per opportunity as del Potro does, suggesting that Fed’s attempts are more difficult than Delpo’s; on those more difficult attempts, Delpo is choosing a different shot. The Argentine is very effective when he opts for the smash, winning 84% of those points, but it seems likely that his rate would not be so high if he hit smashes as frequently as Federer does.

This leads us to a slightly different question: Which players are most effective when dealing with smash opportunities? The smash itself doesn’t necessarily matter–if a player is equally effective with, say, swinging volleys, the lack of a smash would be irrelevant. The smash is simply an effective tool that most players employ to deal with these situations.

Smash opportunities don’t offer the same level of guarantee that smashes themselves do: In the ATP these days, players win 72% of points after being handed a smash opportunity, and 56% of the shots they hit result in winners or induced forced errors. Looking at these situations takes us a bit off-track, but it also allows us to study a broader question with more impact on the game as a whole, because smash opportunities represent a larger number of shots than smashes themselves do.

Here is a list of all the players with at least 99 smash opportunities in the MCP dataset, along with the rate at which they hit smashes (SM/SMO), the rate at which they hit winners or induced forced errors in response to smash opportunites (W/SMO), hit errors in those situations (E/SMO), and won the points when given lobs (PTW/SMO). Like the list above, players are ranked by the rightmost column, points won.

PLAYER              SM/SMO  W/SMO  E/SMO  PTW/SMO  
Jo-Wilfried Tsonga     80%    68%    13%      80%  
Roger Federer          84%    66%    13%      78%  
Pete Sampras           86%    68%    15%      78%  
Tomas Berdych          75%    66%    16%      76%  
Milos Raonic           85%    67%    14%      76%  
Novak Djokovic         81%    60%    13%      75%  
Kevin Anderson         66%    57%    12%      74%  
Rafael Nadal           74%    57%    16%      73%  
Andre Agassi           77%    62%    17%      73%  
Boris Becker           85%    59%    18%      72%  
Stan Wawrinka          79%    58%    15%      72%  
Kei Nishikori          72%    57%    17%      70%  
Andy Murray            63%    52%    15%      70%  
Dominic Thiem          66%    52%    11%      70%  
David Ferrer           71%    57%    17%      69%  
Pablo Cuevas           73%    54%    14%      67%  
Stefan Edberg          81%    52%    23%      65%  
Bjorn Borg             81%    41%    20%      63%  
JM del Potro           54%    48%    19%      60%  
Ivan Lendl             74%    45%    28%      59%  
John McEnroe           74%    43%    24%      56%

The order of this list has much in common with the previous one, with names like Federer, Sampras, and Tsonga at the top. Yet there are key differences: Djokovic and Wawrinka are particularly effective when they respond to a lob with something other than an overhead, while del Potro is the opposite, landing near the bottom of this ranking despite being quite effective with the smash itself.

The rate at which a player converts opportunities to smashes has some impact on his overall success rate on smash opportunities, but the relationship isn’t that strong (r^2 = 0.18). Other options, such as swinging volleys or mid-court forehands, also give players a good chance of winning the point.

Smash value

Let’s get back to my revised question: Who gets the most value out of his smash? A good answer needs to combine how well he hits it with how often he hits it. Once we can quantify that, we’ll be able to see just how much a good or bad smash can impact a player’s bottom line, measured in overall points won, and how much a great smash differs from an abysmal one.

As noted above, the average current-day ATPer wins the point 81% of the time that he hits a smash. Let’s reframe that in terms of the probability of winning a point: When a lob is flying through the air and a player readies his racket to hit an overhead, his chance of winning the point is 81%–most of the hard work is already done, having generated such a favorable situation. If our player ends up winning the point, the smash improved his odds by 0.19 points (from 0.81 to 1.0), and if he ends up losing the point, the smash hurt his odds by 0.81 (from 0.81 to 0.0). A player who hits five successful smashes in a row has a smash worth about one total point: 5 multiplied by 0.19 equals 0.95.

