Implementing Wilson Score in SQL

We have a relatively small table that we would like to sort based on rating, using the Wilson interval or a reasonable equivalent. I'm a reasonably smart guy, but my math fu is nowhere near strong enough to understand this:

The above formula, I am told, calculates a score for a positive/negative (thumbs up/thumbs down) voting system. I've never taken a statistics course, and it's been 15 years since I've done any 开发者_C百科sort of advanced mathematics. I don't have a clue what the little hat that the p is wearing means, or what the backwards Jesus fish beneath z indicates.

I would like to know two things:

1. Can this formula be altered to accommodate a 5-star rating system? I found this, but the author expresses his doubts as to the accuracy of his formula.

2. How can this formula be expressed in a SQL function? Note that I do not need to calculate and sort in real-time. The score can be calculated and cached daily.

3. Am I overlooking something built-in to Microsoft SQL Server?

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Instead of trying to manipulate the Wilson's algorithm to do a 5 star rating system. Why don't you look into a different algorithm? This is what imdb uses for their top 250: Bayesian Estimate

As for explaining the math in the Wilson's algorithm, below was posted on the link in your first post. It is written in Ruby.

require 'statistics2'

def ci_lower_bound(pos, n, power)
    if n == 0
        return 0
    end
    z = Statistics2.pnormaldist(1-power/2)
    phat = 1.0*pos/n
    (phat + z*z/(2*n) - z * Math.sqrt((phat*(1-phat)+z*z/(4*n))/n))/(1+z*z/n)
end

If you'd like another example, here is one in PHP: http://www.derivante.com/2009/09/01/php-content-rating-confidence/

Edit: It seems that derivante.com is no longer around. You can see the original article on archive.org - https://web.archive.org/web/20121018032822/http://derivante.com/2009/09/01/php-content-rating-confidence/ and I've added the code from the article below.

class Rating
{
  public static function ratingAverage($positive, $total, $power = '0.05')
  {
    if ($total == 0)
      return 0;
    $z = Rating::pnormaldist(1-$power/2,0,1);
    $p = 1.0 * $positive / $total;
    $s = ($p + $z*$z/(2*$total) - $z * sqrt(($p*(1-$p)+$z*$z/(4*$total))/$total))/(1+$z*$z/$total);
    return $s;
  }
  public static function pnormaldist($qn)
  {
    $b = array(
      1.570796288, 0.03706987906, -0.8364353589e-3,
      -0.2250947176e-3, 0.6841218299e-5, 0.5824238515e-5,
      -0.104527497e-5, 0.8360937017e-7, -0.3231081277e-8,
      0.3657763036e-10, 0.6936233982e-12);
    if ($qn < 0.0 || 1.0 < $qn)
      return 0.0;
    if ($qn == 0.5)
      return 0.0;
    $w1 = $qn;
    if ($qn > 0.5)
      $w1 = 1.0 - $w1;
    $w3 = - log(4.0 * $w1 * (1.0 - $w1));
    $w1 = $b[0];
    for ($i = 1;$i <= 10; $i++)
      $w1 += $b[$i] * pow($w3,$i);
    if ($qn > 0.5)
      return sqrt($w1 * $w3);
    return - sqrt($w1 * $w3);
  }
}

As for doing this in SQL, SQL has all these Math functions already in it's library. If I were you I'd do this in your application though. Make your application update your database every so often (hours? days?) instead of doing this on the fly or your application will become very slow.

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Regarding your first question (adjusting the formula to the 5-stars system) I would agree with Paul Creasey.

conversion formula: [3 +/- i stars -> i up/down-votes] (3 stars -> 0)

example: 4 stars -> +1 up-vote, 5 stars -> +2, 1 -> -2 and so on.

