Primers | Pythago NRL | rugby league Elo models, player ratings and statistics

Category Archives: Primers

Primer – What variables do the Elo models use?

Posted on April 5, 2017 by pythagonrl

In my previous primer on Elo ratings, I talked about different ways of calculating Elo ratings with a view of measuring form and/or class. This primer will look in a bit more depth at how I arrived at the specific numbers for the variables.

The main variables in an Elo model are:

Starting ratings (discrete versus continuous)
- If continuous, then the reversion to mean discount of ratings
Calculation method (margin vs result/WTA)
K, weighting for each game
h, homefield advantage
p, margin factor

Some are derived from game data, others from optimisation. Let’s tackle them one by one.

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Primer – How do Elo models work?

Posted on March 30, 2017 by pythagonrl

Short answer: with a lot of time spent in Excel and Google Sheets.

Long answer: It depends on what you want to do.

I introduced the Elo rating system in a previous primer. Now it’s time to put it to work.

I think most sport’s fans would agree with the following definitions of form and class –

Form – Short term performance, related to luck, match fitness, weather
Class – Long term performance, related the structural competence of the team in question

Something like “Form wins games, class wins premierships” seems appropriate.

Primers

Primer – What is a Pythagorean expectation?

Posted on March 30, 2017 by pythagonrl

Pythagorean expectation is the idea that you can calculate a team’s winning percentage based solely on its for and against. It originated with baseball nerds but, according to its Wikipedia article, has been adapted for other sports. It is also where the name of this site came from. Pythago is basically what Pythagoras would have been if he had been Australian.

To rip straight from Wikipedia –

“The basic formula is:

Primers

Primer – What are Elo ratings?

Posted on March 30, 2017 by pythagonrl

Elo ratings originated in chess as a way to rate different players. A player starts with a rating of 1500 by convention and then the rating goes up or down depending on whether the players wins or loses. The player’s rating will change proportionally to the rating of their opponent: if the player beats a very highly rated opponent, the player’s rating will go up by more than if they beat a minnow.

This rating system has been adapted for soccer, NFL, AFL and a few systems have been developed for rugby league teams (1, 2 & there was a third but it seems to have gone missing). What intrigued me was how The Arc and 538.com were able to take Elo ratings and, instead of just ranking teams, were able to predict the outcome of matches in advance with a surprising degree of accuracy. I decided that I wanted to try the same thing for the NRL.

Here’s the soccer explanation for how it’s calculated:

Primers