Can Big Data be used in sports?

Is there a place for Big Data analytics in Sports?

Since there is currently a rugby world cup taking place I thought it would be appropriate to discuss whether big data can be applied to sports.  It turns out big data actually has a massive role to play in sports and not just to examine player performance but to manage injuries and analyse fan and spectator behaviour as well.

Which Sports codes use big data?

Several sports codes use big data analytics.  In football (or soccer if you are from North America) Arsenal FC of the English premier league are using a system developed by a sports analytics provider called Prozone.  The system uses cameras installed in a stadium to track players and their interactions every second.  The data is analysed automatically using algorithms embedded in the technology and used to provide insights on player performance.  Big data is also used in Rugby.  IBM have developed a system called TryTracker that uses data from previous matches between two opponents to determine the targets that either team needs to achieve in order to win a game.  A target can be, for example, a certain number of line breaks or goal kicking accuracy percentage.  These are just two examples of sports that use big data analytics.  Other sports such as basketball, tennis, formula one, cycling, baseball and mixed martial arts also make use of big data.

Big Data success stories in sports

One of the more famous examples of big data in sports is the use of big data by the German men’s national football team for the 2014 FIFA World Cup.   A team of German university students in conjunction with SAP and the football team manager compiled a database of football related information called Match Insights.  Match Insights contained information about both the German team and other national teams.  Using this database the team management was able to provide much more specific information to the players about their own individual performance as well as analysis on opposition players.  At a team level the data helped improve several facets of the Germans’ game such as their possession speed.  Analysis of the 2010 world cup showed that the German team had an average of 3.4 seconds on the ball.  Once aware of this the team management adjusted the style of play and the time was reduced to 1.1 seconds.  All teams at the 2014 tournament used data analysis of some sort but Germany seems to be the only one that went to the extent of building a database with a custom built application that provided such in depth analysis.  The extent to which the team performance improved because of Match insights is debateable.  However given that the Germans won the tournament and were the only ones to use Match Insights this does suggest that there was some benefit derived from using Match Insights.

Big data analytics is not only used for analysing players

Big data analytics is also used to analyse fans as well.  According to Christy King, Vice President of IT for Ultimate Fighting Championship (UFC), big data analytics can be used to improve the fans’ experience at a venue.  For example high foot traffic locations can have memorabilia stands close by and real time monitoring of the bathrooms can tell spectators which bathrooms have shorter queues.  Big data analytics can also be used to examine spectator emotions.  Academics did a study using social media analytics on tweets from the U.S.A during the 2014 men’s FIFA world cup football when the U.S.A team was playing.  The findings confirmed what many people already knew which is that when a person is watching a team that they support that person becomes more heavily invested in the game and shows signs of fear and anxiety as well as happiness.  However when a person is watching two teams that they have no interest in there mainly feelings of happiness and enjoyment.  Although this research did not provide any surprising insights it does demonstrate how big data can be applied to fans..

Wearable computing enabling sports analytics

One of the things that stands out for me when considering big data in sports is how big data is enabled by other trends.  In the case of big data and sports, wearable computing plays a big role.  GPS trackers, heart rate monitors and other gadgets to monitor performance are small enough to be worn on the body of players which makes big data analytics in sport possible.  It would be much harder to analyse aerobic performance if training sessions had to continuously be interrupted so a player’s breathing rates could be measured by medical staff.

Big Data does not replace coaches though

One of the questions around big data is whether or not it eliminates the need for human analysis or intuition in decision making.  Are the instincts and knowledge that coaches have gathered over years of involvement with the sport becoming obsolete?  I don’t believe so.  Big data is simply providing better information on which to base a decision on.  Consider fantasy football (or soccer) for example.  You can have information on two opposing teams such as win-loss ratios, goals scored and against, number of shots on target and yellow cards received.  This information will give you an idea of who is likely to win from a statistical point of view.  However statistics do not always tell the full story of how dominant a team was in the last game or how close a game was and how this will impact on the team’s next performance.  Only experience and intuition will inform that aspect of sports knowledge.  Therefore analytics and intuition are not mutually exclusive but rather complementary.