Experiment involving darts, how should I measure the response? I'm conducting a project where I determine which factors (3 in total) contribute to the accuracy in which one would hit a dartboard. Each factor has two levels, low and high. For example, one of the factors is distance and the lowest distance (closest to the dart board) is 2 meters while the highest distance is 4 meters. Another factor I'm using is the left and right hand to throw.
My problem is, how do I measure the response? should I indicate on the dart board two areas where one is considered a bulls-eye (success) and the other a failure? Or should I measure the distance in centimeters between the bulls-eye and the dart to determine which dart has come closest?
which is the appropriate method? 
 A: I would go for the distance and then use a linear model to describe this score. The other option would be a logistic regression approach, but if you have mostly "failures" as you point out in the comments, this will not be very informative.
The only drawback with the distance is that it is not well behaved in terms of distribution. It is certainly not Gaussian, and probably the variance will change with the different conditions (example: right hand versus left hand). So you might already think of a transform to regularize it, perhaps the log of the inverse distance or something like this.
A: I agree that distance is your best option.  The use of a cutoff throws away information about the accuracy.  So accuracy change with distance from the target is better assessed with a continuously changing measure.  If the shooter is unbiased (i.e. his aim is on center and the hits scatter around the center then if the x an y coordinates on the target are normally distributed and independent with equal variance normalized coordinates would have a Rayleigh distribution for their distance.  This is the square root of a chi square and will be skewed.  As gu11aume points out for linear regression you might want to apply a transformation to the distance to make it symmetric so that least squares regression could then be applied.  Handedness could be used as an indicator variable in the model.
