I have two suggestions to improve the prediction algorithm for Trey's Notebook.

(1) The elimination of songs in the last three sets is arbitrary and introduces a sharp discontinuity in eligibility. Instead I would suggest penalties for being played recently, with the penalty decreasing by show separation. To illustrate what I mean, the penalties could be -15 for one show back, -10 for two shows back, -5 for three shows back, -2 for four shows back, and -1 for five shows back.

So if David Bowie has been played 17 times in the past year, but it was played one show ago, DB would get assigned a score of 2. If YEM has been played 13 times in the past year, but it was played three shows ago, it would get a score of 8.

Then whichever songs get the highest scores would be the predicted ones. In essence, this is exactly the scheme being used, except the penalties right now are -1000 for any songs in the past three shows, and 0 for all other songs.

Naturally, there is nothing magical about the penalties I chose above. There would probably have to be some tinkering done. Assuming it's not too tough, you could mess around with the penalties and observe the resulting average prediction accuracy. Then pick whatever penalties maximize average prediction accuracy over the shows so far.

(2) I wonder if the one year cutoff is optimal in terms of establishing the score for each song. Perhaps you could tinker with this. Again if it's not too hard, you could try different cutoff points and see which one maximizes average prediction accuracy.

In the unlikely event that my advice is implemented, I would suggest doing my second suggestion first. The cutoff point is going to make a big difference in terms of which penalties are optimal.