I build an updated free agency model. It is a relatively simple OLS model, and maybe slightly less accurate than the fancier machine learning model I built last year, but it is much simpler, easier to understand, and easier to update live.
None of them are particularly good at projecting value, but they give a broad shape of the overall league evaluation outline. In simple terms Win Shares with a Usage adjustment plus age and games started have performed the best as at mimicking the market of any metrics I have tried, every year for the last three years. I suspect if you converted PER to a win shares style metric with a team adjustment included, as Justin Willard has discussed, it might predict the market even better.
I also added a cap spike variable, which helped a bit, and helped a bit with this year. But I couldn't track down a consistent available cap space at the start of free agency variable for prior years, so I doubt I have captured the tight post July 4th market for this year.
In any case, here's a scatter of where the model looks compared to the overall market, above the line are "over pays" according to the model (It underestimates max players a bit), and below the line are "bargains" according to the model (Click to expand).
And here is a link to the full results, data via Basketball Reference and Spottrac.
The model is high on more of the big men left in the free agent pool. I suspect it and the player's agents are going to have to make an adjustment next year.
