Monte Carlo simulation sounds technical, but the idea is simple: if you do not know exactly how future games will turn out, you roll the dice—many times—and count how often each team ends up where you care about (for example, “top eight in the conference”).

The basic loop. Start from today’s standings and remaining schedule. For each simulated season, pick a winner for every unplayed game using a probability model. Update points after each simulated result. When the schedule is complete, record which teams finished in a playoff position. Repeat ten thousand (or more) times. A team’s playoff probability is the fraction of simulations where they qualified.

Why use probabilities at all? Deterministic standings show what has happened. Monte Carlo answers a different question: given today’s information, how often would random-but-plausible outcomes still land this team in the playoffs? That is why a team can look “safe” in the standings but still show 92% rather than 100%—strange losing streaks and rivals heating up still exist in the tail risk.

Model choices matter. The BlueLine uses Pythagorean expectation built from goals for and against to approximate team strength, then simulates games with that strength profile. That is transparent and reproducible, but no model knows about tomorrow’s starting goalie, travel, or trades. Treat percentages as informed estimates, not prophecies.

Reading the number. A 72% playoff chance is not a guarantee; it means “in roughly 7,200 out of 10,000 synthetic futures, this team finished top eight.” Compare that to magic numbers, which encode clinch math once outcomes are certain.

Takeaway. Monte Carlo turns uncertainty into a single, comparable percentage across teams. Pair it with methodology you trust—and remember every model has blind spots.

The BlueLine is an independent NHL analytics site and is not affiliated with the National Hockey League.