Finals Week 1 - a natural order

We are finally around to the first week of finals, in what is talked about as a fairly open finals race. Read on to find out what we are predicting this week and for the rest of the finals series! Ratings Firstly to our final ratings for the regular season interestingly, our top 8 rated teams all made finals! Sydney surged ahead after Adelaide’s dead rubber loss to WCE. Those two teams were clear of a pretty well ordered top 8 - the only teams whose finishing position on the ladder didn’t match their rating position is Richmond and Sydney. [Read More]

Post round 20 simulations

I haven’t had the time to regularly post stand alone simulation posts this year but given the tightness in the season, I thought I would start for these final few weeks. Read on to see where your team falls in our finals simulations! Ratings I’ll start firstly with our current ratings. We can see that Adelaide and Sydney are clearly the big leaders here - opening up a considerable gap to 3rd. [Read More]

Simulating the 2017 AFL Mens season: Pre-season edition!

As I’ve indicated previously, I’ve finally cleared some time to get back into blogging! Sparked largely by a tweet from the fantastic The Arc blog, I’ve got some very preliminary season simulations together. The competition proposed by The Arc is simple - put forward your probabilities of each team making the finals for 2017. Think you can do a better job of predicting the finalists than our Elo model? [Read More]

Finals Week 2

Following a cracking weekend of finals football, I haven’t had the time I’d like to get this weeks results and predictions up. I also did find out that my simulation of finals script, that I’d hurriedly written back in about round 13 actually couldn’t handle real finals data being thrown at it. Nonetheless, the simulations of the remainder of the finals series are in, and I’ll combine it with my predictions later on in this post! [Read More]

Round 21 Ratings and Simulations - Hawks back to the pack

With a big upset loss on the weekend, the Hawks is the biggest loser from the weekend results. They have dropped back into the pack of clustered teams below our clear leaders and now face a possibility of losing top spot before the season is out. New ratings In fact - despite having a relatively good week for head to head tipping, our ELO model has shown some considerable adjustment to the rankings this week. [Read More]

Round 20 ELO Simulations - almost set

ELO Ratings After seeing Hawthorn take back top stop in our ELO ratings, they’ve quickly lost that due to a relatively poor result against Carlton. The loss of 10 rating points, combined with Sydney’s much larger than expected win, sees those two teams sway places. Combined with Adelaide, these three teams have emerged ahead of the pack. In a distant second tier we see GWS and Geelong, who also swapped places this week. [Read More]

Beyond the 8 point game - estimating match importance in the AFL

As I was watching an enthralling match between Western Bulldogs and Port Adelaide over the weekend, there was a lot of discussion about how important the match was - the Bulldogs needed to win to cement their spot in the top 8, while Port needed to win to have any chance of jumping up. It’s also not uncommon for commentators to discuss the notion of an “8 point game", typically when two teams close on the ladder play each other. [Read More]

Leaping Kangaroos

The Kangaroos, often considered ‘un-sexy’, are putting together a pretty nice season. I’ve discussed previously that early wins in a season is strongly related to wins by seasons end and, although my current ELO rating for North Melbourne only ranks them as the 8th best team[ref] I suspect this is largely due to winning games by less than expected[/ref], their current bank of 7 wins gives them a good base to work off for the rest of the year. [Read More]

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[ref] I believe this is formally known as Monte Carlo Simulation[/ref]. [Read More]