| Short technical explanation: We've extended a pretty cool skill-based model of comparison outcomes called TrueSkill Through Time [1], developed by Microsoft Research. Compared to TrueSkill, our model is able to learn the dynamics of football teams' skill in a more flexible way, and therefore we achieve slightly more accurate predictions. It also makes for great visualizations! For those of you who know the Elo rating system: our model is similar but beats Elo by ~10% [2] Our goal is to use the kickoff.ai platform to continuously evaluate and improve our models. We hope to become the FiveThirtyEight of football! [3] Links to our Euro 2016 model & show HN: https://news.ycombinator.com/item?id=11868863
http://euro2016.kickoff.ai [1] https://www.microsoft.com/en-us/research/publication/trueski... [2] in terms of log-loss, compared to a baseline that gives probability 1/3 to each outcome. [3] Actually, FiveThirtyEight also has football predictions (albeit only for club competitions): https://projects.fivethirtyeight.com/soccer-predictions/ |
FiveThirtyEight actually posted their WC predictions earlier today [1]. Finding where the two models diverge is a fun exercise -- they have Morocco as a 40% favorite tomorrow while your model has Iran as a 46% favorite.
It would be interesting to test the model by applying the Kelly Criterion [2] when you have an odds advantage to a fake initial $1000 bankroll and seeing where you end up at the end of the tournament.
If Iran does end up being a 46% favorite tomorrow then that Kelly test will start to look really good, really quickly - the market is only giving Iran a ~25% chance right now!
[1] https://projects.fivethirtyeight.com/2018-world-cup-predicti...
[2] http://www.elem.com/~btilly/kelly-criterion/ for the background, http://www.albionresearch.com/kelly/ for a useful calculator