Animating Player Movement using Hawk-Eye Optical Tracking Data

Over the last few nights I have been experimenting with ways to animate player movement in tennis, using optical tracking data from Hawk-Eye. Displaying how movement is changing over time (or any temporal data for that matter) has, and still provides a great challenge to data visualization experts, animators and cartographers. This very quick animation highlight some of the advantages and disadvantages of 2D animations.

Over the next month or so as the project progresses I’ll start sharing some of my experiences of what I’ve learnt.

Until then…

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IBM “Data Wall” @ 2013 US Open

Late last year I received a call from the very talented crew at Hush in New York to help on a project they were building for the 2013 US Open and IBM. You can check out a video they put together to promote the work below.

The IBM Data Wall had three layers of interaction, Playground View, Simple View and Detail View. Each digital “tennis ball” represented a match and the wealth of data that every match produces, and sometimes even included predictions of the outcome.

My primary role on the project was to:

  • contribute ideas and design sketches
  • be a source of tennis knowledge
  • contribute as source of interpretation of data

Sean Klassen in a recent article on the Communication Arts website had this to say about the project…

“Sports are chock-full of data enthusiasts, so it’s great to see IBM and the U.S. Open taking advantage of that fact with some of the most beautiful infographics I’ve ever seen.”

The full article by Communication Arts which reviews the application can be read here.

Some of my design sketches and ideas were transformed by the guys at Hush into these fabulous interactive infographics which were used in the final application (see below).

IBM Data Wall Important Point

TennisServe Speed Infographic

Source: http://heyhush.com/work/ibm/

The main interface to exploring the Detail View ended up like this:

US Open 2013 IBM Data Wall

The guys at Hush had some fun with concept of an exploding tennis ball for the Playground View – they literally built this overnight!

Exploding Yellow Tennis Ball

A more comprehensive collection of screen shots from the application can be seen at Hush’s official website.

Enjoy!

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Presenting a Diorama of Player Movement in Sport

Earlier today Sports Performance and Tech magazine published my article on visualizing Hawk-Eye player tracking data. The article explores the value of 3D and Space Time Cubes when displaying spatio-temporal data.

To create the Diorama I used 3D optical tracking data from an official Hawk-Eye tennis match played between Roger Federer and Paul-Henri Mathieu at the Swiss Indoors in Basel, 2012.

Sports Performance and Tech Magazine

A Diorama of Player Movement in Sport

A Diorama of Player Movement in Sport

The full article can be read here.

A Diorama of Player Movement in Sport

To explore the 3D Diorama in more detail please visit the app here.

*** The app is best viewed on a computer or laptop using Google Chrome ***

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How We Made Nadal’s Interactive Game Tree

Late last year we published an interactive game tree celebrating Rafael Nadal’s historic 2013 season. The Game Tree allows users to visually explore how easily, or not, Nadal won each of his 666 service games in the Masters 1000 Tournaments, Grand Slams and World Tour Finals he played in 2013. This rare point-by-point summary shows where Nadal’s history breaking season was won and rarely lost.

Nadal Game Tree

Figure 1. Nadal’s Interactive Game Tree was released after the completion of the World Tour Finals, November, 2013. Click here to view the application.

How the Project Began…

The idea for the project came about after years of frustration by never really knowing how close a match was by just looking at the final score. For example, a 6-4, 6-4 score-line could mean multiple things; one break of serve, or multiple breaks of serve. The winner may have won their service games easily, or they might have been hotly contested. Clearly, the final score gives no indication of the competitiveness of the match. To ease this frustration we set out to find a way to graphically present how hard Nadal was challenged in his matches during the 2013 season.

Our Inspiration

Inspired by Donato Ricci et al’s, (2008) game tree-like infographic (Figure 2), we set out to illustrate the path to victory using game tree theory. Sometimes referred to as a tree of possibilities, a game tree represents paths from a starting point to an end point, often in a game scenario like chess. Tennis plugs perfectly into a game tree as each player starts at 0-0 and makes a move in one direction only through the tree, depending on their success at the 0-0 point.

Ricci et al’s, (2008) map

Figure 2. Mapping relationships between events. Ricci et al’s, (2008) map of the most common research methodologies used by various Italian design firms.

In order to determine the effectiveness of Nadal during his service games we mapped the frequency of paths from one point in a game to the next. To do this, we borrowed concepts from a 19th century cartographic method, called flow mapping. Flow maps were first introduced by Henry Drury Harness in the Atlas to Accompany Second Report of the Railway Commissioners, Ireland (1837) (Figure 3).

