If there's no fraud, the win rate should be the same on deals that had been played before, as on those that have not been played before.
You have wins/losses on previously-played games, and wins/losses on new games. Those are the ratios you're testing. On Sat, Apr 28, 2012 at 7:09 PM, Frank Scholten <[email protected]> wrote: > 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
