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Round 11 Results

After a few weeks struggling to juggle and balance various commitments, I think I’ve finally got into enough of a routine and automated enough of my R scripts that I can release my ELO ratings update early in the week and put my predictions out later in the week! I’ll also hope to write a bit more interesting post mid-week such as my one on the Tackle Machine, or the Round 7 rule. [Read More]

Pre Round 11 Ratings

The congestion at the top of our ELO rankings still remains, although ‘the challengers’ to the top 4 may be slightly sorting themselves out. Only 9 rating points separate the top team (Hawthorn, still!) and our 4th team (tied between WCE and Adelaide) which is within the Home Ground Advantage we apply to the home team of 30 rankings points. After that, Geelong is in a bit of freefall, losing 45 ratings points (roughly the equivalent of a margin of 10 points) and 6 spots in our ranking system in the last 2 weeks! [Read More]

Round 11 Predictions

I’ve separated out the simulations and ratings update into another post and I’ll just focus on this weeks games here. Last week gave my ELO model a 4-peat of 6 out 9 tipping weeks. There were quite a few close tips this week, with an MAE of 23, bringing our season total to 64 (71%) with an MAE of 30.6. To this weeks games, we see some tight matchups. Concretely, Geelong v GWS gives Geelong a slight edge with Home Ground Advantage. [Read More]

Round 10 Predictions

For the third week in a row, my ELO model managed 6 correct tips, with an MAE of 29, bringing our season total to 58 (72%) with an MAE of 31.5. This upcoming weekend shows us a few close matches that are difficult to call. Our current ELO rankings show us that there is more congestion at the top of the ladder, with a clump of 6 teams forming, with the Bulldogs and North (still! [Read More]

My ELO rating system explained

Typical ELO Actual Result Predicted Result Special K Iterating the ratings Results! I’ve been wanting to try my hand at building a rating system to predict AFL results for a while. I’ve decided to begin with a relatively simple ELO rating system. The ELO rating system was originally developed to rank chess players, but more recently has been used for a lot of sports, including AFL, to assess the relative strengths of teams within a competition. [Read More]

Round 9 predictions

Last weekend we again managed 6 correct tips, with an MAE of 33.9, bringing our season total to 52 (72%) with an MAE of 31.8. I’m hoping from here on in, we can maintain greater than 70% tipping and get the MAE under the 30 point mark [ref] I already have some thoughts on improvements of the ELO rating system[/ref] Onto this weekend, it looks like there are two standout games in the Hawks v Swans and the GWS v WB matchup. [Read More]

The tackle machine

Over the weekend, Tom Liberatore managed to equal the record for the most tackles laid in a game in the history of recording the measure (since 1986[ref]at least on[/ref]) with 19. He could have absolutely smashed the record considering he was on 17 tackles at 3⁄4 time. This is also the second time this year the record has been equalled, after Jack Zieballs 19 tackles in Round 3, with the original record set by Jude Bolton in Round 3, 2011. [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]

Round 8 Predictions

Last weekend I managed to tip 6 out of 9, with an MAE of 31.2. A bit better than the first week but also not as well as my models historical performance[ref] In reviewing the data this week, I found a small bug in my “Upcoming Round Prediction script” whereby I was giving the HGA to the away team, thus overestimating the performance of the Away Team by roughly 16 points. [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]