NFL Opponent Scouting Dashboard

A comprehensive analytics platform designed for football coaches and decision-makers to gain actionable insights into opponent performance, game preparation, and strategic planning using advanced NFL statistics.


Dashboard found here: https://jakeblumengarten.shinyapps.io/NFL_Scouting_Dashboard/

🎯 Project Overview

This interactive dashboard provides real-time opponent analysis for the 2025 NFL season, aggregating play-by-play data into actionable scouting reports. Designed for coaching staffs and front office personnel, it delivers:

• Team Overview: Offensive identity, efficiency metrics, and league-wide rankings
• Deep Dive: Situational Analysis: QB performance under pressure, 4th down tendencies, red zone efficiency
• Key Players: Individual player breakdowns including passing splits, receiving targets, and run game schemes
• Visual Analytics: Interactive charts, heat-mapped tables, and color-coded performance indicators

🏈 Use Cases

• Game Preparation: Identify opponent tendencies (pass rate, 4th down aggression, red zone strategy)
• Personnel Evaluation: Track key player performance by down, distance, and field position
• Strategic Planning: Exploit weaknesses in opponent pass protection, run defense, or situational play-calling
• Post-Game Analysis: Compare predicted vs. actual performance metrics

🚀 Key Features

📊 Team Overview Tab

• Offensive Identity:
  • Overall pass rate vs. run rate (pie charts)
  • Red zone pass percentage
  • Total 4th down go rate
• Efficiency Metrics:
  • EPA/Play (Expected Points Added per play)
  • Pass yards per game & Rush yards per game
  • League rankings for all metrics
• Full Team Stat Profile:
  • Success rate, CPOE (Completion Percentage Over Expected), turnovers
  • Explosive pass rate, third down conversion %
  • Division standings

🔍 Deep Dive: Situational Tab

• Quarterback Under Pressure:
  • Completion %, air yards, YPA (Yards Per Attempt)
  • CPOE, success rate, interceptions, touchdowns
• 4th Down Conversions by Distance:
  • Attempts, conversions, and success percentage for 1-3 yds, 4-6 yds, 7+ yds
• Red Zone Efficiency: Scoring rate inside the 20-yard line
• Performance by Down: Passing splits (attempts & completion %) for 1st, 2nd, 3rd, 4th downs

👥 Key Players Tab

• Passing Splits by Down: QB performance breakdown (D1, D2, D3, D4)
• Receiving Targets: Target distribution across downs for WRs/TEs
• Run Game Breakdown:
  • Gap Scheme: Yards by Inside Guard, Off Tackle, Outside End
  • RB Receiving: Receptions, receiving yards, YPR (Yards Per Reception)

📚 Glossary Tab

• Metric Definitions: EPA, Dakota score, CPOE, gap concepts, and more
• Color Coding Legend:
  • 🟢 Green: Top 20% (Elite 1-6)
  • 🟡 Yellow: Mid Tier (7-19)
  • 🔴 Red: Bottom 40% (Weak 20-32)

📁 Repository Structure

team_reporting/
├── Data/                              # Processed datasets
│   ├── offense_stats.csv             # Team offensive metrics
│   ├── defense_stats.csv             # Team defensive metrics
│   ├── qb_stats.csv                  # Quarterback statistics
│   ├── rb_stats.csv                  # Running back statistics
│   ├── wr_stats.csv                  # Wide receiver statistics
│   ├── fourth_down_conversions.csv   # 4th down decision data
│   ├── nfl_25_schedule.csv           # 2025 NFL schedule
│   └── cfb_25_schedule.csv           # 2025 CFB schedule (future expansion)
├── Player_tables/                     # Player-specific HTML tables (generated)
├── Team_tables/                       # Team-specific HTML tables (generated)
├── renv/                              # R environment management
├── 01_Data.R                          # Data collection & preprocessing
├── app.R                              # Shiny dashboard application
├── renv.lock                          # Package version lock file
├── team_reporting.Rproj               # RStudio project file
└── README.md                          # Project documentation

🛠️ Technology Stack

R (100%)

• nflfastR - NFL play-by-play data API (2025 season)
• nflplotR - Team logos and color schemes
• cfbfastR - College football data (future expansion)
• shiny - Interactive web application framework
• tidyverse (dplyr, tidyr, readr) - Data manipulation
• DT - Interactive data tables
• renv - Reproducible R environment management

Data Pipeline

1. Data Collection: nflfastR:: load_pbp() pulls real-time NFL play-by-play data
2. Feature Engineering: Aggregation of 50+ variables (EPA, CPOE, success rate, etc.)
3. Team Matching: Robust team name standardization (LAR→LA, SD→LAC, etc.)
4. Interactive UI: Dropdown team selection, tabbed navigation, color-coded rankings

🚀 Getting Started

Prerequisites

R 4.0+ and RStudio (recommended)

# Install required packages
install. packages(c(
  "shiny",
  "nflfastR",
  "nflplotR",
  "cfbfastR",
  "tidyverse",
  "DT",
  "renv"
))

Installation

1. Clone the repository:

   git clone https://github.com/JakeBlumengarten/team_reporting.git
   cd team_reporting

2. Restore R environment (optional but recommended):

   renv::restore()

3. Run data collection (pulls latest 2025 NFL data):

   source("01_Data.R")

   ⚠️ Note: This script downloads ~500MB of play-by-play data. Runtime: 3-5 minutes.

