With every passing season, statistical analysis is playing an ever-increasing role in how hockey is played and covered. Knowledge of the underlying numbers can help fans stretch their enjoyment of the game. Acting as an invaluable supplement to traditional analysis, Stat Shot: A Fan’s Guide to Hockey Analytics can be used to test the validity of conventional wisdom and to gain insight into what teams are doing behind the scenes or maybe what they should be doing!
Inspired by Bill James’s Baseball Abstract, Rob Vollman has written a timeless reference of the mainstream applications and limitations of hockey analytics. With over 300 pages of fresh analysis, it includes a guide to the basics, how to place stats into context, how to translate data from one league to another, the most comprehensive glossary of hockey statistics, and more. Whether A Fan’s Guide to Hockey Analytics is used as a primer for today’s new statistics, as a reference for leading edge research and hard-to-find statistical data, or read for its passionate and engaging storytelling, it belongs on every serious fan’s bookshelf. A Fan’s Guide to Hockey Analytics makes advanced stats simple, practical, and fun.
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About the Author
A former member of the Professional Hockey Writers Association, Rob Vollman was first published in the Fall 2001 issue of the Hockey Research Journal. He has since co-authored all six Hockey Prospectus books, two McKeen’s magazines, and has authored four books in his own Bill James– inspired Hockey Abstract series, including the highly popular 2016 book, Stat Shot. Rob is one of the field’s most trusted and entertaining voices, and he has helped bring what was once a niche hobby into the mainstream. He lives in Calgary, AB.
Read an Excerpt
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.
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 important to get a good grasp of them up front.
Before moving on to how to put these numbers in context, let's step back to the core of hockey analytics: counting statistics. To explore this, we'll look at goals, which are the source of wins. As previously established, teams win games by scoring more goals than their opponent. That's why there is no closer relationship between any two hockey statistics than the one between goals and wins.
Goals are the best example of a counting statistic, which is exactly what it sounds like — it's anything that can be counted. If you're watching a hockey game and can point at an event and say "Hey look, there's one and there's another one and there's another," then it's a counting statistic. Needless to say, there are an almost endless number of counting statistics at your disposal in any given game, like goals, shots, hits, penalties, passes, tears shed by the Leafs fan next to you, and so on.
You can define counting statistics in many different ways, but the common thread is that they are events that either occurred completely or did not occur at all. For example, a goal occurs when the puck completely crosses the goal line before regulation time expires in the judgment of the goal judge. A team gets absolutely no credit whatsoever if it gets the puck 99.9% across the line or if the puck crosses one microsecond after time expires.
Counting statistics should also have a clear and complete definition. Continuing with goals, a goal must occur without the attacking team committing an infraction, like goalie interference, and in a legal fashion, such as without a high stick or a kicking motion, again in the judgment of the officials.
Unfortunately, you may find that many common statistics lack a clear and complete definition and are therefore subject to the whims and opinions of the scorekeeper. Be wary of any statistic based on these subjective counting stats, like hits or takeaways.
As previously discussed, to account for opportunity, counting statistics can be converted into rates. In this case, a team's goal-scoring rate can be calculated on the same 82-game basis as wins and points, but placing it in per-game terms is another useful context for the average fan. For example, Chicago scored 155 goals in 48 games, which is 3.23 goals scored per game or 264.8 over an 82-game season. These numbers can be more easily understood by the average fan, with no pencil or paper required.
To get technical, some statisticians like to calculate goals per 60 minutes instead of goals per game, since not every game is of the same length. Some games have up to an additional five minutes of overtime, while others do not. But we'll have plenty of time to get into the more pedantic details later in this book. For now, let's keep exploring the relationship between goals and wins.
Consider the following chart, which was taken from Stat Shot. Each dot represents a single team's regular season. On the horizontal axis is the team's goal differential, and on the vertical axis is the number of points they earned in the standings. As you can see, there's a pretty direct relationship between goals and points, and most teams don't stray very far from the trend line.
