Future Clay Court Stars of Tennis

Earlier today Josh Meiseles who writes for the ATP World Tour tweeted some pretty interesting stats about the players who were about to kick off their qualifying campaign for Roland Garros. He said that there were 39 countries represented at the Roland Garros qualifying (1 less than last year). I figured it was worth mapping the 39 countries to see where all of the players were coming from (see below).

Roland Garros Qualifying Map

To create the map I joined up the list of countries that Josh supplied to a country database and then chose a suitable projection to visualise the 39 countries. Since these young players are up and coming future stars I decided to use a Berghaus Star projection. The Berghaus Star projection does exactly what its name suggests. It projects the world’s surface onto a star shape. The Berghaus Star projection was created in 1879 and is perfect for this type of world projection. I also wanted to make sure Roland Garros (Paris) was near the center of the map since it is the focal point of the story. The map is centred on the north pole with Europe and North America dominating the centre of the projection (since that’s where the majority of tennis players who were playing in qualifying come from). Areas highlighted yellow indicate the countries participating in the qualifying.

There’s a huge opportunity in tennis (and all sports) to integrate media stories with maps. So much of the game and its players is about location, like this story was. And it is far more visually stimulating to see the facts presented this way than perhaps in a table or simply in writing. Maps draw an audience, they fascinate people and as a result they drive traffic to stories.

We hope you enjoy this media map. We’ll keep an eye out for further opportunities as the season rolls out over Summer.

Kei Nishikori – Hawk Eye Analysis

Recently Japan’s National Broadcaster (NHK) contacted me to provide Hawk-Eye analytical support for a documentary they were preparing on Kei Nishikori. I was asked to process and analyze the raw Hawk-Eye data. I teamed up with Jordan Montreuil, an animator from LA to provide high-quality 3D scenes that would support the analysis. The documentary aired in Japan on the 13th January prior to the Australian Open. You can watch the program here.

Blog Pic Nishikori

The rise in popularity of Kei Nishikori is illustrated in NHK’s documentary titled “Kei Nishikori: Trails of the Progress” (translated). The documentary explores Nishikori’s growth and development as a player.

Below are a few samples from the project. Unfortunately I can’t share too many details but I hope this gives you some idea of the work completed.

Hawk-Eye Animation

The results of the Hawk-Eye analysis were told using a series of 3D computer generated (CG) animations. The images above are stills taken from the story surrounding Djokovic’s shot depth against Nishikori at the World Tour Finals.

For each scene, storyboards and animation concepts were drafted in order to understand how the story would unfold.

Kei Nishikori storyboard

A typical storyboard which was used to prepare and support the animations. Text is blurred on purpose.

The following clip (2:14 min) from the documentary introduces the viewer to the millions of Hawk-Eye data points that were analyzed for the documentary, and how the data was used to identify trends and patterns in Nishikori’s game.

The video then goes on to compare Nishikori’s hit point location from his 2012 and his 2014 US Open matches against Cilic. One of Nishikori’s strengths under Chang is that he plays ‘up’ on the baseline, taking time away from his opponent. In 2012 before Chang, Nishikori played only 34% of shots 1 m either side of the baseline. We refer to this zone as the attacking zone. In 2014 against Cilic again at the US Open, Nishikori played 49% of his shots in the attacking zone.

Finally we graph Nishikori and Wawrinka’s shot speed trend at the 2014 US Open. Nishikori’s shot speed trended constantly upwards throughout the entire match, while Wawrinka’s trended downwards. This was a distinguishing feature of Nishikori’s game at the US Open.

The complete documentary can be viewed here.

The analysis of the Hawk-Eye data provided ‘scientific’ proof of Nishikori’s strengths and weaknesses. It was also clear during the analysis and cross validating the results against player interviews that the players and coaches don’t always have a clear understanding of why they won or lost a match. We were able to validate the many assumptions, or commentary about a match with the use of such data.

