The Basics of the KFC Elo Ranking System

 VS-Predictions

Last week I made the first KFC Gameweek Predictions post.  It was received well and overall the success rate for the 9 week 33 games was: 5 Correct, 1 Wrong, 3 Draws. The system considers a draw half a win so I think it was a respectable showing. One of the questions several people asked was how the rankings were determined. I’m going to touch on all the basics in this post.

Warning! If you keep reading after the break, there will be math, but I will do my best to keep the explanations simple and understandable. The Basics

Our prediction formula is based on the Elo rating system. This system was developed by Arpad Elo as a better way to calculate the relative skill levels for chess players. Since its implementation in 1960, they system has grown in popularity and is now used by several sports leagues and other competitor-versus-competitor games.

Here’s the formula:

There is also a formula that helps us determine how many points a team/player was expected to score:

The Breakdown

So now you know how it works right? No? Don’t feel bad, it took me several week of looking over all of this to figure it all out. Here’s a some more detail to help explain things further.

Every team in MLS has a rating. Originally, this rating would have been 1500 because the Elo system assumes that an average team will have a 1500 rating (If you want more details on that, checkout the Wikipedia page). Fortunately for us, Ryan Anderson at http://f2f2s.com took the time to go back to 1996 and apply the Elo system to all game through 2012. This provided me with an excellent starting point for the initial 2013 ratings.

Next is the K-factor. As stated above, this changes the significance of the result based on the competition. This means that the more important the game the more impact it will have on a team’s rating. So Regular Season Game (15) < Playoff Game (30) < MLS Cup Final (45).

Now here’s something important I’m going to point out. I do not consider any other league games when making these calculations (so not CCL or things like that). There would just be too many teams for me to keep up with. All of my ratings only reflect MLS games.

Now I’ll jump to the G-Factor since it’s next in the formula. K was how important the game was, so G is basically how much a teams wins by.  If it’s a Draw or a Win by 1 goal, G = 1. Win by 2, G = 1.5. Win by 3, G = 1.75, Win by 4+, G = 1.75 + (N-3)/8.

One of the early mistakes I made was adding in the wrong value for the S variable. It’s important to remember that points scored does not mean the the 3,1,0 the league assigns but the values from the formula. Win = 1, Draw = 0.5, and Loss = 0.

Expected Points: The E value

I thought this one was important enough to explain on it’s own. First let me point out the two R variables from the formula above. R(b) is the opponent’s rating and R(a) is the rating of the team who’s new rating you are currently trying to find. By this point, you should at least know the starting ratings, so you just have to replace the two R variables to get your answer.

Also, as mentioned above, there is a +85 point advantage added to the home team’s rating. This reflects the advantage that all MLS teams have when playing at home. Why 85? Well Ryan Anderson also took the time char the Wins, Draws, and Losses for all game from 1996-2007. He was able to determine that percentage of game a home team did not loose and it ended up equaling a +85 point bump. I’ve recently done the same calculations for 2008-2012 and the new value is +92, but the change in ratings is so small that I’m going to wait until next season to change this.

Closing

So that’s the formula that I use for the Vs gameweek predictions and will soon start using for KFC Power rankings. If anyone have any questions, suggestions, or other thoughts, please feel free to contact us through e-mail, twitter @FantasyMLSTips, or at /r/FantasyMLS on Reddit.

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