Simulating the season

As I’ve promised for a few weeks, my ELO rating system allows me to simulate the season from points in time to assess the chances of various teams finishing positions, based on information we have gathered during the start of the season.

Below, I’ve taken each teams current ELO rating, with their current record, and simulated the season 20000 times. For each match, I use the expected result estimated from the ELO difference between the two teams to draw from a probability distribution around that expected result 1. These simulations are “hot” in the sense that after each simulated match, I update the ELO rankings. I’ll probably write a seperate post 2 on that, but here is FiveThirtyEight’s reasoning on why this is a good idea, which is good enough for me. The nice part about this methodology is that it takes into account a teams draw, their current rating and their current record.

I’m hoping to potentially turn this into an interactive table 3. For now, I’ll update each week with a static image.

finalR7to23_2016C

One interesting point is the big drop-off in percentages for finishing in the top 8 between 8th placed West Coast and 9th placed Port. It seems, as I wrote about previously, that the final 8 is taking shape already this early on. In fact, this seems quite common amongst those generating rating systems.

If the top 8 stays this tight, then we could be in for a super interesting finals series. That’s if we can get through the dullness of the top eight being set already.

Notes:

  1. I believe this is formally known as Monte Carlo Simulation
  2. with my ELO system explainer
  3. anyone with advice? ShinyApps is something I’ve considered)

Leave a Reply