Summary: The local linear smoother usually gives better performance than the Nadaraya-Watson smoother. An exception is the case of data sparsity. Here we discuss a modification of the Nadaraya-Watson smoother by Müller and Song (1993), based on a horizontal shift of the kernel weights towards the local centre of mass of the design points. This gives performance similar to the local linear when that works well and better performance when it does not. The new smoother also preserves monotonicity. Shifting towards the centre of mass is also used to develop a modified kernel density estimate which cancels the well-known peak spreading effect.