The Symmetry of The Tennis Serve

Posted on February 26, 2015 by Damien Demaj

Late last night I was running some analysis on serving, and serve return hit points and I stumbled across this 3D view of the data which made me stop and marvel at how unique the symmetry of a tennis serve is. The image below visualizes over 350 serves. The view of the image is taken from side on to the court.

The blue dots represent the serve trajectories.
The yellow dots are the location of serve bounces.
The red lines are the trajectories of the ball bounces.

This image was created using Hawk-Eye data from an ATP men’s match. Tennis is a unique game in many ways, and the data driving the game is more beautiful than ever. Unfortunately I can’t share any of the results from the analysis but I hope you appreciate the symmetry in this rare image like I did.

Building a Pair of Christmas Trees from Hawk-Eye Data

Posted on December 22, 2014 by Damien Demaj

I want to send out a big thanks to everyone who has shown interest in what is going on at GameSetMap this year. 2014 has been a busy year, and the last couple of months have been super exciting. More news to follow about that in the New Year…

But for now I wanted to send all of you tennis lovers a christmas card made out of Hawk-Eye data! Just because I could!!

It’s just a little bit of fun using player movement paths (the red and green lines) and player strike locations (the white points) from a match I was analysing recently. It was an attempt at creating a pair of Christmas Trees from the data. Kind of worked don’t you think?

Once again thanks for the support in 2014 and I look forward to sharing more insights into the wonderful world of geo tennis analytics!

Damien

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

Posted on October 21, 2014 by Damien Demaj

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.

The 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.

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.

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).

Return Strike Points

Shot Placement

Rally Hit Point

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.

How to bend it like Federer

Posted on October 13, 2014 by Damien Demaj

Roger Federer claimed his 23rd ATP World Tour 1000 Masters title on the weekend by beating Gilles Simon 7-6(6), 7-6(2) in Shanghai. Whilst this wasn’t Federer’s most memorable match of his career, he was able to get the job done when it mattered most. However, the match that everyone is still talking about is his semi-final win over Novak Djokovic. For it was Federer that turned back the clock by putting on a masterclass of serve and volleying.

Last night I pulled down some Hawk-Eye data from a match Federer played against Paul-Henri Mathieu back in 2012 at the Swiss Indoors. I ran a quick visualisation of a serve and volley point played by Federer to illustrate how Federer sets up his serve and volley points using a beautifully executed slice serve.

Figure 1: Federer v Paul Henri Mathieu, Swiss Indoors, 2012. Federer serving. Red lines are Federer. Blue line is Mathieu’s return of serve. Click to enlarge.

In this example Federer slices his serve out wide to Mathieu’s forehand, drawing Mathieu off court. Mathieu picks up the Federer serve (very well actually) and returns it right at Federer’s feet. Unfortunately for Mathieu, Federer makes a rather tricky half court volley look seemingly easily as he punches the Mathieu return into the open court to finish off the point.

Figure 2: Mathieu’s look off of the Federer racket. The red lines are Federer. Blue line is Mathieu serve return. Click to enlarge.

Let’s take a look at how Federer is using sidespin to shape the ball off his racket. Figure 2 gives you a first hand look at what Mathieu sees coming off of the Federer racket. The moment the ball leaves Federer’s racket the ball begins swinging away from Mathieu’s forehand pulling him off court and creating a negative court position for him. The shadow of the serve trajectory illustrates just how much curvature Mathieu has to deal with. Let’s take a look at this from Federer’s end of the court (see Figure 3).

Figure 3: The green line is Federer’s serve trajectory off his racket. This particular serve was recorded at 172 km/h. Click to enlarge.

From Federer’s end the sidespin is even more evident. Take a look at the right to left movement of the ball as seen on the green line above. You will also notice how little margin of error there is as the ball crosses the net, this is a typical property of a sidespin serve. The lack of top spin means the serve doesn’t rip up-and-over the net, instead it’s slicing down towards the court more quickly which results in tighter clearance over the net. In order to generate this amount of sidespin players pull back their serve speed in order to get the racket head around the serve on impact. This first serve of Federer’s was hit at only 172 km/h and landed in an OK position in the service box. Had the location of the serve been closer to the sideline, the serve may have well been an ace, as the ball would have been too far off court by the time it got to Mathieu for him to get a racket on the ball.

Federer has never had the biggest serve on the tour, but his precision and work on the ball has caused many of his opponents a headache or two in their day. The sharp curve and heavy sidespin gives Federer an instant advantage in the point, and puts his opponents in an immediately poor court position. What this does is force the returner to come up with a great return (which in this case Mathieu did, but Federer was too classy in this exchange) otherwise the point is quickly over with a volley, or one-two play. Djokovic experienced the Federer serve in full flight on Saturday, with the Swiss maestro bending it around like Beckham (as they say!). To top it off Federer brought his soft hands to the court and played a number of exquisite volleys, giving Novak no chance of getting into the long grinding baseline rallies that he thrives on. Let’s hope we see more of this attacking serve volley game from Federer as the 2014 ATP season draws to a close!

Visualisations created using 3D ArcGIS.

An interview with Courtney Walsh at the US Open

Posted on September 29, 2014 by Damien Demaj

Recently I met with Courtney Walsh at the US Open. Courtney is a sports journalist at The Australian newspaper, specializing in tennis. He contacted me a while back about the work I was doing with Hawk-Eye data and suggested we meet up at the US Open. We talked about all things tennis, in particular the potential of Hawk-Eye and how it might influence player’s tactical preparation, training and post-game analysis. Courtney’s article titled “Hawkeye data could serve up maps, sports data and analysis” can be read here. Enjoy!

Mining Hawk-Eye data. It’s time to show the tennis world what they’re missing out on.

