Point-by-Point Data From the Last 17 Grand Slams

I’ve been doing a lot of griping lately about the state of tennis data, so I figured now was a good time to start doing something about it.

I’ve just released point-by-point data for most Grand Slam singles matches back to 2011. Beyond the basic point sequence–which is valuable in and of itself–you’ll find serve speed, winner type, and for a few of the slams, rally length for each point.

More detailed notes on the data are available at that link. Enjoy, and if working with it turns up any interesting findings, please let me know.

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Sloan Conference Presentation on Tennis Analytics

Last weekend at the Sloan Sports Analytics Conference in Boston, I gave a talk, “First Service: The Advent of Actionable Tennis Analytics.” The presentation was in three parts:

  1. The sorry state of tennis data
  2. Schedule optimization (based in part on this blog post)
  3. The Match Charting Project (more about that in this post, among others)

The conference video-recorded all presentations, and I understand that video will be posted on the Sloan site. When it becomes available, I’ll post a link here.

In the meantime, many people have asked for my slide deck: First Service.

Also, Jim Pagels wrote a brief piece for Forbes drawing on my talk, which you can read here.

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Who Do You Love, Racket Ralliers?

Many of you probably know by now: Last week, Ben Rothenberg and I launched Racket Rally, a stock-market-style fantasy tennis game. We were overwhelmed by the initial response, getting well over 2,000 signups in only a few days before play began at the Australian Open. If you haven’t joined in yet, we’d still love to have you–you can start building the perfect portfolio for Indian Wells and beyond.

With so much user data, it’s interesting to see which players are most popular among Racket Rally members.

For the uninitiated, here’s how it works. Each member starts with a budget of $100,000. She can spend that money on shares of any player in the top 300 (along with a few injury-protected players), at prices equal to their ATP or WTA ranking points. Last week, Richard Gasquet had 1,350 ATP ranking points, so you could buy one share of Gasquet for $1,350, two shares for $2,700, and so on, up to a maximum of 50 shares or $40,000, whichever comes first.

Each week, sales are limited, so the perfect portfolio isn’t necessarily optimized for the Australian Open. Since users are stuck with many of their players from week to week, their choices reflect both short-term and long-term expectations.

The numbers

Before the Australian Open began, 1,739 members had purchased shares of at least three players–a reasonable cutoff to define active users who built portfolios. They bought over 63,000 shares of 375 different players, spending just short of 169,000,000 fake Racket Rally dollars.

The most popular player, by almost every measurement, was Novak Djokovic. More than half of users (992) bought at least one share of Novak, and the same is true of Roger Federer, who is to be found in 875 portfolios. Here’s the rest of the top ten:

Kei Nishikori      764  
Maria Sharapova    716  
Serena Williams    708  
Andy Murray        697  
Simona Halep       639  
Milos Raonic       571  
Karolina Pliskova  557  
Nick Kyrgios       517

Interesting mix, huh? Pliskova is the big surprise, and shows the savviness of at least 500 users. Since Pliskova reached the final in Sydney last week, her ranking has since gone up, meaning that members who purchased shares last week got her at a discount. Kyrgios is a more Melbourne-optimized choice, as it’s reasonable to expect Nick to perform well at his home slam.

When we switch our focus to shares purchased, many of the same names remain near the top, but the order changes quite a bit. Users bought 2,412 total shares of Kyrgios, most of any player in the game. Pliskova is right behind him, at 1,990. An unexpected name comes in third: 1,921 shares of Viktor Troicki were picked up, presumably by users who think he will return to something much closer to his pre-suspension form.

Here are the other 15 players who garnered enough interest for users to amass at least 1,000 shares each:

Andy Murray         1732  
Novak Djokovic      1723  
Roger Federer       1636  
Bernard Tomic       1563  
Kei Nishikori       1435  
Maria Sharapova     1366  
Borna Coric         1329  
Serena Williams     1292  
Venus Williams      1205  
Thanasi Kokkinakis  1173  
Simona Halep        1158  
Garbine Muguruza    1130  
Vasek Pospisil      1108  
Milos Raonic        1100  
David Goffin        1048

When we turn to total dollars invested–or, to look at it another way, percentage of portfolio allotted–top players take center stage. Djokovic, Federer, Serena, Sharapova, and Murray make up the top five, while Petra Kvitova and Rafael Nadal make their first appearance in a top ten.

