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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 h

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.

ccozan 12y ago
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!
- sanoli 12y ago
  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.
thanatropism 12y ago
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.

ccozan 12y ago

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!

sanoli 12y ago
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.

thanatropism 12y ago

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.