Expected Points, my new short, daily podcast, highlights three numbers to illustrate stats, trends, and interesting trivia around the sport.
The women’s top seed records a flawless start to the tournament, the experienced Feliciano Lopez withstands belated newcomer Li Tu, and Sloane Stephens finds a new way to lose.
You can subscribe on iTunes, Spotify, Stitcher, and elsewhere in the podcast universe.
The Expected Points podcast is still finding its feet, so please let me know what you think.
Expected Points, my new short, daily podcast, highlights three numbers to illustrate stats, trends, and interesting trivia around the sport.
Bianca Andreescu has a winning return after a 15-month layoff, Nick Kyrgios defies the odds with success on both sides of the ball, and almost everybody—even Bernard Tomic—gets through day one injury-free.
You can subscribe on iTunes, Spotify, Stitcher, and elsewhere in the podcast universe.
This is very much still an experiment, so please let me know what you think.
Since 2017, Serena Williams has held 23 major titles, leaving her just one shy of Margaret Court’s 24. The Williams-Court comparison forces us to think across eras in the same way that Federer-vs-Laver does, with the additional complication that Court has earned herself extreme dislike among many fans and fellow champions.
Let’s set aside the off-court stuff and work this out. The pro-Court case is simple: 24 is greater than 23, and you have to evaluate players relative to their own eras. The pro-Serena side is equally straightforward: 11 of Court’s 24 titles came in Australia, before Melbourne was a mandatory tour stop. Regardless of the era, Court’s home event was weaker back then.
As much as possible, I’m going to try to hold to the “relative to their own era” assumption. Everyone seems to accept it when it comes to Laver-vs-Federer. Plus, if we drop that constraint, the whole exercise is meaningless. With improved technology, fitness, and coaching, of course today’s players are better. But that’s not what people are talking about when they pick a side of Serena-vs-Margaret or Rod-vs-Roger.
Attentive readers of this blog might recall I took a stab at this problem back in 2019. That attempt relied on some extreme approximating due to the lack of pre-Open Era women’s tennis data. Regular readers will also know that the state of pre-Open Era women’s tennis data has vastly improved in the last few months. Tennis Abstract, plus the associated GitHub repo, now contains thousands of match results back to the mid-1950s.
Adjusting Australia
Let’s be clear: I’m not about to settle whether Margaret Court or Serena Williams (or someone else) is the GOAT of women’s tennis. That debate depends on much more than grand slam titles.
Today’s question is: How do Williams’s 23 titles stack up against Court’s 24?
That boils down to an even simpler question: How do Court’s 11 Australian titles measure up against other slams, then and now?
The anecdotal evidence is strongly anti-Margaret. As I mentioned in this morning’s Expected Points, the 1960 Australian Championships–Court’s first major title–had a 32-player draw (strike one), and 30 of those players were Australian (strikes two and three). Yes, it was a strong era for Australian women’s tennis, especially a few years later, but the tournament was hardly a showcase of international superstars. As such, it isn’t what we think of as a “major” tournament these days.
I’ve done a lot of “slam adjustments,” mostly to track the difficulty of the majors won by Djokovic, Federer, and Nadal. (Here’s the most recent.) The basic approach is simple. For each tournament, take the winning player’s draw, and for each match, calculate the chance that an average slam winner on that surface would beat that set of opponents. (Odds are determined by my Elo ratings, which are based on results before the event.) Take the resulting probabilities–on average, around 14% between 1952 and 2020–and normalize them, so that a mid-range slam draw is 1.0. Tougher draws are higher than 1, and easier draws are lower.
Equalizing the eras
This type of adjustment gets us most of the way there, but it doesn’t directly confront the “relative to the era” issue. The field in general was more lopsided in the 1960s than it is now, with a handful of very strong players swatting away a pack of also-rans who struggled to win more than a game or two per set against the elites. That in itself is a point in favor of Serena (and modern players in general), but again, on the Laver-vs-Federer principle, that’s not what we’re talking about today.
The easiest way to express this idea that all eras are equivalent is to use as a standard each season’s Wimbledon, the one tournament that everybody always wanted to play, and almost everyone actually did play. To avoid year-to-year fluctuations based on short-term injuries, we’ll make things a bit more resilient and compare the strength of each year’s Australian draw to the average strength of that year’s Wimbledon and US draws.
