Data in Sports.

Introduction to Data in Sports

Sport in today’s world has extended beyond just the playing field. What was earlier two teams entering the field with textbook strategies, has today boomed into a platform with detailed analysis and pin-pointing to every detail.

An athlete or a team’s data can show us where their strengths and weaknesses lie. It tells us a player’s performance probability based on past results:

How many runs has Virat Kohli scored in the death overs of knockout games?
How many penalties has Neymar missed during Champions League Games?
…and so on

Studying data helps teams prepare for the unexpected. Any change that the opposition makes before or during the game can be instantly studied by a team of analysts to see how it will impact the rest of the game.

For example: in an ODI game between India and Australia at Adelaide, the team batting order shows that Tendulkar was moved from #3 to #5.

Australia can then look at data to understand Tendulkar at the #5-

Ø  How he has performed previously at Adelaide,

Ø  The type of bowlers he had least success with,

Ø  His most common way of dismissals,

Ø  His most favored shot selections,

Ø  His aggression and strike rate.

Understanding Tendulkar’s performance pattern at Adelaide, at the #5, will help Australia as a team to be prepared and select the right bowlers and set the right field to minimize the impact that Tendulkar can create for India.

Performance analysis tools don’t limit us only to scoring averages.

Shown above are some of the data tracking tools used in sports, micro-chips that can be attached to equipment and apparels of athletes. The wearable tech tracks the distance covered, hydration & fatigue levels, which then can be analyzed on a heat map. It also helps to understand the areas of excellence and improvement individually and as a team.
 Further, the team manager can use the data to prepare the final team list.

The microchips used on the equipment tracks the manner in which a player moves the bat at each stroke. Once analyzed, it also shows us how much force is applied per stroke, and further areas of improvement.

Commercially, sports data is seen during games on TV to give a perspective to viewers. It builds up a story for the game.
Data also helps understand demographics that can be used for fan engagement. It gives an insight to organizations about the various types of fans that they have and how their experience can be made better. Demographics here analyze age, gender, spending habits, how often they attend games, how much they are willing to spend…and so on.

At a grassroot level, systematic data recording is essential for the development of young athletes. Coaches and clubs need to record the performance of their athletes at every age group level.
This can help track scouts and other stakeholders in analyzing how good they are to play professionally, if it comes to that.
But more importantly, coaches can use that data to understand the area of development for each athlete and to identify elite talent among the group.

Micheal Lewis’ Moneyball followed the story of how MLB’s Oakland Athletics used data analytics to turn their struggling team into a playoff caliber team. Sport is a dynamic field that is constantly evolving. Stakeholders need to keep up with the trend AND stay head-to-head with competitors or be at risk of falling behind.