We can use this simple formula to estimate how much each player’s smash is worth, denominated in points. We’ll call that Point Probability Added (PPA). Finally, we need to take into account how often the player hits his smash. To do so, we’ll simply divide PPA by total number of points played, then multiply by 100 to make the results more readable. The metric, then, is PPA per 100 points, reflecting the impact of the smash in a typical short match. Most players have similar numbers of smash opportunities, but as we’ve seen, some choose to hit far more overheads than others. When we divide by points, we give more credit to players who hit their smashes more often.

The overall impact of the smash turns out to be quite small. Here are the 1990s-and-later players with at least 99 smash opportunities in the dataset along with their smash PPA per 100 points:

PLAYER                 SM PPA/100  
Jo-Wilfried Tsonga           0.17  
Pete Sampras                 0.11  
Tomas Berdych                0.11  
Roger Federer                0.10  
Rafael Nadal                 0.05  
Milos Raonic                 0.04  
Juan Martin del Potro        0.02  
Andy Murray                  0.01  
Kevin Anderson               0.01  
Kei Nishikori                0.00  
David Ferrer                 0.00  
Andre Agassi                 0.00  
Novak Djokovic              -0.02  
Stan Wawrinka               -0.07  
Dominic Thiem               -0.07  
Pablo Cuevas                -0.10

Tsonga reigns supreme, from the most basic measurement to the most complex. His 0.17 smash PPA per 100 points means that the quality of his overhead earns him about one extra point (compared to an average ATPer) every 600 points. That doesn’t sound like much, and rightfully so: He hits fewer than one smash per 50 points, and as good as Tsonga is, the average player has a very serviceable smash as well.

The list gives us an idea of the overall range of smash-hitting ability, as well. Among active players, the laggard in this group is Pablo Cuevas, at -0.1 points per 100, meaning that his subpar smash costs him one point out of every thousand he plays. It’s possible to be worse–in Lorenzi’s small sample, his rate is -0.65–but if we limit our scope to these well-studied players, the difference between the high and low extremes is barely 0.25 points per 100, or one point out of every 400.

I’ve excluded several players from earlier generations from this list; as mentioned earlier, the average smash success rate in those days was lower, so measuring legends like McEnroe and Borg using a 2010s-based point probability formula is flat-out wrong. That said, we’re on safe ground with Sampras and Agassi; the rate at which players convert smashes into points won has remained fairly steady since the early 1990s.

Lob-responding value

We’ve seen the potential impact of smash skill; let’s widen our scope again and look at the potential impact of smash opportunity skill. When a player is faced with a lob, but before he decides what shot to hit, his chance of winning the point is about 72%. Thus, hitting a shot that results in winning the point is worth 0.28 points of point probability added, while a choice that ends up losing the point translates to -0.72.

There are more smash opportunities than smashes, and more room to improve on the average (72% instead of 81%), so we would expect to see a bigger range of PPA per 100 points. Put another way, we would expect that lob-responding skill, which includes smashes, is more important than smash-specific skill.

It’s a modest difference, but it does look like lob-responding skill has a bigger range than smash skill. Here is the same group of players, still showing their PPA/100 for smashes (SM PPA/100), now also including their PPA/100 for smash opportunities (SMO PPA/100):

PLAYER                 SM PPA/100  SMO PPA/100  
Jo-Wilfried Tsonga           0.17         0.18  
Roger Federer                0.10         0.16  
Pete Sampras                 0.11         0.16  
Milos Raonic                 0.04         0.12  
Tomas Berdych                0.11         0.09  
Kevin Anderson               0.01         0.08  
Novak Djokovic              -0.02         0.07  
Rafael Nadal                 0.05         0.03  
Andre Agassi                 0.00         0.01  
Stan Wawrinka               -0.07         0.00  
Kei Nishikori                0.00        -0.03  
Andy Murray                  0.01        -0.03  
Dominic Thiem               -0.07        -0.05  
David Ferrer                 0.00        -0.06  
Pablo Cuevas                -0.10        -0.12  
Juan Martin del Potro        0.02        -0.19

Djokovic and Delpo draw our attention again as the players whose smash skills do not accurately represent their smash opportunity skills. Djokovic is slightly below average with smashes, but a few notches above the norm on opportunities; Delpo is a tick above average when he hits smashes, but dreadful when dealing with lobs in general.

