I have been asked a number of questions about the predictions I post for Rugby and Rugby League competitions. Here are some questions and my answers.
Fascinated by your model and appreciate your input, ever considered doing AFL? (Daniel Levis)
The methodology I use is based on the work of Stephen Clarke and others at Swinburne University and was used for predicting AFL.
My predictions developed from a request to have purely statistical predictions for a TV program. I looked for a quickly implementable approach and chose the exponential smoothing approach because of the minimal data requirements and my knowledge that exponential smoothing is an excellent method for forecasting which is simple to implement.
I am very familiar with AFL as a lifelong Collingwood supporter who grew up in Victoria. I am predicting a number of competitions now which is only possible because I have been able to automate most of the work of obtaining data, creating the predictions and posting them. I have enough to do as just one person with that, so am not keen to take on extra competitions. The cancellations and variations during the covid epidemic have been very time-consuming also, because they can’t be easily automated, but require a lot of individual modification and data entry which is time-consuming and error-prone.
Hi David, when assessing a team, how many points do you think a ‘Home Ground’ advantage should be? (Dr Douglas Wilde)
I aim for my predictions to be statistically based and free of subjectivity as far as possible so I select the home ground advantage as a parameter in my models based on past data.
For each competition I do a grid search over all the parameters in the model to select values which give the best predictions over a number of years. There are actually two home ground advantage parameters for some of the competitions, one for games between two teams from the same country and another where the teams are from a different country.
Home ground advantage is a question where some subjectivity arises. In the NRL for example when two Sydney teams are playing each other, should home ground advantage apply? I do apply it except when they have the same home ground, based on my reading about home ground advantage in other sports. Also what to do about the Warriors based in Australia? Or in Super Rugby the Fijian Drua based in Australia?
Can you explain briefly how we could use the percentage/performance to do calculations ourselves or it is to difficult and can only be done by you? (Eugene Matthew)
My short answer is that I don’t think you could do that.
First of all there is the data problem. My predictions essentially give the mean number of points difference between the first team score and the second team score. To start assigning probabilities to even the probability of the first team winning, you need to model the distribution around that mean value, and for that you require the errors observed in the past as the basic data to model the distribution.
You then have to model the distribution, which is not as straight forward as you might imagine, because the distribution of errors is not a normal distribution, it is heavy-tailed. As it happens I am quite experienced at modelling heavy-tailed distributions since I have written a number of R software packages to handle distributions of that sort which are commonly used in mathematical finance. I have in the past done a preliminary exercise modelling the errors but nothing suitable for prime time.
There have also been requests for probabilities of margins being in particular ranges: 0 to 12, or larger. Here you start to run into problems of accuracy. My guess without doing some actual investigation is that those probabilities would be highly variable.
That sort of calculation is likely done by betting companies which I have at their disposal qualified statisticians and substantial computing power. Talking to betting company statisticians, they have told me that even outside of the betting companies, professional gamblers these days use a lot of data and computer analysis to inform their betting.
It is important to remember here that the only data I use are the scores of past games and home ground advantage.
All in all I think your predictions are very good and could all be near perfect if players had values also like the home advantage value it would make some games more accurate since they are the most important part of the predictions/stats.
example; cowboys vs Storms a few weeks back when Papenhouzen, RSmith, Solomona and Hugh’s didn’t play causing your stats to be significantly wrong. (Eugene Matthew)
There are two reasons why I don’t do this.
The first is practical: there is much, much more data required, which has to be collected and then utilised. I would have to have team sheets for all games and assign some sort of value to each player. That would be very difficult to automate as a single part-time person. It is the sort of thing a betting company would do however because they have the resources.
The second reason is more philosophical. The idea that if you use more data you will get better predictions is somewhat misplaced. There is in team sports inherent randomness that cannot be dealt with no matter how much data is available. It is easy to point to games where you would never pick a team to win using any methodology but they still do win. Extra data and more sophisticated methods generally bring only marginal improvements in accuracy. Statistically the more data is used, the more variability is introduced in estimating effects.
You can always simply use some subjective estimates:
I give – 5 points for a fullback or 5/8 missing from the 17. -3 points for a Marquee player missing. -2 points for a regular player missing. I also give scores to aspects like Home/Away game, Avg points scored in a game, Avg unforced errors and so on. I have 30 key markers/aspects I score to make my prediction. (Dr Doug Wilde)
I would always want to have sound statistically based estimates of any quantities I used in a model, I am a statistician after all.
My aim in producing these predictions is to show the efficacy of very simple statistical methods using only limited data, and exponential smoothing in particular. I don’t recommend betting using these predictions and I never bet myself. I do know that a number of people use the predictions in tipping competitions. My advice is to use the predictions to indicate the form of teams in the competition adjusted for home ground advantage, then if there are other factors you consider important (injuries, the weather, a long-standing voodoo, …) modify my forecasts as you see fit.