Introduction
Last year, I introduced a game theory approach for comparing baseball projection systems. Today, I have once again applied the same methodology in order to evaluate which set of baseball projections excelled in 2019.
Most others who venture in such a comparative exercise make use of some type of statistical analysis. They calculate least square errors, perform a chi-squared test, or perhaps do hypothesis testing. I won’t be engaging in any of these capable methods.
Instead, I will look to determine the profitability potential of each projection system by simulating what would have happened in a fantasy auction draft. Instead, I’ll play a game.
What do I mean by this?
First, think about what happens in a fantasy baseball draft auction.
Suppose that Rudy Gamble of Razzball (or anyone who exclusively uses the Razzball projections) walks into a rotisserie auction league prior to the 2019 baseball season. Let’s say that Rudy decides to participate in an NFBC auction league. Mr. Gamble would take his projections and run them through a valuation method to obtain auction prices. He would generate a list that looked something like this …
Razzball Projected Values: Chris Sale 49, Mike Trout 45, Jacob deGrom 44, Max Scherzer 44. Mookie Betts 42, J.D. Martinez 37, Giancarlo Stanton 36, Justin Verlander 35, … , Brandon Lowe 1, Josh Reddick 1, Mark Melancon 1, etc.
In addition to the raw projected values generated by the Razzball system, Rudy would then establish a price point that he is willing to pay for each player. There might be a premium that he will pay for the top ones, and a discount that he expects to save on lower cost players. He may be willing to bid up to $46 on Jacob deGrom (valued at $44), but would only pay $1 for a $4 Jason Kipnis, etc. Read the rest of this entry »