Henry Drury Harness Map

Figure 3: Henry Drury Harness introduced the first flow map in 1837. The map uses a variety of line thickness to convey a quantity of traffic flow between Irish cities.

The lines connecting each point in the Game Tree became the quantitative flow lines, and were scaled proportionally representing the number of times Nadal played through each point. The various line thicknesses allowed us to very quickly identify the most common path during each service game.

The Data & Technology Behind the Game Tree

To create the game tree we began by downloading all of the appropriate matches from the William Hill sports website as XML files. Each match was available as a separate XML file and these files contained high-level information about the match (players, tournament info, date, etc.), along with a detailed point-by-point breakdown of the match. After a preliminary assessment of the data we developed a javascript application, which looped through the files and began to process the points.

William Hill Data

Figure 4. An extract of data from the xml game files used in the game tree.

We then prepared a series of functions using javascript, to mimic the behavior of the game tree. The Game Tree at present only maps Nadal’s service games, therefore all point values of the opponents’ service games were simply skipped over and tie break points ignored. As the points are looped through and processed, we used the Rapheal javascript library to draw and animate the entire game tree using SVG (Scalable Vector Format). Some additional jQuery code was then added to hook up the tournament and match filters. The application was framed using HTML5, CSS3, SASS, Compass, and the Mueller Grid System.

Designing the Application

Our design work started off defining what the users expectations were from the application, and working out the simplest way of fulfilling their needs.

We defined a number of core functions the app should support:

  • The ability to compare game tree patterns at both the tournament and game level.
  • Multiple filtering at the season, tournament, and match level.
  • Interaction with the flow lines should reveal the exact quantities per line.
  • Tournaments should appear in the order they occurred, and the score should appear alongside each match.

Once we defined the core functions of the app we started sketching out how the game tree would support the application, and how we would visually organize the content for mobile, tablet and desktop devices.

Some of the earlier game tree concepts were centered on a circular game tree, before slowly transitioning to a more conventional representation of the tree diagrams (Figure 5).

Sketching out the Game Tree

Figure 5. Sketching the game tree designs. From here it was a matter of refining the triangular game tree until the design begun to solidify.

It was important that we designed the game tree to be responsive across small and large devices. We needed to ensure a seamless user experience regardless of device type or size. To do this we introduced some mobile ready functions into the design. For example we collapsed the menu on smaller devices so the game tree remained the focal point of the application. And we re-arranged the text on the opening page for smaller devices (Figure 6).

Responsive Design

Figure 6. Designing the optimal viewing experience across tablet and mobile devices forced a reshuffle of some of the key elements of the application.

Each point in the Game Tree was color coded to reflect the momentum in each game. Dark blue representing + positive momentum, red – negative momentum and the neutral points down the spine of the tree were colored white (Figure 7).

Figure7_Nadal

Figure 7. Each point in the Game Tree is color coded to reflect momentum in the match.

Results and Analysis

Nadal’s (6-2, 3-6, 6-4, 6-1) win against Novak Djokovic at last years US Open final illustrates the analytical power of the game tree (Figure 8).

Nadal v Djokovic Game Tree

Figure 8. The US Open final played between Rafael Nadal and Novak Djokovic. The game tree clearly highlights where Nadal played the majority of points on his serve (Deuce to Ad-40 – 12 times)

The score from the match, 6-2, 3-6, 6-4, 6-1 indicates a fairly one-sided match. But the game tree tells us that Nadal won 6 of his service games from Ad-40, (more than any other point). He and Novak wrestled back-and-forth between Deuce and Ad-40 12 times on Nadal’s serve. The frequency/line thickness through this part of the tree suggests that Novak had many opportunities to break Nadal’s serve, and that perhaps this match was much closer than the score suggests.

Nadal’s victory against Stanislas Wawrinka in the final of Madrid (6-2, 6-4) shows us how brutal Nadal can be when serving (Figure 9).

Nadal v Wawrinka Game Tree

Figure 9. An almost perfect service pattern. Nadal’s victory against Wawrinka in the final of Madrid (6-2, 6-4).

In 9 service games, Wawrinka never saw an opportunity to break Nadal in this match, coming close only once at deuce. Nadal’s remaining service games were won from commanding positions in the game (4 times each from 40-15, and 40-0). Nadal was only twice in the red zone (at 0-15). But each time he quickly pulled the momentum in his favor for a quick path to winning each game. Whilst the final score suggests a relatively straight forward win for Nadal, it’s not until we see his service games visualized in this manner that we truly understand his dominance in the match.

