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Dalton · 10-27-2012, 06:27 AM

Comparing 2011/12 Stamkos 60 gs to Selanne's 52 in 1997/98.

First off IMHO 60 gs is an outstanding, rare achievement. Few have done this. I consider it irrelevant if stats show that it was easier to do in this year than that year. I'd bet the same analysis would reveal how many failed to do so under equal circumstances.

I am ignoring the psychological value of the number and simply comparing the performance of both players to their peers in specific subsets.

52 goals represents .041% the production of the top 5% of players who scored 22.5% of the leagues goals that season.

60 gs represents .040% of the production of the top 5% of players who scored 23.0% of the leagues goals that season.

They look pretty equivalent.

10%- Stamkos .024% of .39%
Selanne .025% 0f .37%

Still appear equivalent but a pattern is emerging. Selanne has scored a higher percentage of fewer goals.

20%- Stamkos has .015% of 63%
Selanne has .016% of 59%

Does this make sense?

In the Selanne season 21 players scored at least 30 goals, 75 scored at least 20. 763 players appeared in 38,363 games scoring 5,624 goals. His productivity is comparable to 63 goals amongst the top 10% in Stamkos' season.

In the Stamkos season 31 players scored at least 30 goals, 102 scored at least 20. 894 players appeared in 44,268 games scoring 6,542 goals. His productivity is equivalent to 49 goals amongst the top 10% in Selanne's season.

Selanne was more productive than Stamkos among the top 10% of scorers. It will differ somewhat within 5%, 20% and other subsets of top performers.

What does adjusted scoring say about these two seasons?

I haven't looked at goalies at all, let alone schedules and opponents.

Whatever else this debate is about we can clearly do much better than adjusted stats with respect to analyzing raw data. Adjusted stats are a construct with only the slimmest connection to reality. That connection is just that raw data is the independant variable. Any old wild formula will bear some resemblance to the input. Adjusted data has some careful thought put into it but clearly it is off. Work needs to be done.

Caveat: I tend to make errors. Hopefully I haven't done that here. Feel free to check my calculations.

10-27-2012, 06:27 AM	#88
Dalton Registered User Join Date: Aug 2009 Location: Ho Chi Minh City Country: Posts: 2,096 vCash: 500	Comparing 2011/12 Stamkos 60 gs to Selanne's 52 in 1997/98. First off IMHO 60 gs is an outstanding, rare achievement. Few have done this. I consider it irrelevant if stats show that it was easier to do in this year than that year. I'd bet the same analysis would reveal how many failed to do so under equal circumstances. I am ignoring the psychological value of the number and simply comparing the performance of both players to their peers in specific subsets. 52 goals represents .041% the production of the top 5% of players who scored 22.5% of the leagues goals that season. 60 gs represents .040% of the production of the top 5% of players who scored 23.0% of the leagues goals that season. They look pretty equivalent. 10%- Stamkos .024% of .39% Selanne .025% 0f .37% Still appear equivalent but a pattern is emerging. Selanne has scored a higher percentage of fewer goals. 20%- Stamkos has .015% of 63% Selanne has .016% of 59% Does this make sense? In the Selanne season 21 players scored at least 30 goals, 75 scored at least 20. 763 players appeared in 38,363 games scoring 5,624 goals. His productivity is comparable to 63 goals amongst the top 10% in Stamkos' season. In the Stamkos season 31 players scored at least 30 goals, 102 scored at least 20. 894 players appeared in 44,268 games scoring 6,542 goals. His productivity is equivalent to 49 goals amongst the top 10% in Selanne's season. Selanne was more productive than Stamkos among the top 10% of scorers. It will differ somewhat within 5%, 20% and other subsets of top performers. What does adjusted scoring say about these two seasons? I haven't looked at goalies at all, let alone schedules and opponents. Whatever else this debate is about we can clearly do much better than adjusted stats with respect to analyzing raw data. Adjusted stats are a construct with only the slimmest connection to reality. That connection is just that raw data is the independant variable. Any old wild formula will bear some resemblance to the input. Adjusted data has some careful thought put into it but clearly it is off. Work needs to be done. Caveat: I tend to make errors. Hopefully I haven't done that here. Feel free to check my calculations.