Two New Ways to Chart Tennis Matches

Readers of this site are probably already aware of the Match Charting Project, my effort to coordinate volunteer contributions to build a massive shot-by-shot database of professional tennis. If this is the first you’ve heard of it, I encourage you to check out the detailed match- and player-level data we’ve gathered already.

In the last week, two developers have released GUIs to make charting easier and more engaging. When I first started the project, I put together an excel spreadsheet that tracks all the user input and keeps score. I’ve used that spreadsheet for the hundreds of matches I’ve charted, but I recognize that it’s not the most intuitive system for some people.

The first new interface is thanks to Stephanie Kovalchik, who writes the tennis blog On the T. (And who has contributed to the MCP in the past.) Her GUI is entirely click-based, which means you don’t have to learn the various letter- and number-codes that are required for the traditional MCP spreadsheet.

skoval

While it’s web-based, it has some of the look and feel of a modern handheld app. It’s probably the easiest way to get started contributing to the project.

(Which reminds me, Brian Hrebec wrote an Android app for the project almost two years ago, and I haven’t given it the attention it deserves. It also makes getting started relatively easy, especially if you’d like to chart on an Android device.)

The second new interface is thanks to Charles Allen, of Tennis Visuals. Also web-based, his app requires that you use the same letter- and number-based codes as the original spreadsheet, but sweetens the deal with live visualizations that update after each point:

tvis

With four ways to chart matches and add to the Match Charting Project database, there are even fewer excuses not to contribute. If you’re still not convinced, I have even more reasons for you to consider. And if you’re ready to jump in, just click over to one of the new GUIs, or click here for my Quick Start guide.

 

New at TennisAbstract: Weekly Elo Reports

Starting today, you can find weekly Elo ranking reports on the home page of Tennis Abstract. Here are the men’s ratings, and here are the women’s ratings.

Elo is a rating system originally designed for chess, and now used across a wide range of sports. It awards points based on who you beat, not when you beat them. That’s in direct contrast to the official ATP and WTA ranking systems, which award points based on tournament and round, regardless of whether you play a qualifier or the number one player in the world.

As such, there are some notable differences between Elo-based rankings and the official lists. In addition to some rearrangement in the top ten, ATP Elo ratings place last week’s champion Roberto Bautista Agut up at #12 (compared to #17 in the official ranking) and Jack Sock at #13 (instead of #23).

The shuffling is even more dramatic on the women’s side. Belinda Bencic, still outside the top ten in the official WTA ranking, is up to #5 by Elo. After her Fed Cup heroics last weekend, Bencic is a single Elo point away from drawing equal with #4 Angelique Kerber.

These new Elo reports also show peaks for every player. That way, you can see how close each player is to his or her career best. You can also spot which players–like Bencic and Bautista Agut–are currently at their peak.

Like any rating system, Elo isn’t perfect. In this simple form, it doesn’t consider surface at all. I haven’t factored Challenger, ITF, or qualifying results into these calculations, either. Elo also doesn’t make any adjustments when a player misses considerable time to injury; a player just re-assumes his or her old rating when they return.

That said, Elo is a more reliable way of comparing players and predicting match outcomes than the official ranking system. And now, you can check in on each player’s rating every week.

What Happens After an Unsuccessful First Serve Challenge?

A lot of first serves miss, so every player has a well-established routine between the first and second serve. So much so that, traditionally, if something disrupts that routine, the receiver may grant the server another first serve.

Hawkeye has changed all that. If the server doubts the line call, he or she may challenge it. That results in a lengthy wait, usually some crowd noise, and a general wreckage of that between-serves routine.

The conventional wisdom seems to be that the long pause is harmful to the server: that if the challenge fails, the server is less likely to put the second serve in the box. And if the second serve does go in, it’s weaker than average, so the server is less likely to win the point.

My analysis of over 200 first-serve challenges casts doubt on the conventional wisdom. It’s another triumph for the null hypothesis, the only force in tennis as dominant as Novak Djokovic.

As I’ve charted matches for the Match Charting Project, I’ve noted each challenge, the type of challenge, and whether it was successful. I’ve accumulated 116 ATP and 89 WTA instances in which a player unsuccessfully challenged the call on his own first serve. For each of these challenges, I also calculated some match-level stats for that server: how often s/he made the second serve, and how often s/he won second serve points.

Of the 116 unsuccessful ATP challenges, players made 106 of their second serves. Based on their overall rates in those matches, we’d expect them to make 106.6 of them. They won exactly half–58–of those points, and their performance in those matches suggests that they “should” have won 58.2 of them.

In other words, players are recovering from the disruption and performing almost exactly as they normally do.

For WTAers, it’s a similar story. Players made 77 of their 89 second serves. If they landed second serves at the same rate they did in the rest of those matches, they’d have made 77.1. They won 38 of the 89 points, compared to an expected 40 points. That last difference, of five percent, is the only one that is more than a rounding error. Even if the effect is real–which is doubtful, given the conflicting ATP number and the small sample size–it’s a small one.

