I have a question about computing the loglikelihood scores for this problem.
In bridge, deals are reused inside a tournament. I can see how to figure out which players play more against a specific partner than others. In this case N equals the number of deals, k11 from the loglikelihood contingency table equals the number of deals played by players A and B, k12 deals played by A but not by B, and so on. What I really want is to figure out which players have a lot of wins from deals that were played by others at the same time or in the past. The reasoning is that players who have wins only when someone else has played this deal before are suspect. However how do I account for this temporal aspect, 'number of won deals which were played before by player X' into the loglikelihood counts? It seems I have several subsets, like wins and losses, wins before a certain time and so on. I am not sure how to work these factors into a loglikelihood ratio test. Perhaps there is a different, more suitable method for this type of problem? Cheers, Frank On Tue, Apr 24, 2012 at 7:32 PM, Frank Scholten <[email protected]> wrote: > On Tue, Apr 24, 2012 at 5:20 PM, Sean Owen <[email protected]> wrote: >> OK, this may yet just be an application of statistics. >> >> I assume that my skill in bridge is a relatively fixed quantity, and >> my score in a game is probably a function of the skill of me and my >> partner, and of our opponents' skill. I don't know how IMPs work, but >> assume you can establish some "expected" change in score given these >> two inputs (average skill of my team, their team). Actual changes >> ought to be normally distributed around that expectation. You look for >> pairs whose actual change is highly unlikely (too high) given this, >> like +3 standard deviations above expectation. > > That seems like a good approach. Thanks! > > Cheers, > > Frank > >> >> How's that? >> >> On Tue, Apr 24, 2012 at 3:13 PM, Frank Scholten <[email protected]> >> wrote: >>> Interesting. However, winning in bridge is not a boolean event, each >>> deal gives a number of IMPs, International Match Points, to each >>> player which can be positive and negative. The sum of IMPs of each >>> deal is always zero. >>