Conclusion

We believe this is the first ever-interactive point-by-point Game Tree of a tennis match covering an entire season for one player.

In both the Djokovic and Wawrinka examples presented above the game tree enabled a better understanding of the match than simply seeing the final score. The game tree presents opportunities for further analysis as well. For example we are able to determine where Nadal is most effective on serve. We can see that at Deuce, Nadal beats his opponents more than any other point. He fights back-and-forth between 40-Ad and Ad-40 (like against Djokovic in the US Open Final), but rarely losses when he is serving at Deuce. Across his 666 service games last season, his opponents only had a 1 in 5 chance (0.2) of winning the game from Deuce onwards.

The simplicity of the Game Tree application, and its ability to graphically present traditional statistical data in a unique and informative way allows users to better understand the final score of a match and how games are played out over time.

Craig O’Shannessy, leading tennis analyst for the NY Times, the ATP, and former panelist at the MIT Sloan Sports Analytics Conference labeled the application, “pioneering, and groundbreaking”.  It has featured heavily on well-respected data visualization websites like visual.ly, and visualisingdata.com. Nadal’s Game Tree captured the imagination of tennis analyst, fans, and data visualization experts worldwide for it’s originality and function.

Stay tuned for further interactive sports visualizations in 2014!

Click here to view the Nadal Game Tree application.

This article was written for the MIT Sloan Sports Conference.

Damien Saunder (formerly Demaj) is a Geospatial Designer at Esri where he designs and builds online interactive maps. He is continually rethinking spatial analytics for tennis via GameSetMap.com. @damiensaunder

David Webb is the web team lead at Rady Children’s Hospital-San Diego, where he builds responsive web sites and web applications. He enjoys experimenting and tackling interesting challenges via mor.gd.

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Talk Like a Tennis Player: Word Clouds

Recently ASAP Sports released a bunch of Australian Open press conference transcripts via their website. I thought I’d have some fun and visualize some of the transcripts using a technique called a World Could, or Tag Cloud. A word cloud is a cool way of visualizing text data. A word cloud application ‘tags’ the most frequently used words in a document and makes those words appear bigger than the other less commonly used words. Word clouds allows us to very quickly visualize the most commonly used words in a website, document or in our case a press conference transcript. Ever wanted to know how to talk like a tennis player? Well here’s your chance!

Creating a world cloud is really easy. There are many apps available online that do the tagging and arrangement for you. I used an app called Tagul. Let’s start with the new Swiss star of men’s tennis. Stanislas Wawrinka.

Wawrinka Word Cloud

A blog like this wouldn’t be complete if it didn’t include one of the great characters of women’s tennis, and 2014 Australian Open Champ, Li Na.

Li Na Word Cloud

Now let’s take a look at a couple of the other big names from the Australian Summer. Eugenie Bouchard was the rising star in women’s tennis in 2013, and she kicked off 2014 with a bang making a deep run into the semis at the Australian Open.

Bouchard World Cloud

Rafael Nadal missed last years Australian Open through injury, but made an emphatic comeback in 2014, making the final of the first Grand Slam of the year. Let’s check out his word cloud.

Nadal Word Cloud

The top five most commonly used words by each player were:

Wawrinka: really (102), match (84), play (72), know (65), always (59)

Li Na: think (94), know (73), play (64), match (58), yeah (54)

Bouchard: really (72), think (61), well (47), know (46), bit (33)

Nadal: very (118), play (85), playing (60), great (59), against (58)

THE TOP FIVE: play (221), know (184), really (174), think (155), match (142)

So what are the words you need to know if you want to talk like a tennis player? Well you need to use “Play” a lot in your conversation. Be sure to tell everyone how you “Really Think” the “Match” went and tell the press you “Know” you’re “Playing” “Great”. Don’t forget to drop in some other fillers like “Yeah” (Li Na), “Bit” (Bouchard), “More” (Nadal) and “Always” (Wawrinka).

Given that English is only one of these players native language (Bouchard), there are surprisingly a lot of similarities amongst these four. I guess there is an element of monotony in the questions being asked during each press conference, hence the same words are used each to time to answer them!

OK, so I’m not going to get all geeky and try to run some deep and meaningful analysis on these word clouds, because quite frankly they were just a bit of fun. However we do know word clouds provide an effective way of representing text data in a fun and interesting way. In this case we were able to graphically summarize almost 30,000 words from the Aussie Open press conferences for Wawrinka, Li Na, Bouchard and Nadal (for what’s it’s worth!).