For example, my slam adjustments consider 1960 to be a strong year. Maria Bueno’s Wimbledon title was 40% more difficult than the average slam draw, and Darlene Hard’s US victory was about 30% tougher than usual. Court’s Australian title that year comes out as exactly average, so we compare Australia’s 1.0 to the average of Wimbledon and the US ( (1.4 + 1.3) / 2 = 1.35), and the 1960 Australian title, relative to the era, measures as:
1 / 1.35 = 0.75
The mostly-Australian field wasn’t as weak as the caricature makes it out to be, but it was weaker than the marquee majors that year.
Here is how the strength of the Australian draw has evolved relative to the other grass- and hard-court slams from 1952 to the present:
Except for an outlier in 1965, when Bueno, Billie Jean King, and several other international stars turned up, the Australian Championships was a second class member of the grand slam club until around 1980. It’s had plenty of weak years since then, as well, partly because of players who skipped due to injury, and partly due to contenders losing early, giving the eventual winners easier paths.
The main event
Margaret Court won the Australian 11 times. By this measure of relative strength, those titles were worth 62% as much as the other majors in those years. The strenght of individual titles ranged from a low of 0.29 in 1961, when no international elites made the trip, to a high of 1.02 in 1965, when the field was positively star-studded.
Serena Williams has won the Australian seven times. It is tempting to leave that “7” as is, because Melbourne is now a mandatory tour stop and virtually every woman on tour considers it one of the top targets in her season. However, we should treat Serena’s seven the same way we adjusted Court’s 11. For all the era differences, some things remain the same, like jetlag and the difficulty of playing top-flight tennis only a few weeks into the season.
Williams’s seven were worth, on average, 88% as much as the other majors in their respective years. The weakest of the bunch was her last, in 2017. So many top players lost early that Serena never faced a top-eight opponent.
Court’s 11 titles, then, are equivalent to about 7 non-Australian majors–a penalty of four. Serena’s 7 are worth about 6 non-Australian majors–a penalty of one.
The final, adjusted tally: Williams 22, Court 20.
Margaret Court was one of the greatest players of all time, but her position the all-time grand slam singles list depends too much on the shifting status of her home event. When we properly account for the Australian tournament’s position for decade as the most minor major, Court loses her remaining claim to the top spot. Serena may yet win 24, but to match or exceed Court, she shouldn’t have to.
Expected Points, my new short, daily podcast, highlights three numbers to illustrate stats, trends, and interesting trivia around the sport.
Daniil Medvedev and Andrey Rublev place Team Russia at the top of the international heap, Felix Auger Aliassime’s final-round struggles continue, and Serena Williams chases a record that, adjusted for common sense, she has already passed.
You can subscribe on iTunes, Spotify, Stitcher, and elsewhere in the podcast universe.
This is very much still an experiment, so please let me know what you think.
Tennis players like routine, so maybe that’s what makes left-handed opponents “tricky.” The phrase “tricky lefty” is so common as to be a cliché, leading me to ask on Twitter last night whether there’s such a thing as a lefty who aren’t described as tricky.
A few of you responded, suggesting names such as Petra Kvitova and Rafael Nadal. It’s true, great left-handed players win matches because they’re great, not because they’re unusual. Plenty of adjectives come to mind for Petra and Rafa before “tricky.” Tour coach Marc Lucero suggested a broader framework:
I suspect Marc is right, and he would know better than I would. It doesn’t make sense that all lefties are tricky, even if players don’t face them very often.
Let’s be pedantic and go back to my original question, though: Are there any lefties who aren’t described as tricky? Mihaela Buzarnescu is certainly no Kvitova, but she is more aggressive than the average WTAer, at least according to Match Charting Project stats. To answer this question, I did some hardcore 21st-century research and googled it.
More specifically, I googled the following:
“tricky lefty” tennis
That’s not a perfect filter, because it excludes things like “tricky left-hander,” “a lefty whose tricky game…” and so on. But it gives us a good overview. Skipping over results with instructional content (“how to handle a tricky lefty serve!”) and pages discussing amateur players, here are the first 27 players Google told me are tricky lefties:
Alas, the world’s content writers do not hold to Lucero’s logically consistent definition. While some of the examples that Google gave me come from blogs, which we might not expect to maintain high editorial standards (pot, kettle, etc), one of the mentions of Nadal’s trickiness came from a very respectable publication, written by a pundit whose name you would know. Many of the other players were described as tricky on the tour websites, or in direct quotes from players. (Caroline Wozniacki used the t-word for Buzarnescu.)
