Data Science and Sports - A Smarter Way to ‘Faster, Stronger, Sharper’
It took Hollywood and Brad Pitt – as you would expect - to bring data and sport into the mainstream. Moneyball, the 2011 blockbuster fuelled interest in the way data is used in sports, and since then many have employed the same.
Arsenal, a team synonymous with numbers (particularly the number 4), have invested in developing an analytics team to gather and make better use of the data collected. 8 cameras are installed around the Emirates Stadium to track player movements and their interaction with each other and the opposition.
Like Arsenal, other football teams are also investing in millions to increase the accuracy and value of the analysis. ‘All successful tackles by Paul Pogba’ or ‘All successful passes by Lionel Messi’ are some of the types of metrics tracked. Along with these, off the ball events are also on the tracking radar as well.
While the above is done by cameras, various wearable tracking devices are also being used for the same. Look on your wrist and you’ll find the simplest example of a wearable device with your fitbit band. Most professional sports do not allow players to wear any device during a professional match. However, FIFA (international football’s governing body) has recently announced changes allowing players to wear monitoring equipment during matches for the first time. In tennis too, players can use sensor-equipped racquets during games to record speed, power and spin of the ball. In other sports, players usually wear track-able devices during training to monitor performances.
Data can also help coaches make more informed decisions that could decide the outcome of a game. Coaches can select the best players, field the most effective teams and make smarter decisions on the field or court.
It’s not just players or coaches, but data is targeted towards sports fans as well. Smartphones and a constant stream of social media engagement have changed the in-person experience for fans. These fans can interact with their favourite teams and players on Facebook and Twitter, which has now led to the monitoring and analysis of the same.
One unique way in which data has helped professionalise sport is its use in drawing up contracts. In contract negotiations, players, coaches and teams use data to find evidence that supports whatever contract demand they want to make. Good data insights can make or break a player being hired or a coach being fired.
However, as it would be, there are critics to the increased use of data in sport as well. The union between data analysis and sports takes away from the fundamental principle that sport is about humans competing on the track or field. The ‘sport’ of Formula One is feared to be headed in this direction. Races are now won in team garages as opposed to driver skill on the race track. There is a danger that sport will become more about techies competing in an analytics lab.
At the end of the day, sport, like any other sphere, has to progress. Even if the mixing of Data Science and sport is more of an evolution than a play-by-play tactic. Athletes are now “faster, stronger and sharper” due to the help of Data Science.