The producer’s primary goal was to tell a story that was backed by real data. The animations played a critical part in delivering the story and messaging. They allowed us to simplify the 1 million plus data points that were analyzed, crunched, and spat out. Unfortunately there is only 43 minutes of tape in the final cut, but there were many revealing patterns and trends identified that are no doubt valuable to tennis players, coaches and sports reporters. If you would like to know more please get in touch. But for now, as they say in show business – that’s a wrap!

Video’s copyright NHK. Analysis and images copyright GameSetMap. Do not share the contents of this webpage without permission. 

Official WTA finals app likely to open up a can of worms

Today the Women’s Tennis Association (WTA) and SAP announced the launch of the official WTA Finals mobile app just in time for the BNP Paribas WTA Finals in Singapore – press release.

WTA Official AppThe app has been in progress for much of 2015 so I was happy to finally see SAP and WTA deliver on their promise of “taking the fan experience to the next level.”

The app has all the usual features of a tennis app; live scores, news and videos, schedules, draws etc. But what makes this app “groundbreaking” is a feature called Virtual Replay where users can watch an animated point-by-point replay of the match unfolding right before their very eyes. It’s kind of cool to watch the ball trajectory animate over the net between the players (for what it’s worth). Unfortunately it’s not clear which player is playing at which end as the animation runs through. You will need to read the commentary of the point from below the animation to figure that out.

WTA Virtual Replay

The default view of the animation is a normal camera view (from one end of the court) but users have the ability to change the view to 3 other camera angles which is a nice touch.

IMG_0184

Users can then choose which point they want to watch from the point-by-point breakdown, which is a neatly organized commentary of each point from the match showing the point score, and key actions made by each player.

The app also includes additional visualisations like Serve Direction (below).

IMG_0180

Return Strike Points

WTA App

Shot Placement

WTA Shot Placement

Rally Hit Point

WTA App

All of the visualizations allow you to switch between players, and you can change the Set you want to view at any time. It all makes for a very impressive mobile application, and is certainly light years ahead of any other tennis app I have seen. It is also no mean feat to package all of this content up in a very usable, and engaging mobile app that fans are sure to love and embrace.

So how useful is all of this? Well, to be honest we’ve kind of seen it all before. Hawk-Eye through their various relationships with TV Broadcasters like ESPN and the BBC have been publishing these types of visualizations for a number of years. Admittedly we have not had access to this level of information post match and in the palm of our hand before, so this is new ground definitely. But we are not really seeing anything new here.

The visualizations in the app unfortunately lack some valuable context in order to make them really useful for players, coaches and the fans. For example they are simply static representations of the data. You can’t query them (by touch), or filter them, or overlay one player’s points on another in order to perform any additional analysis. There is no significance attached to the data, like winners, unforced errors, big point plays etc. There is no way of knowing whether the patterns we see are expected, or a cause for alarm given the sate of the match, or past performances against this player. Perhaps we’ll see this kind of contextual information added in future releases. SAP and the WTA claim they have worked closely with the players to develop the app to their needs. However my feeling is most astute coaches and players will see these visualizations as nothing more than eye candy (for now).

As a tennis fan, and analyst of the game, the application naturally left me wanting more, and I suspect coaches and players will feel the same. What the WTA has effectively done is open up a big can of worms. The visualizations in the app leave so many questions unanswered, which is not untypical of a all-in-one app like this. But it does provide a wonderful insight into the potential of these kinds of visualizations. In order for players to really benefit from the true potential of this rich dataset from Hawk-Eye they are likely to still undertake independent analysis which dives much deeper in geographic patterns and tendencies than what we see here.

