Soccer is a game of strategy, skill, and sometimes, luck. But with the rise of advanced analytics, teams, coaches, and fans now have tools to quantify what was once considered unmeasurable.

Advanced metrics in soccer analytics provide deeper insights into player performance, team dynamics, and match outcomes, empowering stakeholders to make data-driven decisions.

If you're ready to dive deeper into soccer analytics and learn how to apply these advanced metrics, check out my Complete Football Analytics in Python Course. This comprehensive course will teach you how to analyze data, build predictive models, and uncover actionable insights to improve performance both on and off the pitch.

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1. Expected Goals (xG)

What It Measures:

Expected Goals (xG) is a statistical measure that evaluates the quality of a scoring opportunity.

It assigns a value to each shot based on factors like distance from goal, angle, and the type of play leading up to the shot (e.g., open play, set piece).

Why It's Important:

xG allows analysts to assess whether a team or player is creating high-quality scoring chances, independent of the actual goals scored. For example, a team with a high xG but low goals scored may indicate poor finishing, while a team with a low xG but high goals scored may be benefiting from luck or clinical finishing.

Real-World Application:

Teams like Liverpool and Manchester City use xG to refine their attacking strategies, identifying areas where players can improve their decision-making or positioning to increase the likelihood of scoring.

Here's a video I made which explains the math behind expected goals:

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2. Expected Assists (xA)

What It Measures:

Expected Assists (xA) quantifies the quality of a pass that leads to a shot. Like xG, xA assigns a probability to each pass based on its likelihood of resulting in a goal.

Why It's Important:

xA provides a more nuanced understanding of a player's creative ability, beyond traditional assist metrics. For instance, a midfielder may consistently deliver high-quality passes that aren't converted into goals, which would be overlooked in basic assist statistics.

Real-World Application:

Kevin De Bruyne's high xA numbers reflect his ability to create high-quality chances for teammates, cementing his reputation as one of the best playmakers in the world.

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3. Passing Efficiency

What It Measures:

Passing Efficiency evaluates the success of a player's passes relative to the difficulty of the passes attempted. Metrics like Pass Completion Percentage by Expected Pass Completion (PERC) are often used to compare actual performance to expected performance.

Why It's Important:

This metric differentiates between players who make safe, low-risk passes and those who attempt ambitious, high-risk passes that break defensive lines. It's particularly useful for analyzing playmakers and central midfielders.

Real-World Application:

Analyzing Passing Efficiency helps teams identify players who excel at progressing the ball under pressure, such as Jorginho or Sergio Busquets.

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4. Defensive Actions Per 90 (D90)

What It Measures:

Defensive Actions Per 90 (D90) quantifies a player's defensive contributions, including tackles, interceptions, clearances, and blocks, normalized over 90 minutes.

Why It's Important:

D90 helps identify players who are consistently active in breaking up opposition play. It's particularly valuable for evaluating defensive midfielders and center-backs.

Real-World Application:

Virgil van Dijk's D90 metrics showcase his ability to read the game and execute crucial defensive interventions without frequent fouling.

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5. Pressing Efficiency

What It Measures:

Pressing Efficiency measures how successful a team or player is at regaining possession within a certain time frame after applying defensive pressure.

Why It's Important:

High pressing efficiency is a hallmark of modern soccer tactics, particularly for teams employing gegenpressing or other high-intensity systems. This metric helps coaches identify which players excel at pressing and which areas of the pitch are most effective for regaining possession.

Real-World Application:

Jurgen Klopp's Liverpool heavily relies on pressing efficiency metrics to optimize their high-energy counter-pressing tactics.

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6. Progressive Carries and Passes

What It Measures:

Progressive Carries track the number of times a player moves the ball toward the opponent's goal at least a certain distance (e.g., 10 yards), while Progressive Passes measure forward passes that significantly advance the ball.

Why It's Important:

These metrics highlight players who contribute to advancing play and breaking defensive lines, even if they don't directly score or assist.

Real-World Application:

Players like Lionel Messi and Jack Grealish often rank highly in Progressive Carries, reflecting their ability to dribble past defenders and create attacking opportunities.

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7. Shot-Creating Actions (SCA) and Goal-Creating Actions (GCA)

What They Measure:

• SCA: The two offensive actions directly leading to a shot (e.g., dribbles, key passes).
• GCA: The two offensive actions directly leading to a goal.

Why They're Important:

These metrics go beyond traditional goal and assist statistics to recognize players who contribute indirectly to scoring opportunities, offering a fuller picture of offensive contributions.

Real-World Application:

Analyzing SCA and GCA helps teams scout creative players who may not get the final credit for goals but are crucial in build-up play.

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8. Goalkeeper Metrics: Post-Shot Expected Goals (PSxG)

What It Measures:

PSxG evaluates the likelihood of a shot becoming a goal after it's been taken, accounting for the goalkeeper's position and reaction. It's often compared to actual goals conceded to assess a goalkeeper's shot-stopping ability.

Why It's Important:

This metric helps isolate a goalkeeper's performance from the quality of the defense in front of them, giving a more accurate picture of their contributions.

Real-World Application:

PSxG highlights standout goalkeepers like Alisson Becker, who consistently outperform the metric by saving high-quality chances.

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Learn Advanced Soccer Analytics

If these metrics intrigue you and you want to take your soccer analytics skills to the next level, don't miss my Football Analytics Course. In this course, you'll learn how to work with real-world data, calculate advanced metrics like xG and xA, and use Python to build predictive models. Whether you're a beginner or a seasoned analyst, this course will equip you with the skills to excel in soccer analytics.

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Conclusion

Advanced metrics in soccer analytics are revolutionizing how we understand the game. From Expected Goals to Pressing Efficiency, these data-driven insights help teams optimize strategies, evaluate players, and gain a competitive edge. Whether you're a coach, analyst, or fan, diving into these metrics will enrich your appreciation for soccer and its complexities.

Start your journey in soccer analytics today by exploring tools like StatsBomb, FBref, and Understat. And if you're ready to go deeper, check out the Football Analytics Course to master these metrics and more.