Article #8: Macro Meta Crunching
- written by Helz
Meta hunting could be summarized as getting to know a player's normal behavior, associating it to their alignment in that game, and comparing it to their current play in order to get an impression of what their current alignment is. This is a popular technique because of the inherent differences between town and non-town motivations that result in perceivable changes in behavior that are individual to each player. You could equate these differences to ?tells? in games of poker. A person will behave differently based upon their current hand. The effective use of statistics is crunching data in order to identify statistically significant information gained through enough replication to identify correlation. This correlation points to the sample data XXXXXX suggesting XXXXXX about (in this case) the player the data was taken from.
By identifying ways to crunch large amounts of data in order to identify patterns that reflect differences in behavior or ?tells? players manipulate this technique to identify a player's alignment. By establishing a large pool of analyzed games players can then simply plug in their method of analysis to a game and apply a calculated level of mathematical certainty to read a player independent of any real analysis.
Here would be 1 simple example:
The ?meta? tell for Bob is that in games as scum he dissociates his perspective from his posts when analyzing a player. This is a ?scum tell? because it reflects him posting from a separate perspective as if attempting to hide his perspective and forgetting to translate his analysis. Player X breaks down the word count of some of Bobs games using online tools and identifies that when playing as a town player Bob frequently uses the words ?I, My, and Me? where as in games as scum the count for these words is much smaller in proportion to the total analyzed word count. In contrast in games where Bob was playing as a scum player Bob he more frequently uses the words ?You and We?. Player X has now established correlation between this word count data and Bobs alignment moving a simple meta tell into something that can be mathematically analyzed. With this in mind Player X breaks down the word count of Bobs analysis posts in the current game and identifies that Bob has a lower than normal use of the words ?I, My, and Me? than observed in Bobs ?town games? while the data for his use of ?You and We? match the analyzed data taken from Bobs ?scum games?. Using this tool Player X is able to apply simple statistical formulas to identify a calculated level of mathematical certainty that Bob has a scum alignment in the current game.
This practice is not limited to breaking down word counts to identify perspective tells. The behavior targeted, method of data collection and different ways of separating sample pools allow for limitless different methods of analysis. Some players use it to identify accounts behind anon games while others use it to clear town or identify patterns that push them to look into a player more as opposed to using it as a substitute for scum hunting.
I am presenting this information to level the playing field. I know of some players using it to build information on the regular players in their site which allows them to constantly pull out good reads and seem skilled. In my opinion some uses of this is unfair to other players and turns Mafia/Werewolf into something it was not meant to be. Although there are also many nice applications of this that allow for players to improve such as analyzing themselves and potentially prevent this from functioning against them or even counter it by manipulating their posts to throw off people who would use it against them. I will note that this is mostly functional against players who stick to 1 play style because by using different play styles you make analyzing the data of your posts much more complex to do with any level of accuracy.
On a less related but associated subject this can also be applied to help determine the accuracy of your reads as a player in order to identify what you are good at finding and should therefore focus on in your analysis. To do this after each game just look at the reasoning behind your correct and incorrect reads and chart your progress. You will find patterns emerging over enough games pointing to you almost never being correct when analyzing one aspect or indicator while you may very often be dead on. Gaining understanding of your ability as a player allows for focused effort to improve. It would be equally easy to apply this to identify why you got lynched as scum, why you got lynched as town, or why you were targeted for some night action (As long as you get the player to explain his reasoning post game).
- Data can be taken from a player's games and analyzed to identify meta trends.
- These trends can then be compared to data taken from a current game to identify specific factors of a player such as alignment.
- A player can analyze their own games to identify their own tells and improve as a player.