Part 7: Capstone Projects
Introduction
These capstone projects integrate concepts from across the entire textbook, challenging you to build complete analytical systems that could be used in professional settings. Each project includes specifications, milestones, evaluation criteria, and extension opportunities.
Choose projects based on your interests and career goals. Each can be completed individually or adapted for team collaboration.
Project 1: Complete Game Prediction System
Overview
Build an end-to-end NFL game prediction system that forecasts point spreads and totals for every game of the season.
Learning Objectives
- Integrate power ratings, efficiency metrics, and contextual adjustments
- Build and maintain a prediction database
- Track and evaluate model performance
- Implement automated weekly updates
Requirements
Core Components: 1. Team power rating system with weekly updates 2. Home field advantage adjustments (venue, schedule, travel) 3. Weather impact modeling 4. Injury adjustment framework 5. Point spread and total predictions 6. Performance tracking and calibration
Technical Requirements: - Python implementation with pandas/numpy - Database storage (SQLite or PostgreSQL) - Automated data collection pipeline - Visualization dashboard (matplotlib/plotly)
Milestones
Week 1-2: Foundation - Set up data pipeline for historical games (2018-present) - Implement basic Elo rating system - Calculate initial power ratings
Week 3-4: Adjustments - Add home field advantage (static and dynamic) - Implement schedule factors (bye weeks, rest) - Add weather adjustments
Week 5-6: Injuries and Context - Build injury impact model by position - Add playoff/rivalry adjustments - Integrate market data for validation
Week 7-8: Automation and Evaluation - Build weekly update pipeline - Create performance tracking dashboard - Document calibration and accuracy metrics
Deliverables
- Working prediction system with documentation
- Historical backtest results (3+ seasons)
- Weekly prediction export mechanism
- Performance report with accuracy metrics
Evaluation Criteria
| Criterion | Weight | Description |
|---|---|---|
| Technical Implementation | 30% | Code quality, architecture |
| Prediction Accuracy | 25% | ATS performance, MAE |
| Integration | 20% | Successful component integration |
| Documentation | 15% | Clear explanation of methodology |
| Extensions | 10% | Additional features beyond requirements |
Extensions
- Add player prop predictions
- Implement Monte Carlo simulation for uncertainty
- Build web interface for predictions
- Add live game probability updates
Project 2: Fantasy Football Analytics Platform
Overview
Create a comprehensive fantasy football analysis platform that provides projections, rankings, and decision support.
Learning Objectives
- Apply VORP and positional scarcity concepts
- Build player projection models
- Implement DFS optimization
- Create actionable decision tools
Requirements
Core Components: 1. Player projection engine 2. VORP-based ranking system 3. Draft optimization tool 4. Weekly start/sit recommendations 5. DFS lineup optimizer 6. Waiver wire analyzer
Technical Requirements: - Python with pandas/scipy - Linear programming for DFS - Web interface (Flask/Streamlit optional) - Data integration with fantasy platform APIs
Milestones
Week 1-2: Projections - Build player projection model - Implement regression to mean - Create efficiency and volume forecasts
Week 3-4: Rankings - Calculate replacement levels - Implement VORP rankings - Create position scarcity analysis
Week 5-6: Decision Tools - Build DFS optimizer - Create start/sit recommender - Implement waiver analyzer
Week 7-8: Integration - Build unified platform - Create export/visualization features - Test with live data
Deliverables
- Projection database with weekly updates
- VORP rankings with positional analysis
- DFS optimizer with multiple contest types
- Documentation of methodology
Evaluation Criteria
| Criterion | Weight | Description |
|---|---|---|
| Projection Accuracy | 25% | Compare to expert consensus |
| VORP Implementation | 25% | Correct value calculations |
| Optimization Quality | 20% | Valid lineups, value generation |
| Usability | 20% | Clear outputs, actionable |
| Documentation | 10% | Methodology explanation |
Extensions
- Dynasty/keeper valuations
- Trade analyzer
- Auction value calculator
- Best ball optimization
Project 3: NFL Draft Prospect Evaluator
Overview
Build a comprehensive prospect evaluation system that ranks draft prospects and projects NFL success.
