The Use Of Data Analytics In Football Is A Testament To The Fact That Man Has Truly Learned To Harness The Power Of Technology.
No matter which country we look at, football is an important aspect of the lives of common folk. Whether it drives in a spirit of patriotism, entertainment, or a sense of relaxation, football shall forever remain an integral component in our lives, for it never bores the viewer.
It pays to know that the world of football is much more than the single 90-minute game in which we wait with bated breaths- the background dynamics that go behind the play are often overlooked. There’s a common saying- There’s more than meets the eye.
So, It means that there is a lot more than what catches our eye or what we comprehend out of the situation. The popular idiom is often an understatement in football. Especially when it comes to analyzing team and player performances.
How did it all begin?
The trend of using data for sports get unnew. first popularized thanks to the David vs Goliath story of Oakland Athletics, an American Major League Baseball(MLB) team.
an uphill task of assembling a competitive team in 2002 after losing its star players to richer teams and having a limited budget.
The manager hired an economics graduate from Yale, to work with him to build up a new team. They use data to come up with players who are suitable for their style of play and even use it to help players work on their weaknesses.
There was no looking back for Data Analysts ever since then. Major sports tried replicating the success in their own fields.
Fast forward to 2020, several football teams harness data analytics in Football for player assessment and assessing opponent team play through various techniques.
So how does Data Analytics in Football is supported?
The most popular sport in the world has teams, employ data analytics in Football to extract the best out of their respective players.
Various approaches like clustering, KNN algorithm, regression, Etc. – used to analyze the given data. Additionally, data gets extensively collected, store, and retrieve with few high-end urbane techniques. The international governing body of football, FIFA, has also made it a point to make available certain player data to the public as well.
Let’s see in what aspects does data analytics in Football come into a team’s dynamics.
Team Training:
In order to get the result of a 90 minutes football match in their favor, teams train their squad for the entire week rigorously. With the use of many sensors, data is convene, which gives managers, coaches, physios, and supporting staff a huge idea of what is going right and what isn’t with the team.
Through gathering data, each and every player’s attributes get known, which gives teams ample knowledge to rework their strategy.
- If a player excels in counter-attack, then play him down deep.
- Or If a player is good at finishing, then play him in the opposition goal box.
- If a player is good at interception, make him the playmaker and counter-attack.
Huge datasets helps via, so quality data scientists also employed. Player movement – scaled on a model and aggregated to incorporate into team play style.
All these tactical decisions – taken by analyzing player performances.
Player recruitment:
Often there are situations where teams need to choose any one player to recruit, out of several players. The parameters are several, which are revealed to teams by data. Scouts are specially trained employees hired to look for talent. In the past, scouts used their experience and judgment to make a list of players. With the involvement of data analytics in Football. the task is simplified. The scouts just make a note of player performances and feed into the models, which give the desired analysis. They then make a list of players who are more suitable to the team’s style of play.
Also, With clustering algorithms, players reports build on whether they can used for the team or not.
Match Analysis:
Match analysis includes pre as well as post-match analysis. Prior to the match, teams often use it to analyze the previous performances of their opponent teams and put in place necessary tactics to keep them in check. Data scientists use several techniques to predict the most likely actions of opponent players. This data could be of immense use to teams.
Post the match, coaches use the match data- distance covered by players, a number of dribbles, tackles, goals, and shots on target, passes, etc. to ascertain the team collective performance on the match day.
Written By: Viivek Uppalapu
Reviewed By: Viswanadh
If you are Interested In Machine Learning You Can Check Machine Learning Internship Program
Also Check Other Technical And Non Technical Internship Programs
