While it’s probably impossible to expect fans of any non-championship winning team to acknowledge their satisfaction with their team, one can reasonably expect a fan to be content if their team has given them as much joy as sorrow. Not even the most advanced analytics writer will pretend to be able to quantify those emotions, but that is essentially the idea behind a metric like .500.
.500 almost seems too simple to call it a statistical metric, both because it is easy to calculate and because it has been used for so long. It’s also used in the three other traditionally understood major North American sports. In baseball, basketball and football, .500 means having the same number of wins as losses, except when a football game ends in a tie, but even then it still works.
In this context, .500 has a lot of meaning. Perhaps the most important is the qualitative understanding that a team playing at or above .500 is respectable. Numerically speaking, besides winning half of their games a .500 team is almost certainly better than half of the other teams in the league, and if half of all teams make the playoffs, a .500 record should be good enough to allow a team to qualify. There will always be variations in where individual teams finish, but in all of these other sports, .500 will always be the mean average of all teams winning percentages (assuming they have all played the same number of games).
Hockey analysts use .500 in their conversations too, but .500 means something else in hockey than it does in all of the other sports. Since all of the other major leagues rely mostly on wins, for them .500 is a winning percentage (WP). In hockey, .500 is a points percentage (PP). These are not the same thing.
One could argue that because of the possibility of ties in football theirs is a PP too, but in football, just like the NHL before the extra point was introduced in the 1999-2000 season, a tie was worth half of a win. In those settings, the league either awarded a win at the end of a game, or they awarded two half-wins.
For instance, if the 2014 Carolina Panthers had finished with 7 wins, 7 losses and 2 ties, instead of going 7-8-1, it would be debatable if they were as good as a team that finished 8-8, it would certainly be debatable if they deserved to make the playoffs with that record, but it would be indisputable that they had a .500 record. That hypothetical .500 would be both a WP and a PP.
WP is calculated by dividing the total number of wins by the total number of games played. In leagues with a WP, if a team has the same number of wins and losses, they will have a WP of .500. PP is calculated by dividing the number of points earned by the number of points available. These percentages are only slightly different in calculation, but can be very different in its meaning.
As a test case, let’s use the Atlantic Division in the current 2014-15 season up to the All-Star break. The teams with their W-L-OTL records are as follows: Tampa Bay 30-14-4, Detroit 27-11-9, Montreal 29-13-3, Boston 25-16-7, Florida 20-14-10, Ottawa 19-18-9, Toronto 22-23-3, Buffalo 14-30-3. So which teams are under .500 and which teams are over? By both measures, the top four teams are over five hundred. Tampa Bay, Detroit, Montreal, and Boston are all averaging more than a point a game and have more total wins than total losses. Both Toronto and Buffalo are below .500 by both definitions, but Ottawa and Florida are both averaging more than a point per game but have more combined losses than wins. So, of these eight teams, four are over .500, two are under, and two are sort of.
At first glance it would simplify things to simply use the WP .500 as the metric of success. It fits the criteria of what .500 should be, that is mean, median and mode. No matter what happens, if all teams have played the same number games, the mean average of all winning percentages will be .500. We can expect half of all teams to finish with a WP above .500 and half to finish below (there will always be variations, but the median should never really be far off of .500. Ideally, as long as there were an even number of games played, we could expect .500 to be the most frequent WP, but the NHL doesn’t follow a binomial distribution, and that’s a little above the scope of this article anyway. The main problem with WP .500 is that two teams that both have the same number of wins and losses could have vastly different point totals. For example, in the lockout shortened 2012-13 season, four teams finished with a WP of .500. Two of them made the playoffs, Detroit with 56 points and the New York Islanders with 55, and two missed the playoffs, Columbus with 55 points and Winnipeg with 51.
What is needed is a kind of “.500” that satisfies the mean and median criteria but is consistent with the current NHL point system.
A few simple formulae will produce the same result that is in keeping with our previous understandings of .500.
Both of these will produce an average points per game or PP that will more accurately serve as a benchmark of success. That number for the 2013-14 season was 0.562 and for the 2014-15 season before the All-Star break was 0.565. Also, if we knew the average OL/team, we could easily measure the number of points the benchmark should be. Up to the All-Star break in 2014-15, there were 179 games decided in extra time, meaning that roughly 6 points over a point a game is a better benchmark. In the 2013-14 season, 307 games were decided in extra time, so the benchmark was roughly 10 games over a point a game.
This may not solve any real problems, but our current use of .500 is either incorrect or insignificant. Consider that in the 2013-14 NHL season, only 5 teams finished below 82 points (a PP of .500). 82 points was 9 short of a playoff spot in the West and 11 short of a playoff spot in the East. Whereas .500 once represented the 50th percentile (ie. A .500 team was better than 50% of all other teams) last season it represented the 18th percentile (ie. A .500 team was better than 18% of all other teams). No fan base would be satisfied to know that their team was merely in the 18th percentile.