Soccer data—aka football data—is the analysis and forecasting of soccer team or player performance. Users of this data are interested in both historical and current data at a highly granular level. They also tend to enrich their datasets with a wide range of external information, from stadiums to weather conditions.
Most soccer data comes from matches, so anyone can collect and analyze it. Further, with cameras covering every inch of a field, even hobbyist data analysts can collect data at a previously-unheard-of granular level, down to the movement of a supporting player yards away from the ball’s current position.
Additional sources of this data include open-source historical match data and proprietary club or stadium data. However, this proprietary data is, of course, very difficult to acquire, making certain data providers with partnerships with clubs and leagues very valuable resources for football data.
As mentioned earlier, there is a huge range of possible data attributes for football data, though it does tend to be organized by league and team or match. Much of the data available comes in the form of odds per game, as one of the main uses of soccer data is sports betting.
Other attributes of this data include expected goals and assists, sequences of play, possessions, and individual player analysis and predictions.
Fans use soccer data to place bets or to back up predictions. Meanwhile, coaches use the data to plan training modules; both coaches and managers use it to determine which new players will make the best additions to their teams.
Sports broadcasters also use analysis of real-time data to create visualizations to present to viewers, drawing fans in.
Users of football data demand live data feeds; APIs and widgets, then, should update constantly. Even analysis and predictions based off the live data should update immediately if possible. Machine learning programs would be of enormous help here.
One of the most important tests of quality soccer data, however, is relevance. Gamblers require different sets of data from youth coaches, for example.
The concept is simple: a player is selected as the subject of the search and the algorithm then produces a list of players with similar statistical profiles, ranked on a scale of 0-100, with 100 being an exact match. The subject could be a well-known player, a potential transfer target or even one of a team’s own players for whom they require a backup or future replacement.
It is also possible to adjust the importance assigned to each metric. For instance, if a team is looking for a player capable of replicating the passing and creative output of a league-leading attacking midfielder but are not as concerned about matching the defensive output of that player due to team play style or other factors, the former elements can be weighed more heavily in the similarity analysis.
OneFootball Data provides match data, stats, trends, and news for all the major European leagues: EPL, La Liga, Bundesliga, Serie A
Football-Data Historical Data—aka, Football-Data’s Historical Football Results and Betting Odds Data—provides historical football (soccer) data on all the major leagues. Fans usually use this information to supplement their online bets. To that end, Football-Data also offers free resources for learning how to bet, testing your first bets, and more.
Football-Data Livescore provides live football (soccer) data to fans for betting purposes, primarily. They provide data on all the major leagues, with odds and ratings. In addition, they have free resources for learning how to bet, testing your first bets, and more.