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Show HN: Euro 2016 predictions using Bayesian inference

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Re: Show HN: Euro 2016 predictions using Bayesian inference

#21

Post by lum »

galfarragem wrote: post #12
lum wrote: post #2

For those who are interested in the details: we model the strength of teams using the well-know Elo model (used e.g., for official chess ratings). We innovated on two aspects of the traditional Elo model, in which every team has an independent parameter: 1) We actually model the strength of players instead of teams. This makes it possible to learn from games that were played in championships between clubs, and transf…

One specific criticism regarding Portugal-Iceland: main squad will not be exactly as considered there. E.g. Cristiano Ronaldo is considered a substitute.. Does it make a difference to your predictions?

You are correct about Portugal, not exactly the squad that will likely play against Iceland. We used the starting lineup of the last official game (I believe in this case it was Portugal vs England on June 2nd), which did not include e.g. Cristiano Ronaldo.

The actual lineup does impact the predictions - we will update the lineups before the start of each game, when the lineup is anounced.

In a future version, we would like to make it possible for visitors to change the players in the team, and automatically update the prediction.

Re: Show HN: Euro 2016 predictions using Bayesian inference

#22

Post by lum »

ancymon wrote: post #19
lum wrote: post #2

For those who are interested in the details: we model the strength of teams using the well-know Elo model (used e.g., for official chess ratings). We innovated on two aspects of the traditional Elo model, in which every team has an independent parameter: 1) We actually model the strength of players instead of teams. This makes it possible to learn from games that were played in championships between clubs, and transf…

Where did you get player/club data from?

We scraped it from soccerway.com.

Re: Show HN: Euro 2016 predictions using Bayesian inference

#24

Post by vicow »

tremon wrote: post #13
lum wrote: post #2

For those who are interested in the details: we model the strength of teams using the well-know Elo model (used e.g., for official chess ratings). We innovated on two aspects of the traditional Elo model, in which every team has an independent parameter: 1) We actually model the strength of players instead of teams. This makes it possible to learn from games that were played in championships between clubs, and transf…

Has there been any work (by you or others) towards factoring in the performance effect of the coaches on the teams?

We tried to consider it as an extra player, but it didn't help.

Re: Show HN: Euro 2016 predictions using Bayesian inference

#26

Post by lum »

antouank wrote: post #6
lum wrote: post #2

For those who are interested in the details: we model the strength of teams using the well-know Elo model (used e.g., for official chess ratings). We innovated on two aspects of the traditional Elo model, in which every team has an independent parameter: 1) We actually model the strength of players instead of teams. This makes it possible to learn from games that were played in championships between clubs, and transf…

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...

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.

Re: Show HN: Euro 2016 predictions using Bayesian inference

#27

Post by tfgg »

lum wrote: post #26
antouank wrote: post #6

Earlier quoted context omitted.

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...

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"…

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 :)

Re: Show HN: Euro 2016 predictions using Bayesian inference

#28

Post by dmoo »

lum wrote: post #21
galfarragem wrote: post #12

Earlier quoted context omitted.

One specific criticism regarding Portugal-Iceland: main squad will not be exactly as considered there. E.g. Cristiano Ronaldo is considered a substitute.. Does it make a difference to your predictions?

You are correct about Portugal, not exactly the squad that will likely play against Iceland. We used the starting lineup of the last official game (I believe in this case it was Portugal vs England on June 2nd), which did not include e.g. Cristiano Ronaldo. The actual lineup does impact the predictions - we will update the lineups before the start of each game, when the lineup is anounced. In a future version, we wou…

I think the same is also true for Ireland. The last friendly match was used to view mainly fringe players. I guess 7/8 of the likely starters are here as substitutes. Perhaps you should look at the last competitive game in the qualifiers.

Re: Show HN: Euro 2016 predictions using Bayesian inference

#29

Post by psuter »

antouank wrote: post #10
heinrich5991 wrote: post #8

Earlier quoted context omitted.

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.)

Re: Show HN: Euro 2016 predictions using Bayesian inference

#30

Post by lum »

tfgg wrote: post #27
lum wrote: post #26

Earlier quoted context omitted.

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"…

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…

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 :-)

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