Editor’s note: Please welcome guest author Lowell! He’s a prolific contributor to the Match Charting Project, and the author of the first guest post on this blog. The Problem Quantifying aggression in tennis presents a quandary for the outsider. An aggressive shot and a defensive shot can occur on the same stroke at the same place on the court at the same point in a rally. To know whether one occurred, we need information on court positioning and shot speed, not only of the current shot, but the shots beforehand. Since this data only exists for a fraction of tennis matches (via Hawkeye) and is not publicly available, using aggressive shots as a metric is untenable for public consumption. In a different era, net points may have been a suitable metric, but almost all current tennis, especially women’s tennis, revolves around baseline play. Net points also can take on a random quality and may not actually reflect aggression. Elina Svitolina , according to data from the Match Charting Project , had 41 net points in her match against Yulia Putintseva at Roland Garros this year. However, this was not an indicator of Svitolina’s aggressive play so much as Putintseva hitting 51 drop shots in the match. The Match Charting Project does give some data to help with this problem however. We can use the data to get the length of rallies and whether a player finished the point, i.e. he/she hit a winner or unforced error or their opponent hit a forced error. If we assume an aggressive player would be more likely to finish the point and would be more likely to try to finish the point sooner rather than later in a rally, we can build a metric. The Metric To calculate aggression using these assumptions, we need to know how often a player finished the point and how many opportunities did they have to finish the point, i.e. the number of times they had the ball in play on their side of the net. To measure the number of times a player finished the point, we add up the points where they hit a winner or unforced error or their opponent hit a forced error. For short, I will refer to these as “Points on Racquet”. To measure how many opportunities a player had to finish the point, we calculate the number of times the ball was in play on each player’s side of the net. For service points, we add 1 to the length of each rally and divide it by 2, rounding up if the result is not an integer. For return points, we divide each rally by 2, rounding up if the result is not an integer. These adjustments allow us to accurately count how often a player had the ball in play on their side of the net. For brevity, I will call these values “Shot Opportunities”. If we divide Points on Racquet by Shot Opportunities we will get a value between 0 and 1. If a player has a value of 0, they never finish points when the ball is on their side of the net. If the player has a value of 1, they only hit shots that end the point. As the value increases, a player is considered more aggressive. For short, I will call this measure an “Aggression Score.” The Data Taking data from the latest upload of the Match Charting Project , I found women’s players with 2000 or more completed points in the database (i.e. all points that were not point penalties or missed points). Eighteen players fitted these criteria. Since the Match Charting Project is, unfortunately, a nonrandom sample of matches, I felt uncomfortable making assessments below a very large number of data points. Using 2000 or more data points, however, an overwhelming amount of data would be required to overcome these assessments, giving some confidence that, while bias exists, we get in the neighborhood of the true aggression values. The Results Below are the results from the analysis. Tables 1-3 provide the Aggression Scores for each player overall, broken down into serve and return scores and further broken down into first and second serves. They also provide differences between where we would expect the player to be more aggressive (Serve v. Return, First Serve v. Second Serve and Second Serve Return v. First Serve Return). Table 1: Aggression Scores Name Overall On Serve On Return S-R Spread S Williams 0.281 0.3114 0.2476 0.0638 S Halep 0.1818 0.2058 0.1537 0.0521 M Sharapova 0.2421 0.2471 0.2358 0.0113 C Wozniacki 0.1526 0.1788 0.1185 0.0603 P Kvitova 0.3306 0.347 0.309 0.038 L Safarova 0.2475 0.2694 0.2182 0.0512 A Ivanovic 0.2413 0.247 0.2335 0.0135 Ka Pliskova 0.256 0.2898 0.2095 0.0803 G Muguruza 0.231 0.238 0.2214 0.0166 A Kerber 0.1766 0.2044 0.1433 0.0611 B Bencic 0.1742 0.1784 0.1687 0.0097 A Radwanska 0.1473 0.1688 0.1207 0.0481 S Errani 0.1232 0.1184 0.1297 -0.0113 E Svitolina 0.1654 0.1769 0.1511 0.0258 M Keys 0.3017 0.3284 0.2677 0.0607 V Azarenka 0.1892 0.1988 0.1762 0.0226 V Williams 0.2251 0.247 0.1944 0.0526 E Bouchard 0.2458 0.2695 0.2157 0.0538 WTA Tour 0.209 0.2254 0.1877 0.0377 Table 2: Serve Aggression Scores Name Serve First Serve Second Serve 1-2 Spread S Williams 0.3114
