Mark
I was trying to get some sort of definitive answer from Tom Dunne about Links 100 ranking/points. Below is the cut n' paste of the conversation.
Sean
Tom Dunne - Out of curiosity, of what significance is the score? For instance, is an 8 meant to be twice as good as a 4? If there is no significance, why is the info given?
Tom D
The significance of the score is to show relationships. #1 Cypress Point and #2 Pine Valley are within .1 of a point of each other, but the data indicates that there's some daylight between them and #3. Of course, it also shows that after the "super-courses", many courses are tightly bunched. A rank without a score would eliminate that context.
Sean
Tom - Okayyyy, so how much better is .1, .5, 1.0? To me, this is an alien way to see a course so I genuinely don't understand the relationship of score-quality-ordinal ranking.
Tom D
Sean,
This is the work of a collective in which each voter uses his or her own criteria in assigning rankings to courses, so whatever scores the statistical model spits out should only be interpreted in the most general sense.
From the original feature:
"At the heart of the methodology is a tool known as logistic regression, or logit for short. In the LINKS100, every course on your ballot competes in head-to-head combat against every other course in the system, generating a Carl Saganesque number of data points. The logit takes that data—wins and losses—and spits out a number, or coefficient, for each course. That coefficient itself changes every time a course appears on a ballot—based on whether you have Pebble Beach, hypothetically speaking, ranked 1st, 10th, or 100th on your list. The bottom line: The bigger the difference between two coefficients, the higher probability that one course is truly better than another."
Coefficients are converted into scores to create the rankings and to show these relationships. If CPC is a 9.2 and PV is a 9.1, that means there is a slight chance the former is "better" (broadly speaking) than the latter.
Sean
Tom
Okay, we are getting closer. Using your example, how much of a chance of being better is .1, .5, or 1.0? For instance, does .1 represent a 50.1 to 49.9 ratio? Also, how large a gap is actually meaningful? Is it .1, .5, 1.0...? I think you can see what I am driving at. 3.4 is meaningless unless it is assigned a value. In which case, either a value for each point must be set or it is pointless to offer scores because as is they are meaningless. I mean, I can't tell how much the likelyhood of how much better CPC is over PV if I don't know the value of 9.2 and 9.1. It could well be and I suspect it is the case here, that the .1 difference statistically is not enough to support any reasonable conclusions. At what point can we do this?
Tom D
Sean,
No, it's more than that. Based on the difference between the two raw coefficient scores, CPC has a 52.5% chance of actually being "better*" than PV. That difference is probably within the margin of error, but our opinion is that readers don't want to see 1A and 1B. They want "gun to your head, which do your voters think is better?" I had a conversation about this stuff with one statistically-minded former GCAer who strongly believed we should include margin of error data throughout--I personally believe that would be TMI for the vast majority of users. We could readily produce the tiered system that some advocate, as well. Just a matter of making choices about how and how much information to provide.
*"Better" according to 135 panelists and +2600 users. Different groups can and do generate different results.
Ciao