As it turns out, we can measure the best smashes in tennis, both to compare players and to get a general sense of the shot’s importance. What we’ve also seen is that smashes don’t tell the entire story–we learn more about a player’s overall ability when we widen our view to smash opportunities.

Smashes in the women’s game

Contemporary women hit far fewer smashes than men do, and they win points less often when they hit them. Despite the differences, the reasoning outlined above applies just as well to the WTA. Let’s take a look.

In the WTA of this decade, smashes result in winners (or induced forced errors) 63% of the time, and smashes result in points won about 75% of the time. Both numbers are lower than the equivalent ATP figures (69% and 81%, respectively), but not dramatically so. Here are the rates of winners, errors, and points won per smash for the 14 women with at least 80 smashes in the MCP dataset:

PLAYER               W/SM  E/SM  PTS/SM  
Jelena Jankovic       73%    9%     83%  
Serena Williams       72%   13%     81%  
Steffi Graf           61%    9%     81%  
Svetlana Kuznetsova   70%   10%     79%  
Simona Halep          66%   11%     76%  
Caroline Wozniacki    61%   16%     74%  
Karolina Pliskova     62%   18%     74%  
Agnieszka Radwanska   54%   13%     74%  
Angelique Kerber      57%   15%     72%  
Martina Navratilova   54%   13%     71%  
Monica Niculescu      50%   15%     70%  
Garbine Muguruza      63%   19%     70%  
Petra Kvitova         59%   22%     68%  
Roberta Vinci         58%   14%     68%

Historical shot-by-shot data is less representative for women than for men, so it’s probably safest to assume that trends in smash success rates are similar for men than for women. If that’s true, Steffi Graf’s era is similar to the present, while Martina Navratilova’s prime saw far fewer smashes going for winners or points won.

Where the women’s game really differs from the men’s is the difference between smash opportunities (lobs) and smashes. As we saw above, 72% of ATP smash opportunities result in smashes. In the current WTA, the corresponding figure is less than half that: 35%. Some of the single-player numbers are almost too extreme to be believed: In 12 matches, Catherine Bellis faced 41 lobs and hit 3 smashes; in 29 charted matches, Jelena Ostapenko saw 103 smash opportunities and tried only 10 smashes. A generation ago, the gender difference was tiny: Graf, Martina Hingis, Arantxa Sanchez Vicario, and Monica Seles all hit smashes in at least three-quarters of their opportunities. But among active players, only Barbora Strycova comes in above 70%.

Here are the smash opportunity numbers for the 17 women with at least 150 smash opportunities in the MCP dataset. SM/SMO is smashes per chance, W/SMO is winners (and induced forced errors) per smash opportunity, E/SMO is errors per opportunity, and PTS/SMO is points won per smash opportunity:

PLAYER                SM/SMO  W/SMO  E/SMO  PTW/SMO  
Maria Sharapova          12%    57%    11%      76%  
Serena Williams          55%    58%    18%      72%  
Steffi Graf              82%    52%    17%      71%  
Karolina Pliskova        47%    52%    16%      70%  
Simona Halep             14%    41%    11%      69%  
Carla Suarez Navarro     25%    33%     9%      69%  
Eugenie Bouchard         29%    50%    18%      68%  
Victoria Azarenka        35%    52%    17%      67%  
Angelique Kerber         39%    42%    14%      66%  
Garbine Muguruza         43%    51%    18%      66%  
Monica Niculescu         57%    41%    19%      65%  
Petra Kvitova            48%    50%    19%      65%  
Agnieszka Radwanska      44%    42%    18%      65%  
Johanna Konta            30%    47%    21%      64%  
Caroline Wozniacki       36%    44%    18%      64%  
Elina Svitolina          14%    38%    14%      63%  
Martina Navratilova      67%    42%    26%      58%

It’s clear from the top of this list that women’s tennis is a different ballgame. Maria Sharapova almost never opts for an overhead, but when faced with a lob, she is the best of them all. Next up is Serena Williams, who hits almost as many smashes as any active player on this list, and is nearly as successful. Recall that in the men’s game, there is a modest positive correlation between smashes per opportunity and points won per smash opportunity; here, the relationship is weaker, and slightly negative.

