I have one question on player pool and average values. The purpose of the player pool is to determine what the average category values (and StdDevs) will be for players in your league in the coming season. But since you and your leaguemates are not clairvoyant, you will not select the best 108 players for your league in your auction (or indeed during subsequent free agent pick-ups). Instead, the expected production of some players will not materialize and leave them out of the top 108. Therefore, doesn’t it make more sense to determine league average values not by looking at the top 108 players in last season’s stats (or this year’s projections) and instead add another 20-30 players to the list to account for inherent inefficiency in player selection?

I’m planning to predict the average values by a) determining the number of hitters on rosters in my league (active and bench players) and then b) adding 20% to that number. This will lower the average values to account for sub-optimal player selection that will naturally occur.

Taking all Hitters, sorting them by position, ranking by points at a position, assessing how many players at each position would get drafted, then figuring out how much better at a position is a player than a reference player, and using this for valuations.

Your thought process really is helpful. Thanks.

Instead of average player, I was using the last expected player drafted at a position’s value to set positional value. Example:

If I thought 20 2B were going to be taken (in a 12 team league, 12 + 8 MI…yeah, I know cruder than your method), I would take the points associated with the 20th player and divide that into each 2B points to get a “positional ratio.” This told me how much better than replacement level a player was. Doing this for all players and positions (a couple players I had to decide upon for which position), I would use this factor times their expected points to calculate a value I would use for valuation :

If Ian Kinsler was worth 100 points, but was 20% better than a replacement 2B, he would be 100*1.20 = 120 points for valuation.

So, Am I over / undervaluing players by using the last player taken value for scarcity instead of average player taken?

I have a couple questions though, which seem not to have been addressed in these articles.

First, you didn’t quite specify what you did about the bench. I’m assuming you ignored it, but please correct me if I”m wrong.

Second, and this might just show my lack of statistical knowledge, why do you divide the difference of the players stats in a category and the average players stats in that category by the standard deviation? It seems as though the higher the standard deviation of a category, the more that category would be sought after; yet, in your system, you seemingly punish categories with higher standard deviations. I understand that by dividing, you accomplish normalizing these categories; however, if there is some other method of normalizing that doesn’t make it harder to value categories with higher standard deviations, it seems as though that might be preferable.

Now, I’m trying to implement a method similar to yours to rank and value fantasy football players for an auction draft. Am I correct in skipping the normalizing step and simply using the points I project the players minus the average points of all of the starters; then, taking the lowest valued player at each position and subtracting their value from each player of their position; finally, dividing the players value by the total draftable value and multiplying that by the total marginal dollars and adding one? This approach seems sensible, but it appears to be pumping out somewhat low dollar values for even the top players from last season.

Regardless of whether you choose to respond to this, I would like to thank you for sharing your methodology of pricing players.

@mays,
just came across this site (thanks to our mutual love of AJ Mass’s “math” skills) and i love it. i’ve been doing the same type of thing as your price guide (without the auction prices) with almost identical results… and might just abandon it for yours, seeing as you have much more customizable rankings and better projections. keep up the good work!