DanRoM;n3546107 said:

So

MXM, how did you calculate the percentage values per pair? I can't get my head around that.

Read the fine print

This is how I implemented it, say I'm comparing your vote with Matt2000's vote:

1. Make a list of all car pairs. Car a vs car b. Like:

*Lexus vs Disco*,

*Lexus vs 156*,

*Lexus vs Tesla*... then

*Disco vs 156, Disco vs Tesla*... etc. That's what "14 choose 2" operator does. You get 91 combinations of car pairs.

2. For each car pair, check whether you prefer a or b. So get a list like: a a a b a b b, except it's 91 letters long.

3. Do the same for Matt's vote, say the list for him is b a a b b b b.

4. If you both have the same letter for a pair of cars, you get a score +1 (concordant pair), if you have a different letter you get -1 (discordant pair). For the 7 pair example above the score is 5-2=3. The percentage is score/amount of pairs, so 3/7 = 43%.

5. Repeat steps 2-4 for every voter pair to fill the table above.

Why do it like this? Because it simulates the decision-making when ordering a list of items. It shows similarity between two people's decisions. It does not care about absolute position of the car in the list. Say you have Disco and Lexus placed 5 and 11, but Matt has them 1 and 3. That's still a positive correlation.

Could be done better? Yes, I could've assigned weights to the distance between pairs, or make it "top heavy".

https://en.wikipedia.org/wiki/Kendal...on_coefficient
My "modifications" to the algorithm are about dealing with incomplete lists, where I assume that every missing car has lower placement than cars in the list. Except if both cars in a pair are missing, then I can't make a decision and the score remains the same.

The score in the last column is just a sum of all scores on one line, but it's not very representative, as it favours people who sent a more complete list.