Because most women hit so few smashes, there isn’t quite as much to be gained by using point probability added (PPA) to measure WTA smash skill. Graf was exceptionally good, comparable to Tsonga in the value she extracted from her smash, but among active players, only Serena and Victoria Azarenka can claim a smash that is worth close to one point per thousand. At the other extreme, Monica Niculescu is nearly as bad as Graf was good, suggesting she ought to figure out a way to respond to more smash opportunities with her signature forehand slice.

Here is the same group of women (minus Navratilova, whose era makes PPA comparisons misleading), with their PPA per 100 points for smashes (SM PPA/100) and smash opportunities (SMO PPA/100):

PLAYER                SM PPA/100  SMO PPA/100  
Maria Sharapova             0.03         0.21  
Serena Williams             0.09         0.15  
Steffi Graf                 0.15         0.14  
Karolina Pliskova          -0.01         0.09  
Carla Suarez Navarro        0.04         0.08  
Simona Halep                0.00         0.07  
Eugenie Bouchard           -0.02         0.03  
Victoria Azarenka           0.08         0.00  
Angelique Kerber           -0.03        -0.02  
Garbine Muguruza           -0.07        -0.03  
Petra Kvitova              -0.07        -0.04  
Monica Niculescu           -0.13        -0.06  
Caroline Wozniacki         -0.01        -0.07  
Agnieszka Radwanska        -0.02        -0.07  
Johanna Konta              -0.12        -0.08  
Elina Svitolina             0.01        -0.09

The table is sorted by smash opportunity PPA, which tells us about a much more relevant skill in the women’s game. Sharapova’s lob-responding ability is well ahead of the pack, worth better than one point above average per 500, with Serena and Graf not far behind. The overall range among these well-studied players, from Sharapova’s 0.21 to Elina Svitolina’s -0.09, is somewhat smaller than the equivalent range in the ATP, but with such outliers as Sharapova here and Delpo on the men’s side, it’s tough to draw firm conclusions from small subsets of players, however elite they are.

Final thought

The approach I’ve outlined here to measure the impact of smash and smash-opportunity skills is one that could be applied to other shots. Smashes are a good place to start because they are so simple: Many of them end points, and even when they don’t, they often virtually guarantee that one player will win the point. While smashes are a bit more complex than they first appear, the complications involved in applying a similar algorithm to, say, backhands and backhand opportunities, are considerably greater. That said, I believe this algorithm represents a promising entry point to these more daunting problems.

Forecasting the Laver Cup

This weekend brings us the first edition of the Laver Cup, a star-studded three-day affair that pits Europe against the rest of the world. The European team features Roger Federer and Rafael Nadal, and even though several other elites from the continent are missing due to injury, the European team is still much stronger on paper.

Here are the current rosters, along with each competitor’s weighted hard court Elo rating and rank among active players:

EUROPE                  Elo Rating  Elo Rank  
Roger Federer                 2350         2  
Rafael Nadal                  2225         4  
Alexander Zverev              2127         7  
Tomas Berdych                 2038        14  
Marin Cilic                   2029        15  
Dominic Thiem                 1995        17  
WORLD                   Elo Rating  Elo Rank  
Nick Kyrgios                  2122         8  
John Isner                    1968        22  
Jack Sock                     1951        23  
Sam Querrey                   1939        25  
Denis Shapovalov              1875        36  
Frances Tiafoe                1574       153  
Juan Martin del Potro*        2154         5

*del Potro has withdrawn. I’ve included his singles Elo rating and rank to emphasize how damaging his absence is to the World squad.

“Weighted” surface Elo is the average of overall (all-surface) Elo and surface-specific Elo. The 50/50 split is a much better predictor of match outcomes than either number on its own.

Nick Kyrgios can hang with anybody on a hard court. But despite some surface-specific skills represented by the American contingent, every other member of the World team rates lower than every member of team Europe. This isn’t a good start for the rest of the world.

