Hacker News Reader

Comment by antouank | Hacker News Reader

But how does that make sense in a game where you can have a draw? By definition 100% cannot cover only home/away. Draw must be factored in. So doesn't that make all the Elo assumptions false? Just wondering how you can take draw into consideration...

View on HN

Excellent point - this one is definitely on our todo list. There are several simple extensions of the Elo model that take draws into consideration (i.e., give a non-zero probability to draws), for example the Rao-Kupper model. There are only minimal changes needed w.r.t. the original model, but still we didn't manage to make the changes in time for this version of the site.

In short: at its core, the "Elo assumption" postulates that every team can be represented by a real number (that can be interpreted as the strength of the team), and that the probability of the outcome depends on on the difference in strength. In the vanilla Elo model, the outcome is binary, but it's easy to make it ternary.

Have you considered using a TrueSkill-alike with extensions for scores/teams, such as PoissonOD?

http://research.microsoft.com/pubs/193839/sbsl_ecml2012.pdf

A few years ago I had a similar idea for trying to build team models, so you can make a better guess at the performance of national teams, since they don't play very often, or for league teams due to transfers at the start/during the season, but hadn't got as far as you :)

We did some preliminary experiments in this direction. Basically, we tried to do a regression on the score difference instead of using only binary outcomes. In our experiments it didn't improve the predictive accuracy - but there are many more things to try. It does feel a bit wasteful not to take score data into account.

Nice that you had some similar ideas :-)

The thing is that football is a time based sport, so draw is an outcome with pretty good chances. Usually weak teams will try to delay as much as they can to get the draw. Given enough time, the strong team would have much bigger chances to win. Also, depending on the context ( points needed for each time ) a team might have bigger motive to go for a draw than a win.

Predicting outcome possibility in football is a very complicated story, I doubt it can be solved in a simple way like elo ranking the players or teams.

That said, the knock-out phase might be more suitable for that model.

Kudos for the effort anyway, and nice UI.

In chess you can also have a draw.

Either by having the player agree to a draw, or by killing every piece that is not the kings.

Right. But then again, you cannot say it's 100% that player A or player B will win, right? There's a chance for draw that is not negligible.

One common way to represent the probability of outcomes in chess as a single number (e.g. in comparing opening lines) is to say "white gets 55% of the points", which aggregates wins and draws. (So for instance if white wins 50%, draws 25% and loses 25% of the games, it gets 0.5 * 1 + 0.25 * 0.5 + 0.25 * 0 = 62.5% of the points.)

In fact, at top levels of play, draw rates are 40% or higher.

Reply on news.ycombinator.com