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In his upcoming book, Winning Fantasy Baseball, Larry Schechter has a chapter subheading called Why Mixed League Value Formulas Are Mostly Worthless. While this statement contains some degree of hyperbole, in my experience it is mostly accurate.

However, it is certainly a worthy endeavor to try to measure what a mixed league hitter is worth. Below are the steps I went through to derive what I consider valid mixed league values, a cursory examination of some truisms surrounding mixed league values and whether they are correct or not, and how you can apply these values to your mixed league and—perhaps more importantly—how you can’t.

The values below assume 12-team mixed leagues with the same rules as a “standard” Rotisserie-style league (5×5 category scheme, \$260 budget per team, etc.)

Step 1: Establishing the Baselines
When deriving National League or American League valuation formulas, I use auction rosters to establish a baseline for mono-league values. I do this because the \$3,120 that is spent in a 12-team, NL or AL-only league auction with a \$260 per team salary cap is spent on auction day, not throughout the regular season.

Distributing \$3,120 worth of value across the best 276 players is an interesting exercise, but isn’t useful in measuring what a player is worth on a league’s typical auction day. Including the complete duds and the players who didn’t quite live up to their expectations produces dollar values that are far too low and won’t provide earnings that can be applied to an auction environment.

As a result, the baseline for mixed league dollar values is less than it is for NL or AL only stats, but not as extreme as you might expect.

Table 1: NL Only, AL Only, and Mixed Hitter Baselines by Player

 Type AB R H HR RBI SB BA \$ Mixed 475 63 128 16 61 9 .269 \$12.5 AL 383 50 100 12 48 7 .261 \$12.5 NL 354 45 93 10 43 6 .263 \$12.5 Mixed Best 168 500 69 139 17 66 11 .278 \$13.7

Table 1 takes the 168 most commonly auctioned/drafted hitters in the American League, National League and mixed leagues and provides the average stats on a per player basis. Looking at this on a per player basis, it doesn’t sound like much of a difference. One home run, five runs batted in, six runs, and two stolen bases. It doesn’t sound like a lot, but using the best 168 mixed league hitters instead of the 168 most likely to be auctioned adds an additional 177 HR, 826 RBI, 230 SB, and 858 runs. It adds up, and would have quite an impact on player value.

Likewise, looking at the difference between a mixed league player and an only league player sounds trivial when you look at it on a per player basis; however the difference between each format’s average auctioned hitter is quite dramatic.

Table 2: NL Only, AL Only, Mixed Hitter Values For Each Counting Stat

 Type AB R H HR RBI SB Mixed (\$0.06) \$0.06 \$0.22 \$0.19 \$0.06 \$0.23 AL (\$0.07) \$0.07 \$0.27 \$0.24 \$0.08 \$0.31 NL (\$0.07) \$0.08 \$0.27 \$0.29 \$0.09 \$0.35

Table 2 shows what one of each at bat, run, hit, home run, RBI, and stolen base are worth using a raw, auction-based formula. Again, it doesn’t sound like much on a per-stat basis, but it makes a difference the more stats each player accumulates. The better the player, the bigger gap there is going to be between his only league stats versus his mixed league stats. Tables 3 and 4 illustrate this gap and the differences upon the impact on the top hitters and the hitters toward the bottom of the (mixed) league heap.

Table 3: Mixed League Versus Only League Values, Top 10 Hitters

 # Player \$ Only \$ 1 Miguel Cabrera \$32 \$42 2 Mike Trout \$31 \$41 3 Paul Goldschmidt \$28 \$41 4 Chris Davis \$27 \$36 5 Andrew McCutchen \$26 \$38 6 Jacoby Ellsbury \$26 \$34 7 Carlos Gomez \$24 \$36 8 Alex Rios \$24 \$32 9 Adam Jones \$24 \$32 10 Robinson Cano \$23 \$31 Average \$27 \$36

Table 4: Mixed League Versus Only League Values, 161-170 Ranked Hitters

 # Player \$ Only \$ 161 Matt Joyce \$9 \$12 162 Wilson Ramos \$8 \$13 163 Lorenzo Cain \$8 \$12 164 Neil Walker \$8 \$13 165 Mike Aviles \$8 \$11 166 Yunel Escobar \$8 \$12 167 Dayan Viciedo \$8 \$11 168 Nolan Arenado \$8 \$13 169 Charlie Blackmon \$8 \$12 170 Jose Iglesias \$8 \$11 Average \$8 \$12
Since there is more of each counting stat to obtain in a typical mixed league, it stands to reason that the better hitters would get penalized more. In other words, Miguel Cabrera stands out for more from the average American League hitter than he does from the average mixed league hitter. Dayan Viciedo is also better using a raw mixed league valuation formula, but because he isn’t as productive as Cabrera, the gap is less. The larger gap for National League players from the mixed league values in Table 4 is because the average National League hitter is weaker than the average American League hitter.

