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Comment by ccozan | Hacker News Reader

I think you need to factor some other datapoints too:

- probable weather - some teams play differently depending on conditions

- yellow and red cards - some major player could miss a match. If team A is missing a defender, might be that is more vulnerable and thus, may lose a match.

and so on.

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I find comments like this unconstructive, and they always surface when people present forecasts. It's easy to play the shoulda-woulda-coulda game with data. "You should account for the breaking strengths of each team's players' ACL tendons. It could affect the outcome if they get injured!" Okay, so what? Did you do it? Does the data exist? Do you have any quantitative evidence of a statistically significant link between the weather and team performance? If you do, do you also have the evidence to show that the game forecast is not rate limited by the error of the weather forecast?

I'm not saying you have to write a thesis to suggest new data sources, but I am saying that "you need to do XYZ" is not a productive piece of advice without the evidence to back it.

Good points. Now I am not going to excuse for expriming my thoughts on a public forum, but I will give right to you that one need to have data to sustain his points.

Now strictly to subject, would be indeed a nice exercise to take a team performance ( under any scoring formula ) and correlate it with the weather at that time. This data ( team data and weather data) exists. If we find any correlation - not sure. Could be that for some teams it doesn't exist - they play good no matter the atmospheric conditions.

The second exercise is the yellow cards, but this is indeed hard to factor since is very dependent of the coach strategy ( we cannot assume that the same player always plays). So, yes, OK, let's leave that out.

It's obvious that weather is a huge factor and if you think this is just a "shoulda-woulda-coulda game", then maybe you need to take a step back and admit you have no domain knowledge?

Also, if you're doing statistical analyses of your own, hopefully this isn't how you react to domain experts giving you advice on factors you should consider. If you react the way you just did, it's quite likely people will just shut up and you'll be left floundering the darkness.

Though I agree with you on the weather bit, there is already a history of yellow and red cards from the tournament already, which could have huge implications on the tournament ahead. (For eg, Luis Suarez getting booted is one.)

It would be useful to have a database of football punishments so teams and fans could see whether a player has been dealt with fairly or not. Perhaps one already exists?

Also: Local support. For example, Colombia have a HUGE support in the stadium, they say are playing as a local all the time.

Plus: History against X. If a team have played against another a lot of times it build some model in how perform. Latin america teams know more about each others than teams abroad.

I see in this model not much discussion in how latin-teams have the upper hand here: The climate, the people, the shared-history, the kind of game, etc

Indeed, it would be interesting to add more features. However, for international games, I saw that it was quite hard to improve accuracy beyond what I already have. One other approach would be to create alternative ranking systems (modified Elo...) and use them as features.

I tend to agree with you, these factors would not improve the already good accuracy, however due to KO phase the games are all-or-nothing ( unlike other types of international games ), so some little factor ( extreme heat or a missing player ) could incline to a victory or not.

Anyway, good work!

In the case of missing a player, take Uruguay for example: they just lost their best player after he bit (yes, à la Mike Tyson) the shoulder of a player on his last game. Fifa banned him for some 8 games, so he'll be absent for the rest of the cup. Uruguay just took a big blow.

Elo ratings (such as those kept at eloratings.net) actually work quite well. Ultimately, they work like a weighted moving average.

The thing that I find hard to predict and build into my models is style of play. By style I mean: spatially-intensive, high-time-inpossession-the-ball-time (e.g. Spain with tiki-taka and Germany to some extent) versus time-intensive, opportunity-seeking/opportunity-creating (such as Brazil, Argentina, etc.)

Why? Because passing-intensive teams seem to display more of an own effect -- they fall or rise on the strength of their team, since it's an intricate, very technical and collaborative style. The results of opportunity-seeking teams are much more dependent on the strength of the adversary -- i.e. much more Elo-like.

Ideally, I'd be able to infer from the data a (exponentially biased to recent games) own-team/spatial play dependence factor as opposed to a strength-of-opponent/opportunity-seeking factor. In principle if all victories were explainable by a combination of those two variables the Elo residual/surprise would be a measure of this, but hey, teams get better/worse at opportunity-seeking too, even teams specialized in tiki-taka.

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