The differences among dollars spent are enormous. Members spent nearly $20 million (more than 10% of in-game currency) on Djokovic, $16 million on Federer, and just over $10 million each on Serena and Sharapova.  10 players are over the $5 million mark, 22 over $2 million, and 30 over $1 million.

Plenty of notable players are another order of magnitude less–Bethanie Mattek-Sands, the best Racket Rally investment, as of this writing–is held in only 49 portfolios, for a total of $120,000. Carina Witthoeft, the unheralded German who has reached the third round, appears in only nine portfolios, for a total of $44,000. One lonely user took a chance on Evgeniya Rodina (5 shares for $2,375)–members spent more money on at least 20 players who aren’t even in the Melbourne main draw.

It may be that not every share purchase was based entirely on interest or potential. 76 players–most of them out of action this week–are held in only one portfolio. I suspect that the member who spent $146 on one share of Anastasia Grymalska had about $146 left in his or her portfolio when that choice was made.

In the near future, I’ll put together a page on the Racket Rally website to show all of this data on a weekly basis. It will also be fascinating to see what players are the most traded each week.

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You Can’t Win Over Our Aussie Sam Stosur … Or Can You?

With the possible exception of the first movement of Schubert’s Bb major piano sonata (D960), the greatest work of art to emerge from the western musical tradition is, of course, “Sam vs OVAs.”

After the six or seven hundredth time through this song, I untied my dancing shoes, put my tennis statistician hat back on, and wondered: Is the conventional wisdom valid? Is it true that players whose names end in “ova” can’t win over Sam Stosur?

Let’s delve into the database and find out.

“Sam vs OVAs” lists 24 potential opponents: 23 Ovas and 1 Galina Voskoboeva. Stosur has faced 21 of the 24 in her career, missing only Nina Bratchikova, Barbora Zahlavova Strycova, and Kristyna Pliskova. (For the record, Sam has faced Kristyna’s sister Karolina, losing in their one meeting.)

Sure enough, Ovas usually don’t win over Sam Stosur. The Aussie owns winning records against 13, has losing records against 7, and is even with 1, Yaroslava Shvedova.

Despite all those positive head-to-heads, the numbers aren’t so rosy upon closer inspection. Only 5 of 21 truly “can’t win” over Sam Stosur–Sam has lost at least once to 16 others. (That group of 16 includes Anastasia Rodionova and Jarmila Gajdosova, so the song is correct in those cases.) And while she has a positive aggregate record against the players in the song–holding at 56-52 as we head into the 2015 season–it is heavily weighed down by poor performances against Maria Sharapova (2-14), Petra Kvitova (1-7), and Lucie Safarova (2-9).

However, in Sam’s defense, the song’s lyricist didn’t cherry-pick in her favor. Stosur has faced 36 Ovas (plus Voskoboeva) in her career, 16 of whom weren’t named in the song. Against those players, she is undefeated against 10, and her overall record is a slightly better 15-12. Take out her abysmal 0-6 mark against Nicole Vaidisova, and you could put together a compelling (if biased) case that, as we have been led to believe, Ovas can’t win over Sam Stosur.

As my mother always taught me, a song can only reach its true potential once you thoroughly fact-check it. With that in mind, let’s listen again!

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The Match Charting Project: One Year On

Just over a year ago, I launched the Match Charting Project, a collaborative effort to track every shot of as many professional matches as possible. Many of you have contributed, and a few of you have given more time to the project than I could have ever hoped. Thank you.

To make the MCP possible, I devised a relatively simple notation system, tracking every type of shot and its direction, along with an Excel document to make recording each point easier. Earlier this year, I beefed up the stats generated for each match, showing not only hundreds of rates and totals for each player, but also player and tour averages for comparison.

The project has recently passed a number of milestones, and even more are coming soon. The database now includes at least one match for every player in the ATP and WTA top 100. There’s depth as well as breadth: 18 players (10 men and 8 women) are represented with at least 10 matches each.

The WTA portion of the database just passed 200 total matches, and by the end of the year, the combined total will cross the 500-match mark. Earlier this year, I hesitated to pursue too much research using this dataset because it was too small and biased toward a few players of interest, but those reservations can increasingly be put to bed.

Frequently on this site, I have reason to vent my frustration with the state of data collection in tennis, and an excellent recent article illustrates how, in many ways, the state of the art is no more advanced than it was thirty years ago. If the professional tours won’t even release all the data they have, let alone lead the way in improving the state of analytics in the game, it’s up to us–the fans–to do better.

