This project uses a Bayesian statistical model to simulate and predict the outcomes of the NHL playoffs. The methodology is designed to provide robust, probabilistic estimates for both series and individual games, leveraging the full season’s game data and advanced simulation techniques.
The model incorporates several key data sources:
The predictions are broken down into several key components:
Calculated using Monte Carlo simulations of the entire series, factoring in home-ice advantage shifts.
Individual game win probabilities that account for home-ice advantage, rest days, and prior
Probability distribution of series ending in 4, 5, 6, or 7 games, derived from the game-by-game prediction model and historical patterns.
Detailed forecasts of specific series scorelines, like the probability of a team winning 4-2 or 4-3.
The model is continuously validated against actual outcomes and updated daily throughout the playoffs to incorporate:
All probabilities should be interpreted as the likelihood of an outcome based on the model's current assessment. Even high-probability outcomes are not certainties, and low-probability events do occur in sports. The model provides a data-driven framework for understanding the relative likelihood of various scenarios.
The model employs a combined approach using:
The model is compared to an Elo baseline for improvement.