Why PPDA Fails: Building a Six-Dimensional Pressing Metric

2026-02-17 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Sports Analytics · Depth: Advanced, short

Summary

A new six-dimensional framework for analyzing pressing in football, developed using tracking data, addresses the limitations of the widely used PPDA (Passes Per Defensive Action) metric. While PPDA measures defensive action density and intensity, it fails to capture the coordinated spatial and temporal aspects of effective pressing, such as defensive block compression or synchronized team movement. The proposed framework includes dimensions like pressure intensity, compactness, local pitch control, pass option suppression, anticipation, and synchronization. Empirical analysis of a 90-match dataset showed that pressing phases with high coordinated strength, as measured by this new system, more than doubled turnover probability compared to low-coordination phases, demonstrating that structure, not just frequency of actions, drives defensive disruption.

Key takeaway

For AI Scientists developing football analytics models, you should move beyond event-based metrics like PPDA when tracking data is available. Incorporate spatiotemporal dimensions to capture the coordinated nature of pressing, as this approach reveals a stronger correlation between defensive structure and game outcomes, such as turnover probability. Your models will better reflect tactical realities and provide more actionable insights for coaching.

Key insights

Effective football pressing is a coordinated spatiotemporal system, not merely a count of defensive actions.

Principles

• Pressing quality depends on coordinated team movement.
• Structure, not frequency, drives defensive disruption.

Method

A six-dimensional framework uses tracking data to measure pressure intensity, compactness, local pitch control, pass option suppression, anticipation, and synchronization for coordinated pressing analysis.

In practice

• Use tracking data for deeper pressing analysis.
• Focus on collective behavior over individual actions.

Topics

• Football Analytics
• Pressing Metrics
• PPDA
• Tracking Data
• Spatiotemporal Systems

Best for: AI Scientist, Data Scientist, Analytics Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.