25 Jul The Power of Data Analysis in Player Selection: A Hypothetical Case with Toni Kroos
In professional football, precision in player selection is crucial, especially when it comes to replacing key figures like Toni Kroos of Real Madrid. LaLiga, through its Mediacoach tool, provides equitable access to advanced data that facilitates this decision-making process. In this post, we explore a hypothetical scenario using a data model that deeply analyzes Kroos’ passing typology, focusing on game distribution.
A Data Model for Game Distribution
The first step in our analysis was to create a data model that replicates the type of passes made by Kroos. It wasn’t just about counting total or completed passes, but understanding the specific nature of his passes, including the area of the field, difficulty, and danger level. These characteristics are crucial for assessing how a player can influence game control and create opportunities.
Some key metrics considered in this analysis include:
- Successful Difficult Passes (xP<0.7): This metric evaluates a player’s ability to complete passes with a low probability of success, a skill Kroos has consistently demonstrated. For example, Aleix García showed an average of 42 successful difficult passes per game, similar to Kroos.
- Successful First Touch Passes: This metric indicates a player’s ability to maintain the flow of the game, especially under pressure, allowing for quick transitions and surprising the opposition.
- Passes Beyond the Midfield Line: This is crucial for evaluating a player’s ability to advance the ball from a safer area to a more dangerous one, creating attacking opportunities.
- Passes that Break Lines: Similar in concept, this type of pass is essential for breaking defensive blocks and connecting with attackers in advanced positions.
- Pre-Area Action Passes: This metric assesses a player’s decisiveness in the phase leading to a goal-scoring opportunity, which is fundamental in constructing dangerous plays.
These metrics, although not exhaustive, represent examples of advanced pass qualification through analytics that go beyond conventional statistics.
Mediacoach and Contextual Analysis
This data analysis is complemented by advanced techniques such as the use of Convex Hull, which measures the difficulty of passes based on the configuration of the opposing team on the field. For example, a pass from the front of the opponent’s defensive block to the back is extremely valuable and challenging, as it must completely bypass the defensive line. In contrast, a horizontal pass within the front of the opponent’s block, without breaking their defensive structure, is generally easier and less impactful.
The accompanying video illustrates how the Convex Hull concept is used to evaluate pass difficulty, providing a more detailed and rich view of a player’s skills in different tactical situations.
Conclusion
Advanced data analysis, such as that provided by Mediacoach, is an invaluable tool for LaLiga clubs in making strategic decisions. In this hypothetical case, we have shown how it is possible to identify players who not only replicate the quantity of Kroos’ passes but also emulate his style and quality in game distribution.
This analytical capability not only helps identify the right talent but also ensures decisions are based on objective and contextualized data, minimizing risk and maximizing potential success on the field. In an environment where every pass can be crucial, access to advanced analytical tools is essential to remain competitive at the highest level of football.