## Primer – How does the Stocky work? The Stocky, which is short for stochastic simulation, is a Monte Carlo simulation of the season using Elo modelling to work out what the outcome of that season might be.

The basic premise of a Monte Carlo simulation is that if you have a few pieces of the puzzle, an idea of how they relate and then throw enough random numbers at it, you’ll get a pretty good idea of what the puzzle picture is.

Let’s say you have a circle inside a square with sides the same length as the circle’s diameter. Then throw a bunch of sand onto the square/circle combination and count how many grains of sand end up in the circle. If you know the length of the square’s side and the proportion of sand that ends up in the circle, you can work out a value for π.

(You want more detail? Fine: the side of the square can be used to calculate the area of the square, multiply that by the proportion of sand inside the circle will give you an estimate of the circle’s area, divide the circle’s area by square of half the square’s length and you will get an estimate of π).

The more grains of sand you throw at the square/circle, the closer the estimate will be to the actual answer.

## Primer – What variables do the Elo models use? 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.