Hockey Stats 101

Hockey stats really aren't that difficult — once you break things down.

One of the reasons I rarely use the term "advanced stats" is that there's really nothing terribly advanced about what hockey statisticians do. Everything starts with a simple counting statistic, then we account for opportunity, and then we place the data in context. As I hope to demonstrate, this is a simple three-step process that is easy to grasp, even for those without a mathematical background.

Hockey stats are at their best when they serve as a sober second thought and help point out things that we missed. After all, it's easy for our eyes to deceive us. We get so swept up in the emotion of the moment — and we all have our biases about teams and players — that we sometimes don't really see what's happening on the ice. Even when we see the game clearly and objectively, we rarely remember the important details the next day.

However, without a proper understanding of how to use them, stats can be just as deceiving as the perspective of the most emotional and biased fan. Just as in any other field, we can only achieve a clear interpretation of hockey statistics by taking clearly defined and accurate measurements, adjusting those measurements for opportunity, and placing them in context. Even if you choose to skip this chapter, understanding that means you have understood the essence of hockey analytics.

There are so many simple examples of that clear interpretation, that there's no need to look at any stats with fancy names, like Corsi, Fenwick, and PDO. In this chapter, we stick to simple stats like goals and wins. These are excellent base statistics. Since everyone understands and uses them, they have a clear and universally accepted definition and their importance is obvious.

Following those base stats, we'll explore how to take opportunity into account, by calculating stats like goals per 60 minutes and winning percentage. The third and final step is to place that information in context by using charts, rankings, and comparisons to the league average. Finally, for the particularly ambitious, we'll close by introducing goals created, which is a compound statistic meant to replace points.

Team Stats

There is no better place to start than with wins. It is the entire point of hockey and a concept that everybody understands. It's the only stat that truly matters, and everything in the world of hockey analytics either boils down to wins or is utterly meaningless.

So let's start with wins. Better yet, let's start with 36 wins. What does 36 wins mean, other than a team outscored its opponents in 36 separate games? Quite frankly, it doesn't mean much.

Wins may be the ultimate statistic, but they mean nothing without opportunity. For example, if we're studying the Chicago Blackhawks, who won 36 games in the 2012–13 season, which was shortened to 48 games because of a lockout, then 36 wins is an incredible achievement. It means that the Blackhawks were one of the most dominant teams in NHL history. However, if we're talking about the 36 wins by the Vancouver Canucks the following season, which was over an 82-game schedule, then it doesn't mean quite so much.

That's why the only truly important statistical adjustment accounts for opportunity. In this case, the number of games a team plays represents the number of opportunities that it had to win. Chicago had 48 opportunities, and Vancouver had 82. Dividing 36 wins by the number of games produces each team's winning percentage. For Chicago it's 0.750, meaning Chicago earned 0.750 wins per game. Given that you can either earn 0 wins or 1 win in a game, that also means that Chicago had a 75.0% chance of winning any given game. For Vancouver, it was 43.9%. That's a big difference.

Besides percentages, the other way to account for opportunity is to calculate the rate at which teams accumulated wins. For example, Chicago had a rate of one win every 1.33 games, which is 48 games divided by 36 wins, and Vancouver had a rate of one win in every 2.28 games.

You will generally never see a team's winning rate presented in those terms, since it's not easy for us to place such numbers in context. When presented with no other information, it's hard to accurately figure out exactly how good 1 win in every 1.33 games actually is. After all, teams play one game or two games but never 1.33 games. (Well, maybe the old Toronto Maple Leafs would sometimes play only 0.33 games in a night, but things have changed.)

As fans, we think in terms of an 82-game schedule, so it's better to present a team's winning rate in those terms: Chicago had a rate of 61.5 wins per 82 games, while Vancouver obviously had a rate of 36 wins in 82 games. That statistic is much easier to place in context, and it helps illustrate the importance of finding the right terms in which to express a rate statistic.

We now need to take a step back because I actually wrote a little bit of a fib at the top of this section. Wins may be the ultimate statistic in the playoffs, but they don't actually hold that distinction in the regular season, where points are king. As it turns out, teams can earn points not only from winning but also from ties (which existed prior to 2005–06) and even for certain types of losses (since 1999–00).

That means that a team can make the playoffs despite winning fewer games than another. In fact, it happens all the time. Florida won 42 games in 2016–17, but Toronto, who had 40 wins, made the playoffs. So wins are not the ultimate statistic. Sorry about that.

This is why the NHL doesn't actually use winning percentage anymore, but rather points percentage. Points percentage works the same way as winning percentage, by dividing a team's points by their opportunity to earn points. Since a team can earn up to two points per game, their actual points are divided by the maximum number of points they had the opportunity to earn, which is the number of games they played multiplied by two.

Continuing the example, Chicago earned 77 points in 48 games, and 77 divided by 96 (which is two points per game over 48 games) works out to a points percentage of 0.802. That means Chicago earned an average of 0.802 multiplied by the maximum two points per game, which equals 1.604 total points per game. However, since teams can earn zero, one, or two points per game, it no longer means that they have a 80.2% chance of earning a point in any given game.

Rates are also calculated the same way for points as they were for wins. In this case, Chicago earned points at the rate of 131.5 points per 82 games. That's a much more meaningful number than 0.802.

These are admittedly very simple concepts, but the principles of taking opportunity into account and calculating rates gets trickier when we apply them to other statistics and other situations, so it's...