In 2013, statistician Kevin Dayaratna and mathematician Steven J. Miller provided theoretical justification for applying the Pythagorean Expectation to hockey. In particular, they found that by making the same assumptions that Miller made in his 2007 study about baseball, specifically that goals scored and goals allowed follow statistically independent Weibull distributions , that the Pythagorean Expectation works just as well for hockey as it does for baseball. The Dayaratna and Miller study verified the statistical legitimacy of making these assumptions and estimated the Pythagorean exponent for hockey to be slightly above 2. 
This formula includes two different constants, with (b) being as the aggregate winning percentage of all teams in any given season is .500. The second constant (m), the "slope", is calculated based on the results of all games played from 1998 to 2013, . from the time Major League Baseball reached its current number of 30 teams until the final season for which statistics were available when Prof. Rothman devised his formula. He came up with the constant . The theory behind this number is that most games are not decided by exactly one run and that not all extra runs scored or allowed have an equal impact on winning percentage.