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Elizabeth O'Brien, Worldwide Sponsorship Strategy, IBM

Elizabeth O’Brien, Worldwide Sponsorship Strategy, IBM

By Elizabeth O’Brien

Big Data is a term we hear a lot about in the business world. But these days, thanks to the insatiable hunger for player, team and league stats and analysis, it’s also becoming widely used in the world of sports.

In tennis, for example, Big Data includes tournament, match and player stats, things like serve speeds, rally counts, winners and aces. But more important than what Big Data includes, is how it is used to enhance and, in many ways, transform how we experience and enjoy the sport of tennis.

This week marks the 28th year of IBM’s partnership with the French Tennis Federation in support of Roland Garros (also known as the French Open).  IBM brings a suite of solutions to Roland Garros, all centered on real time and historic Grand Slam data. We capture, analyze, secure, store and distribute the data—in fact Big Data is the heart of our collaboration with the FFT.

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There are many ways we use Big Data to enhance the game of tennis, bringing the action at Roland Garros to fans, coaches, players and media around the world. One example is through SlamTracker, an analytics tool available on rolandgarros.com that has changed the way many fans watch and enjoy tennis.

SlamTracker analyzes 8 years of Grand Slam tennis data (41 million data points per match) to identify the 3 key strategies that will affect the dynamics of a particular match for each player. We call these ‘keys to the match.’ Before matches, fans can go on the website to check out each player’s keys to the match and during the match, watch player’s progress against these keys in real time—point by point.

At this year’s tournament, we focused on helping fans gain a better understanding of how Big Data has affected and impacted coaches, players and media, IBM convened a panel discussion of tennis experts including

  • Justing Gimelstob, a former player, Roland Garros mixed doubles winner (with Venus Williams) and current Tennis Channel commentator
  • Sebastien Grosjean, a former #4 ATP player and current coach of Richard Gasquet
  • Leo Levin a former coach and tennis analyst for whom this is his 107th Grand Slam

These experts, with their combined 70+ years of tennis playing, coaching, analyzing and commentating, participated in a lively discussion featuring their views of the importance of Big Data in the sport of tennis, how it impacts Roland Garros and insights about players they have both coached and played against.

 The action at Roland Garros runs through June 9, and we’ll be analyzing the Big Data daily. Follow the action and insights via SlamTracker on rolandgarros.com and join the conversation on Twitter (@eryanobrien) and via daily blog posts  at http://asmarterplanet.com/gamechangers.

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14 Comments
 
September 9, 2014
8:49 am

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the web for that reason, and obtain the most up-to-date information.


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June 22, 2014
8:38 pm

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Posted by: Jenni
 
June 14, 2013
6:04 pm

I wish i could have been there because Leo is a true tennis stat Legend. I personally have helped to create a new app that helps you Map and track the serve&return patterns of play. this is a key element of the matches of today. every point starts with a serve&return and now everybody can use this free app and grow personal skills in tennis. more info at http://www.gpstennis.com
or get your free download here .goo.gl/tPDPz


Posted by: Sven Groeneveld
 
June 11, 2013
8:54 am

I followed SlamTracker during the final and although it was clear from the beginning that Rafa would make it, something disappointed me: having worked in predictive coding before, I wouldn’t says Big Data is a tool for predicting the future (business or else). The criteria were changing in real time. It is a statistical tool. Kaya can still rely on the uncertainty of sport diagnostics, … especially in Tennis. Are we selling this as a “prediction” tool (like I heard) ? Have we tried BigData on stock options ?


Posted by: Emmanuel Lancon
 
June 11, 2013
8:46 am

I followed the SlamTracker during the final and although it seemed clear that Rafa was making it (2 criteria met among 3), I was disappointed by something: having worked on predictive coding before I would not say this is a predictive system. It is a statistical system. Sport lovers like Kaya can still enjoy the uncertainty of diagnostics,.. especially in Tennis. Let’s not make our customers believe we can predict anything. Have we tried SlamTracker in the Stock Option world ?


Posted by: Emmanuel Lancon
 
June 7, 2013
3:02 am

Interesting news for Tennis lovers..


Posted by: Radhika
 
June 6, 2013
8:03 am

Its really very interesting converstaion.. How bigdata IBM is maintaing in such a systematic way.. Its good news for Tennis fans


Posted by: Anjan
 
June 6, 2013
7:26 am

I love seeing IBM involved at such major events. Makes me proud to be an IBM’er.


Posted by: Egmondt
 
June 5, 2013
10:31 pm

This is great for Tennis Fans,


Posted by: Ravikiran
 
June 5, 2013
4:35 pm

Interesting… Do the commentators have access to the most current stats as a match progresses?

Then again, what is the use of all this if Federer is no longer in the tournament? :(


Posted by: Vasantha
 
June 5, 2013
1:56 pm

I would argue that there is some truth in the statement that tennis is at times a ‘head game,’ and not just purely about analyzing statistics to ‘find patterns’ to help improve ones game. True, it does help to know your opponents strengths and weaknesses going into a match, and data will help with that, but just like any other highly competitive sporting match, the players are the ones that sometimes defeat themselves because they are not mentally prepared or are defeated mentally easily during the course of the match. Tiger Woods is one of the best examples of an athlete with a tremendous mental game.

In terms of capturing and analyzing data, when does it start and end? In other words, do players and coaches only analyze the data from the match itself? What about other factors (that can be measured) that happened leading up to the match? For example, sleep patterns, weather, dietary information, etc. These are just some examples of other data points that in my opinion as an athlete can be and should be used to strategize and improve ones game.

And I agree with Justing’s comment “if you’re 5% better than someone in everything, you don’t beat them 5% of the time, you beat them 95% of the time.” (@8:50). To simplify this statement even further, if you’re better than someone in anything (let’s say even by 1%), logically you should always beat them as long as you remain better than that person.


Posted by: Kaya Spiers
 
June 5, 2013
1:22 pm

Very very interesting article. Thanks for sharing. I always admired about the IBM speed gun.


Posted by: Bharat
 
June 5, 2013
7:03 am

very very interesting and intriguing how Big Data does all this. The game is going to be all the more interesting to watch now.


Posted by: Surekha Jain
 
June 5, 2013
6:38 am

Thank you for sharing this video.

Very interesting discussion. Really demonstrated how Big Data Analytics is used in French Open by different stakeholders.


Posted by: Yushma
 
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