As a soccer analyst, you may be tempted to make a decision based on your feelings and emotions, but it’s important to know the facts. For example, the number of goals that Real Salt Lake has conceded in 2019 is a more accurate measurement of the success of its defense than its offense. And, you shouldn’t make the same mistake that other futbolpronosticos has made. After all, you can’t make such an informed decision based on the whims of other coaches or managers.
There are several ways to use sports data to improve your coaching. One of the most obvious ways is to combine event data with tracking data. This data is more useful for predicting off-ball patterns because it shows players’ physical characteristics, such as their gaze and poses. But, it’s far from perfect, and it doesn’t show everything. For this reason, you can also use the world football R package to analyze individual passing behavior in Fifa top-five domestic European soccer leagues.
As technology advances, the development of soccer statistics has evolved. Earlier, only the most basic statistics were measured. These included pass counts, possession time, and pass completions. Those were very useful, but these were only the first steps in the development of soccer analytics. The development of sensor technology has led to high-fidelity data streams. Soccer logs and video-tracking data describe the trajectories of players during games and training sessions.
Data analytics is important in sports, and there are many ways to analyze them. Opta, the leader in soccer sports data, can determine every player’s movement in a specific zone on the field. You can also measure the distance that each player runs during a game. Opta’s advanced analytics cover over 100 match-specific statistics, including shots, goals, and assists, yellow and red cards, and won and lost duels. You can also determine how effective a corner is in the box.
Soccer logs also help researchers analyze player interactions. With this data, they can reconstruct a team’s passing network, which represents how the ball moves between players during a match. This data can be used to identify key players. For example, this research reveals how much a player’s pass is worth. Similarly, the size of an edge or node reflects how many times that player made a pass. A team can also use soccer logs to determine which player makes more or less passes in a match.
In soccer analytics, one of the most widely used metrics is expected goals. Teams that have more shots on goal than their opponents usually perform better than those that don’t. Expected goals also help understand the strength of a team. Similarly, players with plenty of high-quality shots are more likely to score than players with few chances. So, expect goals to be a better indicator of a team’s strength than goal difference. But before you start applying these metrics, remember to get the best data possible.
While football coaches have long trusted their coaches’ eyes, it’s only recently that the author of Moneyball has turned his attention to soccer. While the game is less quantitative than baseball, applying analytics to soccer is a challenging task. But, the potential is huge, and you’d be surprised how many coaches and managers are now using the data to improve their players’ performance. That’s where data analytics can play a major role. It’s time to embrace this emerging technology in soccer.