The Bundesliga has become one of the most technologically advanced leagues in the world. Football has always relied on statistics and analysis, but in recent years, experts have been using vast amounts of data collected from various sources to predict outcomes and develop strategies.
The league uses optical tracking, sensors, and high-frequency positional monitoring in partnership with high-tech companies. In this article, we’ll dive deep into how that data is collected and used to make the Bundesliga a leader in the field.
From Shots and Possession to Millions of Data Points
Just a decade ago, match analysis was about a few simple metrics that both fans and teams could track. These included shots on target, possession percentage, fouls committed, and a few other niche metrics.
Today, Bundesliga generates roughly 3.6 million data points per match. Cameras are positioned around the stadium and track all players at all times, as well as the ball itself, capturing micro-movements. These include: defensive shifts, off-ball runs, passing lanes, and pressing triggers. Every point of action, such as tackles, interceptions, pressures, sprints, body orientation, and recovery runs, is tagged and marked.
These data points are then combined with master data such as player profiles, historical tendencies, formations, substitutions, and tactical phases. This shifts the data from descriptive to predictive uses.
The New Metric Toolbox: xG, xT, EPV & More
Expected Goals (xG) and Expected Assists (xA)
Expected goals are a metric used to quantify the probability of scoring in any given situation on the field. This is determined by using location, angle shooting, and defensive pressure as metrics. On the other hand, expected assists are used to evaluate how often a pass or cross would generally result in a goal.
Expected Threat (xT) and Expected Possession Value (EPV)
Xt is used to answer the question of how much more likely a team is to score after each ball movement. The pitch is divided into zones, and xT measures the value of passes to quantify how dangerous a player is, even without taking a shot.
EPV goes a step further than this and evaluates every action in terms of how much threat it poses to the opposing team. This includes passes, presses, and clearances.
Pressing and Pressure Metrics
The Bundesliga has introduced metrics to quantify pressure and pressing. These evaluate how players respond when they are repeatedly pressed. By keeping track of this data, it becomes easier to identify pressure-resistant midfielders and how often the team’s structure can cause opponents to make mistakes.
Momentum, Attacking Zones & Shot Speed
Advanced momentum visualization combines xG with attacking territory and keeper efficiency. This data shows which team controls the game’s rhythm. When further combined with the shot speed, this information gives fans and analysts a deeper technical understanding of the match.
How Clubs Use These Metrics in Day-to-Day Analysis
Clubs have already started using the metrics they get from these complex analyses to improve their strategy. Before every match, analysts map out where opponents generate their highest xT values. This reveals the opposing team’s weaknesses and allows them to identify zones where they are vulnerable to pressure.
Coaches monitor live xG, momentum charts, running intensity, and pressure-resistance data during the match itself. This shows them how well their strategy is playing out in real time and whether it translates into actual scoring opportunities. Substitutions are therefore made based on real-time data.
Clubs also rely on this data to inform their recruitment decisions. Sporting directors use data and its long-term implications to identify undervalued players and arrange their transfers once they become a burden to the team. German teams are known for using information to make their scouting more scientific and precise.
From Data to Predictions: Beating the Table and the Bookies?
Betting sites were always among the first to adopt new technologies. This is evident in how crypto sports betting was among the first to accept crypto, and now mainstream industries are following suit. Cryptocurrencies allow players to place wagers while based abroad and without providing personal data.
Research shows that in many ways, xG-based models can outperform bookmaker odds. Bettors now track all the metrics we mentioned, and they can do so in real time. They quickly adapted to the new technology and used it to make better predictions. Spotting value bets becomes easier for the tech-savvy bettors.
Machine learning used to create odds for betting apps is also plugged into this data. That means the predictions will get better over time as they continue to use large datasets and learn from their own mistakes. Other leagues will follow along since the Bundesliga is among the most tech-savvy, but it’s not the only one.
To Sum Up
The Bundesliga is among the first in Europe and the world to use advanced data analytics to inform and improve its strategies and outcome predictions. This is done by monitoring the games to the smallest detail and by computing how dangerous a team and their strategy could be when it comes to scoring.
The process involves the league, the team, the fans, and the bettors at different stages and levels. As more data is collected, it will become more sophisticated and more accurate in its predictions. AI will play a role in making such predictions as it relies on the data collected from more matches and teams.
