How to determine individual contribution when data is aggregated by team
Two alternatives

TrueSkill:

You might want to try a variant of TrueSkill. As Wikipedia says: 


  Factor graphs are used to "pack up" each team into $(\mu ,\sigma )$
  pairs on which the update formulas are run; the skill updates are then
  correctly distributed to each player.


Here's the TrueSkill paper with more details. From the abstract:


  The new system tracks the uncertainty about player skills, explicitly
  models draws, can deal with any number of competing entities and can
  infer individual skills from team results. Inference is performed by
  approximate message passing on a factor graph representation of the
  model.


Simple Regression:

You can try to set this up as a multivariate regression problem. Inputs are tuples representing who was involved in the sale, outputs are # of sales. So something like this, assuming tuples are (Fred, Susan, Tom, John, Wayne, Conner, Winona):

(1, 1, 1, 0, 0, 0, 0), 25
(0, 0, 0, 1, 1, 1, 0), 12
(1, 0, 0, 1, 1, 0, 0), 16
etc...


Now each person's coefficient should be proportional to their contribution, assuming no interaction effects.