Plenty of it. Good review is in "Practical Portfolio Performance Measurement and Attribution" by Carl R. Bacon. You could also use variety of DEA methods. As far as I remember, DEA is not covered by Bacon, but you could find many publications (see fore example Babalos et al., 2007, 2008).
I have written few articles on fund performance and I found that factor models are commonly used. In the case of a fund, you are really measuring "net selectivity", Fama (1990) provides an a framework for measuring net selectivity.
To make a long story short, you really need to clean (orthogonalize) all non-security selection noises. The usual approach is a 4-factor CAPM (RPM, SMB, HML, MOM) which means that your alpha (the intercept) is netted from style effects. Further, you can augment the model with a market timing component to account for cash balance management (add RPM^2).
I found that accounting for time-varying betas--conditional betas--provides significant improvement in the measurement of alpha (I used a conditional 5-factor CAPM in my papers). That is, you need to condition your 4 (or 5) factors with time-varying constructs-instruments- such as financial, economic, political risk factors--or using Ross' APT parameters: change in industrial production, unemployment, default spreads, maturity spreads, and unexpected inflation.
I provided an academic answer to your question. If you wanted a practitioner answer...well, sharpe, treynor, jensen, IR (information ratio), and R^2 are sufficient. Adding more measures (MM, sortino, etc.) won't provide practitioners with added operational efficiency.
Selection ability could also be nicely captured by Brinson, Hood, Beebower attribution, which is also covered in Bacon. But to use it, you have to collect data on portfolios returns with asset classes breakdown. I'm writing research paper on russian pension fund performance, and the only thing which prevents me from Brinson style attribution is lack of such data.