Data Analytics in Sports

Why is data analytics important in sports?

Sports data analytics is driving the industry to erstwhile unfathomable heights in team/player performance optimization and corporate success. More than ever before, sports teams and organizations are making very profound strides in using cuttind edge data and analytics to make well informed, educated and winning decisions. The naked eye and simple stats are no longer the way teams decide on players, scout opposing teams, and build championship winning sides.

What kind of data is recorded?

There is a plethora of data being tracked by sports teams around the world. Statistics such as goals, points, and individual performances can all be tracked in real time. Yet, far more insights can be generated when this data is analyzed autonomously in real time.

Internet of Things Wearables are being deployed by teams and athletes in a variety of sports. NFL players are now using wearables embedded within their helmets to analyze tackles in an attempt to minimize head injuries.

Wearables can also be used in other ways. Soccer players wear devices under their shirts which track their heart rates and distance covered. There are also wearables players can put inside their boots that track exactly how many times they touch the ball. A world of insight is now at the touch of a fingertip and can change the fortunes of a team or player.

How to use data in sports?

There are thousands of different data sports organizations can track. Yet, not all of the data points are necessary for success. In many ways the mining of data and putting an emphasis on information that isn’t vital to success can be confounding to performance optimization.

It is also vital teams read and understand the data provided. In 2011, Liverpool Football Club paid £35 million for striker Andy Carroll. The club later bought winger Stewart Downing for £20m. The idea behind their purchases was that Carroll scored the most headers in the Premier League while Downing was statistically the best at crossing the ball into the box. Unfortunately, the data used by Liverpool’s management was misleading as to score headers, players must be close to goal. It also ignored Carroll’s awful injury history and Liverpool’s team tactics.

Insights from analytics can tell sports teams what they want to hear, but without studying the analytics thoroughly, it can leave the savvy game manager short handed.


Moneyball continues to be the driving force in sports analytics. Finding undervalued players and succeeding to win championships is the most important part of analytics. Leicester City’s improbable 2015–16 Premier League championship is one of the best examples of Moneyball in action. The team was assembled with players few other Premier League clubs wanted at the time. Yet, after they won the title, the value of each member of the starting XI increased dramatically. The best example of the team’s use of analytics was the purchase and then sale of N’Golo Kante. Bought for £5.6m, Kante was sold after winning the title to Chelsea for £32m.

Analytics are shaping sports on and off the field. Whether it is soccer, baseball, the NFL, or another sport or league, analytics are changing the face of the industry.

Leave a Reply

Your email address will not be published. Required fields are marked *