Posted on September 5, 2014 by Damien Demaj

It’s been a little quiet around here recently, so I wanted to update you all on what’s been happening at Gamesetmap. The good news is there has been plenty of great work going on.

As many of you know I have been chasing down access to Hawk-Eye data for over 18 months now. One of my earliest posts on Gamesetmap outlined the challenges of getting access to the data. You can check out my post titled “Unlocking Hawk-Eye data: What it means for tennis, the ATP, WTA and ITF”.

Today, I’m please to announce that I now have access to a select number of matches, with the opportunity to purchase further matches in future. This is an enormous breakthrough for tennis, and for Gamesetmap!

Let the Hawk-Eye data mining journey begin. A screen dump of the raw ball trajectory data from Hawk-Eye.

Generating the ball trajectory from the raw Hawk-Eye data was made possible with the help of Darren O’Shaughnessy. Darren runs a small consulting company in sports analytics and informatics called Ranking Software.

Late last year I created a 3D Diorama using Hawk-Eye player tracking data from the same matches (see below):

The two datasets (player tracking and ball trajectory) provide an insight into tennis matches that has rarely be seen before.

Stay tuned over the next few months as I dig through these fabulous datasets to uncover the spatial patterns that exist in tennis.

Visualisations created using 3D ArcGIS.

Roland Garros Men’s Final 2014 – Game Tree

Posted on June 9, 2014 by Damien Demaj

Rafael Nadal continued his dominance on court Philippe Chatrier with an emphatic 3-6, 7-5, 6-2, 6-4 win over Novak Djokovic to take out the French Open for an incredible 9th time. Below are Novak and Rafa’s interactive Game Trees. You can explore the interactive game tree here.

Roland Garros Men’s Final 2014 Game Trees (click to enlarge).

How the Game Tree Works

Each point is color-coded to reflect the momentum in each game. A match that is dominated by the server is highlighted with a thicker, outside flow through the ‘positive’ blue points of the Game Tree. More tightly contested service games result in thicker lines through the ‘neutral’ (white) and ‘negative’ (red) points of the Game Tree. You can click on each line to reveal how many times the score passed through that point in the match.

A Summary of the Roland Garros Game Tree

Nadal won 16 service games. He was broken 3 times.
Djokovic won 14 service games. He was broken 6 times.
Nadal won 6 service games from 40-30 (38%), and 5 from 40-0 (31%).
Djokovic won 9 service games from 40-15 (64%), and 2 from 40-30 (14%).
Djokovic lost his serve 3 times (50%) from 40-Ad. He lost his serve twice at 30-40 (33%), and once from 15-40 (16%).
Nadal lost his serve 2 times from 30-40 (67%). The one other time he lost serve was at 40-Ad.
Nadal won 74% of first points while serving.
Djokovic won 70% of first points while serving.
Neither player served out a shocker of a service game (loosing from 0-40). However both players found themselves at 0-30 once, but each time they were able to push the score back to 15-30.

Most Frequently Played Points

Roland Garros Men’s Final Most Frequently played points (click to enlarge).

Nadal played most of his points down the outside positive section of the game tree (as indicated by the rich yellow markers on the above diagram). Other commonly played points by Nadal were played at 15-15, 30-30 and 40-30 (as indicated by the lighter yellow markers).

Djokovic played most of his points down the positive section of the game tree, but with a little less safety than Nadal. Most of Djokovic’s points were played within one point of the neutral section of the game tree, whereas Nadal’s were played within two points of neutral section of the game tree.

The Big Difference: 30-30

The big difference between the two player’s game trees is at 30-30.

Nadal played 30-30 seven times, and was able to convert 100% of those into a very positive position, 40-30 (see below). Djokovic also played 30-30 seven times, but was only able to get to 40-30, 3 times (43%). He was forced into a very dangerous position (30-40) 4 times (57%).

Nadal Djokovic

Conclusion

The Game Tree was originally developed to give us a better understanding of the final score, and how close a match was. It provides one of many ways to analyse the final result of a tennis match. In this match the game trees for each player were remarkably similar hinting that the match was tightly contested. No player likes to see too many (if any) thick lines extending from the bottom four red circles of the game tree like. In this match Djokovic saw his service broken 6 times (once at 15-40, twice at 30-40, and three times at 40-Ad). (Note: lines extending from any of the four bottom red circles means a player lost his serve). Djokovic played 15-15 point better than Nadal, but clearly Nadal played 30-30 far better than Djokovic. Nether player played through deuce a lot (in comparison to other matches). However, Nadal was able to convert more winning games from deuce than Djokovic. Nadal converted 75% of games played through Deuce, while Djokovic converted only 50% of games played through deuce. At 40-30, Nadal also had a better conversion rate than Djokovic (66% to 40%).

Like we see in so many of these matches, games are won and lost at a few very important points. The game tree suggest there wasn’t much in this match between these two players. It does however suggest that Nadal was perhaps the better ‘big point’ player, and as we know in tennis, big points win matches.

Make your own conclusion about the final using the interactive Game Tree here.

@damiensaunder

IBM “Data Wall” @ 2013 US Open

Posted on March 3, 2014 by Damien Demaj

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).

Source: http://heyhush.com/work/ibm/

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

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

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

Enjoy!

Presenting a Diorama of Player Movement in Sport

Posted on February 19, 2014 by Damien Demaj

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.

The full article can be read here.

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 ***

Sneak Peek. Visualizing Player Tracking Data in 3D.

Posted on February 18, 2014 by Damien Demaj

Later this week a leading sports magazine will feature an article of mine about visualizing Hawk-Eye player tracking data in 3D. Below is a sneak peek taken from the 3D web scene. More to follow soon…

GameSetMap

Rethinking analytics for tennis using Hawk-Eye data.

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