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.
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjpjX-4bKWRp61n2DMHv_buhCKEbf9sy9c_9z6WOFaVSyX1oouIq4mgSmLeB6HjQNbsR78XPrXWSuXwE5AGYJks1K6V3r9Ze6NjbWMFVwHLw0MZaWnEko14qcnx2VRAZEj1egAwkQCNkc8/s1600/Germany.jpg)
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.