I developed a probabilistic model to forecast the 2025 NCAA Football Playoff outcomes using Monte Carlo methods and multi-source data aggregation. Technical Implementation:
Integrated datasets from ESPN FPI, 247Sports, PFF, and EA Sports player ratings Engineered 11 weighted features including quarterback experience metrics, composite talent rankings, and historical coaching performance Executed 3,000+ game simulations to generate confidence intervals for playoff scenarios Built in Python using NumPy for numerical computing and Pandas for data processing
Sample Output: The simulation generates:
Probability distributions for each team's final ranking Heat maps showing likelihood of teams finishing in top 10/25 Comparative analysis of different ranking methodologies Statistical measures of ranking confidence
Key Observations: This season presents an anomaly in the dataset. Traditional models weight quarterback experience heavily, but 2025 features statistically significant representation of underclassmen starters at top programs: Stockton (Georgia), Sayin (Ohio State), Simpson (Alabama). This introduces higher variance in outcome predictions. Model Output - Top 4 Playoff Teams:
Texas - Strong offensive metrics, home-field advantage in Big 12 Penn State - Consistent coaching performance, above-average QB development trajectory Clemson - Veteran QB experience, improved NIL infrastructure Ohio State - Defensive efficiency ratings, institutional depth
Future Enhancements:
Incorporate real-time game data through API integration Add machine learning models to improve performance predictions Expand analysis to include playoff probability calculations Build interactive dashboard for visualization Include additional metrics like recruiting rankings and coaching statistics
Technical Skills Demonstrated: Statistical modeling and simulation, Python programming and data analysis, probability theory and stochastic processes, data visualization and communication, sports analytics methodology. Methodology Note: While quantitative analysis provides baseline predictions, the model acknowledges limitations in capturing qualitative factors like team chemistry and in-game adjustments. Coaching system discipline remains a significant but difficult-to-quantify variable.