Neural network design when amount of input neurons vary I'm looking to design a neural network that can predict which runner wins in a sports game, where the amount of runners varies between 2-10. In each case, specific data about the individual runners would be fed into the neural network.

What design would be most advantageous for such a neural network? 

Essentially this is a ranking problem where the amount of inputs and outputs are variable.
The best option in your case would probably be zero padding or padding up. This is simply zeroing out inputs for cases in which there is no data. It's done a lot on the borders of images for CNNs.

That or you could just use a RNN which can handle your variable length inputs with ease.