Note: The Tagal word cloud filters out common words like “a”, “some”, “this”, “us” etc.

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ATP World Tour Interactive Map – 2014

With the 2014 ATP World Tour underway in Brisbane, Doha and Chennai this week, I thought I would put together a quick interactive map that locates this year’s 61 tournaments around the world. The ATP World Tour map allows fans to follow the world’s greatest players around the world as they battle it out in 2014.

ATP World Tour Map 2014

The ATP World Tour Map – 2014. Click here to access the map.

The map shows the location of the four Grand Slam tournaments, the nine Masters 1000′s, the eleven 500 and the forty 250 series tournaments, as well as the season ending World Tour Finals played in London.

Use the map to check out the site of the newest tournament on the tour this year, The Rio 500 in Brazil. Remind yourself of where the clay tournament in Umag (Croatia) is played. A tournament that returns to the circuit this year after a one year absence. Check out the latest stop in China in 2014 at the Shenzhen Open, a city that is already familiar with the women on the WTA tour.

Explore each tournament in more detail by clicking on each of the icons to reveal further information about the tournament like the prize money on offer, who is the defending champion, what the tournament surface is and other important information.

ATP World Tour Map WimbledonInteract with each tournament to reveal important statistics about each event.

The map features some of the highest resolution available satellite imagery of the globe, meaning you can see the tournaments up close like never before. Click on the “Zoom To” link in the pop-up to quickly navigate to each tournament. You will find yourself being blown away by some of the global landscapes that sit at the doorstep of some of the lesser known tournaments, like the Credit Agricole Suisse Open.

Gstaad TournamentThe 250 series Credit Agricole Suisse Open, set against the backdrop of the beautiful Swiss Alps.

Valencia Open

Discover the Valencia Open 500 (Spain) played at the stunning Ciutat de les Arts i les Ciències where Mikhail Youzhny triumphed in 2013.

In each of the pop-up’s there is a link to the official tournament website where you can see who’s down to play in 2014, and where you can get tickets. I hope you have fun exploring the map throughout the year, and I hope it inspires you to plot your next tennis adventure!

Read the ATP press release about the 2014 tour here.

To view the full 2014 ATP World Tour calendar in PDF format, click here.

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Where are you most likely to win a point on Nadal’s serve?

A couple of weeks back I released an interactive Game Tree of Nadal’s stellar 2013 season. The Game Tree was an experimental infographic that mapped Nadal’s service dominance, and showed us his most common path to victory.

Nadal Game Tree

Nadal’s Game Tree captured the imagination of many for its originality and groundbreaking way of visualizing tennis games. 

The flow lines through the original game tree (above) allow us to see some interesting patterns emerging from his 666 service games.

Using the data from the flow lines, I developed the Proportional Symbol Game Tree (see below). It maps the chances an opponent has of winning a point on Nadal’s serve.

Nadal Proportional Symbol mapA Proportional Symbol Game Tree. Mapping the chances an opponent has of winning a point on Nadal’s serve. <click to enlarge>

Below I’ll talk you through a few interesting observations I’ve made about the Proportional Symbol Game Tree. If you see other patterns, and would like to share them drop a comment at the bottom of the page!

The Bad News (for Nadal’s opponents).

The bad news for Nadal’s opponents is that your best chance at winning a point on his serve is at best, only half a chance! 15-15, 0-30, 30-15 and 30-0 are where your best chances are of taking a point from Nadal on his serve. But even at these points, the data tells us that Nadal’s opponents are on average likely to win only 1 in every 2 (0.5) points. Of these points, 30-15 is your absolute best chance of winning a point, representing a 1 in 1.8 chance (0.56), which is hardly encouraging!

The story only get’s worse…

You might as well head to the chair when you get Nadal to Deuce. You have virtually no chance of winning the game from Deuce onwards (see the smallest circle on the proportional symbol diagram above). Nadal dominates his opponents at Deuce more than any other point. He teases his opponents by going back-and-forth between 40-Ad and Ad-40, but according to his 666 service games, he only gives his opponents 1 in 5 chance (0.2) of winning the game from Deuce onwards. OK, so heading for the chair at Deuce might be slightly over doing it, but you had better step up your game big time at Deuce otherwise Nadal will be notching up another game on serve!

