Projecting 2015-16 NHL Standings by Identifying the Best Predictive Statistics

Written by Bob Sullivan at Sporting Charts

“If you can look into the seeds of time and say which grain will grow and which will not, speak then to me, who neither beg nor fear your favors nor your hate.” ~ Lord Banquo from Macbeth

Those who follow the NHL are fanatical and devoted. As every new season approaches, each and every fan gathers with a buddy over a beverage and discusses the outlook for their franchise of choice. Each of us share one thing in common with Banquo, Macbeth’s brave and ambitious right-hand man in Shakespeare’s play. You see, Banquo desperately wants to know what the future holds and urges some otherworldly prognosticators to shed light on who will become king, or should I say, sip from the Cup.

I never considered myself otherworldly, or worldly for that matter, but I do appear in front of you now with a forecast of events to come. Last season, I investigated which fancy stat possession statistic would be the best predictor of future results. This season, I have taken my prognostication to the next level. I have used both simple (i.e., one variable) and multiple linear regression analysis to determine which combination of statistics most enhances the clarity of the crystal ball.

Macbeth and Banquo were as eager as you are to have the future foretold. But Banquo was skeptical of the forecast. Macbeth, on the other hand, seemed to find the outlook quite reasonable. I have presented my analysis and summarized results below. Are you like Macbeth and ready to trust the numbers? Or are you another Banquo?

IDENTIFYING INDEPENDENT VARIABLES

The plan is this: I want to find one, or several, team statistics from the prior season (i.e., the independent variables) which act as strong predictors and can be used alone, or in combination, to reduce the expected variability in my forecast of each team’s current season point total (i.e., the dependent variable).

I started with the following groups of team statistics:

Each statistic can be evaluated under different game situations. For example, assessing puck possession of a player while he’s on the powerplay or shorthanded isn’t necessarily relevant. A team with the man advantage would naturally possess the puck more, you would hope, in that situation. Consequently, I started my analysis researching 5-on-5 situations only. This could be at any point in time of the game; however, I also narrowed my data review to critical game situations such as “Within 1” and “Close”. A “Close” situation is when the game is within one goal in the first two periods or tied in the final period.

The question now becomes: Which of these metrics can best predict future outcomes? Before fine tuning the model, I evaluated each of these statistics on their own to determine which had the strongest correlation to a team’s point total the following season.

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