The Wild bowed out early yet again, the Flames looked every bit the team with a <1% chance of winning the cup, and the Model went 7 for 8 in first round series predictions. The big (and only) miss came on the heels of the Chicago Blackhawks, who apparently decided they would rather be golfing than playing hockey. Despite the addition of Bruce Boudreau, the superstar-less Minnesota Wild proved once again that they lack a star goal scorer needed to make a deep run in the playoffs, such as a Vladimir Tarasenko. While Round 1 presented zero game sevens, the Model predicts we might see a nail biter or two in the second round.
Using the average probability from our NHL Playoff Models, we have run a large-scale simulation for the 2017 NHL Playoffs. This allows us to estimate how often each team makes the semifinals, conference championship, Stanley Cup Finals, and ultimately win the Stanley Cup. In order to do this, we took each series and generated a random number between 0 and 1. If the random number is less than our predicted probability of the home team winning, then we advance the home team as the winner of that series (and vice versa if the random number is greater than our probability). For example, Model 1 has a probability of 0.74 that the Chicago Blackhawks will beat the Nashville Predators. If the random number is 0.95, we would pick the Predators to advance, and if the random number is 0.55, we would pick the Blackhawks to advance. This methodology was applied to the entire NHL Playoff bracket, generating 10,000 brackets and 10,000 Stanley Cup champions.
Our NHL playoff models use team-level and individual player statistics to predict the probability that a team will win a given series. We build our bracket by advancing the team with the higher win probability for each series. For example, this year, our models give the Chicago Blackhawks a 74% chance of winning their first round series against the Nashville Predators, so we advanced the Blackhawks to the following round in our bracket. While we utilize multiple models to generate predictions for each series, the bracket below represents the average probability of all models for the 2017 NHL Playoffs. For more information on our NHL Playoff Models, see our methodology article here.
The NHL Playoff Model uses team-level and individual player statistics to predict the probability that a given team will win a series (rather than predicting each game individually). Theoretically, one would think predicting a winner of a series would be easier than predicting the probability a team wins a single game, but many things can happen throughout the course of a series, most significantly, injuries that make predicting the series winner difficult.