It usually goes something like this:
"Aff wins more rounds than the neg, so you should give us conditional counterplans. Whaaaaaaaah"
Besides the really whiny tone, the funny thing about this argument is that it is presented on both sides of the topic, with absolutely no data.
Come on people. It's the 21st century here. Quit making statistical claims without statistics.
In an attempt to lay half of these arguments to rest, I knocked up a little python script that counts rounds in results packets and determines affirmative win percentage. As an arbitrary data set, we'll use "the tournaments lucy has been to so far this year." Here's the output of my analysis (rearranged chronologically):
============ valley.txt ===============
Affirmative Wins 108
Negative Wins 126
Aff win percentage 0.461538461538
============ bj.txt ===============
Affirmative Wins 25
Negative Wins 17
Aff win percentage 0.595238095238
============ u-of-m.txt ===============
Affirmative Wins 52
Negative Wins 57
Aff win percentage 0.477064220183
============ hopkins.txt ===============
Affirmative Wins 38
Negative Wins 42
Aff win percentage 0.475
============ sibley.txt ===============
Affirmative Wins 28
Negative Wins 44
Aff win percentage 0.388888888889
============== TOTAL ==================
Affirmative Wins 251
Negative Wins 286
Aff win percentage 0.467411545624
I included only varsity policy divisions, since if a side bias exists in JV or Novice I'm sure I'd care even less. This is far from scientific, since we've got a mix of "national" and "local" tournaments and one of the tournaments was challenge format, but its a start. Looks like we've got a slight "bias" towards the negative so far on the Energy topic. So if you make the "side bias" argument on the aff it's a bad argument, and if you make the "side bias" argument on the neg it's bad argument that's factually inaccurate.