Hats off to the WTA for leading the way with this new-age tennis app. It has raised the bar and expectation going forward, and it definitely takes the mobile fan experience to a new level. I look forward to hearing what the players and coaches really think. My understanding is they will be given a more comprehensive app for on-court coaching, which may pack a few more tricks than what we see here. That may or may not be a good thing given visualisations like these tend to take time to digest, assess, and decide what action to take. This will be a new challenge for coaches, particularly in the heat of the battle. My sense is this kind of information will be primarily used post-match when emotions and the tension from a match have passed. It will also be interesting to see how the ATP respond over the coming months/years. Perhaps they too will partner with SAP to deliver a similar app for the mens tour if this takes off.

The WTA application was tested on an iPhone 6.

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 ***

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.

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.

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!

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!

Pinpointing the serve. Who’s better? The Big Boys or the School Boys?

(Part 2 of 3)

In part 1 of this 3 part series, I set out to find which player out of Federer, Murray and two NCAA Division 1 players were able to land the highest proportion of their serves in the USTA target zones.

Surprisingly the School Boys outranked the big boys in this simple comparison. However once we moved the target to include zones closer to the lines, Federer’s serving clearly stood out as being the most accurate. See part 1 for the complete results of the analysis. In order to gain some real value out of this analysis, I set out to determine if there was a positive relationship between serve position and outright serve success.

To explore this relationship I classified each serve into an ‘outright success’ category. Throughout the blog I will refer to an outright success point as a free point (to keep things simple).

Free Point definition: An error made by the player returning serve OR an ace made by the server. The remaining serves were either classified as being “returned in play” or “out” (fault).

For each player I generated a Serve Map (see Figures 4 A-D) showing the position of their serves in relation to the three target zones and their free point success.

Click to enlarge each map.

Federer ServeFigure A. Federer’s Serve Map

Murray ServeFigure B. Murray’s Serve Map

NCAA Tennis PlayerAFigure C. School Boy A Serve Map

NCAA Tennis PlayerBFigure D. School Boy B Serve Map.

Mapping the relationship between serve location and the effectiveness of serve. The Serve Maps also show where each player served when it mattered most.

School Boy A was able to collect 3 (50%) free points from his serves inside the zones, compared to 5 (42%) for School Boy B.

Federer picked up 13 (76%) free points from his serves inside the zones, compared to 18 (82%) for Murray.

Summary: The Big Boys picked up 31 (79%) free points from serves that landed in the target zones, compared to 8 (44%) for the School Boys.

Across all four players, 39 (68%) serves out of 57 that landed in the target zones earned the players a free point.

Serves that missed the zones: To test the importance of serve position I calculated how many free points each player picked up off of their serve that landed outside the target zones, but still within the service box.

Federer picked up only 4 (24%) free points on serves outside the zones compared to 13 (76%) inside the zones. While Murray picked up 4 (18%) outside the zones, compared to 18 (82%) inside the zones.

School Boy A picked up 3 (50%) free points when serving outside the target zones, which equalled his inside count 3 (50%), while School Boy B picked up 7 (58%) free points outside, which was more than his inside count of 5 (42%).

Summary:  The Big Boys picked up only 8 (21%) free points from serves that landed outside the target zones, compared to a surprisingly high 10 (56%) for the School Boys.

Across all four players, 18 (31%) serves out of 57 that landed outside the target zones earned the players a free point.

Take-aways:

Based on the data in this analysis the Big Boys clearly had more success on their serve when they landed their serve into the target zones (79% to 21%). This is a significant difference. At this level the Big Boys almost quadruple their chances of getting a free point off of their serve if they land it in the target zones!

Interestingly, the same trend didn’t occur for the School Boys. Player B recorded more success outside the zones than inside (58% to 42%), while School Boy A had the same level of success inside to out. So does it mean at the lower levels of the game that serve position is not all that important? Well it is quite possible. However we need to be a little careful about the above statement given the small-ish sample size and the fact that the study only included two players. It would be interesting to see what the numbers would do over a larger sample size, and with more players. Likewise for the Big Boys, would the high level of success remain with a larger sample spread over different players?

Overall across the four players free points were easier to get inside the target zones than out.