If you’re lucky enough to score the first point on Nadal’s serve then history shows that he pulls out all punches to prevent the score line from going to 0-30. The 0-15 point is a clear turning point in the game tree. However if you can get to 0-30 on Nadal’s serve then you are back in with half a chance to take him to 0-40!

The far right bottom three points (40-30, 40-15 and 40-0) indicate that once Nadal get’s a sniff of the finish line, he makes sure he closes it out.  From this position his opponent only ever has on average 1 in 3.3 (0.3) chance of pegging him back to Deuce.

Interestingly at the most important points in each game (15-30, 30-30, 15-40, 30-40 and Deuce) Nadal gives you a very small window of opportunity compared to other points in the game tree. At Deuce we know he dominates, but he’s not putting the hammer down quite as much as expected at these big points.

Summary

Nadal’s 2013 season was historic – no doubt. He rarely lost any games on serve. In fact he won 88% of his 666 service games that we studied.  Apart from the Deuce point there is little variation between the chances his opponents have of winning a point on his serve (0.35 to 0.55). His brutal consistency is clearly evident in these figures, and in the above graphic. The variable scaling of the circles in the Proportional Symbol Game Tree allows us to easily identify opponents areas of opportunity (even if they are only half chances)!

So it’s now off to the video tape and other supplemental tennis stats to see what Nadal is consistently doing at Deuce that makes him so dominant!

—-

Source: Nadal’s Game Tree. The Game Tree app is coded so we can plug it into any ATP, WTA or Challenger event. Let us know who you’d like to see mapped next!

Original data source: William Hill

References: C. Morris, “The most important points in tennis”, In Optimal Strategies in Sports, vol 5 in Studies and Management Science and Systems, , North-Holland Publishing, Amsterdam, pp. 131-140, 1977.

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The Comeback. An interactive Game Tree of Nadal’s extraordinary 2013 season.

On February 5, 2013, following a 222 day break from the game, Rafael Nadal returned to tennis ranked #8 in the world. Just 9 months later he completed an almost flawless comeback ending the year ranked the #1 player in the world for the third time.

To celebrate Nadal’s historic season we present his 2013 interactive Game Tree. Nadal’s Game Tree allows you to explore how his 600+ service games played out in the Grand Slams, Masters 1000 and World Tour Finals.

NadalGameTree_new

Click here to access Nadal’s interactive Game Tree application. 

This rare point-by-point summary shows where Nadal’s history breaking season was won and rarely lost.

The Game Tree presents an alternate way of visualizing game momentum. The challenge was to come up with a visualization that better reflects game momentum, and therefore shows how easily, or not a player wins their service game.

Each point in Nadal’s Game Tree is colour coded to reflect the momentum in each game. Blue representing positive momentum, and red negative momentum. The spine of the game tree is coloured white indicating neutral territory for Nadal.

About Nadal Game Tree

Mapping momentum through the Game Tree. Each point in the Game Tree diagram is tagged with a colour that matches its momentum classification.

When Nadal dominates a match you will see him flow through the outside ‘positive’ points of the Game Tree. When he struggles to hold serve, or looses his serve his flow will tend to move the through the ‘neutral’, or outside ‘negative’ points. The Game Tree clearly shows how Nadal dominated his opponents on serve this season by the frequency of points through the positive side of the Game Tree.

Game Tree’s are perfect for coaches and players to see where in the game a player is making it, or breaking it. Once they identify the breaking points, they can go to the tape and see what’s happening on court.

And the great news is we have coded the app so we can plug in any ATP, WTA or Challenger match/es into their own Game Tree!

We believe this is the first interactive point-by-point Game Tree that maps an entire season of service games for one player. The Game Tree interactive is an experiment to see what we can do with traditional forms of tennis data. Please let us know what you think; we’d love to hear from you.

A massive thanks to David Webb for coding this up, and Ella Ling for allowing me to use her fantastic Nadal pic on the opening page. Next week we’ll go into a little more detail about the concept, design and development of the app.

Enjoy!

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Pinpointing the serve. Who missed, and by how much.

(Part 3 of 3)

In the final part of this three part series, I determine who picks up the most free drinks as a result of hitting the centre of the USTA target zone, and by how much. I also extend the analysis to see how much each player missed the ‘optimum’ serve locations.

Who picks up the most free drinks?

For a bit of fun let’s see who would have picked up the most free drinks by hitting the ‘imaginary’ cone in the center of each target zone. We know coaches run this drill with their players, so let’s see how well each player fared in a match environment. Let’s assume the cone is 20 cm in diameter.