4. Launch the Shiny app:

   shiny::runApp("app.R")

   The dashboard will open in your browser at http://127.0.0.1:5865/ (or similar port).

Quick Start (Pre-loaded Data)

If you want to skip data collection and use existing CSV files:

# Just launch the app (uses Data/*. csv files)
shiny::runApp("app.R")

📈 Methodology

Data Collection Pipeline

1. Play-by-Play Scraping:

   • nflfastR::load_pbp(2025) retrieves every play from the 2025 NFL season
   • Data includes: down, distance, field position, play type, yards gained, EPA, CPOE, pressure data, etc.

2. Team-Level Aggregation:

   • Group by posteam (possession team) and aggregate by game/season
   • Calculate per-game averages, success rates, and efficiency metrics

3. Player-Level Aggregation:

   • Extract QB, RB, and WR statistics from play-level data
   • Filter for statistical significance (e.g., QBs with 200+ attempts)

4. Ranking & Color Coding:

   • Percentile-based rankings (Top 20% = Green, etc.)
   • Contextual coloring for easy pattern recognition

Key Metrics Explained

Metric	Description	Why It Matters
EPA/Play	Expected Points Added per play	Best overall efficiency measure (accounts for field position, down, distance)
CPOE	Completion % Over Expected	Measures QB accuracy beyond difficulty of throws
Dakota	Composite score (EPA + CPOE)	Holistic QB performance metric
Success Rate	% of plays gaining 40%+ of yards needed (1st/2nd), 100% (3rd/4th)	Consistency indicator
Explosive Pass Rate	% of passes gaining 15+ yards	Big-play capability
4th Down Go Rate	% of 4th downs where team goes for it (not punt/FG)	Aggressiveness indicator
Gap Scheme	Yards by run direction (Inside/Tackle/Outside)	Run game tendencies

📊 Sample Insights

Example: Scouting the Arizona Cardinals (ARI)

Offensive Identity (from Image 1):

• Pass Rate: 52% (balanced offense)
• Red Zone Pass: 42% (slightly run-heavy in RZ)
• 4th Down Go Rate: 15% (conservative)

Efficiency Metrics:

• EPA/Play: -0.021 (Rank #25) ⚠️ Below average
• Pass Yds/Gm: 256.1 (Rank #5) ✅ Elite passing volume
• Rush Yds/Gm: 93.1 (Rank #31) ❌ Weak run game

Key Takeaway: Arizona is pass-dependent with a struggling run game. Expect high pass volume, but inefficient offense overall.

QB Under Pressure (from Image 2):

• C. Williams: 25. 3% completion, 6.7 YPA, 0.139% CPOE when hit
• 4th Down: 48% success on 1-3 yds, 43% on 4-6 yds, 100% on 7+ yds (small sample)

Defensive Strategy: Pressure the QB heavily. Williams struggles under duress.

⚠️ Limitations

• Data Availability: Only includes 2025 NFL season data (no historical trends yet)
• Sample Size: Early-season data may have small sample sizes for situational stats
• Team Name Mapping: Relocation teams (LAR, LAC, LV) require manual mapping
• CFB Integration: College football data included in repo but not yet integrated into dashboard

🔮 Future Enhancements

• Historical Trends: Multi-season comparison (2020-2025)
• Defensive Dashboard: Mirror offensive metrics for defensive performance
• Predictive Modeling: Win probability, score prediction based on team stats
• Play-Calling Sequencing: Analyze play-calling patterns (e.g., run after incompletion)
• Injury Impact: Integrate injury data to adjust projections
• CFB Integration: Expand to college football scouting reports
• Export Reports: Generate PDF scouting reports for offline use

📚 Data Sources

• nflfastR: Official NFL play-by-play data (2025 season)
  • Gilani, S., Easwaran, A., Lee, J., & Hess, E. (2021). *nflfastR: Functions to Efficiently Access NFL Play by Play Data. * https://www.nflfastr.com/
• nflplotR: Team logos, color schemes, and wordmarks
• cfbfastR: College football data (SportsDataverse)

👤 Author

Jake Blumengarten
📧 blumengartenjake@gmail.com
🔗 LinkedIn
💻 GitHub

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This project showcases data engineering, sports analytics, and interactive dashboard development for football operations roles. All code and analysis are original work.

📄 License

This project is available for educational and portfolio purposes. Data sourced from publicly available NFL APIs (nflfastR).

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⭐ **If you find this dashboard useful for your scouting process, please consider starring the repository! **

🤝 Contributing

Contributions are welcome! If you have ideas for new metrics, visualizations, or features:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-metric)
3. Commit your changes (git commit -m 'Add new metric: X')
4. Push to the branch (git push origin feature/new-metric)
5. Open a Pull Request

📞 Support

For questions, bug reports, or feature requests:

• Open an issue on GitHub
• Email: blumengartenjake@gmail.com

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Built for football coaches and analysts