The USTA suggest that improving and practicing your serve location will help strengthen your game, and with some luck you might just pick up some free points along the way! Well that may well be the case, but it also might depend on which level of the game you’re playing!

In part 3…

In the final part of this three-part blog we are going to have some fun and address the most important question of all. Which player picked up the most free drinks by landing their ball in the center of the target zones? I present another series of maps showing spider diagrams to visualize how far each player was from the centre of each zone!

Pinpointing the serve. Who’s better? The Big Boys or the School Boys?

(Part 1 of 3)

We have all been there, standing on the baseline when the coach places three cones in each service box and says “There’s your target, if you hit the cones you’ll get a free can of drink”.  If you were like me, you rarely hit the cone, and if you did, it was more luck than anything else!

Coaches have been using these types of serving drills for many years. Why? Well, in order to develop a successful serve, you need to practice the placement of your serve. In the USTA book titled Tennis Tactics, Winning Patterns of Play, drill 4.2 (p 45) outlines four target zones in each service court to aim for (see Figure 1).  It is in these zones where coaches place their cones to improve the serve placement of their players (and give away free drinks!).

USTA Target Serve Zones

Figure 1. The four recommended serve target zones in each service court as recommended by the USTA. Down the T (T1), a body serve (T2), a wide serve (T3) and short-ish out-wide serve (T4). Source: Tennis Tactics, Winning Patterns of Play, USTA.

Given the continuous emphasis on serve placement I set out to run a simple analysis to see who was the more ‘accurate’ server, the Big Boys (professional players) or the School Boys (college level players)? Included in this analysis are Roger Federer and Andy Murray representing the Big Boys, and the School Boys (whom shall remain nameless) are from the NCAA Division 1 tennis competition.

Some Context:

  • Murray defeated Federer: 6-2, 6-1, 6-4
  • School Boy A defeated School Boy B: 6-1, 6-1

Total number of serves hit by each player:

School Boy A: 58   School Boy B: 54   Federer: 95   Murray: 111

Total number of serves hit IN:

School Boy A: 44 (76%)   School Boy B: 45 (83%)   Federer: 78 (82%)   Murray: 86 (77%)

Total number of serves hit OUT:

School Boy A: 14 (24%)   School Boy B: 9 (17%)   Federer: 17 (18%)   Murray: 25 (23%)

In order to determine which player landed the highest percentage of balls in the four USTA zones (and therefore could claim they were the most accurate server!) I ran a simple select by location algorithm between each serve bounce and the four target zones in each service court. This enabled me to very simply return a count of how many balls landed in each box, for each player. Figure 2 shows the results of the selection.

PinpointingYourServeFig1

Figure 2. The percentage of serves that landed in the USTA defined target zones for each player.

Surprised? Most of us would expect the Big Boys to place a higher percentage of their serves in the target zones than the School Boys right? However the results showed that School Boy A landed 15 out his 58 (26%) serves into the target zones, making him arguably the most ‘accurate’ server of the four players. School Boy B closely followed with 12 out 58 (22%). Murray was next up, landing 23 of 111 (21%) serves into the boxes, while Federer brought up the rear with only 16 out of his 95 (17%) serves landing in the boxes.

Accuracy: If we loosely define accuracy as being how close a measured value is to an actual value, where the actual value are the USTA target zones, then we can with some caution claim the School Boys out served the Big Boys in the accuracy department. Hard to believe I know.