So that’s that, right? Since there is so much value at the bottom of the heap, it makes sense that Cabrera, Trout, and the other superstars get dinged the most while the guys in the middle get dinged less. Thank you for reading Mike Gianella’s 2013 Mixed League Hitter Valuation at Baseball Prospectus. Have a nice day.

Unfortunately, it’s not that simple.

Table 4 wasn’t included here by accident. In a player auction, the 168th-best hitter in a 12-team league should cost \$1. This doesn’t mean that the 168th-best hitter should earn \$1, because hitters who aren’t purchased at auction do wind up earning more than hitters who are. However, eight dollars for the hitters at the bottom of the heap is too high for this bracket. The valuation formulas for mixed leagues should come closer to replicating the valuation formulas for AL and NL-only leagues. Last year in AL-only, the 168th-best hitter, Nate Freiman, earned \$4. This is also true for the 168th-best hitter in the NL, Erik Kratz. \$4 is a reasonable baseline for these hitters in this income bracket, not \$8.

This isn’t an arbitrary decision on my part. The shallower the league, the more players at the top should be paid and the more players at the bottom should get penalized. This fits in nicely with the concept of a replacement level player. Dayan Viciedo, Andy Dirks, and Mike Carp all put up \$8 seasons in 12-team mixed leagues 2013. If you owned Viciedo, you could have easily flipped Dirks or Carp in for Viciedo and had the same relative level of production. In mono formats, Dirks was owned while Carp was owned in some formats. Maybe you get Carp in an AL-only, maybe you don’t, but you definitely have your pick of players like this in the free agent pool if Viciedo doesn’t work out… or if you decide that you’re tired of Viciedo and want to roll the dice on Dirks or Carp.

On the other hand, there is only one Miguel Cabrera and—if you lose him to injury—the player who plug in for him isn’t going to measure up. Even if you hit the lottery in your 12-team mixer and grabbed Josh Donaldso— the best player who wasn’t among the top-168 choices in a mixed league, there is still a \$12 gap between what Cabrera earned and what Donaldson earned. This, of course, is assuming someone else didn’t take Donaldson or that you were lucky/savvy enough to get to Donaldson first.

This is why the players at the top get a bump. The hitters at the bottom are interchangeable cogs, whereas the hitters at the top are not. The question then becomes, what is the best way to do this?

One way would be to take the best hitters by dollar value and bump their values up, take the hitters in the middle and leave their values static or downgrade them slightly, and then downgrade all of the other hitters. This is easy, since it only requires one manual intervention. However, the problem with this approach is that it ignores a crucial difference between mixed league and mono league formats: one category players are far more of a liability in mixed leagues than they are in mono formats. There are enough dead spots in AL/NL only leagues that it is (mostly) irrelevant if you put a 20 steal guy who does nothing else into one of your outfield spots. In a mixed league, with so much production sitting in the free agent pool, your goal is to get production at every slot from every category if possible.

Chart 1: Valuation Adjustment by Category, Mixed Leagues

The chart above is a representation of the adjustments I made to mixed league hitters by category. The x-Axis lists how each adjustment was made by bracket, while the Y-Axis shows the adjustment. For example, for the 12 best home run hitters in Major League Baseball in 2013, I took their raw adjusted valuation from the original mixed league formula (above) and added 21.49 percent of value to the hitters in those categories. Without breaking down the chart into granular detail, the top 60 hitters in each category received an increase in value while anyone below that was docked.

There were 634 hitters who had at least one and another two—Freddy Guzman and John Hester—who either scored a run or stole a base without the benefit of an AB. However, the chart above does not continue docking hitters past the negative percentage shown on the chart above. The baseline dollar value for a player who doesn’t play is somewhat arbitrary, but I selected negative seven dollars for a player who didn’t play at all in 2013 but was inserted into a typical Rotisserie league squad’s lineup.

For an example of how this works for one player, here is 2013 Miguel Cabrera:

Table 5: Miguel Cabrera, AL-Only, Mixed Raw, and Mixed Adjusted Values

 Type HR RBI SB R BA \$ AL \$10.74 \$10.50 \$0.94 \$7.67 \$11.70 \$42 Mixed Raw \$8.34 \$8.23 \$0.71 \$5.97 \$8.70 \$32 Mixed Adj \$10.13 \$9.99 \$0.20 \$7.26 \$10.55 \$38

By adjusting the best fantasy hitter in 2013 back up using a scarcity model, Cabrera gets a six dollar bump. However, he doesn’t quite make it back to his AL-only stratosphere (and he does get docked in stolen bases, the one category where he isn’t a strong contributor).