Team Goal Differential vs Points, 2007–08 to 2013–14
Goal differential is simply the number of goals a team scored minus those it allowed. For example, Vancouver scored 196 goals in 2013–14 and allowed 223 goals, for a goal differential of –27. Having earned 83 points in the standings, the Canucks are the dot located almost exactly on the line, at -27 on the horizontal axis and 83 on the vertical.
In the larger sense, differentials are a difference within a single counting statistic. For instance, you can calculate a team's penalty differential by subtracting the penalties they took from those of their opponents. However, it doesn't make statistical sense to create a differential from two different counting statistics, like subtracting passes from shots to create a shot-pass differential. That sort of comparison usually involves creating a compound statistic, which we're not exploring just yet.
Differentials make the most sense when each side has the same opportunity to generate the counting statistic in question. That's certainly the case with goals, since both teams are on the ice at all times.
Getting back to the chart, if teams win games by outscoring their opponents, then why aren't all the dots exactly on the trend line? Why would they vary at all?
Since winning isn't the only way to get points, teams that earned a lot of points for losing games in overtime or the shootout are above the line. Plus, teams that won a lot of close, one-goal games but lost a few massive blowouts are also above the line. That type of data tends to even out over the long run, but not always over 82 games.
There is another limitation with differentials that can explain some of the variance on the chart. Differentials indicate how many more counting events occurred over a certain period of time, but they offer absolutely no indication of scale. For example, a goal differential of +15 over an entire season is really nothing special, but it's incredible over a single game.
For a real-life example, consider the 2009–10 Vancouver Canucks, who had a goal differential of +50, and the 2011–12 St. Louis Blues, who had a similar goal differential of +45 in the same number of games. On the surface, the Canucks appear to have been slightly more effective at outscoring their opponents and ought to have finished with one or two more points. However, if we dig deeper into the numbers that make up the differential, it becomes clear that St. Louis actually had a larger share of all of the goal-scoring.
In raw terms, the Blues outscored their opponents 210-165, while Vancouver outscored their opponents 272-222. Placed in terms of a goal percentage, St. Louis was responsible for 56.0% of all the goals in its games, which is 210 divided by the sum of 210 and 165, while Vancouver was responsible for 55.1%. That may be a subtle difference, but it could be one of the reasons why St. Louis actually had more points than the Canucks, 109 to 103.
While counting statistics are sometimes presented as differentials in mainstream coverage, they are almost never offered in terms of a percentage. Unless you're a long-time reader, you have probably never seen information presented this way. In essence, this is the point where we are finally starting to pierce the skin of the world of hockey analytics and bite into the delicious fruit inside.
As we enter this world, the greatest challenge is how to place new and unfamiliar statistics in context. Most seasoned hockey fans have a good sense of what it means for a team to earn 109 points or to average 3.23 goals per game, but many of the metrics in this book will be new to most fans. Be honest, do you have any idea how good a goal percentage of 56.0% actually is? I mean, you know that it's above an average 50%, but is that really far above average or just a little?
That's why it's absolutely critical to find ways to place these new stats in context. The simplest way is to mention where a result ranks. In this case, the Blues ranked third (out of 30 teams) in 2011–12. Using ranks is a very effective way of placing a new or unfamiliar stat in context, especially when it's expressed visually, as follows. At a glance, you get a real sense of how good 56.0% is, since it's now relative to the rest of the league. (See page 122 to 148 for more information about how visualizations can help place individual stats in context.)
Another method of placing stats in context is to express them relative to the league average or some other point. In this case, the Blues' goal percentage of 56.0% would be +6.0% relative to the league average of 50.0%. This is commonly referred to as a relative statistic. That is, the Blue's relative goal percentage is +6.0%.
To take a trickier example, Chicago's average of 3.23 goals per game in 2012–13 is greater than the league average of 2.73 by 0.50 goals per game in absolute terms, which is 18.3%. In this case, Chicago's relative scoring rate is +0.50 goals per game, and its relative scoring percentage is +18.3%.
