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Who will stop Italy?


han

Who will stop Italy?  

51 members have voted

  1. 1. Who will stop Italy?

    • Somebody.
      33
    • Nobody.
      18


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If so she should have presented her assuptions and how she computed these probabilities and asked the question what's wrong. Since it should be obvious that none of the figures presented could be anywhere near correct.

That's what I did. Matt, Richard and Frances suggested some improvements. I agree that my model is probably too simple, although it is not clear in what direction it would be more fruitful to generalize it. Taking all the problems mentioned into account would obviously lead to an overfit. This data set is very small. Therefor any model will either be very crude, or too complex to fit with these data.

 

I'm sure someone has made much more serious models for bridge events, based on much larger databases.

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I suspect there is little to choose between the players in the final stages of the BB.

This is not true. If you think it's true though we can definitely bet on the outcome at even money.

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And 5th segment :(

 

Maybe I could say something about Frances' hypothesis that there will be more swings now that Italy is trailing, but other than that I'm afraid a statistical analysis is not the kind of analysis you would like. Obviously the performance of SA is far too good to be merely based on statistical fluctuations, but whether this is a time thing or an H2H thing I cannot say.

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Anyone who wants to back South Africa to beat Italy with me, despite the current score (48/96 boards played) please form an orderly queue.

 

nickf

sydney

alright, all those who placed bets know where to find me. Well done to you all.

 

nickf

sydney

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I think that the results of the South Africa - Italy match expose some of the limitations with the modeling techniques that folks have been using:

 

The approach that I have been advocating (and that Gerben and Alex implemented) is based on Monte Carlo simulation. To date, the model that we were using made an explicite assumption that the expected value and the variance for board results was constant across the length of a match. A more sophisitcated model should probably build in the notion of deliberately trading off expected value for variance when one is behind by a large amount. I don't think that this would be that much more difficult to program, however, parameterizing the model would become a lot more difficult.

 

From what I can tell, Helene's approach suffered from a different problem. A number of follks, myself included seemed to feel that Helene's model gave a bot too much credit to the Italians. In particular, the model suggested that the Italians were massive favorites against a number of teams that beat them in head-to-head matches during the Round Robin stage. (In defference to Helene, it seems clear that the Italians also believed that they were clear favorites to beat the SA team despite losing to SA in the RR). I suspect that Helene's model may have fallen victim to one of my favorite theories: Bridge isn't transitive (furthermore, I beleive that Helene's model does have transitivity assumptions built into it). While Italy lost to SA, they scored well against a number of teams that SA didn't fare quite so well against. In turn, this suggested that the Italy v SA was an outlier....

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Bridge isn't transitive  (furthermore, I beleive that Helene's model does have transitivity assumptions built into it).

It is a linear model, i.e. if the coefficients are

Dekistan: 10

Oktistan: 8

Quintistan: 5

 

so Dekistan is expected to beat Oktistan by 2 IMPs and Oktistan to beat Quintistan by 3 IMPs, then it follows that Dekistan is expected to beat Quintistan by 2+3=5 IMPs.

 

It could be that the general response function is slightly convex (Dekistan will beat Qintistan by less than 5 IMPs) or concave (more than 5 IMPs). This would not be so difficult to identify and I could probably make up some parametrization of the response function.

 

Some have implied that the response functions may be different for different teams (e.g. Italy being particulare good at beating weak teams). This may be true but it's not clear how to model it, and in any case it would require a lot more data.

 

The same goes with independency of the residuals, and constancy of the coefficients throughout the event. Obviously not true either but quite difficult to say in what way dependency between the residuals and variation of the coffeicients should be modeled, and again more data would probably be needed.

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Quintistan: 5

I think Quintistan is overrated

 

I don't know about running monte-carlos to simulate outcomes, but it does strike me that this is more of a graph theory problem than anything (and, those who have their nose in math more than I do, can tell me how much I am wrong there).

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Bridge isn't transitive  (furthermore, I beleive that Helene's model does have transitivity assumptions built into it).

It is a linear model, i.e. if the coefficients are

Dekistan: 10

Oktistan: 8

Quintistan: 5

 

so Dekistan is expected to beat Oktistan by 2 IMPs and Oktistan to beat Quintistan by 3 IMPs, then it follows that Dekistan is expected to beat Quintistan by 2+3=5 IMPs.

 

It could be that the general response function is slightly convex (Dekistan will beat Qintistan by less than 5 IMPs) or concave (more than 5 IMPs).

I suspect it would be less than five imps.

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SA is pwnzzoring italy ^^

Anyone who wants to back South Africa to beat Italy with me, despite the current score (48/96 boards played) please form an orderly queue.

 

nickf

sydney

So, how much did you lose?

None, because I didn't get to see nick's offer before it was all over... :)

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Yes, there is a standard deviation of 24 IMPs on the RR matches. This scales with the squareroot of the size so it becomes sqrt(8)*24 for 128 boards.

Answering other critics, that's not so small. That suggests std dev of ~68 IMPs. And for a 92% chance of winning (assuming normal distribution) versus South Africa that suggests on average Italy should win by 1.4 std dev, so by a mean of 95 IMPs (assuming they always play all segments at top form and don't stop if Italy is up triple digits early). Italy losing by at least 12 IMPs (since carry over wasn't counted) is thus a 5.68% event or a bit better than 1 in 20.

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I think that the results of the South Africa - Italy match expose some of the limitations with the modeling techniques that folks have been using:

 

The approach that I have been advocating (and that Gerben and Alex implemented) is based on Monte Carlo simulation. To date, the model that we were using made an explicite assumption that the expected value and the variance for board results was constant across the length of a match. A more sophisitcated model should probably build in the notion of deliberately trading off expected value for variance when one is behind by a large amount. I don't think that this would be that much more difficult to program, however, parameterizing the model would become a lot more difficult.

 

From what I can tell, Helene's approach suffered from a different problem. A number of follks, myself included seemed to feel that Helene's model gave a bot too much credit to the Italians. In particular, the model suggested that the Italians were massive favorites against a number of teams that beat them in head-to-head matches during the Round Robin stage. (In defference to Helene, it seems clear that the Italians also believed that they were clear favorites to beat the SA team despite losing to SA in the RR). I suspect that Helene's model may have fallen victim to one of my favorite theories: Bridge isn't transitive (furthermore, I beleive that Helene's model does have transitivity assumptions built into it). While Italy lost to SA, they scored well against a number of teams that SA didn't fare quite so well against. In turn, this suggested that the Italy v SA was an outlier....

I think it is much simpler than that. Bridge maybe isn't transitive, but I would think this effect is very very small.

The simple explanation is:

1. South Africa played better in the quarter-final than in the Round Robin, and the Italians (well, maybe just one of their pairs) worse. I don't think Helene's model allowed for that.

2. South Africa had more luck than Italy in the quarter final.

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Arend, when you say SA had more luck, isn't that just what Richard was already saying with "this suggested that the Italy v SA was an outlier"?

 

Your point 1. is well taken though.

No, I think Richard was saying the exact opposite. He was saying that Helene's analysis wrongly assumed that the the win by SA vs Italy in the round robin was an outlier (probably implying that the quarter final result may have proven this wrong).

 

I have to say that in all the quarters I have been watching, the Italians were playing a bit better than the South Africans.

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