The Evolution of Soccer Analysis with AI Technology

The Evolution of Soccer Analysis with AI Technology

With the advancement of artificial intelligence (AI) technology, the use of data in soccer games has become more prevalent and essential. Previously, subjective analysis held more importance than data in the preparation and evaluation of soccer matches. However, recent developments in AI technology have opened up a new era in which data plays a significant role in understanding and strategizing the game.

One crucial aspect of data collection in soccer is the filming of matches. Traditionally, game footage was captured manually by various individuals within the club or through third-party companies. However, AI-based sports analysis solution companies, like Bepro, have successfully automated the filming process using AI technology. By installing fixed network cameras in high positions within the stadium, the footage from these cameras is stitched together to create a single video. This stitching technology allows for the tracking of player movements, enabling the extraction of physical, tactical, and technical data. Additionally, portable cameras such as Cerberus have made it easier to film matches without the need for fixed cameras. Cerberus can recognize players and automatically track their movements, providing a wealth of data for analysis.

One of the significant advancements in data analysis is the introduction of Expected Goals (xG). xG is a metric that calculates the probability of a shot resulting in a goal based on factors such as distance from the goal, angle, number of defenders, body part used for the shot, and the process leading to the shot. This metric provides a more comprehensive evaluation of a player's scoring ability, taking into account the specific circumstances surrounding each shot. By comparing xG values to actual goals scored, a player's finishing ability can be objectively assessed. For example, if a player consistently scores below their xG value, it suggests a lack of finishing prowess, whereas exceeding the xG value indicates effective goal-scoring.

Another significant metric is Expected Threat (xT), which evaluates a player's ability to deliver the ball to high-scoring areas. xT considers passing, dribbling, and carrying the ball into different zones of the pitch, assigning values to each zone based on its potential for creating goal-scoring opportunities. This metric provides a more nuanced evaluation of midfielders and defenders, who play a crucial role in creating attacking chances. Similarly, Expected Goals on Target (xGOT) evaluates a goalkeeper's shot-stopping ability. By calculating the probability of shots on target resulting in goals, xGOT allows for a more objective assessment of a goalkeeper's performance, independent of their team's defensive capabilities.

These new metrics, along with others like Expected Assists (xA), Goal Contribution (xGC), and Passes Allowed per Defensive Action (PPDA), are reshaping the way soccer is analyzed. The availability of data-driven insights allows coaches and analysts to make more informed decisions and strategies. The game is no longer solely reliant on subjective analysis but incorporates objective data to better understand and evaluate performance.

The paradigm shift brought about by AI technology in soccer analysis highlights the indispensability of data in the game. Coaches and analysts must now leverage data to gain a competitive edge. The continuous development of AI technology promises even more meaningful metrics and insights, making data interpretation and analysis skills increasingly crucial.

To make the most of this new era of soccer analysis, here are three actionable pieces of advice:

1. Embrace AI Technology: Incorporate AI-based analysis tools into your coaching and analysis processes. Explore solutions like Bepro's automated filming technology or portable cameras for easy and efficient data collection.

2. Understand and Utilize New Metrics: Familiarize yourself with metrics like xG, xT, and xGOT. Learn how to interpret and apply these metrics to gain deeper insights into player performance and team strategies.

3. Invest in Data Literacy: Develop the skills necessary to interpret and analyze data effectively. Stay updated with the latest advancements in AI technology and learn how to extract meaningful information from the data collected.

In conclusion, AI technology has revolutionized soccer analysis by providing objective and comprehensive data. The use of metrics like xG, xT, and xGOT has transformed the way players, teams, and coaches are evaluated. Embracing AI technology, understanding new metrics, and investing in data literacy are crucial steps in maximizing the potential of this new era of soccer analysis.