538 nhl playoff odds

bravadoaustralia.com.au › /10/10 › nhl-season-predictions East playoff field ; Carolina Hurricanes *. % ; New York Rangers *. % ; Pittsburgh Penguins. % ; Boston 538 nhl playoff odds *. %. Colorado Avalanche +; Toronto Maple Leafs +; Vegas Golden Knights +; Carolina Hurricanes +; Edmonton Oilers + If you feel. Probability of making the Stanley Cup playoffs.

NHL Post Season Probabilities

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Can you complete the grid. We're Social New York Rangers. Carolina Hurricanes. New York Islanders. Washington Capitals. Pittsburgh Penguins. 538 nhl playoff odds Philadelphia Flyers. New Jersey Devils. Columbus Blue Jackets. Dallas Stars. Winnipeg Jets. Colorado Avalanche. Nashville Predators. Louis Blues.

Minnesota Wild. Arizona Coyotes. Chicago Blackhawks. Florida Panthers. Boston Bruins. Toronto Maple Leafs. Tampa Bay Lightning. Detroit Red Wings. Buffalo Sabres. Ottawa Senators. Montreal Canadiens. Vancouver Canucks. For the NHL, we found that a K-factor of 6 gives us the most accurate adjustments to team ratings after each game, for the purposes of predicting future games.

Our NHL Elo system not only cares if you win, but how you win — a blowout is worth more than eking out a close win. We adjust for this with the margin-of-victory multiplier , which accounts for diminishing returns:. Since we include scoring margins within our Elo ratings, we also need to adjust this multiplier to account for a pesky side effect known as autocorrelation.

Generally speaking, autocorrelation is the tendency of a time series to be correlated with its past and future values. In our NHL Elo system, that means autocorrelation wants to inflate the ratings of already good teams and suppress the ratings of not-so-great teams. Since Elo gives more credit to bigger wins, and favorites tend to run up the score in their wins more often than underdogs even in a low-scoring sport like hockey , top-rated teams could see their ratings rise disproportionately without an adjustment.

So we multiply the margin-of-victory multiplier by the following autocorrelation adjustment formula, which curbs Elo gains for teams that were bigger favorites going into the game:. Because Elo is constantly adjusting itself to hone in on the true strength of each squad, teams should also gain more points for winning a game they were expected to lose — games in which the model was wrong about the relative strength of each team — and drop more points for losing a game the model thought they should have won.

We account for this with the pregame-favorite multiplier :. We experimented with other Elo adjustments specific to the NHL. The beta version of our Elo model, for instance, accounted for the circumstances of the result i. This process is repeated for every game in a season, through the last game of the Stanley Cup Final. For our NHL forecast, teams retain 70 percent of their rating from the end of the previous season and are reverted 30 percent toward Using reverted end-of-season ratings makes sense for teams that played in the NHL in the previous season, but where does that leave expansion teams?

The era of expansion teams needing a handful of years to become competitive was over — it was this dynamic that allowed the Vegas Golden Knights to reach the Stanley Cup Final in their first year of existence. But if expansion teams in the salary cap era should have a higher rating than the previous expansion team benchmark of , exactly how much better should their rating be?

This question is especially pertinent for the Seattle Kraken, who happened to begin play the same season we rolled out our Elo model. To answer this question, we looked at things through a few different lenses, taking the average Elo between:. We then averaged these three approaches together, giving us a rating of for salary-cap era expansion teams — still below average, but much more competitive than We use this amended rating as the inaugural preseason Elo for expansion teams established after the season, like the Golden Knights in and the Kraken in We implement Monte Carlo simulations for this, using randomness to simulate every remaining game in the regular season and playoffs thousands of times, keeping tabs on what happens in each simulation.

We then use a logistic regression to determine the probability that this simulated game went into overtime, using the following formula:. Once we have two simulated scores one for each team , we check those against the results we just generated. We can use our newly generated scores as the goal totals for this game simulation, so long as two conditions are true:.

We then construct a simulation of the remainder of the regular season and playoffs, built on real results from already completed games and these simulated game results. In each season simulation, we keep tabs on how many points each team accrues, 8 who makes the playoffs, who wins each round of the playoffs and who wins the Stanley Cup. We then run this full season simulation thousands of times, averaging results across all simulations for each team.

So, for example, when you see that a team has a 37 percent chance of making the playoffs in the forecast interactive, that means that team made the playoffs in 37 percent of the simulations we ran, each of which takes its current record and remaining schedule into account. After every NHL game is played, we store the results of that game, rerun our thousands of simulations and update our interactive with the latest figures.