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#11
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To attempt to isolate aggressiveness as a property, valued between 0 and 100, may be a suboptimal approach because of the high degree of intertwining of aggression along the range of very loose to very tight.
Aggressiveness from a loose player is very different from aggressiveness from a tight player. You welcome the LAG and avoid the TA type in most cases. Consequently, since adjusting to Aggression requires context info to get the adjustment "just right", it may be suboptimal to isolate Agg as a property. Perhaps a value from 0 to 100 where 0 is loose passive (calling station) and 100 is super-tight super aggo may be closer to an optimal approach. |
#12
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You are absolutely right that aggression from a loose player is different than aggression from a tight player.
However, I'm not trying to come up with a stat on how good a player plays, just how aggressive. A loose aggressive player and a tight aggressive player will have pretty similar aggression frequency stats. Look at two examples I posted above: 54/16/1.3 = 42% 22/12/1.2 = 42% The first player is very loose, and is no doubt a very bad player. However, you know that this player is semi aggressive. The second player is tighter, but he bets/raises/calls/folds just as frequently as the first player. When this player is in the hand, expect his actions to be very similar to the loose player. This isn't meant to be a universal stat that displays how good a player is, just how aggressive they play their hands. You will need to use it in conjunction with other stats to determine the skill of the player. |
#13
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OK... isn't this just bet% + raise%?
(Which I think is fine as an indicator of aggression) |
#14
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It's close, but not exactly.
I actually added those stats as well: Bet Street %, Check Raise Street %, and Raise Street %. |
#15
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Oh ok, you don't have times_check(ed) in there.
Why do you think it works better without the checks? As far as classifications with limited context go, they would be very close, with the main difference being the previous action facing the player (bet/raise vs check), IMHO. |
#16
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I don't want one tidy number to fit it all in. Here are the stats I want.
Bets flop when checked to: 47% Raises flop when confronted with a bet/raise: 23% Calls flop when confronted with a bet/raise: 32% Bets turn when checked to: 47% Raises turn when confronted with a bet/raise: 21% Calls turn when confronted with a bet/raise: 42% Bets river when checked to: 45% Raises river when confronted with a bet/raise: 11% Calls river when confronted with a bet/raise: 53% |
#17
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I've decided that checking should be considered a neutral action. Check raising is aggressive while check folding/calling is passive. Since there's no way to determine the player's motive, it's better just to remove them from the formula all together.
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#18
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[ QUOTE ]
Bets flop when checked to: 47% [/ QUOTE ] This is the new "Bet Street %" [ QUOTE ] Raises flop when confronted with a bet/raise: 23% [/ QUOTE ] This is the new "Raise Street %" [ QUOTE ] Calls flop when confronted with a bet/raise: 32% [/ QUOTE ] There is no direct stat that shows this, but looking at the "Raise Street %" and "Folds to Street Bet %" should give you an idea how often the player just calls. |
#19
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Good move! On making checking neutral.
I think you ought to weight raising more than betting. Such as TA = Bets + 2xRaises / calls. I think raising is much more indicative of aggression than betting. |
#20
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You might be onto something.
Perhaps we should make raising count as more aggressive than betting and calling as more passive than folding. So the formula would look like this: (times_bet + (times_raised * 2)) / (times_bet + (times_raised * 2) + (times_called * 2) + times_folded) Thoughts? |
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