But wait a minute, what if the Big Boys weren’t actually aiming for the USTA target zones, and instead were aiming outside of those zones? Perhaps they were aiming for the lines, which are outside the USTA defined target areas but still legally within the service court? What would the results look like if we extended the target zones further towards the lines? Let’s see…

Playing the Lines

You could argue that the service line is the optimum position for the placement of your serve, and that the corners of each service box are the ultimate targets. However targeting the lines brings a higher degree of risk, and a lower margin or error. Which is why coaches & the USTA don’t recommend us amateurs to go-for these targets every time! However at the top level where the Big Boys play, where there is so much on the line and so little margin for error (in all facets of the game) they are more likely to take the risk. By sending their serves as close to the lines as possible they give themselves a greater chance of setting up the point in their favor. We would also expect that they are more likely to consistently execute a higher level of accuracy, given their higher-level skill set. We shall see…

In order to test this I added two more 12.5cm (4.7 inch) wide target zones around the original USTA target zones. I call these Medium and High risk zones, where the High risk zone abuts and includes the service lines. By running the selection again using these two extra zones we will see who is taking the risk and pushing their serve towards the lines more, the School Boys or the Big Boys?

PinpointingYourServeFig2

Figure 3. The percentage of serves that landed in the two additional High and Medium risk serve zones for each player. The width of each additional zone is 12.5cm (4.7 inches) (roughly twice the width of a tennis ball). In the second part of this blog we will see the spatial spread of serves across all target zones and all services boxes.

Figure 3 starts to tell a different story. By moving the target Federer was now clearly winning the most accurate server competition, landing 13 (14%) serves in the medium risk zone, and 18 (19%) in the high-risk zone. Murray’s success in these zones was a littler lower than Federer, with 10 (9%) for the medium risk zone, and 13 (12%) in the high-risk zone. School Boy A scored, 3 (4%) in the medium risk zone, and 5 (13%) in the high risk zone, while School Boy B scored, 2 (5%) and 7 (8%).

Clearly Federer was able to consistently pop more serves in the high-risk zones than any of the other three players. This would suggest that the Fed is arguably the most accurate server of the bunch? Most commentators of the game are unlikely to argue with that statement, but of course it depends on where the target is and where the players are aiming! School Boy A has every right to claim he is the most accurate server given he landed the highest proportion of his serves in the USTA target zones.

Some Further Ponderings

Given that each of the four USTA target zones in each service box are roughly 0.75m (2.46 ft) square I am surprised that the Big Boys are not landing a higher percentage of serves in these areas. No disrespect to the School Boys, they aren’t playing NCAA Level 1 tennis for no reason, but I expected the professional players to have a higher percentage of serves land in the target zones than the School Boys. I also expected Federer and Murray to land more serves in the higher risk zones. The results showed this was partly the case. Murray’s numbers in these zones are a little surprising given he swept aside Federer in straight sets on that day.

Perhaps at the highest level, simply aiming your serve at the USTA zones is not enough. Maybe the margin is too great. And in doing so you make life a little too easy for the returner?

So why do the School Boys have such a high percentage of serves in the USTA zones (compared to the Big Boys)? Is it because they serve with less speed and spin, therefore allowing them to slow things down and hit the ‘safe’ targets? Perhaps at this level, the players are taught to play the percentages? Perhaps their skill level forces them to do so?

The School Boys will no doubt develop their serving skills, and pop more serve speed and aggressive ‘kick’ on the ball as they mature. Being able to maintain that accuracy as they increase their serve speed and spin will be on ongoing player development challenge.

It is worth noting that each School Boy in the study served just over 50 times in their match, less than half that of Federer and Murray. Would they be able to maintain their high serve percentage into the USTA zones over a longer match where they may be required to serve 100+ serves? Would we see the same consistency, or could we expect it to see it drop off?

So what do these figures mean, if anything? What if I miss the USTA zones by a ball width or two? Am I still an accurate server? What if I’m only a little bit too short, or a little bit too central to the service box on my serve? Will I still win the same number of points if I’m a few centimeters or inches wide of the mark?

In part 2…

In the second part of this three-part blog I will endeavor to determine if there is a positive relationship between serve position, and outright success. I’ll explore if it’s possible to determine if the game of serving is really about a few centimeters or inches here and there? And in part 3 we will answer the most important question of all, who takes home the most free drinks!

Note: This study only looked at a very small sample of data from all players, so we need to be careful about making gross assumption based on the findings.