Group work, where students work on projects to overcome challenges together, has numerous advantages, including learning of important transferable skills, better learning experience and increased motivation. However, in many academic systems the advantages of group projects clash with the need to assign individualised marks to students. A number of different schemes have been proposed to individualise group project marks, these include marking of individual reflexive accounts of the group work and peer assessment. Here, we explore a number of these schemes in computational experiments with an artificial student population. Our analysis highlights the advantages and disadvantages of each scheme and particularly reveals the power of a new scheme proposed here that we call pseudoinverse marking.