Learning Objectives
- Apply production normalization techniques
- Integrate combine and athletic testing
- Build position-specific models
- Create comparable player identification
Requirements
Core Components: 1. College production database 2. Conference adjustment system 3. Combine analysis module 4. Position-specific evaluation models 5. Draft value calculator 6. Historical validation framework
Technical Requirements: - Python with scikit-learn for modeling - Historical prospect database (2015+) - Visualization for profiles - Exportable reports
Milestones
Week 1-2: Data Foundation - Build college stats database - Implement conference adjustments - Create production metrics (YPRR, Dominator, etc.)
Week 3-4: Athletic Profiles - Process combine data - Calculate Speed Score, RAS equivalents - Build athletic comparison system
Week 5-6: Models - Build QB, WR, RB projection models - Implement comparable identification - Create draft range predictions
Week 7-8: Validation - Backtest against historical classes - Calculate accuracy metrics - Document strengths/weaknesses
Deliverables
- Prospect database with comprehensive metrics
- Position-specific evaluation tools
- Draft board generation system
- Validation report with historical accuracy
Evaluation Criteria
| Criterion | Weight | Description |
|---|---|---|
| Data Quality | 20% | Complete, accurate data |
| Model Accuracy | 30% | Historical prediction performance |
| Feature Engineering | 20% | Creative, useful metrics |
| Usability | 15% | Practical output format |
| Documentation | 15% | Methodology transparency |
Extensions
- Add film-based metrics (PFF integration)
- Build trade value calculator
- Create team-specific fit analysis
- Implement machine learning models
Project 4: Advanced EPA Analysis System
Overview
Build a complete Expected Points Added (EPA) analysis system using play-by-play data.
Learning Objectives
- Calculate and interpret EPA from first principles
- Build success rate and efficiency metrics
- Create player and team evaluation systems
- Implement advanced analytics visualizations
Requirements
Core Components: 1. EPA calculator from play-by-play 2. Expected Points model 3. Success rate framework 4. Player EPA attribution 5. Team efficiency dashboards 6. Situation-specific analysis
Technical Requirements: - nflfastR or equivalent data source - Python/R for analysis - Interactive visualizations - Database for calculated metrics
Milestones
Week 1-2: EPA Foundation - Load play-by-play data - Calculate expected points by field position - Implement basic EPA
Week 3-4: Success Rate - Define success thresholds by down/distance - Calculate success rate by team/player - Build explosive play metrics
Week 5-6: Player Analysis - Attribute EPA to players (QB, RB, WR) - Create CPOE calculations - Build receiver efficiency metrics
Week 7-8: Visualization - Create team dashboards - Build player comparison tools - Implement situation analysis
Deliverables
- EPA calculation engine
- Team and player efficiency reports
- Interactive visualization dashboard
- Methodology documentation
Evaluation Criteria
| Criterion | Weight | Description |
|---|---|---|
| Technical Accuracy | 30% | Correct EPA calculations |
| Analysis Depth | 25% | Insights beyond basic metrics |
| Visualization | 20% | Clear, informative charts |
| Usability | 15% | Accessible outputs |
| Documentation | 10% | Clear methodology |
Extensions
- Add win probability model
- Implement clutch metrics
- Create coaching evaluation system
- Build play-call analysis
Project 5: Sports Betting Analysis Tool
Overview
Create a comprehensive betting market analysis tool that tracks lines, identifies value, and evaluates performance.
