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.

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.