Federer Murray Serve Map Spider DiagramFigure 1. Federer v Murray. Mapping spatial serve patterns from the centre of each target zone. (click to enlarge)

The results show us that Federer picked up 4 free drinks, while Murray picked up only 3.   I don’t feel too bad since each player hit 100 or so serves each. That’s a pretty poor strike rate given these guys are best players in the world!

Each player missed the target by almost the same amount. Federer was on average     0.76 m from the centre of the each target  zone, while Murray was out by an average of 0.82 m.

Let’s take a look at the School Boys…

NCAA Tennis Serve Spider DiagramFigure 2. School Boy A v School Boy B. Mapping spatial serve patterns from the centre of each target zone. (click to enlarge)

The results show us that School Boy A picked up only 1 free drink, while School Boy B went thirsty not hitting the center of any of the targets! Ok, so now I’m feeling really good.

School Boy A on average missed the centre of the target zone by 0.94 m, while School Boy B was only out by an average of 0.80 m.

As discussed in part 2 of the blog, it’s reasonable to assume that perhaps the players weren’t targeting the centre of each zone. What if they were aiming for a ‘optimum’ but higher risk serve position? In part 2 of the blog we argued that the corners and lines were the ‘optimum’ positions to land your serve. So let’s see how far each player was from these ‘optimum’ serve positions.

Federer Murray Serve Map Spider Diagram 2Figure 3. Federer v Murray. Mapping spatial serve patterns from the ‘optimum’ serve locations. (click to enlarge)

Figure 3 shows us that Federer missed the ‘optimum’ serve locations on average by     0.88 m, while Murray missed on average by 1.04 m.

NCAA Serve map Spider DiagramFigure 4. School Boy A v School Boy B. Mapping spatial serve patterns from the ‘optimum’ serve locations. (click to enlarge)

Figure 4 shows us that School Boy A missed the ‘optimum’ serve locations on average by 1.15 m, while School Boy B missed on average by 1.22 m.

What can we learn from this?

Well we know that Federer takes home as many free drinks as the other three put together! We also know that Federer was on average serving closer to the ‘optimum’ locations than Murray which supports our analysis in part 2 of the blog, where we found Federer to target the high risk zones more than any other player.

We all expected the spread of the School Boy serves around the ‘optimum’ zones to be greater than the Big Boys due the results in part 2, where the Big Boys landed more balls in these ‘optimum’ areas. When we changed the target position back to the centre of each zone the School Boys and Big Boys numbers pretty much evened up, again supporting the results in part 2.

Spider Diagrams: The spider diagrams allowed us to visually link the serves to their target points and see the spread (length and direction) around each point. The spider lines for each zone allow us to very quickly see any bias in direction and distance towards the spread of serve around the points.  Without the lines it would be difficult to identify the serve clusters, and which central point they belong to.

Outliers: There were a couple of serve outliers for the Big Boys but these didn’t affect their averages enough to remove them from the calculations. The School Boys certainly had some big misses, but because there were multiple instances of these so they were left in the calculations.

More Data: With a larger dataset across different players we would be able to determine what is the expected norm, and whether these results are above or below that. Unfortunately, large serve datasets that are easily accessible to the players, coaches or analyst do not exist in tennis (hint hint ATP and WTA).

0.75 m: Let’s think back to part 2 of the blog for a minute. The size of the USTA target zones are 0.75 m square. Perhaps this tells us something. On average the four players missed by 0.83 m. Maybe the USTA set their targets knowing these missed averages and that is the reason for the particular size of the boxes?

To Summarize…

Over the course of the three blogs I have presented an alternative way of assessing a player’s serve accuracy using the USTA defined serve zones, and an additional two ‘higher risk’ zones. When comparing serve accuracy around the USTA zones there was very little difference between the four players. However once we started to analyze the serve towards the higher risk zones (the ‘optimum’ serve areas) the results started to lean in favor of the Big Boys, Federer and Murray.

I also set out to determine whether serve location really matters in tennis. The results suggest that it depends on what level of tennis is being played. The Big Boys clearly had more outright success on serves that landed in the USTA zones, and the higher risk zones than if they missed these zones. It was a different story for the School Boys however, as it didn’t appear to make any difference to their outright success rate whether they served in or outside the zones.

There is much work to be done in expanding the analysis of serve accuracy, serve success, and general serve patterns. Let’s hope we start to see more meaningful statistics from broadcasters and commentators about the serve in order to better understand who really are the best servers in the game!

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