Are lefties tricky?
As I said at the outset, tennis players like routine. Unless you’ve reached the finals at Roland Garros, facing a lefty is out of the ordinary. It’s the same type of unusual as drawing an opponent with a monster serve (Ivo Karlovic is incessantly deemed “tricky”) or a finely-honed backhand slice. There’s a whole range of tired tennis tropes for the underspinners–they “slice and dice” (really? they chop up the tennis balls into small cubes?), and their trickiness is rivaled only by how “crafty” they are.
We can’t quantify this unless we reframe the question. If lefties are tricky–or, let’s say, they have more capacity to be tricky than right-handers do–it’s roughly equivalent to saying that left-handers have an advantage. And if southpaws have an edge, we’d expect to see more of them in high-level tennis than in the population as a whole.
Is there a disproportionate number of lefties? This was one of the first tennis analytics questions I tried to answer, almost exactly a decade ago, and my conclusion then was: not really.
In February 2011, 12 of the top 100 players in the ATP rankings were left-handed. That includes Nadal, who complicates things a bit, as he’s a natural righty. 10% of the population is left-handed, so 11 natural-born lefties out of 100 players is awfully close to what we’d expect if there was no advantage.
Don’t read too much into this, but things have changed a bit! At the moment, 15 of the top 100 ATPers are left-handed. (Still including Rafa, of course.) There’s only about a 4% chance that there would be so many lefties purely due to chance, or 7% if you class Rafa with the natural-born righties. It’s hardly a statistical slam dunk, and the case gets weaker when we broaden our view. There are 12 lefties among the next hundred male players, and only 18 lefties–fewer than we’d expect from chance alone–in the WTA top 200.
Paradoxically, the more lefty regulars on tour, the less uncomfortable they are to face. Put another way, the trickier they are, the less tricky they are.
There may well be an advantage to left-handedness, and its inherent trickery, in the junior or amateur ranks. (There is certainly an advantage when facing me!) But the evidence is flimsy that it extends to the highest level of the game. The real trick would be convincing everyone to start using a different adjective or–gasp!–treating non-superstar lefties as individuals with games that aren’t interchangeable, even if they do all use the same dominant hand.
Expected Points, my new short, daily podcast, highlights three numbers to illustrate stats, trends, and interesting trivia around the sport.
Garbine Muguruza posts her fourth consecutive easy victory, Felix Auger Aliassime is hitting untouchable first serves, and Sofia Kenin will have to beat the odds to defend her Australian Open title.
You can subscribe on iTunes, Spotify, Stitcher, and elsewhere in the podcast universe.
This is very much still an experiment, so please let me know what you think.
Expected Points, my new short, daily podcast, highlights three numbers to illustrate stats, trends, and interesting trivia around the sport.
Some of the biggest names on the women’s tour advance via the just-instituted third-set match tiebreak, Khachanov will play Jannik Sinner in the only meeting between seeds in Saturday’s men’s semi-finals, and Tony Trabert leaves a legacy of excellent play and longtime service to the game.
You can subscribe on iTunes, Spotify, Stitcher (click the “subscribe” button in the player), and elsewhere in the podcast universe.
This is very much still an experiment, so please let me know what you think.
Expected Points, my new short, daily podcast, highlights three numbers to illustrate stats, trends, and interesting trivia around the sport.
On today’s episode: Thursday’s action in Melbourne was canceled due to a positive coronavirus test, Dayana Yastremska won’t be returning to action anytime soon, and Benoit Paire gets back in the swing of things with an ignominious service game.
You can subscribe on Spotify or Stitcher (click the “subscribe” button in the player), and iTunes is coming soon.
This is very much still an experiment, so please let me know what you think.
I’m trying something new: A short, daily(!?) podcast to keep you up to date with the tennis world. Patterned after the Numbers by Barron’s finance podcast, Expected Points highlights three numbers to illustrate stats, trends, and interesting trivia around the sport. Today’s pilot episode is under four minutes long, and I’ll aim to keep each installment around this length.
Joining me for the inaugural episode is Carl Bialik of the Thirty Love podcast. Given the short duration, this will probably be a solo podcast most of the time, but I look forward to including other voices as time and logistics permit.
Today’s episode features Carlos Alcaraz, superstars falling early in the women’s tournaments, and the imminent return of Roger Federer.