Of course, the potential benefit of challenging the call on your first serve is big: If you’re right, you either win the point or get another first serve. Of the challenges I’ve tracked, men were successful 38% of the time on their first serves, and women were right 32% of the time.

There’s no evidence here that players are harmed by appealing to Hawkeye on their own first serves. Apart from the small risk of running out of challenges, it’s all upside. Tennis pros adore routine, but in this case, they perform just as well when the routine is disrupted.

First and Second Serves: Another ATP Info-miss

Breaking news, everybody: First serves are better than second serves!

That’s what I learned, anyway, from the latest article in the “Infosys ATP Beyond the Numbers” series:

When you average out the Top 10 players in the 2015 season, they are saving break points 72 per cent of the time when making a first serve. On average, that drops to 53 per cent with second serves. That 19 per cent difference is one of the most important, hidden metrics in our sport.

Is the difference between first and second serves “important?” Definitely. Is it in any way “hidden?” Not so much.

The melodramatic phrasing here suggests that break points are different from regular points, perhaps with a much larger spread between first and second serve winning percentages. But no, that’s not the case.

Last year, top ten players won 75.6% of first-serve points and 55.4% of second-serve points. Combined with the Infosys numbers–which I can’t verify, because the ATP doesn’t make the necessary raw data available–that means that top ten players win 5% less often when making a first serve on break point, and 5% less often when missing their first serve on break point.

At the risk of belaboring this: When it comes to the importance of making your first serve, break points are no different than other points.

Even that 5% difference is less meaningful that it looks. Break points don’t occur at random–better opponents generate more break opportunities. If you play two matches, one against Novak Djokovic and one against Jerzy Janowicz, you’re likely to face far more break points against Novak than against Jerzy … and of course, you’re less likely to win them.

Pundits tend to focus on break points, and in part, they are right to do so, because this small subset of points have an outsized effect on match outcomes. However, because of the small sample, it’s easy–and far too common–to read too much into break point results. My research has repeatedly shown that, once you control for opponent quality, most players win break points about as often as they do non-break points.

The ATP is sitting on a wealth of information. If we’re going to learn anything meaningful when they go “beyond the numbers,” it would be nice if they took advantage of more of their data and offered up more sophisticated analysis.

Match Charting Project February Update

At the beginning of the year, I announced an ambitious goal: to double the number of matches in the Match Charting Project dataset. That’s a target of 1,617 new matches in 2016–about 135 per month, or 4.5 per day.

So far, so good! In January, ten contributors combined to add 162 new matches to the total. Our biggest heroes were Edo, with 35 matches, including many Grand Slam finals; Isaac, with 33; and Edged, whose 22 included some of the dramatic late-round men’s matches from Melbourne.

As we close in on the 1,800-match mark, I’m excited to announce a new addition to the stats and reports available on Tennis Abstract. Now, for every player with at least two charted matches in the database, there’s a dedicated player page with hundreds of aggregate data points for that player.

Here’s Novak Djokovic’s page, and here’s Angelique Kerber’s. I’m still working on integrating these pages into the rest of Tennis Abstract, but for now, you’ll be able to access them by clicking on the match totals next to every player’s name on the Match Charting home page.

These pages each feature four charts, which compare the player’s typical rally length, shot selection, winner types, and unforced error types to tour average. The other links on each page take you to tables very similar to those on the MCP match reports. Move your cursor over any rate to see the relevant tour average, as well as that player’s rates on each surface.

I hope you like this new addition, which owes so much to the amazing efforts of so many volunteer charters.

I hope, too, that you’ll be inspired to contribute to the project as well. When you’re ready to try your hand at charting, start here. As always, the more matches we have, the more valuable the project becomes.

Is Milos Raonic’s Return Game Improving?

It’s no secret that Milos Raonic‘s return game is a liability. He has reached the game’s elite level with a dominant serve, and he broke into the top five on the strength of a historically-great record in tiebreaks.

Last year, Raonic’s tiebreak record fell back to earth (as these things usually do) and he dropped out of the top ten. Now, in a new season with a new coach, Carlos Moya, Raonic reeled off nine straight victories, finally losing in five sets to Andy Murray in today’s Australian Open semifinal.

Until today’s match, when Raonic won a dismal 25% of return points, the numbers were looking good. Milos won 36.5% of return points in his four matches in Brisbane, which is a little bit better than the 35% tour average on hard courts. With his serve, he doesn’t need to be a great returner; simply improving that aspect of his game to average would make him a dominant force on tour.

This is a crucial number to watch, because it could be the difference between Milos becoming number one in the world and Milos languishing in the back half of the top ten. It’s incredibly rare that players with weak return games are able to maintain a position at the very top of the rankings.

