Bridging Football Analytics with Biological Systems Thinking
A bioinformatician's lens on 22 years of English Premier League evolution
This project treats the English Premier League (2000-2022) as a dynamic biological system where:
- Teams = Organisms in an ecosystem
- Seasons = Generational timepoints
- Points/Goals = Fitness metrics
- Promotion/Relegation = Speciation/Extinction events
- European Qualification = Ecological niches
- Financial Resources = Environmental selection pressure
| Biological Concept | Football Analogue | Implementation |
|---|---|---|
| Phylogenetics | Team evolutionary trees | Neighbor-joining based on performance metrics |
| Population Genetics | League competitive balance | Wright-Fisher modeling of point distributions |
| Metabolic Networks | Goal scoring networks | Network analysis of scoring patterns |
| Microbiome Dynamics | Team promotion/relegation cycles | Diversity indices and community assembly |
| Gene Expression | Form fluctuations | Time-series clustering of performance |
python
from epl_evolution import LeaguePhylogeny
phylogeny = LeaguePhylogeny(seasons_data) tree = phylogeny.build_distance_matrix( metrics=['points', 'goal_difference', 'consistency'] )
- Evolutionary Phylogenetics · Team relationship trees based on 22-season performance · Molecular clock analysis of tactical evolution · Ancestral state reconstruction of playing styles
- Population Dynamics · Fitness landscape visualization · Selective pressure quantification · Genetic drift in mid-table regions
- Ecological Network Analysis · Promotion/relegation flux networks · European competition ecosystem · Resource flow (financial) modeling
- Temporal -Omics · "Transcriptomic" analysis of form fluctuations · "Proteomic" profiling of team characteristics · "Metabolomic" snapshot of league state
🛠 Technical Architecture
Data Flow Pipeline graph TB A[EPL Excel Dataset] --> B[Data Preprocessing] B --> C[Evolutionary Analysis] C --> D[Network Modeling] D --> E[Dashboard Visualization] E --> F[Interactive Insights]
style A fill:#e1f5fe
style F fill:#f1f8e9
Tech Stack
Layer Technology Purpose Analysis Pandas, NumPy, Scikit-learn Data manipulation & ML Evolution Biopython, DendroPY Phylogenetic modeling Networks NetworkX, Graph-tool Relationship mapping Viz Plotly, Matplotlib, Streamlit Interactive dashboard Compute Multiprocessing, Dask Scalable analysis
🧫 Installation & Usage
Quick Start
git clone --recurse-submodules https://github.com/blueprint-fx/epl-excel-dashboard.git
conda env create -f environment.yml conda activate epl-evolution
streamlit run app/main.py For Bioinformatics Colleagues
pip install biopython dendropy 📈 Sample Analyses
- Team Phylogenetics
from epl_bio.phylogeny import PerformanceTree
tree_builder = PerformanceTree() mcc_tree = tree_builder.build_maximum_clade_credibility() tree_builder.visualize_ancestral_states()
- Competitive Landscape
from epl_bio.ecology import FitnessLandscape
landscape = FitnessLandscape() adaptive_peaks = landscape.identify_evolutionary_stable_strategies()
🎓 Research Applications
This project demonstrates how bioinformatics approaches can be applied to complex systems beyond biology:
· Evolutionary Dynamics in competitive environments · Network Theory for relationship mapping · Systems Biology for holistic understanding · Time-Series Analysis of complex systems
📁 Repository Structure
epl-excel-dashboard/ ├── data/ # Raw and processed datasets │ ├── raw/ # Original Excel files │ └── processed/ # Analysis-ready formats ├── src/ │ ├── epl_bio/ # Bioinformatics-inspired modules │ │ ├── phylogeny.py # Evolutionary trees │ │ ├── ecology.py # Ecosystem dynamics │ │ └── networks.py # Relationship networks │ ├── visualization/ # Plotting utilities │ └── utils/ # Data processing ├── apps/ # Streamlit dashboard ├── notebooks/ # Exploratory analysis └── tests/ # Test suite
🤝 Contributing
We welcome contributions from:
· Bioinformaticians interested in complex systems · Data Scientists exploring novel applications · Sports Analysts with domain expertise · Computational Biologists expanding methodologies
See our Contributing Guidelines for details.
📜 License
MIT License - see LICENSE file for details.
🧠 About the Author
🧬 Microbiologist → Bioinformatician | Data Alchemist 🔬 BSc Microbiology @ UniLag | 🎓 MSc Bioinformatics (Canada-bound) 💻 Full-Stack Dev | ☁ Cloud Architect | 📊 Data Storyteller 🚀 Building bridges between wet lab & dry lab
This project exemplifies my philosophy: the same analytical frameworks that decode biological complexity can illuminate patterns in any complex system.
⭐ If this bio-inspired approach to sports analytics intrigues you, please star the repository!