I would note though that instead of the lower bound of the interval that both ruby and php functions compute, I would just compute the much more simple wilson midpoint:

(x + (z^2)/2) / (n + z^2)

where:
n = Sum(up_votes) + Sum(|down_votes|)
x = (positive votes)/n = Sum(up_votes) / n
z = 1.96 (fixed value)

---

Taking Williams link to the php solution http://www.derivante.com/2009/09/01/php-content-rating-confidence/ and making your system such that it just postive and negative (5 stars could be 2 pos, 1 start could be 2 neg perhaps) then it would be fairly easy to convert it to T-SQL, but you'd be much better off doing it in the server side logic.

---

The author of the first link recently added an SQL implementation to his post.

Here it is:

SELECT widget_id, ((positive + 1.9208) / (positive + negative) - 
               1.96 * SQRT((positive * negative) / (positive + negative) + 0.9604) / 
                      (positive + negative)) / (1 + 3.8416 / (positive + negative)) 
   AS ci_lower_bound FROM widgets WHERE positive + negative > 0 
   ORDER BY ci_lower_bound DESC;

Whether this can be accommodated to a 5-star rating system is beyond me too.

---

I have uploaded an Oracle PL/SQL implementation to https://github.com/mattgrogan/stats_wilson_score

create or replace function stats_wilson_score(

/*****************************************************************************************************************

Author      : Matthew Grogan
Website     : https://github.com/mattgrogan
Name        : stats_wilson_score.sql 
Description : Oracle PL/SQL function to return the Wilson Score Interval for the given proportion. 
Citation    : Wilson E.B. J Am Stat Assoc 1927, 22, 209-212

Example:
  select 
    round(29 / 250, 4) point_estimate, 
    stats_wilson_score(29, 250, 0.10, 'LCL') lcl, 
    stats_wilson_score(29, 250, 0.10, 'UCL') ucl
  from dual;

******************************************************************************************************************/

  x integer,  -- Number of successes
  m integer,  -- Number of trials
  alpha number default 0.95,  -- Probability of a Type I error
  return_value varchar2 default 'LCL' -- LCL = Lower control limit, UCL = upper control limit
)

return number is

  z float(10);
  phat float(10)  := 0.0;
  lcl float(10)   := 0.0;
  ucl float(10)   := 0.0;

begin

  if m = 0 then
    return(0);
  end if;

  case alpha
    when 0.10 then z := 1.644854;
    when 0.05 then z := 1.959964;
    when 0.01 then z := 2.575829;
    else return(null); -- No Z value for this alpha
  end case;

  phat := x/m;

  lcl := (phat + z*z/(2*m) - z * sqrt( (phat * (1-phat) ) / m + z * z / (4 * (m * m)) ) ) / (1 + z * z / m);
  ucl := (phat + z*z/(2*m) + z * sqrt((phat*(1-phat)+z*z/(4*m))/m))/(1+z*z/m);

  case return_value
    when 'LCL' then return(lcl);
    when 'UCL' then return(ucl);
    else return(null);
  end case;

end;
/
grant execute on stats_wilson_score to public;

---

The Wilson score is actually not a very good of a way of sorting items by rating. It's certainly better than just sorting by mean review score, but it still has a lot of problems. For example, an item with 1 negative review (whose quality is still very uncertain) will be sorted below an item with 10 negative reviews and 1 positive review (which we can be fairly certain is bad quality).

I would recommend using an adaptation of the SteamDB rating formula instead (by Reddit user /u/tornmandate). In addition to being better suited to this sort of thing than the Wilson score (for reasons that are explained in the linked article), it can also be adapted to a 5-star rating system much more easily than Wilson.

Original SteamDB formula:

( Total Reviews = Positive Reviews + Negative Reviews )
( Review Score = frac{Positive Reviews}{Total Reviews} )
( Rating = Review Score - (Review Score - 0.5)*2^{-log_{10}(Total Reviews + 1)} )

5-star version (note the change from 0.5 (a 50% score with up/down votes) to 2.5 (a 50% score with 5-star ratings)):

( Total Reviews = total count of all reviews )
( Review Score = mean star rating of all reviews )
( Rating = Review Score - (Review Score - 2.5)*2^{-log_{10}(Total Reviews + 1)} )

The formula is also much more understandable by non-mathematicians and easy to translate into code.