Sensor selection is an often under-appreciated aspect of state estimator or Kalman filter design. The basic minimum requirement for the choice of a sensor set while designing Kalman filters is that all states are observable. In addition, the sensors should be chosen with a view towards estimating the states with a desired accuracy. Often observability is treated as true/false check during filter design. Beyond observability -- the observability degree -- which measures \emph{how observable} the states are, has been used as the metric of choice to for sensor selection or placement applications. The higher the degree of observability, the better the possibility of designing Kalman filters that achieve the desired state estimation accuracy and consistency requirements. When a wide variety of sensors are available, sometimes with cost and physical constraints involved, sensor selection plays a crucial role in filter design. In such situations it is important to know the expected contribution of each sensor towards observability degree. Shapley values, developed in cooperative game theory for fair allocation of the payout of a multi-player game to individual players, are widely used in machine learning to assess feature importance. This paper shows that Shapley values can indeed be leveraged to quantify the expected marginal contribution of each sensor in any given sensor set towards the observability degree. This quantification of the fair contribution of each sensor towards the observability degree can be leveraged by filter designers for sensor selection, placement and filter (state estimator) design.