The Challenges of Data-Driven Goalkeeper Analysis in Football

Goalkeepers are fascinating. Not in the sense that every goalkeeper is supposed a little weird. No, I mean in the sense that goalkeeper analysis and scouting is a different part of the game, altogether. It’s a specific skill and while we want to use data to analyse all players, this might prove to be very difficult when we look at goalkeepers.

Analysing a goalkeeper in football can be a complex task that poses several challenges. In this article, we will explore the reasons why it is difficult to use data to analyse a goalkeeper’s performance in football. We will delve into various aspects such as limited data points, the subjectivity of performance evaluation, contextual factors, team dynamics, the lack of standardized metrics, and the variability of match situations. Despite these challenges, advancements in technology and data analytics have paved the way for more sophisticated goalkeeper performance analysis.

Limited data

One of the primary reasons why analysing a goalkeeper using data is difficult is due to the limited data points available. Compared to outfield players, goalkeepers have fewer measurable actions and events during a match. While outfield players have numerous touches, passes, and duels, goalkeepers’ involvement is relatively lower. They often have fewer opportunities to interact with the ball, leading to a limited dataset for analysis. With fewer data points, it becomes challenging to draw comprehensive conclusions about a goalkeeper’s performance solely based on data.

Subjectivity of judgement

Another challenge lies in the subjectivity of performance evaluation for goalkeepers. Goalkeeping involves a significant degree of subjective judgment. Factors such as positioning, decision-making, anticipation, and handling can be difficult to quantify objectively. While data can provide some insights, accurately assessing a goalkeeper’s performance often requires expert judgment or qualitative analysis. Evaluating elements such as their ability to organize the defense, communication with teammates, or command the penalty area may not be easily captured by data alone.

Contextual factors

Contextual factors also play a significant role in analysing a goalkeeper’s performance. A goalkeeper’s performance can be influenced by several contextual factors that need to be taken into account. The quality of the opposition, the defensive organization of their team, or the style of play adopted by the opposition can all impact a goalkeeper’s actions and decision-making. For example, a goalkeeper facing a barrage of shots from a strong attacking team may concede goals despite putting in a commendable performance. Isolating a goalkeeper’s performance from these contextual factors can be a challenge.

Performance of the team

Furthermore, goalkeeping performance is closely interconnected with the performance of the entire team. The defensive line, communication with defenders, and the overall defensive strategy all have an impact on a goalkeeper’s performance. Evaluating a goalkeeper without considering the team dynamics can lead to skewed conclusions. For instance, a goalkeeper may appear to have made several saves, but if their defenders are consistently leaving them exposed or the team’s defensive structure is weak, it can affect their overall performance.

Lack of standardised metrics

The lack of standardized metrics specifically designed for goalkeepers is another hurdle in analysing their performance. Unlike outfield players, where metrics such as goals scored, assists, or successful passes are commonly used, there is a relative lack of standardized metrics for goalkeepers. Metrics such as save percentage, clean sheets, or goals conceded provide some insights but only offer a partial understanding of a goalkeeper’s performance. They may not capture their overall contribution effectively. Developing more comprehensive and tailored metrics for goalkeepers remains a challenge.

Variability of match situations

Additionally, the variability of match situations faced by goalkeepers adds to the complexity of the analysis. Goalkeepers encounter a wide range of scenarios during matches, including different types of shots, crosses, or long-range efforts. Each situation requires different skills, decision-making, and positioning. The variability of these match situations makes it difficult to generalize a goalkeeper’s performance based on limited data points. An exceptional performance against one type of situation may not necessarily translate into consistent performance across all situations.

Despite these challenges, advancements in technology and data analytics have brought about improvements in analysing a goalkeeper’s performance. Tracking technologies, such as player-tracking systems and ball-tracking systems, provide detailed data on a goalkeeper’s movements, positioning, and actions. These technologies enable the extraction of valuable insights from available data. Machine learning algorithms can be applied to analyse large datasets and identify patterns that may contribute to goalkeeper performance. These advancements offer opportunities to enhance the understanding of a goalkeeper’s performance.

However, it is important to note that while data analytics can provide valuable insights, subjective analysis and expert judgment remain crucial for a comprehensive evaluation of a goalkeeper’s performance. Experts in the field can consider factors that are difficult to quantify, such as decision-making under pressure, distribution skills, or leadership qualities. By combining data-driven analysis with qualitative evaluation, a more holistic understanding of a goalkeeper’s performance can be achieved.

So what can we do?

1. Shot maps: Shot maps illustrate the location of shots faced by the goalkeeper on the field. By plotting the shots on a graphical representation of the pitch, patterns can emerge regarding the areas where the goalkeeper faces the most shots or where they are more effective at making saves. This visualization can help identify strengths and weaknesses in a goalkeeper’s positioning and shot-stopping abilities.
2. Save percentage over time: Creating a line graph that tracks the goalkeeper’s save percentage over a period can visually represent their consistency and performance trends. It allows for the identification of any fluctuations or improvements in their shot-stopping abilities over time.
3. Distribution heatmaps: Heatmaps can visualize a goalkeeper’s distribution patterns and effectiveness in distributing the ball to different areas of the field. This can include the frequency and accuracy of their throws, kicks, or passes. Heatmaps can reveal the areas where the goalkeeper tends to distribute the ball most frequently, highlighting their ability to initiate attacking plays or relieve pressure on the defense.
4. Sweeper-keeper analysis: A radar chart or spider chart can be employed to assess a goalkeeper’s ability to act as a sweeper-keeper, showcasing their involvement outside the penalty area. This visualization can display metrics such as successful sweeps, interceptions, or actions taken outside the box, providing an understanding of their proactive approach to anticipating and dealing with through balls or long passes.
5. Cross claim success rate: For goalkeepers, successfully claiming crosses is a vital aspect of their role. A stacked bar chart or a grouped bar chart can be used to illustrate the goalkeeper’s success rate in claiming crosses from different positions or heights. This visualization can help identify areas of strength or areas where improvement is needed in dealing with aerial threats.
6. Time series analysis: A time series analysis can reveal patterns in a goalkeeper’s performance throughout a match or a season. This visualization can include metrics such as the number of saves, goals conceded, or clean sheets over time. By observing fluctuations or trends, it becomes possible to identify periods of exceptional performance or areas that require improvement.

Final thoughts

In conclusion, analysing a goalkeeper’s performance in football using data is a challenging task due to various reasons. Limited data points, the subjectivity of performance evaluation, contextual factors, team dynamics, the lack of standardized metrics, and the variability of match situations all contribute to the complexity. While advancements in technology and data analytics have improved the analysis of goalkeeper performance, it is important to strike a balance between data-driven analysis and subjective evaluation to gain a comprehensive understanding of a goalkeeper’s contribution to the team.