Abstract

This analysis examines some of the key metrics that differentiate teams which made the knock-out phase of Champions League from those that do not. The goal is to identify what makes these teams unique and provide insights into how other teams could improve their performance. By analyzing statistical features such as passing accuracy, shooting efficiency, and long-ball effectiveness, we aim to uncover the attributes most strongly linked to success.

Introduction

The UEFA Champions League represents the pinnacle of club football, featuring the strongest teams across Europe. However, not all teams perform equally in the league phase, and some consistently qualify for the knockout rounds while others fall short. What separates these successful teams from the rest?

This analysis seeks to answer this question by examining key performance metrics. We explore whether technical quality outweighs sheer quantity of play, and whether specific playing styles—such as long-ball strategies—offer an advantage. The findings could provide valuable insights for coaches, analysts, and teams aiming to optimize their tactical approach.

Data & Methodology

The dataset, sourced from fbref, includes match data from all teams that participated in the 2024/2025 Champions League league phase. 

To provide a comprehensive assessment, team statistics were aggregated over the entire league phase using sum values to capture overall performance trends. Additionally, normalization techniques were applied where necessary to account for variations in match tempo, playing style, and the number of matches played, ensuring a fair comparison between teams.

We first analyzed correlations between the various performance metrics and achieved points to identify general trends. To further quantify the importance of these features in predicting match outcomes, we conducted a SHAP (SHapley Additive exPlanations) analysis.

SHAP values provide a breakdown of each feature’s contribution, revealing whether higher or lower values positively or negatively influence predictions. 

To determine whether specific performance metrics significantly differ between qualified, playoff, and knocked-out teams, we conducted an ANOVA (Analysis of Variance) test. 

By combining correlation analysis, SHAP feature importance, and ANOVA, we aim to identify the most influential statistical attributes associated with successful qualification, while minimizing the influence of external factors that could distort the results.

Results

Points vs. Features Correlations

Our analysis of the correlation data reveals on Figure 1 the strongest positive correlations with points are :

• Goal Difference (0.92) & Team Goals (0.79) – Goal difference is by far the strongest predictor of success. Naturally, this is closely linked to the number of goals a team scores—teams with a high goal difference almost always have strong attacking output, making these two metrics heavily intercorrelated.
• Total Saves by Opponent (0.68) – Indicating teams that force more saves from opposing goalkeepers perform better

Interestingly, several possession and accuracy metrics show moderate positive correlations (around 0.54-0.60):

• Total Passes Team (0.6)
• Average Shots on Target % (0.6)
• Average Passing Accuracy % (0.59)
• Touches Team (0.58)
• Total Shoots Team (0.57)
• Average Ball Possession Team %(0.54)

Since these metrics are highly correlated, the key insight is that quality is just as important as quantity, but neither alone guarantees a high number of points.

On the negative side, opponent-related metrics show moderate to strong negative correlations with points, particularly:

• Opponent Goals (-0.71) – The strongest negative correlation, as expected. If a team concedes a lot of goals, it is unlikely to accumulate many points.
• Total Shots by Opponent (-0.64) – Suggests that allowing too many shots increases defensive vulnerability.
• Total Saves by Team (-0.63) – Indicates that when a team’s goalkeeper is forced to make more saves, it often means the opponent is dominating the game.
• Opponent Corners (-0.58) – A high number of corners conceded appears to correlate with poorer performance. Interestingly, a team’s own number of corners only has a 0.33 correlation with high points, suggesting that simply winning many corners does not strongly translate into success.

In Figure 2 we can see that :

• Aerial Duels Won (0.12) & Tackles (-0.0098) – Despite being key defensive actions, these metrics show a surprisingly weak correlation with points. This suggests that while they contribute to defensive efforts, they do not directly translate into better team performance or success.

• Interceptions (-0.053) – Although interceptions indicate defensive awareness, their low correlation with points implies that simply winning back possession is not enough to guarantee better results.

On the weaker end, opponent-related metrics also show minimal influence:

• Opponent Passing Accuracy (-0.21) – Teams do not necessarily perform better just because their opponents have lower passing accuracy, indicating that disrupting passing lanes alone is not a major success factor.
• Total Passes Opponent(-0.25) – Allowing many passes does not strongly correlate with poor performance, suggesting that passive defending does not always lead to losing points.
• Opponent Shots on Target (-0.29) – While conceding shots is generally bad, this weak correlation implies that shot quality and defensive structure matter more than just the number of attempts faced.

Overall, these findings highlight that defensive volume does not always equate to effectiveness—strategic defensive actions and quality offensive play seem to have a greater impact on success.

This data reinforces the idea that while attacking efficiency is crucial, defensive stability is equally important. The high correlation between goal difference and points (0.92) highlights that teams succeed by excelling on both ends of the pitch—scoring efficiently and minimizing defensive weaknesses.

Figures 3-6 illustrate the relationships we found between Goal Difference, Total Passes by Team, Total Saves by Opponent, and Corners Conceded with Points. By observing the general direction and clustering of data points, we can identify whether a relationship exists—for example, an upward trend suggests a positive correlation, while a scattered distribution indicates a weaker or no correlation.

