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February 9, 2015updated 22 Sep 2016 11:45am

Data in sports, who’s doing it right?

Which sports are excelling with their use of data?

By James Nunns

Data analytics is starting to become commonplace in some sports, Basketball, Tennis, Golf and to some extent Football have all started embracing data usage as a means to improve players performances and to enhance the fans experience.

CBR provides you with a list of who is and who isn’t using data to their competitive advantage.

Tennis

IBM has been working with tennis in some form or another for 25 years now. At Wimbledon they provide data on aces, serve speed, winners and other key statistics which are all rendered in real time to give immediate match data. IBM has also been working with the US Open and Australian Open, for these events they provide analytics in a similar way to Wimbledon. One of the tools that IBM provides is the Open Crowd Tracker, which helps fans to navigate around events.

Providing data for the fans is not the only reason behind IBM’s sports analytics, analytics is also made available to players in the hope that it can be used to improve performance. Data obtained by using tracking can be used to analyse plays, this allows players to see how they are performing, view their technique, spot flaws and make improvements.

Golf

SAP are aiming to make life simple by using big data analytics tools in everyday life and also in sports. Working within golf, SAP has become the official global partner of the 2015 Solheim Cup. SAP’s HANA analytics will be used to collect data from wearables and sensor technologies, which can then be analysed to improve player performance, training efficiency as well as other improvements.

It is not only player data that is collected but also ball and player position, course layout and weather conditions. The end development will be to improve all players’ abilities from professional to amateur and make a complicated game simpler.

Football

In preparation for the World Cup, the German national team worked with SAP to develop a "Match Insights" software system. The prototype was delivered in March 2014 and the national team has been using it ever since.

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Data captured by video cameras around the pitch was turned into information that could be viewed on tablets or mobile devices. The plan was to help the team’s performance and to gain insights into the strenghts and weaknesses of opponents, and clearly it worked as Germany won the World Cup.

Basketball

SAP’s partnership with the NBA has helped the organisation to quickly analyse and create some of the most detailed sports records you are ever likely to find. Statistics on every player from 1996, every shot, success rate, success rates when opponents are 1 foot away, 2 feet away, 3…the list goes on, you could spend months looking at all the data.

Not only is this data fascinating for the fan and may help resolve many an argument over who is better, Michael Jordan or Kobe Bryant (you can compare stats) but the system also has potential for being used for training purposes, scouting and the development of tactics.

 

Not all sports are jumping on board with data analytics, and the following sports are ones that need to step up their game. Although these sports are producing vast amounts of data and have large numbers of fans who enjoy looking at game and player data, most of it is done through independent sites or frequently through fan run sites.

NFL

The NFL has masses of data produced from matches, much of it is used in analysis by pundits and undoubtedly will be used by coaches and fans alike to look at player performances. However, it is up to individual teams to hire data analysts if they want to do it. Plenty of fan sites offer analytics but the data would be much more accessible and trustworthy if it came directly from the league.

It is quite surprising that the NFL isn’t using data analytics to its full potential, perhaps this is because of the fame of Moneyball. Although, perhaps it is because of the failings of the Moneyball system that has made teams shy away from adopting similar systems.

MLB

Major League Baseball is another sport which has the potential for rich data analytics, and while there has been some adoption, no centralised data analytics are easily available to the fans. Trackman is used to track the game for in game analysis and is used to create stats but the analytics is predominantly done in house by the MLB and its media partners, with no specialist input, the MLB seems to be missing a trick.

Cricket

For a sport that is as stats obsessed as cricket is, it is very surprising to see that there is no one supplier of statistics or data analytics. You can find stats on every player by searching around the internet, but it is time consuming and not always from the most accurate sources.

One of the issues facing cricket and data adoption appears to be because of a split between the ‘old fashioned’ coaching and the ‘new school’. One wants to use data to help improve the players, such as Andy Flower. Flower had great success in the short term but too heavy a focus on it may have assisted in the downfall of the team. On the other side, the non data using side, the enigmatic ‘old school’ approach of coaches like Darren Lehmann has at the moment come out on top and it could be a while till we see cricket using data heavily again.

NHL

The sport that is widely popular across North America and large parts of Europe is starting to show some examples of the beginning of data analysis adoption. Some teams are starting to look a bit more closely at player statistics, possession and passing stats, but a similar case of unwillingness to change looks to be hindering adoption. The Chicago Blackhawks have used data to improve their game and the Toronto Maple Leafs went from dismissing data analytics to creating a data department, the sport appears to be on the cusp of accepting data analysis on a larger scale.

An issue with adoption seems to be players not understanding it or not wanting to use data, however, the more data is implemented then the more data savvy players are likely to get.

 

Football

Yes, football was mentioned as being good at using data analytics, but really it isn’t being as widespread as it perhaps should be.

Football is developing a bad reputation for shunning technology and when you consider the success of the German national team using data analytics, you wonder why it isn’t being used more.

Football leagues need to follow the example of the NBA. Fed up with independent sites and competitors using their statistics, they worked to develop a one point of access site to all the stats, so fans wouldn’t have to trawl the internet for different sites showing the data that they wanted.

As with other sports, you can find plenty of statistics from multiple sources on football in different leagues, but for the fan, or player or coach it would be much more convenient to have all the data compiled in one place.

 

Overall, the future for sports analytics looks bright, as more teams throughout the sporting community look for a competitive edge an increase of data analysis is likely to take place. Certainly a number of coaches are looking at ways to improve teams, develop game tactics and to use it to scout for players.

With companies like Under Armour expanding the depth of data knowledge that they have, sports can also expect to have more technologically focussed clothing and analysis to look at. In the future this is likely to trickle down to the average sports player and consumer of sporting goods.

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