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Comment by corry | Hacker News Reader

Any soccer/football fans will tell you that it's a tough sport to predict for a variety of reasons: low scoring games, lots of potential for human error in officiating, high levels of competence of even weak teams, team dynamic vs individual skill, etc.

I'd guess that it's one of the harder sports to build a prediction model for. Good luck!

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But in many ways it is actually a sport that can be modelled fairly accurately. The betting markets have remarkable consistency, in that they 'agree' on what the odds should be for any particular match. Compare this with horse racing, where the odds on any horse vary wildly before the start. For example, a horse might be 10/1 in the morning, 3/1 half an hour before the race and 6/1 at the off. Whereas the odds on a football team will stay fairly constant.

So the matches may be difficult to predict the winner but the markets price up all non-random factors very well.

Yes, this is something every soccer fan knows. Also, the world cup is every 4 years and it's difficult, if not impossible, to predict how a renewed team will work. You can make a cluster of few teams that will win in 99% of the cases but not optimize much more from that.

If someone wants to know why it's so difficult, look at how a single player can change the whole game: https://www.youtube.com/watch?v=KY40__rBvSk

In the case of the "hand of god" goal, it is not just the player, but the (unseeing) referee that allowed that goal.

A complete predictive model would not only have data about players, coaches, weather conditions, etc but also referees.

The point is: you can't predict such outliers. Maradona passed 6 players before the goal.

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