What about doubles? Here are the D-Lo (Elo for doubles) ratings and rankings for all twelve participants, plus Delpo:

EUROPE                  D-Lo rating  D-Lo rank  
Rafael Nadal                   1895          4  
Tomas Berdych                  1760         28  
Marin Cilic                    1676         76  
Roger Federer**                1650         90  
Alexander Zverev               1642         99  
Dominic Thiem                  1521        185  
WORLD                   D-Lo rating  D-Lo rank  
Jack Sock                      1866          8  
John Isner                     1755         29  
Nick Kyrgios                   1723         45  
Sam Querrey                    1715         49  
Denis Shapovalov**             1600        130  
Frances Tiafoe                 1546        166  
Juan Martin del Potro*         1711         55

** Federer hasn’t played tour-level doubles since 2015, and Shapovalov hasn’t done so at all. These numbers are my best guesses, nothing more.

Here, the World team has something of an edge. While both sides feature an elite doubles player–Rafa and Jack Sock–the non-European side is a bit deeper, especially if they keep Denis Shapovalov and last-minute Delpo replacement Frances Tiafoe on the sidelines. Only one-quarter of Laver Cup matches are doubles (plus a tie-breaking 13th match, if necessary), so it still looks like team Europe are the heavy favorite.

The format

The Laver Cup will take place in Prague over three days (starting Friday, September 22nd), and consist of four matches each day: three singles and one doubles. Every match is best-of-three sets with ad scoring and a 10-point super-tiebreak in place of the third set.

On the first day, the winner of each match gets one point; on the second day, two points, and on the third day, three points. That’s a total of 24 points up for grabs, and if the twelve matches end in a 12-12 deadlock, the Cup will be decided with a single doubles set.

All twelve participants must play at least one singles match, and no one can play more than two. At least four members of each squad must play doubles, and no doubles pairing can be repeated, except in the case of a tie-breaking doubles set.

Got it? Good.

Optimal strategy

The rules require that three players on each side will contest only one singles match while the other three will enter two each. A smart captain would, health permitting, use his three best players twice. Since matches on days two and three count for more than matches on day one, it also makes sense that captains would use their best players on the final two days.

(There are some game-theoretic considerations I won’t delve into here. Team World could use better players on day one in hopes of racking up each points against the lesser members of team Europe, or could drop hints that they will do so, hoping that the European squad would move its better players to day one. As far as I can tell, neither team can change their lineup in response to the other side’s selections, so the opportunities for this sort of strategizing are limited.)

In doubles, the ideal roster deployment strategy would be to use the team’s best player in all three matches. He would be paired with the next-best player on day three, the third-best on day two, and the fourth-best on day one. Again, this is health permitting, and since all of these guys are playing singles, fatigue is a factor as well. My algorithm thus far would use Nadal five times–twice in singles and three times in doubles–and I strongly suspect that isn’t going to happen.

The forecast

Let’s start by predicting the outcome of the Cup if both captains use their roster optimally, even if that’s a longshot. I set up the simulation so that each day’s singles competitors would come out in random order–if, say, Querrey, Shapovalov, and Tiafoe play for team World on day one, we don’t know which of them will play first, or which European opponent each will face. So each run of the simulation is a little different.

As usual, I used Elo (and D-Lo) to predict the outcome of specific matchups. Because of the third-set super-tiebreak, and because it’s an exhibition, I added a bit of extra randomness to every forecast, so if the algorithm says a player has a 60% chance of winning, we knock it down to around 57.5%. When I dug into IPTL results last winter, I discovered that exhibition results play surprisingly true to expectations, and I suspect players will take Laver Cup a bit more seriously than they do IPTL.

Our forecast–again, assuming optimal player usage–says that Europe has an 84.3% chance of winning, and the median point score is 16-8. There’s an approximately 6.5% chance that we’ll see a 12-12 tie, and when we do, Europe has a slender 52.4% edge.

If Delpo were participating, he would increase the World team’s chances by quite a bit, reducing Europe’s likelihood of victory to 75.5% and narrowing the most probable point score to 15-9.

What if we relax the “optimal usage” restriction? I have no idea how to predict what captains John McEnroe and Bjorn Borg will do, but we can randomize which players suit up for which matches to get a sense of how much influence they have. If we randomize everything–literally, just pick a competitor out of a hat for each match–Europe comes out on top 79.7% of the time, usually winning 15-9. There’s a 7.6% chance of a tie-breaking 13th match, and because the World team’s doubles options are a bit deeper, they win a slim majority of those final sets. (When we randomize everything, there’s a slight risk that we violate the rules, perhaps using the same doubles pairing twice or leaving a player on the bench for all nine singles matches. Those chances are very low, however, so I didn’t tackle the extra work required to avoid them entirely.)

