For most people, sports is something fun to pass the time and perhaps to stay fit. You swing at a target, kick a ball or run across the touchline. When it flies into your opponent’s goal, you cheer, and vice versa. The idea of unleashing data analysis on this may sound somewhat bizarre. However, the possibilities are in fact enormous. For an increasing amount of professional companies, the use of abundant data is increasingly playing an important role in the game, as well as to get the most out of the team in the face of limited budgets.
Data in sports is about more than merely analyzing the opponent and your team. Data can also be used to create training drills as effectively as possible for players at an individual level. That can be very useful, for instance, it can be used to regulate the intensity in such a way that there is less chance of costly injuries.
Another interesting area for data in sports lies in scouting new talent, especially for clubs with a relatively small budget and few scouts. By passing the multiple data sources with information about players and their performance through a digital filter, it is possible to follow players that specifically match the interests and criteria of the club. Those criteria could be things like age, or budget, for example.
Way of playing and strategy
However, the most important use of data in sports at the moment is the ability to analyze the game and using those insights to improve strategies and approaches in the game, allowing you to adapt to the opponent. So, how do you achieve that? Take into consideration the following steps:
In some sports, data is collected centrally, such as in the Eredivisie. In other sports, you might have to do this yourself with the help of some creative thinking, cameras, and perhaps some sensors. Although this can be quite difficult, the real challenge is mainly how to set up the collection of data in a manner that the data is readily usable and accessible to gain insight from. It is best to collect all relevant data sources in one place: for example, in the cloud.
The power of data is to do more with the same data. Or put it another way: data plus more data is better data. The example below shows this well.
The first image shows the position from which a shot at goal was fired. This might be useful to know, but it says little about the circumstances or context of the shot. The second image now reveals the positions of the players at the time of the shot. It becomes rather clear now that the goalkeeper (the orange arrow) is completely out of position, making it easy for the opponent to score.
Now that you can link important data, it is possible to analyze further what happened, and how to do things differently in the future. This kind of analysis is already happening in sports, but there is a lot of manual work involved still. By collecting and linking data, you can automate much of this, making analysis less of a challenge. See the example below.
Above you can see the position from which a shot was placed on the target. However, there are three opponents in the path of the shot, so the chances of a goal are very small. A likely better option would have been to pass the ball at number 19 or to cross it over to the running number 23.
Where sports and IT meet
Collecting and linking data properly is certainly not a cakewalk, however, it offers many benefits and can unlock hidden information. To carry this out properly, good cooperation between the sports club and IT team is vital. These are two completely different worlds, but as a team, they can work wonders.
Would you like to know more about the possibilities to use data within the sport? And what LINKIT’s experiences are in that area? Please contact one of our experts for more information.