Tennis Note #33

Time Violation Warning Study: Motivation & Preliminary Results

“Time Violation Warning, Mr. Nadal.”

Something every single person has heard when Rafael Nadal plays tennis. It makes sense. He always takes more than the allotted 25 s (20 s for Slams) between serves. This is well known. The question is — when is the time violation warning issued? Is it immediately on opening points or much later in the scoreline? Is it in the opening games of the first set or the tail end of later sets? Who issues the most time violation warnings [TVWs]? How often are these warnings issued at Slams, Masters, etc? How often are they issued on various surfaces? How often are they issued between ATP vs WTA? How often on serve vs. return? After warning was issued, did the person the person win the point or double fault? Did they win the game?

These questions are just a sample of my many interests in TVWs but the main problem is a lack of data about any of this [and if there is, please highlight here and send me a comment]. Thus, after witnessing yet another time violation warning on breakpoint, I decided to take matters into my own hands in two different ways.

|GOOGLE FORM| I created a simple form as shown below, with the link here. Every single time you see a TVW, fill out this form. We need diverse data and a lot of entries to make this a better study.

|TWITTER| I extracted all tweets that used the phrase Time Violation Warning in the past and decoded all of these tweets to create a simple database. It is incomplete information but can help guide us in our search for past matches with TVWs. In addition to past tweets, I already have a program in place to extract and save every tweet message and date that relates to TVWs.

Test Case: Rafael Nadal

If you look at the Twitter extracted data, you will notice one thing: NADAL. More people tweet about Nadal when it comes to TVWs, and in quite a bit of detail. Between twitter and the form, we accumulated close to 100 matches involving Nadal [removing any obvious overlaps, giving priority to the form]. From this, I eliminated any entry that had no information about the game scoreline from anyone (either the score or terminology like ‘BP’ or ‘breakpoint’). This left me with 60 matches to analyze, in which 46 matches had the precise game scoreline. Of course, it is important to note that there may be a bit of bias here because most people tweet inconvenient TVWs. Nevertheless, the visuals below illustrate how this breaks down.

The heatmap helps put things in perspective with all the combinations for a score. At the same time, with such little sample size, the histogram gives more precision. It is entirely possible that there is a bias because we sampled Twitter data and people are more likely to mention break points. At the same time, many of the overlaps included a scoreline. More data [not from twitter] would prove more sufficient and reliable.

Among the 46 matches sampled, there is a clear bias for when Nadal receives a TVW and if I were to hypothesize, more data would further support Nadal receiving a TVW at high pressure moments.

The second piece of important information is the actual set scores and how it corresponds to these game scorelines. This drops many of the matches included above because set scoreline was unknown. As you saw with the distribution, you could actually place the scorelines into four categories: 1) Not Lethal [15–15], 2) Breakpoints [30–40], 3) Gamepoints [40–30], or 4) Potentially Leads to BP [30–30 or Deuce or 15–30]. These categories are illustrated through the color code as shown below. For convenience, I sorted it based on set number (if known) and game number.

When reading this, look at the game number the time violation warning was issued and the set (if it is available). Then look at the color of the set scoreline (in columns) and this will tell you what type of game scoreline was on the board when the TVW was issued.

As you can see, while I was able to extract set scores, I had no idea where it belonged with regards to set number. Ignoring set number for a second, you can see how many instances umpires issued TVW at game 6 or later [22] vs. before [10]. This also helps you visualize the game scoreline — majority of which are at break points or potentially lead to one.

Now there are so many arguments that can justify these later stage TVWs such as Nadal is slowing down towards the tail end of the set. However, this does not justify when it is issued. Why are so many TVWs at these crucial game, set or match-defining moments? This is the true purpose of this study — to expose the bias. As I mentioned before, Twitter introduces its own bias with what we choose to report (although, it seems for Mr. Nadal, everyone reports it for at least the major tournaments). Thus, this need for a diverse set of data, from every single possible match in ATP and WTA, could remove any skepticism. There is much more we can explore including player psychology (did they win or lose the point/game?), umpire bias (do umpires have a tendency to issue TVWs at certain moments?), slams vs. other tournaments, and many more questions. I hope this motivates you to bookmark this form and participate. Do not worry about double counting, I will automatically delete any duplicates. My goal is to reach at least 500 matches [at least 250 for ATP and 250 for WTA] before I make all of this data public in addition to interactive visuals made for exploration. The best way to do this is through you — whether you use the Twitter database to find past matches or watch new ones, all data is welcome!

Special thanks to those who filled out the form already. If you enjoy reading these tennis notes, make sure to follow the publication, ‘Recommend’ and share! Check us out on Facebook! Made a cool observation? Interested in certain topics and writing? Are you a tennis photographer? Comment, add notes, and check out the submission guideline. Let me know which visuals are good and which are not so great. Cheers!