We can also tweak roster usage by team, in case it turns out that one captain is much savvier than the other. (Or if a star like Nadal is unable to play as much as his team would like.) The best-case scenario for our World team underdogs is that McEnroe chooses the best players for each match and Borg does not. Assuming that only European players are chosen from a hat, the probability that the favorites win falls all the way to 63.1%, and the typical gap between point totals narrows all the way to 13-11. The chance of a tie rises to 10%.

On the other hand, it’s possible that Borg is better at utilizing his squad. After all, it doesn’t take an 11-time grand slam winner to realize that Federer and Nadal ought to be on court when the stakes are the highest. This final forecast, with random roster usage from team World and ideal choices from Borg, gives Europe a whopping 92.3% chance of victory, and median point totals of 17 to 7. The World team would have only a 4% shot at reaching a deadlock, and even then, the Europeans win two-thirds of the tiebreakers.

There we have it. The numbers bear out our expectation that Europe is the heavy favorite, and they give us a sense of the likely margin of victory. Tiafoe and Shapovalov might someday be part of a winning Laver Cup side, but it looks like they’ll have to wait a few years before that happens.

Update: One more thing… What about doubles specialists? Both captains have two discretionary picks to use on players regardless of ranking. Most great doubles players are much worse at singles, but as we’ve seen, a player can be relegated to a lone one-point singles match on day one, and as a doubles player, he can have an effect on three different matches, totaling six points.

Sure enough, swapping out Dominic Thiem (a very weak doubles player for whom indoor hard courts are less than ideal) for Nicolas Mahut would have increased Europe’s chances of winning from 84.3% to 88.5%. On the slight chance that the Cup stayed tight through the final doubles match and into a tiebreaker, the doubles team of Mahut-Nadal (however unorthodox that sounds) would be among the best that any captain could put on the court.

There’s even more room for improvement on the World side, especially with del Potro out. At the moment, the third-highest rated hard court player by D-Lo is Marcelo Melo, who would be a major step down in singles but a huge improvement on most of the potential partners for Sock in doubles. If we give him a singles Elo of 1450 and put him on the roster in place of Tiafoe and pit the resulting squad against the original Europe team (with Thiem, not Mahut), it almost makes up for the loss of Delpo–World’s chances of winning increase from 15.7% to 19.3%.

Unfortunately, Borg and McEnroe may have missed their chance to eke out extra value from their six-man rosters–this is a trick that will only work once. If both teams made this trade, Mahut-for-Thiem and Melo-for-Tiafoe, each side’s win probability goes back to near where it started: 85.8% for Europe. That’s a boost over where we started (84.3%), just because Mahut is better suited for the competition than Melo is, as an elite doubles specialist who is also credible on the singles court. No one available to the World team (except for Sock, who is already on the roster) fits the same profile on a hard court. Vasek Pospisil comes to mind, though he has taken a step back from his peaks in both singles and doubles. And on clay, Pablo Cuevas would do nicely, but on a faster surface, he would represent only a marginal improvement over the doubles players already playing for team World.

Maybe next year.


A Preface to All GOAT Arguments

Earlier this week, The Economist published my piece about Rafael Nadal’s and Roger Federer’s grand slam counts. I made the case that, because Nadal’s paths to major titles had been more difficult (the 2017 US Open notwithstanding), his 16 slams are worth more–barely!–than Federer’s.

Inevitably, some readers reduced my conclusion to something like, “stats prove that Nadal is the greatest ever.” Whoa there, kiddos. It may be true that Nadal is better than Federer, and we could probably make a solid argument based on the stats. But a rating of 18.8 to 18.7, based on 35 tournaments, can’t quite carry that burden.

There are two major steps in settling any “greatest ever” debate (tennis or otherwise). The first is definitional. What do we mean by “greatest?” How much more important are slams than non-slams? What about longevity? Rankings? Accomplishments across different surfaces? How much weight do we give a player’s peak? How much does the level of competition matter? What about head-to-head records? I could go on and on. Only when we decide what “greatest” means can we even attempt to make an argument for one player over another.