Through the quarterfinals in Melbourne, the positive signs kept piling up. For each of his 2016 opponents, I tallied their 2015 service points won on hard courts. In 6 of 10 matches this month, Milos kept their number below their 2015 average. In a 7th match, against Gael Monfils, he was one return point away from doing the same.

By comparison, in 2015, Raonic held hard-court opponents to their average rate of service points won only 9 times in 35 tries. Even in his career-best season of 2014, he did so in only 15 of 41 matches. Even with the weak return numbers against Murray, this is Raonic’s best ever 10-match stretch, by this metric.

The difference is more dramatic when we combine all these single-match measurements into a single metric per season. For each match, I calculated how well Milos returned relative to an average player against his opponent that day. For example, against Murray today, he won 25% of return points compared to an average hard-court Murray opponent’s 33.7%. In percentage terms, Raonic returned 26% worse than average.

Aggregating all of his 2016 matches, Raonic has returned 6% better than average. In 2015 hard-court matches, he was 10% below average; in 2014, 3% below average, and in 2013, 7% below average.

A nine-match stretch of good form is hardly proof that a player has massively improved half of his game, but it’s certainly encouraging. While all know that Milos is an elite server, it’s his return game that will determine how great he becomes.

How Dangerous Is It To Fix a Single Service Game?

Earlier this week, I offered a rough outline of the economics of fixing tennis matches, calculating the expected prize money that players forgo at various levels when they lose on purpose. The vast gulf between prize money, especially at lower-level events, and fixing fees suggests that gamblers must pay high premiums to convince players to do something ethically repugnant and fraught with risk.

So much for match-level fixes. What about single service games? In Ben Rothenberg’s recent report, a shadowy insider offers the following data points:

Buying a service break at a Futures event cost $300 to $500, he said. A set was $1,000 to $2,000, and a match was $2,000 to $3,000.

In other words, a service break is valued at between 10% and 25% the cost of an entire match. The article doesn’t mention service-break prices at higher levels, so we’ll have to use the Futures numbers as our reference point.

Selling a service break might be a way to have your cake and eat it too, taking some cash from gamblers while retaining the chance to advance in the draw and earn ranking points. But it won’t always work out that way.

I ran some simulations to see how much a service break should cost, based on the simplifying assumption that prices correspond to chances of winning and, by extension, forgone prize money. It turns out that the range of 10% to 25% is exactly right.

Let’s start with the simplest scenario: Two equal men with middle-of-the-road serves, which win them 63% of service points. In an honest match, these two would each have a 50% chance of winning. If one of them guarantees a break in his second service game, he is effectively lowering his chances of winning the match to 38.5%. dropping his expected prize money for the tournament by 23%.

If our players have weaker serves, for instance each winning 55% of service points, the fixer’s chances of winning the match fall to about 42%, only a 16% haircut. With stronger serves, using the extreme case of 70% of points going the way of the server, the fixer’s chances drop to 34%, a loss of 32% in his expected prize money.

This last scenario–two equal players with big serves–is the one that confers the most value on a single service break. We can use that 32% sacrifice as an upper bound for the worth of a single fixed break.

Fixed contests have more value to gamblers when the better player is guaranteed to lose, and in those cases, a service break doesn’t have as much impact on the outcome of the match. If the fixer is considerably better than his opponent, he was probably going to break serve a few times more than his opponent would, so losing a single game is less likely to determine the outcome of the match.

Let’s take a few examples:

  • If one player wins 64% of service points and other wins 62%, the favorite has a 60% chance of winning. If he fixes one service break, his chances of winning fall to just below 48%, about a 20% drop in expected prize money.
  • When one player wins 65% of service points against an opponent winning 61%, his chances in an honest match are 69.3%. Giving up one fixed service break, his odds fall to 57.4%, a sacrifice of roughly 17%.
  • A 67% server facing a 60% server has an 80.8% chance of winning. With one fixed service break, that drops to 70.7%, a loss of 12.5%.
  • A huge favorite winning 68% of service points against his opponent’s 58% has an 89.5% chance of advancing to the next round. Guarantee a break in one of his service games, and his odds drop to 82%, a loss of 8.4%.

With the exception of very lopsided matches (for which there might not be as many betting markets), we have our lower bound, not far below 10%.

The average Futures first-rounder, if we can generalize from such a mixed bag of matches, is somewhere in the middle of those examples–not an even contest, but without a heavy favorite. So the typical value of a fixed service break is between about 12% and 20% of the value of the match, right in the middle of the range of estimates given by Rothenberg’s source.

Even in this hidden, illegal marketplace, the numbers we’ve seen so far suggest that both gamblers and players act reasonably rationally. Amid a sea of bad news, that’s a good sign for tennis’s governing bodies: It promises that players will respond in a predictable manner to changing incentives. Unfortunately, it remains to be seen whether the incentives will change.