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ottoneu Values Refreshed

Posted By Chad Young On March 12, 2013 @ 9:15 am In Auction,Featured,Ottoneu,Projections,Rankings | 29 Comments

Back when we launched FG+ earlier this year, I included a table that put dollar values on players for year one and future year ottoneu leagues. Since then, I have mentioned a few times that I wanted to refresh those values with new projections.

Well, I have auctions coming up the next two weekends and finally got my act together to get my spreadsheets in shape; today, I release them into the wild!

Actually, those of you who follow me on twitter (@chadyoung) may have seen these already, but today I will give a bit more info on what you are looking at.

Before sharing the link, I’ll start with an update on my methodology (you can see the original here).

The biggest difference is that I used a combination of three projection systems – Steamer, ZiPS and PECOTA – rather than only a single system. I do this because I like to smooth out the outliers from each. Some people have told me they think this is a mistake, but it’s a process that works for me. If all three (or even two of the three) agree that a guy is a candidate for a big season, it shows up. If only one does, it shows up but much less clearly. Instead of getting caught up in the particulars of a single system, I am using a modified version of the “wisdom of the crowds” and it has served me well in the past. I do not, by the way, weight them equally – I have a preference for ZiPS in general and Steamer for pitchers, and I take that into account.

The next change is that I made a change to the way I calculate Points Above Replacement (PAR) for points leagues. As discussed with reader Chok in the comments of the article linked to above, I was likely under-weighting the value of a player leading off (or just playing every day) and getting large numbers of plate appearances, by breaking everything down to a per-PA ranking. I still use Pts/PA as my primary method to determine value, but now any plate appearances past #600 are compared to a replacement level of 0. So, for his first 600 PA, Ryan Braun gets credit for his 1.613 Pts/PA minus the OF replacement level of 1.117 Pts/PA, but for the other 76, he gets the full 1.613. I think this will help the issue of undervaluing guys who play a lot and overvaluing platoon players. For catchers, I used 500 PA instead of 600.

I also limited the player pool, only using players who were ranked by all three systems, leaving me with 644 hitters and 652 pitchers.

And, finally, rather than basing inflation on a theoretical set of keepers based on owners only keeping the players my spreadsheet says they should keep, I based it on the actual results of the cut deadline in my leagues.

With that said, here is the link to the spreadsheet.

A couple interesting notes:

1) Inflation is definitely less drastic than it appeared before. This is to be expected – we are not perfectly logical (at least not defined as only keeping guys whose value is mathematically better than his price) in our keepers. We keep players who we think are underrated (I kept a $30 Matt Moore because I believe he will produce at a much higher level than the rankings do); we keep players who we think have more value in the future than they do now (my $5 Trevor Bauer has negative value in the rankings, but I expect he will far outperform the $7 he costs me NEXT season); and we keep players because we fear we can’t fill needs otherwise (I don’t love a $4 Ryan Dempster, but pitching will be hard to come by in the original league). All of this results in more dollars per production being spent before the auction, limiting inflation. That said, in my leagues that are one and two years old, you can see a couple dollars inflation on the top guys and that jumps to closer to $10 in the long-standing original league.

2) Positional strength varied by format. In 4×4, the replacement level values were, from lowest to highest, SS, 2B, C, OF, 3B, 1B. In 5×5, C, SS, 2B, OF, 3B, 1B. For points leagues, it matched 4×4. The big difference here is where catchers rank. I assume this has to do with catchers providing little speed and with the catcher position having a number of low-average, high-power players who play better in 4×4 and points leagues.

Alright, dive in, let me know what you think!


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