How Sports Club Managers Use Data to Transfer and Scout Players

Sports data is big. And big data means big business. You might be surprised to find out that sports club managers don’t simply watch matches anymore. Instead, they rely on hefty data collected through multiple methods to figure out their next drafts.

We’ll be taking a look at what these methods are, how managers and experts work together to implement them, and how this, in turn, entices fans, bettors, and other sports enthusiasts to invest their time, money, and nerves into the industry.

How They Collect Sports Data

At a base level, managers can consult historic data for multiple teams from official sources like FIFA, UEFA, and other organizations or partners who keep track of these kinds of stats. If they want to go deeper, they can also track the data of individual players and see how they have performed in time.

To do this, you’ll want an algorithm that can handle scout data analysis and interpret it to the best possible degree alongside the help of experts. Scouting experts and machine learning algorithms working in tandem are by far the most effective method of predicting individual players’ potential.

This data is collected through field analysis, smart cameras, and even smartwatches that players can wear to determine their heartbeat, speed, efficiency, agility, and so forth. Managers can then use this information to determine the best course of action possible.

Are Video Games the Next Big Sports Data Enhancers?

During the previous decade, clubs have started using the Football Manager franchise to help out with transfers. That is because the team that created the game has used real-life scouts to gather that information and place it into the game. As for the game itself, it features a fantastic football club managing simulation.

This way, inexperienced managers can test out their skills within the game before applying their knowledge in real scenarios. Furthermore, this method is best-suited as a training simulation rather than an actual trading tool because the game only gets an update every so often (once per year). And the teams within the game are also affected by your decisions. So by playing more and more, the stats become far less grounded in reality.

Player and Team Pairing Considerations

Of course, this data would be totally useless if it wasn’t for the ability to pair individual players alongside the team that you want to transfer them to. Those statistics need to be put in context for them to work and avoid any misleading appearances.

As an example, you might see a player that has excellent passing abilities. However, if the team that they are part of is one that usually keeps the ball for the majority of the game, then it doesn’t necessarily mean the said player is a great passer.

Similarly, if you see a player with a lot of goals, but the said player has only competed in minor leagues, then there’s no guarantee that he’ll be good for the major leagues. Plenty of other examples could arise if you just stop and think about the context for a while. This is why we believe that having an artificial intelligence or machine learning algorithm can be advantageous as long as you don’t forgo real, expert scouts.

How This Affects the Fan Experience

Fans are always on their toes whenever a new season begins. If you’re a club manager, then you can keep the fanbase growing strong by recruiting all sorts of renowned players to your team. Similarly, scouting for new talent can also spice things up and increase the ratings, ticket sales, and, ultimately, the sponsorships you’ll be able to bring in.

And, if you’re a fan, we’d like to hear your opinion as well. What do you think of all this information and about sports data in general? Let us know in the comments below what you would like to see more of or what you would like to change within this domain.

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