How has Data Analytics Impacted European Football?

Analytics in sports is not a new concept. Crunching big data to make informed decisions has already transformed sports in the US. For example, baseball players focus on home-run at-bat thanks to analytical data, and it is all or strike-out philosophy. As a result, baseball lost some of its appeal and nuances, but the end data suggested it is the most efficient way of play.

Analytics also changed basketball and the greatest shooter of all time Steph Curry. The three-point shot is dominant, and centers who can't shoot have become obsolete. As a result, the midrange game is no longer a desirable shot selection.

Data analytics made a massive entry in European football, and here is how such analysis is changing the most popular sport.

Better broadcast and more information for fans

IoT development and advanced sensors enabled tracking players' movement at all times. More than 10 data points each second for all footballers on the pitch equals 1.4 million data sets.

Those statistics and measurements translate into more analytical data for fans, and broadcasters can create overlays and graphic presentations with accurate additional data. The iGaming industry benefits from such overwhelming data sets. For example, bettors now have better than ever insight into players' performance and can make informed predictions in football betting.

Finding new talents

Numerous sensors track players off the field to assess their health and training metrics. Giant datasets combined with body measurements help trainers recognize new talents more accurately. For example, if your team likes pass-first players, you can monitor youth selection and find a suitable player that will fit your system based on the movement charts and player tendencies.

Experienced coaches rely on man-analytics and eye tests, and there is value to the experienced beholder. Still, if you combine it with complex data analytics, you can find things you didn't notice at first.

Player performance

Most top-flight teams in Europe have dedicated personnel for data analysis. For example, Manchester City hired astrophysicist Laurie Shaw for data analysis, but the English champions are not alone. Other clubs also try to create a competitive edge by analyzing millions of stats and complex data sets.

Players have wearable devices for workload, movement, and fatigue measurements. Data is poured into systems and directly to training staff. Coaches can assess the level of form, anticipate potential injury and have a complete overview of players' performance.

Trainers or players can optimize the training process, and in a matchday scenario, data can help with timely substitutions.

Preparing for the opponent

If you don't have an analytical department, you will be at a disadvantage on game day in top European leagues. Most clubs study opponents with film sessions, but now they have a robust set of statistical data about tendencies, formations, movement, and player performance.

When you prepare a match, you can accurately anticipate the opponent's weak and strong sides and adjust your tactics based on analytics. The only problem is that your rival is doing the same thing, so better analytics will help you win some games. But, naturally, players still have to perform, and data analytics can only do so much to prepare the game.

This article does not necessarily reflect the opinions of the editors or management of EconoTimes