A comprehensive tutorial demonstrating how to transform NFL play-by-play data into process mining event logs using advanced SQL methodology. This repository showcases the complete pipeline from raw sports data to process-analyzable format, enabling strategic analysis of offensive drive patterns and success factors.

After working through this tutorial, you will understand:

• Process Mining Fundamentals: How to identify cases, activities, and timestamps in domain-specific data
• Advanced SQL Transformations: Sophisticated data engineering techniques for event log construction
• Domain Expertise Application: Strategic NFL knowledge applied to process activity classification
• Data Engineering Best Practices: Reproducible, scalable transformation pipelines

This project transforms NFL play-by-play data into a process mining event log by:

1. Defining Process Boundaries: Each NFL offensive drive becomes a distinct process instance (case)
2. Strategic Activity Mapping: Play types are classified with strategic context (e.g., "pass short left" vs. "run right tackle")
3. Temporal Alignment: Game timeline is converted to process mining compatible timestamps
4. Outcome Enrichment: Drive-level results enable analysis of which process paths lead to scoring

Input: 43,000+ NFL plays from 2007 season (nflverse data)
Output: Process mining event log ready for PM4PY, ProM, or Celonis analysis

• Python 3.8 or higher
• Internet connection (for downloading nflverse data)

# Clone the repository
git clone https://github.com/nickblackbourn/nfl-process-mining.git
cd nfl-process-mining

# Install dependencies
pip install -r requirements.txt

# Run the complete transformation pipeline
python run_transformation.py

That's it! The script will:

• Download 2007 NFL play-by-play data from nflverse
• Execute the SQL transformation pipeline
• Validate the results
• Export outputs/nfl_eventlog.csv ready for process mining

The resulting event log contains:

Core Process Mining Columns:

• case_id: Unique drive identifier (e.g., "2007_01_NE_NYJ_1")
• activity_name: Strategic play classification (e.g., "pass short left", "run right tackle")
• transformed_time: Process mining timestamp (chronological ordering)

Enrichment Columns:

• drive_any_score: Did this drive result in points? (process outcome)
• yards_gained: Activity outcome measurement
• down, desc: Situational context
• Plus 20+ additional NFL context attributes

Why Drives as Cases?
An NFL offensive drive represents one complete execution of the "offensive possession" process - from gaining possession to either scoring, turning over the ball, or reaching a natural stopping point (end of half/game). This creates meaningful process boundaries for analysis.

Why Strategic Activity Classification?
Simple classifications like "run" vs. "pass" lose critical strategic context. Our sophisticated mapping preserves decision-making nuance:

• pass short left vs pass deep middle represent different strategic choices
• run left tackle vs run right end show distinct tactical executions
• Outcome-specific activities like sacked and interception capture process failures

Why This SQL Approach?
The transformation uses advanced SQL techniques that demonstrate:

• Proper data engineering methodology (intermediate tables, clear steps)
• Scalable approach suitable for production environments
• Complex domain logic handling with sophisticated CASE statements
• Professional ETL patterns for process mining transformations

With this event log, you can perform:

Process Discovery: Identify common play-calling patterns and sequences
Conformance Checking: Compare actual vs. expected offensive strategies
Performance Analysis: Correlate process paths with drive success rates
Variant Analysis: Compare different types of drives (scoring vs. non-scoring)
Resource Analysis: Study personnel usage in different game situations

nfl-process-mining/
├── README.md                  # This comprehensive guide
├── ATTRIBUTION.md            # nflverse data credits and sources
├── run_transformation.py     # Single-command execution script
├── requirements.txt          # Python dependencies
├── src/
│   └── transform_data.sql    # Heavily commented SQL transformation
├── data/                     # (Data downloaded automatically)
├── outputs/
│   └── nfl_eventlog.csv     # Final process mining event log
└── .gitignore               # Excludes large data files

This repository serves as a comprehensive example of:

• Advanced SQL for Data Science: Complex transformations with clear business logic
• Process Mining Methodology: Proper event log construction from domain data
• Data Engineering Best Practices: Reproducible, documented, scalable pipelines
• Domain Expertise Application: Strategic knowledge driving technical decisions

Perfect for data scientists learning process mining, SQL practitioners seeking advanced techniques, or sports analytics enthusiasts interested in strategic analysis.

1. Explore the SQL: Read src/transform_data.sql to understand the transformation methodology
2. Analyze the Results: Import outputs/nfl_eventlog.csv into your process mining tool
3. Extend the Analysis: Modify the SQL to include additional teams or seasons
4. Apply the Methodology: Use this approach as a template for other domain transformations

Found an improvement or have a question? Open an issue or submit a pull request. This repository aims to be a learning resource for the community.

GNU General Public License v3.0 - see LICENSE file for details.