The Match Charting Project is one way to do that. Every additional match added to the database increases our knowledge of a specific matchup, of a pair of players, of surface tendencies, and of the sport as a whole. We’ll probably never be able to chart every tour-level match, but as the first (almost) 500 matches have shown, the database doesn’t have to be complete to be extremely valuable.

If you’ve already contributed, thank you. If you’re interested in contributing, start here.

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The Almost Neutral Let Cord

Once I started charting matches–carefully watching and notating every shot–I thought I noticed a trend after “let” serves. It seemed that players missed far more first serves than usual after a let, and when players landed a post-let first serve, their offering was weaker than usual.

Now that we have nearly 500 pro matches in the Match Charting Project database, including at least 200 each from both the ATP and the WTA, there’s plenty of data with which to test the hypothesis.

To my surprise, there’s no such trend. If anything, players–men in particular–are more likely to make a first serve after a let cord. When they do, they are at least as likely to win the point as in non-let points, suggesting that the serve is no weaker than usual.

Let’s start with the ATP numbers. In over 1,100 points in the charting database, the server began with a let. He eventually landed a first serve 62.8% of the time, compared to 62.0% of the time on non-let points. When he made the first serve, he won 73.3% of points that began with a let serve, compared to only 70.6% of first-serve points when there was no let.

More first serves in, and more success on first serves. The latter finding, with its difference of 2.7 percentage points, is particularly striking.

Of the trends I had expected to see, only one is borne out by the data. Since a net cord let is only millimeters away from a fault into the net, it seems logical that net faults would be more common immediately after a let than otherwise. That is the case: 15.7% of men’s first serves result in faults into the net, but after a let,  that figure jumps to 17.0%.

When we turn to WTA matches with available data, we find that the post-let effect is even stronger. In non-let points, first serves go in at a 62.8% rate. After a first-serve let, players record a 65.3% first-serve percentage. Given that first-serve percentages are usually concentrated in a relatively small range, a difference of 2.5 percentage points is quite significant.

The WTA data tells a different story than the ATP numbers do when we look at the end result of those first serves. On non-let points, WTA players win first-serve points at a 62.8% rate, while after a first-serve let, they win these points at only a 61.8% clip. It may be that some women approach post-let first serves a bit more conservatively, and they pay the price by winning fewer of those points.

WTA players also appear to miss a few more post-let first serves into the net, though the difference is not as striking as it is for men. On non-let points, net faults make up 16.2% of the total, and after first-serve lets, net faults account for 16.7% of first serves. Of all the numbers presented here, this one is most likely to be no more than random noise.

It turns out that let serves don’t have much to tell us about the next serve or its outcome–and that’s not much of a surprise. What I didn’t expect was that, after a let serve, professionals are a bit more likely than usual to find success with their next offering.

If you like watching tennis and think this kind of research is worth reading, please consider lending a hand with the Match Charting Project. There’s no other group effort of its kind, and the more matches in the database, the more valuable the analysis.

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New “Event Records” View at TennisAbstract.com

TennisAbstract.com now offers another way to look at stats for every player on the ATP tour.

The new “Event Records” view shows–you guessed it–records by event, summarizing a player’s performance at a given tournament, including his career record, career tiebreak record, years played, best result, and the usual complement of aggregate statistics such as return points won and break points saved.

To access a player’s event records, click here, in the upper left corner, right next to the link to the head-to-head view I introduced recently:

er1

Then you’ll see something like this:

er2

 

The events names are links, so you can click on any of those to see the full list of matches the player contested at that tournament.

Three columns in the middle of the table–“First” (the player’s first year at the event), “Last,” and “Best” (his best result at the tournament)–are loaded with additional information. Mouseover the data in those columns to see a description of the player’s last match (for “First” and “Last”) and the years in which he achieved his best result:

er3

 

er4

 

If you’re interested in particular subsets of matches, most of the filters in the left-hand column function as they normally do. For instance, let’s say you’re interested in Stan Wawrinka’s performance at various events as a top-ten player:

er5

 

You can also use the filters to reduce the number of tournaments on view. Use the “Level” filter to show only Grand Slams or Masters. Use the “Surface” filter to show only events on a particular surface. I also added a “Minimum Years” filter so that you could limit the list to tournaments that the player entered a certain number of times.

In the context of event records, some of the filters are more useful than others (would anyone ever have a use for tournament-by-tournament records in matches with bagel sets?), but at the very least, there are a ton of tools here to play around with.

Enjoy!

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