This technique makes it possible to compare Chicago to great teams in other leagues or from other eras, like the dynastic Edmonton Oilers, who scored 5.58 goals per game in 1983–84, the season in which they won their first Stanley Cup. The league average that season is 3.95 goals per game, meaning that Edmonton's relative scoring rate is +1.63 goals per game, and their relative scoring percentage is +41.1%.
As great as Chicago was in 2012–13, the 1983–84 Oilers were a better team, offensively. This kind of comparison is only possible when statistics are placed in context.
That covers a lot of the basics for team-based stats, and it's everything you'll need to know to enjoy the rest of this book: start with a clearly defined counting statistic, account for opportunity by calculating a rate, and then place the information in context using charts or techniques like ranks, percentages, or commonly understood formats. Now let's apply this knowledge to individual players.
Individual Player Stats
Since the earliest days of the sport, goals have been the most common statistic used to evaluate individual hockey players. Given that the meaningfulness of goals is obvious, it's a great place to start.
Every goal is awarded to the single attacking player who was deemed to be ultimately responsible for the goal. Typically, that is the player who either took the shot or last deflected it, intentionally or otherwise. In almost every case, goals are assigned to the attacking player who last touched the puck before it crossed the goal line, which also helps cover rare situations, like when a goal is accidentally scored by a defensive player or the goalie.
While the process of counting an individual player's goals scored is relatively simple and, at the team level, accounting for opportunity and placing those results in context is straightforward, it poses more of a problem at the individual level.(Continues…)
Excerpted from "Stat Shot A Fan's Guide To Hockey Analytics"
Copyright © 2018 Rob Vollman.
Excerpted by permission of ECW PRESS.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.
Table of Contents
Hockey Stats 101 11
Team Stats 12
Individual Player Stats 20
Goals Created 28
Closing Thoughts 32
Who Is The Most Valuable Goalie? 34
Projecting the Next Season 38
Adjusting for Age 45
When Do Careers End? 47
Converting to Goals and Dollars 49
Closing Thoughts 53
How Can We Compare A Player's Stats Between Leagues? 55
Ontario Hockey League 63
Western Hockey League 69
Quebec Major Junior Hockey League 74
Western Collegiate Hockey Association 77
National Collegiate Hockey Conference 78
Big Ten 80
Central Collegiate Hockey Association 81
ECAC Hockey 83
Hockey East 84
Kontinental Hockey League 87
American Hockey League 94
Swedish Hockey League 100
Finland SM-liiga 104
Switzerland NLA 107
Other Leagues 110
Closing Thoughts 112
Can A Goalie's Stats Be Compared Between Leagues? 114
Closing Thoughts 121
How Can Stats Be Placed In Context? 122
Simplified Player Usage Charts 125
The 2010-11 Vancouver Canucks 128
The 2000-01 Colorado Avalanche 131
The 2014-15 Frolunda HC 134
The 1976-77 Montreal Canadiens 136
New Developments in Visualizations 139
Closing Thoughts 148
Who Is The Best Women's Hockey Player? 149
The Subjective View 151
The World Championship 153
Translating Data from Other Leagues 159
North American Professional Leagues 160
U Sports 162
U.S. College Hockey 169
European Leagues 175
Closing Thoughts 184
Who Has The Best Coaching Staff? 187
Setting Expectations 189
How Valuable Are Coaches? 193
Outside the NHL 198
American Hockey League 202
Canadian Hockey League 204
U.S. College Hockey 208
Top Coaches Outside the NHL 212
Looking at the Entire Staff 214
Closing Thoughts 216
Are There Careers In Hockey Analytics? 218
Manual Trackers 221
Data Scientists 226
Career Advice 235
Closing Thoughts 241
Questions And Answers 243
Which Is Better, a Penalty Shot or a Power Play? 243
When Should Teams Pull the Goalie? 245
Will Ovechkin Catch Gretzky? 247
How Can the NHL Boost Scoring? 248
What's the Key Stat for Individual Players? 252
Super Glossary 255