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.