Expected Points isn’t yet on iTunes … or anywhere else, for that matter. It will be soon. In the meantime, you can listen right here using the player below:
Please let me know what you think–format, content, whatever. I’ve opened up comments on this post so you can respond, and you may also send comments my way on Twitter.
(Don’t worry, the long-form Tennis Abstract Podcast isn’t going anywhere–we’ll continue with our sporadic schedule throughout the year.)
Great! Here’s some data.
Maybe you’ve got a class project that will allow to you pick your own dataset. Or perhaps you just think that tennis analytics are cool, and you’d like to jump in. One of the more common questions I get is from people in this situation who are looking for a little guidance in choosing a subject. Here are a few tips.
1. Scratch your own itch
I try not to pick topics for others, because I generally find that people do better work (and are more likely to stick with it) when they are “scratching their own itch,” working on what they find particularly interesting. If nothing comes to mind, keep reading.
2. Get skeptical
When you’re watching tennis or reading about it, get in the habit of questioning everything. Does that player really hit more wide serves on break points? Does that guy really play better when he’s leading? If you listen with this type of mindset, you can come away from watching a single match with half a dozen new ideas.
This tip presupposes what might be step 0 — watch and read about tennis! I assume that if you’ve found my blog and want to do analytics, you’re already a pretty big fan. Keep it up–any analyst can benefit from attentively watching more tennis. Reading analytical work is also key, both to get ideas, and to learn what effective studies look like.
3. Think analogically
Many of us who do tennis analytics also work in other sports. Others are academics such as economists and statisticians whose “real jobs” have them working in fields far from athletics. Non-tennis subjects aren’t irrelevant–quite the contrary! If you do an interesting hockey study, or read about an interesting experimental design in development economics, think about how else you could apply a similar approach. Sometimes it’s a dead end with no direct application to tennis, but the exercise itself has value–practicing this kind of thinking eventually pays off.
This tip can be particularly useful for those of you doing a class project. If your professor provides examples of the type of work they’d like to see, consider if there’s a close cousin in tennis analytics. That first thought might not be where you end up, but it’s a good way both to get ideas and to ensure that you’re doing roughly the sort of work that’s asked of you.
4. Chart a match (or ten)
The Match Charting Project is the largest public dataset of shot-by-shot tennis data. It can be overwhelming at first, so if you are considering doing research with the dataset, I strongly recommend charting a match or two as a way to get familiar with it.
Charting a match is also a great way to generate more questions. It forces you to watch closely, so you’ll notice tactics that you might not have otherwise seen. As you chart, you might find yourself dreaming up hypotheses–say, that a player’s service return is particularly effective when she steps inside the baseline. The rest of the match will offer more data to confirm or contradict, and it might help you develop more ideas about where to go from there.
5. Collect your own data
There’s more than enough tennis data out there to keep you busy for a very long time. But don’t be afraid to strike out in a new direction. Perhaps you’d like to study whether certain players are more effective under the lights, which would require tracking the start time of matches. Maybe you’d like to see if certain coaches are particularly good at extracting better performances from their charges, which means you’d need to build a database of coaches, look up when they worked with each of their players, and how the players fared during that time.
Many analysts think that their job is just that–analysis. But in some areas, there more to be gained from better data than from better analysis. Plus, building a new dataset doesn’t have to be a monumental task. The coaches example I gave might include only a few dozen coaches, who worked with a handful of players each.
6. Start small
Following some of my suggestions above can lead you into a huge, ambitious project. the most common result of taking on a huge project is an unfinished project, as I can tell you from experience. Before going big, try to find a “proof of concept” both to get your feet wet, and to see whether you’re on a useful track.
In the coaches example I just gave, you might look at what happened to the WTA rankings of Wim Fissette’s players when they worked with him. I don’t know if there’s a “Fissette effect,” and now that I mention it, I’m curious! That’s a mini-project you could do in an afternoon, and it gets you started on the path of a more thorough study.
Ok, ok, here’s a list
Still stuck? A few years ago, Carl and I put together a list of potential research topics. I’ve since taken it down, but Peter forked it, so it still exists on GitHub.
Some of the topics have already been done, and several others are beyond the scope of what’s possible with publicly-available data. That still leaves you with dozens of ideas.
Finally, once you’ve completed a study–big or small–be sure to post it on twitter and share with other tennis analysts. Your work might be the key that gives the next graduate student or hobby analyst the spark to start a project of their own.
