7:30 AM ET

The 2022 college football season was plenty of things. It was the year of the burgeoning Georgia dynasty. It was the year of great playoff semifinals.

Most of all, though, it was the year of the transfer. Thanks to the NCAA’s long-time-coming decision to allow first-time transfers to change schools without sitting for a year, and thanks to the general proliferation of the transfer portal as a means for roster building, FBS coaches made full use of transient talent.

A decade ago, it was a pretty big deal if a school took five or six transfers in one class; in 2022, FBS teams averaged more than seven. It was a dizzying experience.

Last summer, I took a look at the trends I found from portal usage as we headed into such a transfer-heavy season. Now that 2022 is over and we’ve turned the page to 2023, let’s take on the same exercise.

Once again, I am using stats to compare what players produced at their previous schools in 2021 (or, where applicable, earlier seasons) to what they produced at their 2022 destinations.

Because the requirements of different positions are so varied, and because the statistics for measuring performance at given positions are too, I again kept things simple. I created a quick scoring system based primarily on two things: How much did a transfer play, and how well did he play?

Loosely speaking, the scoring scale was set up like this:

1. Almost never played (or played at a level lower than FBS)

2. Played a little

3. Played quite a bit and performed at an average or worse level

4. Played quite a bit and did well

5. Absolute star, one of the best in college football at their position

(Note: For this exercise, I looked only at offensive and defensive players, not specialists. Apologies to all the kickers and punters out there.)

I gave players a score for their performance at both their old school and their new school, then looked at the resulting changes and averages. This is an extremely subjective process, but the goal was to scope out macro-level trend data for an increasingly huge data set, and this was a solid process for achieving that.