The second step–answering the questions posed by the first–is more work-intensive, but much less open to debate. If we decide that the greatest male tennis player of all time is the one who achieved the highest Elo rating at his peak, we can do the math. (It’s Novak Djokovic.) If you pick out ten questions that are plausible proxies for “who’s the greatest?” you won’t always get the same answer. Longevity-focused variations tend to give you Federer. (Or Jimmy Connors.) Questions based solely on peak-level accomplishments will net Djokovic (or maybe Bjorn Borg). Much of the territory in between is owned by Nadal, unless you consider the amateur era, in which case Rod Laver takes a bite out of Rafa’s share.

Of course, many fans skip straight to the third step–basking in the reflected glory of their hero–and work backwards. With a firm belief that their favorite player is the GOAT, they decide that the most relevant questions are the ones that crown their man. This approach fuels plenty of online debates, but it’s not quite at my desired level of rigor.

When the big three have all retired, someone could probably write an entire book laying out all the ways we might determine “greatest” and working out who, by the various definitions, comes out on top. Most of what we’re doing now is simply contributing sections of chapters to that eventual project. Now or then, one blog post will never be enough to settle a debate of this magnitude.

In the meantime, we can aim to shed more light on the comparisons we’re already making. Grand slam titles aren’t everything, but they are important, and “19 is more than 16” is a key weapon in the arsenal of Federer partisans. Establishing that this particular 19 isn’t really any better than that particular 16 doesn’t end the debate any more than “19 is more than 16” ever did. But I hope that it made us a little more knowledgeable about the sport and the feats of its greatest competitors.

At the one-article, 1,000-word scale, we can achieve a lot of interesting things. But for an issue this wide-ranging, we can’t hope to settle it in one fell swoop. The answers are hard to find, and choosing the right question is even more difficult.


Fun With Service Point Ratios

In Rafael Nadal‘s comprehensive victory over Kevin Anderson in the 2017 US Open final, Nadal didn’t face a single break point. Anderson didn’t even earn very many deuces. Nadal, on the other hand, constantly challenged in his opponent’s service games.

This produced an unusual ratio: Anderson had to play way more service points than Nadal did, even though they served the same number of games. Rafa toed the line only 72 times to the South African’s 108, for a ratio of 2/3 or, rounded, 0.67. In this week’s podcast, I speculated that this service point ratio is a handy way of spotting winners–if one man is getting through his service games much quicker than the other, it’s probably because he is holding easily and his opponent is not.

It wasn’t the best hypothesis I’ve ever put forward. It’s true, but not by an overwhelming margin. In the average ATP match, the ratio of the winner’s service points played to the loser’s service points played is 0.96 — equivalent to Rafa serving 88 times to Anderson’s 92. The winnner plays fewer service points in 57% of contests. We’ve hardly discovered the next IBM Key to the Match here.

Instead of discovering a useful proxy for success in the most basic of match stats, we’ve come upon yet another item to add to the list of Nadal’s extreme accomplishments. Of nearly 13,000 completed grand slam singles matches since 1991, only 147 of the winners–barely one percent–had service point ratios below 0.67. Out of 106 major finals with stats available, Rafa’s ratio on Sunday was the lowest on record. He just edged out Roger Federer‘s 0.68 ratio from the 2007 Australian Open final against Fernando Gonzalez.

It turns out that the service point ratio is as fluky for Rafa as it is for men as a whole. Of his 16 victories in grand slam finals, he has posted a ratio below 1.0 in eight of them, equal to 1.0 once, and above 1.0 seven times. His average is an uninteresting 0.98.

There you have it: Over the course of a single week, we’ve seen an oddity, devised a stat to capture it, and determined that it doesn’t tell us much. Analytics, anyone?

For a more serious look at Rafa’s career accomplishments after bringing home his 16th major title, check out my analysis posted yesterday at The Economist’s Game theory blog.

Denis Shapovalov and Fast ATP Starts

18-year-old Canadian lefty Denis Shapovalov has had one heck of a summer. In Montreal, he defeated Juan Martin del Potro and Rafael Nadal in back-to-back matches, and at the US Open, he qualified for the main draw, upset Jo Wilfried Tsonga, and reached the fourth round in only his second appearance at a major.

