[Nichomacheo]

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Must be some kind of sharkscope manager.

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Not Sharkscope at all. This is not what I want to talk about, so lets stick to the project.

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then calculate an average winrate for the different buyins.

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This is a popular request, but, eh, its 50% across the entire population. Think about it: for every 1 win, there is 1 loss. Four-man games will be different though, but thats not what you asked.

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PS if u want me to analyze the data in SAS

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Dont know what SAS is, but, the data is not in a standard Access database. I had to create my own directory hasing scheme to make it fast.

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I don't know HOW he finds the time while still playing a dozen games or so a day to record thousands of games

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Its called multitasking, jackass. [img]/images/graemlins/wink.gif[/img]

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I think the best way to analyse these is by stakes, then by player, then by winrate.

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Penguin, great response as always. I was thinking about doing something like this and I'm glad you've put some thought into it. I'm going to spend my operations research class today thinking about it and with everyone's help I want to do something like what you were saying. At a high level, it would be breaking the winrates up for each buyin (ie what % of players at the $50s have a winrate 45-50%, 50-55% etc). Just what Penguin said.

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Another thing you might try to do is rank the players (or even compute Elo-type ratings as in chess) rather than just look at a simple statistic like 1) ROI or 2) dollars/day.

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I dont know what ELO is, but I understand what you're saying. You want to come up with a number that represents skill level. I've tried long and hard to do this, but its amazing complex. If you can come up with a smart way to do it that'll factor in the different buyins etc, then I'd do it.

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My interest here would be to evaluate the "learning curve" effect. Bascially, within each limit of play I would look at the overall win rate, then look at the win rate after removing the first 100 games played per player, then look at it after removing the first 200 games played, etc etc...Naturally, this analysis should be on players with at least 400 or so games played.

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I dont think this would be informational. My data is not 100% complete for any period of time. Its a sampling of 900k games out of... who knows. I probably have 60-75% of the game results. Any analysis that involves profit would be pointless (ROI wouldnt). I dont think games 0-100 of a sample would be any different from 200-300 other than variance. Unless you follow a player's ENTIRE results (without blanks), you couldnt do the analysis you're talking about.

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Another analysis we should do is a prediction of winrate in a logistic model. I'd set it up to regress win rates against buyin(mandatory), time of day(perhaps categorized into morning, evening, night), and number of games played by this player. From this we would get very useful info like the amount of % win/loss we can expect by playing under different conditions.

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I like this, but you'd have to explain how to do it.