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Topic: Is there a way to calculate an average ranking from uneven lists?
Replies: 12   Last Post: Nov 2, 2013 12:55 PM

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 Virgil Posts: 8,833 Registered: 1/6/11
Re: Is there a way to calculate an average ranking from uneven lists?
Posted: Oct 28, 2013 3:14 AM

In article <j0sr695ffuelprtlh8akljk2118t3buhpl@4ax.com>,
Jennifer Murphy <JenMurphy@jm.invalid> wrote:

> On Sun, 27 Oct 2013 17:01:48 -0600, Virgil <virgil@ligriv.com> wrote:
>

> >In article <2ivq699o8a81ppiu5qognbecbgm9et2sov@4ax.com>,
> > Jennifer Murphy <JenMurphy@jm.invalid> wrote:
> >

> >> On Sun, 27 Oct 2013 13:36:29 -0600, Virgil <virgil@ligriv.com> wrote:
> >>

> >> >In article <chpq69prq63kh364qqmphkmqedhgm5ti6h@4ax.com>,
> >> > Jennifer Murphy <JenMurphy@jm.invalid> wrote:
> >> >

> >> >> There are many lists containing rankings of great books. Some are
> >> >> limited to a particular genre (historical novels, biographies, science
> >> >> fiction). Others are more general. Some are fairly short (50-100 books).
> >> >> Others are much longer (1,001 books).
> >> >>
> >> >> Is there a way to "average" the data from as many of these lists as
> >> >> possible to get some sort of composite ranking of all of the books that
> >> >> appear in any of the lists?

> >>
> >> >One way to compare rankings when there are different numbers of objects
> >> >ranked in different rankings is to scale them all over the same range,
> >> >such as from 0% to 100%.
> >> >
> >> >Thus in all rankings a lowest rank would rank 0% and the highest 100%,
> >> >and the middle one, if there were one, would rank 50%.
> >> >Four items with no ties would rank 0%, 33 1/3%, 66 2/3% and 100%,
> >> >and so on.
> >> >
> >> >For something of rank r out of n ranks use (r-1)/(n-1) times 100%.

> >>
> >> In the lists I have, the highest ranking entity is R=1, the lowest is
> >> R=N. For that, I think the formula is (N-R)/(N-1). No?

> >
> >Works for me!

> >>
> >> Two questions:
> >>
> >> 1. Do I then just average the ranks across the lists?

> >
> >That ought to work. but th effect of your averaging will be to compress
> >the pattern of rankings towards o.5 with fewer near either 1 or 0.

>
> Yes, but isn't this what we want? Are you suggesting that this is a
> problem?

NO, but it is an effect you shuold be prepared for.
>
> If a book is ranked high and low on different lists, then the "average"
> rank would be more in the middle. If a book is close to the top in most
> lists, then the average ranking would be closer to the top.
>
> The term regression to the mean" comes to mind...
>

> >> 2. What scaled rank do I use for a book that is not ranked in a list?
> >
> >If no preferences are evident, I would either leave it out entirely

>
> Are you suggesting that the composite list only include books that are
> on ALL lists? That would have the effect of making the final list
> smaller and smaller as the number of lists increases. This is the
> opposite effect that I want to achieve.

What I meant was to give any book not mentioned in a particular list the
lowest possible score for that list or even invent a "lower than
mentioned" score for each list.
>
> >or give each book mentioned the same score of 0.5 ( or 50%).
>
> Do you mean that we add all of the books that are any list to all of the
> lists and assign any that do not have a ranking the 0.5 value? On a list
> of 1,000 books, this would have the effect of giving a book that did not
> even make the list, a ranking higher than half of the books that did.

No, what I mean is something like giving each book a plus score for each
mention but no score at all for a non-mention.
>
> Let's consider some actual data. Here are 3 sample lists each containing
> 5 books, but not the same 5 books:
>
> Rank List 1 List 2 List 3
> 1 A B F
> 2 B A H
> 3 C E C
> 4 D G D
> 5 E D A
>
> When listed by book, the data looks like this:
>
> List 1 List 2 List 3
> Books Rank Rank Rank
> Book A 1 2 5
> Book B 2 1
> Book C 3 3
> Book D 4 5 4
> Book E 5 3
> Book F 1
> Book G 4
> Book H 2
>
> How would you calculate average rankings?

Any way you try will have several drawbacks.
Clearly books D and E reviewed fairly well and book G did not,
but the reasom why a particular reviewer did or did not review a
particular book may well have nothing at all to do with the book's
quality but be related to its subject matter, its publisher, or sheer
happenstance.
--

Date Subject Author
10/27/13 Jennifer Murphy
10/27/13 Virgil
10/27/13 Jennifer Murphy
10/27/13 Virgil
10/28/13 Jennifer Murphy
10/28/13 Virgil
10/28/13 David Bernier
10/28/13 Graham Cooper
10/29/13 Jennifer Murphy
10/29/13 Graham Cooper
10/29/13 David Bernier
10/29/13 David Bernier
11/2/13 Jennifer Murphy