Thanks to those wins and the big stages on which he achieved them, he has cracked the ATP top 60, despite playing fewer than 20 tour-level matches. The Elo rating system, which awards points based on opponent quality, is even more optimistic. By that measure, with his win over Tsonga, Shapovalov improved to 1950–good for 34th on tour–before losing about 25 Elo points in his loss to Pablo Carreno Busta.

While an Elo score of 1950 is an arbitrary number–there’s nothing magical about any particular Elo threshold; it’s just a mechanism to compare players to each other–it gives us a way to compare Shapovalov’s hot start with other players who made quick impacts at tour level. Since the early 1980s, only 13 players have reached a 1950 Elo score in fewer matches than the Canadian needed. As usual with early-career accomplishments, there are a few unexpected names in the mix, but overall, it’s very promising company for an 18-year-old:

Player               Matches   Age  
Lleyton Hewitt             7  16.9  
Jarkko Nieminen            7  20.2  
Juan Carlos Ferrero       10  19.4  
David Ferrer              12  20.4  
Kenneth Carlsen           12  19.4  
Tommy Haas                13  19.1  
Peter Lundgren            13  20.7  
John Van Lottum           14  21.8  
Sergi Bruguera            14  18.4  
Julian Alonso             15  20.0

Player               Matches   Age   
Xavier Malisse            16  18.6  
Jan Siemerink             16  20.9  
Ivo Minar                 16  21.2  
Florian Mayer             17  20.7  
Cristiano Caratti         17  20.7  
Nick Kyrgios              17  19.3  
Denis Shapovalov          17  18.4  
Martin Strelba            17  22.1  
Jay Berger                17  20.2  
Andy Roddick              18  18.6

I identified just over 350 players who, at some point in their careers, peaked with an Elo score of at least 1950. On average, these players needed 75 matches to reach that level (the median is 59), and two active tour-regulars, Gilles Muller and Albert Ramos, needed almost 300 matches to achieve the threshold.

Shapovalov’s record so far is equally impressive when we consider it in terms of age. Again, he’s among the top 20 players in modern tennis history: Only 11 players got to 1950 before their 18th birthday. The Canadian is only a few months beyond his. And many of the other ATPers who reached that score at an early age needed much more tour experience. I’ve included the top 30 on this list to show how Shapovalov compares to so many of the game’s greats:

Player                  Matches   Age  
Aaron Krickstein             25  16.4  
Michael Chang                32  16.5  
Lleyton Hewitt                7  16.9  
Boris Becker                 27  17.5  
Mats Wilander                27  17.5  
Guillermo Perez Roldan       26  17.6  
Andre Agassi                 46  17.6  
Pat Cash                     66  17.6  
Goran Ivanisevic             35  17.7  
Andrei Medvedev              22  17.8  

Player                  Matches   Age
Rafael Nadal                 44  17.9  
Sammy Giammalva              21  18.0  
Horst Skoff                  19  18.1  
Jimmy Arias                  61  18.2  
Kent Carlsson                56  18.3  
Sergi Bruguera               14  18.4  
Denis Shapovalov             17  18.4  
Andy Murray                  22  18.4  
Juan Martin del Potro        31  18.4  
Fabrice Santoro              59  18.5  

Player                  Matches   Age
John McEnroe                 28  18.5  
Roger Federer                40  18.5  
Stefan Edberg                40  18.5  
Andy Roddick                 18  18.6  
Pete Sampras                 56  18.6  
Thomas Enqvist               28  18.6  
Xavier Malisse               16  18.6  
Novak Djokovic               33  18.8  
Jim Courier                  51  18.8  
Yannick Noah                 41  18.8

There are no guarantees when it comes to tennis prospects, but this is very good company. On average, the 23 other players to reach the 1950 Elo threshold at age 18 improved their Elo ratings to 2100 before age 20, and rose to 2250 at some point in their careers. The first number would be good for 12th on today’s list, and the second would merit 5th place, just behind the Big Four. Nadal and del Potro were the first of Shapovalov’s high-profile victims, and judging from this sharp career trajectory, they won’t be the last.