Difference between revisions of "Research:Mother Feelbright's Busy Bees"

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(Shaping research)
 
m (Still don't know how much bonus/penalty is associated with which category)
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At the moment I only have two data sets; the categorizations seem to fit pretty well, but I think more research might be needed to confirm the formula for the pivot point.
 
At the moment I only have two data sets; the categorizations seem to fit pretty well, but I think more research might be needed to confirm the formula for the pivot point.
 +
 +
It's also not currently clear what the relationship between the categorizations and the bonus/penalty to throwing that results from them is - so more research is needed there.  --[[User:Chat|Chat]] 22:49, 3 September 2009 (UTC)

Revision as of 17:49, 3 September 2009

Shaping effect

I have the following research on shaping effects:

Dollops 239 bonus 311 bonus
1 Professional honey handler Professional honey handler
2 Professional honey handler Professional honey handler
3 Professional honey handler Professional honey handler
4 Professional honey handler Professional honey handler
5 Ease Professional honey handler
6 Ease Professional honey handler
7 Ease Ease
8 Relative ease Ease
9 Relative ease Ease
10 (none) Relative ease
11 Slight difficulty Relative ease
12 Slight difficulty (none)
13 Difficulty Slight difficulty
14 Difficulty Slight difficulty
15 Difficulty Slight difficulty
16 Severe difficulty Difficulty
17 Severe difficulty Difficulty
18 Severe difficulty Difficulty
19 Severe difficulty Severe difficulty
20 Severe difficulty Severe difficulty

From this I've deduced:

  • There's a 'pivot point', which seems to be 'sqrt(bonus) - 5.5'
  • Dollops <= floor(pivot * 0.5) dollops is 'professional honey handler'
  • floor(pivot * 0.5) < dollops <= floor(pivot * 0.75) is 'ease'
  • floor(pivot * 0.75) < dollops <= floor(pivot * 0.95) is 'relative ease'
  • floor(pivot * 0.95) < dollops <= floor(pivot * 1.05) results in no adverb.
  • floor(pivot * 1.05) < dollops <= floor(pivot * 1.25) is 'slight difficulty'
  • floor(pivot * 1.25) < dollops <= floor(pivot * 1.55) is 'difficulty'
  • floor(pivot * 1.55) < dollops is 'severe difficulty'

At the moment I only have two data sets; the categorizations seem to fit pretty well, but I think more research might be needed to confirm the formula for the pivot point.

It's also not currently clear what the relationship between the categorizations and the bonus/penalty to throwing that results from them is - so more research is needed there. --Chat 22:49, 3 September 2009 (UTC)