Part III: Player and Team Analysis

Overview

Part III shifts our focus from individual metrics to comprehensive analysis frameworks for evaluating players and teams across all dimensions of the game. While Parts I and II established foundational concepts and core metrics, this section demonstrates how to synthesize these tools into actionable insights for defensive evaluation, goalkeeper analysis, set piece optimization, and holistic performance assessment.

Chapters in This Part

Chapter 12: Defensive Metrics

Modern soccer analysis has historically emphasized attacking metrics, but defensive contribution is equally crucial for team success. This chapter provides a comprehensive framework for quantifying defensive actions, positioning, and effectiveness. We examine individual defensive events (tackles, interceptions, clearances, blocks), spatial defensive coverage, pressing coordination, and advanced metrics like defensive expected goals prevented. Students will learn to evaluate defenders and defensive midfielders objectively, moving beyond simplistic counting statistics to nuanced performance profiles.

Chapter 13: Goalkeeper Analysis

Goalkeepers occupy a unique position in soccer analytics due to their specialized role and the challenge of evaluating rare events. This chapter develops a complete analytical toolkit for goalkeeper evaluation, covering shot-stopping metrics (post-shot expected goals, save percentage above expected), distribution analysis, sweeper-keeper metrics, and positioning evaluation. We address sample size challenges inherent in goalkeeper analysis and explore how tracking data enables more sophisticated evaluation.

Chapter 14: Set Piece Analytics

Set pieces account for approximately 25-30% of goals in professional soccer, yet remain under-analyzed in many contexts. This chapter provides systematic methods for analyzing corner kicks, free kicks, penalty kicks, and throw-ins. We cover delivery optimization, movement patterns, defensive organization, and conversion modeling. Students learn to identify set piece strengths and weaknesses, design evidence-based routines, and evaluate set piece specialists.

Chapter 15: Player Performance Metrics

Building on all previous chapters, this chapter synthesizes individual metrics into comprehensive player evaluation frameworks. We examine position-specific analysis, style categorization, similarity algorithms for player comparison, and performance trends over time. The chapter addresses practical applications including contract negotiations, transfer decisions, and team building, while acknowledging the limitations and proper interpretation of composite metrics.

Chapter 16: Team Performance Analysis

This chapter scales analysis from individual players to team-level evaluation. We explore team style fingerprints, performance prediction, schedule difficulty adjustment, and league-wide comparisons. Students learn to identify systemic patterns that emerge from collective behavior, evaluate tactical approaches quantitatively, and build comprehensive team profiles that capture both attacking and defensive dimensions.

Learning Objectives

By completing Part III, students will be able to:

  1. Evaluate Defensive Performance: Quantify defensive contributions using appropriate metrics, understanding the distinction between ball-winning actions and positional excellence
  2. Analyze Goalkeepers: Apply specialized methods for evaluating goalkeepers across shot-stopping, distribution, and sweeping dimensions
  3. Optimize Set Pieces: Analyze and design set piece routines using data-driven approaches
  4. Build Player Profiles: Create comprehensive, position-adjusted player evaluations suitable for scouting and recruitment
  5. Assess Team Performance: Develop holistic team analysis frameworks that capture style, effectiveness, and contextual performance

Technical Skills

This part develops intermediate analytical skills including:

  • Multi-dimensional clustering for player and team categorization
  • Regression techniques for expected value calculations
  • Spatial analysis at both individual and team levels
  • Time series analysis for form and trend detection
  • Radar charts and comprehensive visualization techniques
  • Database design for player and team profiles

Data Requirements

Chapters in this part utilize: - Event data (StatsBomb format) - Aggregated season statistics - Optional: Tracking data for advanced positioning analysis - Optional: Video data for set piece analysis

Connection to Other Parts

Part III builds directly on the metrics introduced in Part II (xG, xA, xT, passing networks, possession) and applies them within evaluation frameworks. The analytical techniques developed here provide the foundation for Part IV's advanced analytics and Part V's practical applications. Specifically:

  • Chapter 12's defensive metrics inform Chapter 19's machine learning features
  • Chapter 13's goalkeeper analysis connects to Chapter 18's tracking data methods
  • Chapter 15's player profiles feed directly into Chapter 21's scouting applications
  • Chapter 16's team analysis supports Chapter 22's tactical analysis

A Note on Defensive Analysis

Historically, defensive analysis has lagged behind attacking analysis in sophistication. Goals scored are visible and celebrated; goals prevented are counterfactual. This asymmetry has led to systematic undervaluation of defensive contributions.

Modern analytics seeks to correct this imbalance by: 1. Crediting defenders for actions that prevent goal-scoring opportunities 2. Evaluating positioning that deters dangerous play before it develops 3. Measuring pressing coordination that disrupts opponent buildup 4. Quantifying the value of winning possession in advantageous locations

Part III embraces this challenge, providing tools that treat defense with the same analytical rigor as attack.

Prerequisites

Before beginning Part III, students should have: - Completed Parts I and II, or equivalent preparation - Strong understanding of expected goals and expected threat - Familiarity with passing network concepts - Proficiency with pandas, matplotlib, and basic statistical analysis - Understanding of spatial coordinate systems in soccer data

Practical Applications

The frameworks developed in Part III have immediate practical applications:

  • Scouting: Build position-specific player profiles for recruitment
  • Performance Analysis: Evaluate player contributions for coaching staff
  • Contract Negotiations: Quantify player value objectively
  • Opposition Analysis: Identify team and player weaknesses to exploit
  • Development Tracking: Monitor young player progression over time

Let's begin our exploration of comprehensive player and team analysis with defensive metrics—the often-overlooked foundation of successful soccer.

Chapters in This Part