Feature Importance Analysis

To complement our correlation analysis, we conducted a feature importance analysis using a Random Forest model. This approach helps us understand which variables have the most predictive power in determining match outcomes beyond simple linear correlations.

The top features identified by our model were:

• Goal Difference – The most influential feature, which makes sense as it directly impacts match success.
• Team Goals – The number of goals scored by a team significantly contributes to predicting match outcomes.

• Opponent Goals – The number of goals conceded also plays a crucial role.

• Total Shots Opponent – This metric highlights the pressure exerted by opponents.

• AVG Saves Opponent % – The ability of the opposing team’s goalkeeper to prevent goals affects match outcomes.

• Other important factors included Touches Team, Total Passes Team, and Interceptions Team, emphasizing the role of ball control and defensive actions.

Metrics Comparison by Qualification Status

Using the ANOVA method, we analyzed whether the most correlated metrics are also the ones where qualified teams outperformed others.

Metric	F-Statistic	P-Value
Goal Difference	50.1931	9.7982e-11
Team Goals	26.1578	1.5614e-07
Total Saves Opponent	15.4500	1.8396e-05
Total Passes Team	13.1062	6.4662e-05
Touches Team	12.5482	8.8516e-05
AVG Passing Accuracy Team%	11.1563	1.9901e-04
AVG Shots on Target Team%	10.4592	3.0327e-04
Opponent Goals	10.3911	3.1616e-04
Total Shots Team	8.5440	1.0230e-03

Table 1 highlights that goal difference and goals conceded are the metrics with the most significant differences across the qualified, playoff, and knocked-out groups.

An interesting observation is that while total passes and shots are correlated with success, the number of passes appears to be more impactful than passing accuracy. This suggests that a possession-heavy, pass-based playstyle can be beneficial.

On the other hand, qualified teams differ significantly in shooting accuracy, implying that precision in finishing is more critical than the sheer volume of shots taken.

Figure 7 provides further insights into how different performance metrics impact a team’s qualification status. The comparison of goal difference, total passes, and shooting accuracy reveals a consistent pattern—teams that advance further tend to excel in these areas.

Shot Quality & Scoring Efficiency: The Key to Success

As observed, scoring goals is the most fundamental aspect of football, but how and where teams score can be just as important as how often they shoot. In this section, we take a deeper look at shooting metrics to understand their impact on a team’s success.

Figure 8 confirms our earlier finding—there is a clear correlation between shooting accuracy and the number of points a team earns. Teams with higher shooting accuracy tend to accumulate more points, reinforcing the idea that precision is more valuable than sheer shot volume.

If we analyze the distance from where the different teams score goals an interesting pattern emerges. The boxplot reveals that as teams advance from the “Knocked Out” stage to the “Playoff” round and eventually to “Qualified,” their goal-scoring distances become more concentrated within a specific range.

As Figure 9 shows, qualified teams tend to score from shorter and more controlled distances, as indicated by their more compact interquartile range. This suggests a higher level of precision in their finishing, likely due to better shot selection, tactical discipline, and superior offensive play. In contrast, teams that were knocked out exhibit a wider spread, implying a reliance on long-range efforts, which may indicate a struggle to create high-quality chances closer to goal.

The “Playoff” teams sit between these two extremes, showing a mix of both approaches. This transitional pattern reinforces the idea that elite teams have a more refined shot selection, increasing their efficiency by capitalizing on close-range opportunities rather than relying on speculative long-range strikes.

While the ANOVA test for shooting distance (F-statistic: 1.0318, p-value: 0.3676) does not indicate a statistically significant difference between groups (p ≥ 0.05), this could be due to the limited number of data points rather than the absence of a meaningful trend. The pattern observed still suggests that teams advancing further in the tournament tend to score from more favorable positions, supporting the idea that precision and shot selection are critical factors in success.

Conclusion

This analysis highlights key factors that drive success in the Champions League. While goal difference remains the strongest predictor of qualification, shooting accuracy emerged as a crucial factor, correlating more strongly with success than possession or total shot volume. This suggests that teams focusing on high-quality chances rather than sheer shot volume are more likely to thrive.

Defensive stability also plays a key role—teams that allow fewer shots and concede fewer corners tend to perform better. This reinforces the idea that controlling the game is not just about possession but also about minimizing opponent opportunities.

Ultimately, the data suggests that efficient finishing, defensive solidity, and the ability to create quality chances are what set top teams apart.

While this study provides valuable insights, further research could explore:

• Expected Goals (xG) & Shot Selection: Assessing whether teams consistently outperform or underperform xG.
• Pressing & Defensive Intensity: Evaluating how different defensive setups impact qualification chances.
• Player-Level Analysis: Investigating individual contributions, such as shot-creating actions or defensive duels.
• Tactical Formations: Examining how formations influence attacking and defensive efficiency.

Exploring these areas could further refine our understanding of what separates the best teams in European football.

Data source: www.fbref.com