Learning Objectives
- Work with odds and probability conversions
- Track market movements
- Implement CLV analysis
- Build bankroll management systems
Requirements
Core Components: 1. Odds database and tracking 2. Implied probability calculator 3. Model vs market comparison 4. CLV tracking system 5. Performance analytics 6. Bankroll optimization
Technical Requirements: - Market data integration - Time-series tracking - Statistical analysis tools - Reporting system
Milestones
Week 1-2: Market Data - Build odds tracking database - Implement probability conversions - Track line movements
Week 3-4: Model Comparison - Integrate prediction model - Calculate edge vs market - Implement CLV tracking
Week 5-6: Performance - Build betting log system - Calculate ROI metrics - Analyze by market/bet type
Week 7-8: Optimization - Implement Kelly Criterion - Create bankroll projections - Build reporting dashboard
Deliverables
- Market tracking database
- Edge calculation system
- Performance analytics dashboard
- Bankroll management tool
Evaluation Criteria
| Criterion | Weight | Description |
|---|---|---|
| Data Accuracy | 25% | Correct odds handling |
| Analysis Quality | 25% | Meaningful insights |
| Risk Management | 20% | Sound bankroll approach |
| Performance Tracking | 20% | Complete metrics |
| Documentation | 10% | Clear explanations |
Extensions
- Add prop bet analysis
- Implement arbitrage detection
- Create market efficiency tests
- Build automated alerts
Project 6: Comprehensive Team Analytics Report
Overview
Create a season-long analytical report for a specific NFL team, similar to what a team analytics department might produce.
Learning Objectives
- Apply all analytical frameworks to a single team
- Create executive-level summaries
- Build comprehensive evaluation systems
- Produce professional reports
Requirements
Core Components: 1. Team efficiency profile 2. Personnel analysis 3. Scheme evaluation 4. Schedule/opponent analysis 5. Prediction and simulation 6. Executive summary
Technical Requirements: - Complete data integration - Professional visualization - Written analysis components - Exportable report format
Milestones
Week 1-2: Team Profile - Calculate team power ratings - Build offensive/defensive efficiency analysis - Create personnel valuations
Week 3-4: Deep Analysis - Evaluate coaching decisions - Analyze play-calling tendencies - Study situational performance
Week 5-6: Season Context - Analyze schedule strength - Project remaining games - Simulate playoff scenarios
Week 7-8: Report Creation - Compile executive summary - Create visualizations - Document methodology
Deliverables
- Complete team analytics report (20+ pages)
- Supporting data and visualizations
- Executive summary (2 pages)
- Methodology appendix
Evaluation Criteria
| Criterion | Weight | Description |
|---|---|---|
| Analysis Depth | 30% | Comprehensive coverage |
| Insight Quality | 25% | Actionable findings |
| Presentation | 20% | Professional format |
| Data Integration | 15% | Multiple sources combined |
| Documentation | 10% | Clear methodology |
Extensions
- Add video/film integration
- Create interactive dashboard
- Build real-time updates
- Produce coach-specific recommendations
General Guidelines
Project Selection
Choose a project that: - Aligns with your career interests - Challenges your current skills appropriately - Can be completed within the timeframe - Provides portfolio-worthy output
Timeline
Most projects are designed for 8 weeks at 10-15 hours/week. Adjust milestones if working on a different schedule.
Collaboration
These projects can be: - Individual (recommended for portfolio building) - Team-based (divide responsibilities clearly) - Mentored (with instructor guidance)
Submission
Include: 1. Working code (GitHub repository recommended) 2. Written documentation 3. Sample outputs/visualizations 4. Reflection on methodology
Grading Rubric (If Academic)
| Component | Weight |
|---|---|
| Technical Implementation | 35% |
| Analytical Quality | 30% |
| Documentation | 20% |
| Presentation | 15% |
Resources
Data Sources: - nflfastR/nflfastpy for play-by-play - Pro Football Reference for historical - ESPN API for current stats - FantasyPros for projections
Tools: - Python (pandas, numpy, scipy, scikit-learn) - R (tidyverse, nflfastR) - Visualization (matplotlib, seaborn, plotly) - Database (SQLite, PostgreSQL)
References: - All chapters from this textbook - Further reading sections - Code examples provided
Conclusion
These capstone projects represent the culmination of your NFL analytics education. Each integrates multiple concepts and challenges you to build complete, professional-quality systems. The skills developed here directly transfer to careers in team analytics, fantasy sports, betting markets, and sports media.
Select a project that excites you, plan your approach carefully, and build something you're proud to show. The best analytics work combines technical skill with creative insight—let these projects showcase both.