The new mixed league Top 10 looks like this.

Table 6: Top 10 Mixed League Hitters 2013

 # Player HR RBI SB R BA \$ \$ Raw 1 Miguel Cabrera \$10.13 \$9.99 \$0.20 \$7.26 \$10.55 \$38 \$32 2 Mike Trout \$5.65 \$6.43 \$8.49 \$7.68 \$7.58 \$36 \$31 3 Paul Goldschmidt \$8.29 \$9.11 \$3.69 \$7.26 \$4.36 \$33 \$28 4 Chris Davis \$12.20 \$10.06 \$0.42 \$7.26 \$2.05 \$32 \$27 5 Andrew McCutchen \$4.17 \$5.37 \$6.94 \$6.84 \$6.77 \$30 \$26 6 Adam Jones \$7.60 \$7.87 \$3.44 \$7.05 \$2.12 \$28 \$24 7 Jacoby Ellsbury \$0.52 \$2.32 \$14.70 \$5.90 \$3.53 \$27 \$26 8 Carlos Gomez \$4.85 \$4.25 \$11.31 \$4.92 \$1.50 \$27 \$24 9 Robinson Cano \$5.65 \$7.80 \$1.28 \$4.98 \$6.52 \$26 \$23 10 Alex Rios \$3.13 \$5.15 \$11.87 \$5.11 \$0.88 \$26 \$24

These are the same hitters that were in Table 3 (above). Some of them are in a different order and some of them receive different adjustments than others. Ellsbury sees the smallest increase of all of the hitters because a good chunk of his value is wrapped up in stolen bases, but it is important to note that he does receive an increase. One thing my methodology does that is somewhat different than other mixed league valuation is that it doesn’t penalize one category players as much as other systems do…particularly if that one player is a categorical monster like Ellsbury was in 2013. In Ellsbury’s case, he is penalized in home runs and RBI but sees a slight bump in batting average in runs and a large bump in stolen bases.

For comparative purposes, here are the 161-170th ranked players using this modified mixed valuation model:

Table 7: 161-170 Mixed League Hitters 2013

 # Player HR RBI SB R BA \$ \$ Raw 1 Evan Gattis \$4.17 \$3.34 -\$1.75 \$0.98 -\$2.62 \$4 \$9 2 Mark Reynolds \$4.17 \$3.65 \$0.20 \$2.16 -\$6.06 \$4 \$7 3 Brian McCann \$3.68 \$2.69 -\$1.75 \$0.85 -\$1.36 \$4 \$9 4 Ryan Raburn \$2.58 \$2.52 -\$1.75 \$0.59 \$0.07 \$4 \$9 5 Wilson Ramos \$2.58 \$2.90 -\$1.75 -\$0.02 \$0.10 \$4 \$8 6 Mike Aviles \$0.52 \$1.39 \$1.54 \$2.03 -\$1.74 \$4 \$8 7 Charlie Blackmon \$0.11 -\$0.15 \$1.28 \$0.33 \$2.15 \$4 \$8 8 Alcides Escobar -\$0.34 \$2.11 \$5.49 \$2.28 -\$5.82 \$4 \$8 9 Mitch Moreland \$4.17 \$3.34 -\$1.75 \$0.98 -\$2.62 \$4 \$8 10 David Freese \$4.17 \$3.65 \$0.20 \$2.16 -\$6.06 \$4 \$8

Adjusting the formulas shuffles the deck (some of the players here are different) but the adjustment does what I intended it to do: shifts value of the players in this value range from \$8-9 down to \$4. If the players at the top of the valuation spectrum are going to get some additional money, the players at the to bottom are going to get robbed.

I can’t possibly anticipate all of the questions that will derive from this pricing, but I can anticipate three of them before they are even asked. So to preempt the objections I know I’ll get, here are those questions and here are my answers:

1. Some mixed league formulas assign greater value to the top players while cutting even more value from the players at the bottom? Why don’t you do this?

Some mixed league pricing models do add more money to the top players in the pool while taking even more money from players at the bottom. And I can’t argue that this isn’t a valid method either, depending on what it is you are trying to reflect with your valuation.

In my case, I would argue that the “replacement level player” isn’t the 168th player in the pool at the end of the season, but a player with a much lower level of statistics. Even in mixed leagues, there is a difference between the level of anticipated statistics and the level of predictability that the players in the pool have. Danny Espinosa, Tyler Colvin, Corey Hart, and Derek Jeter were all owned in at least some mixed leagues on Opening Day last year. While in a perfect world we would all like to think that we would have plugged Jed Lowrie, Domonic Brown, and Michael Brantley into our starting line-ups, the reality is that we sometimes do far worse. The “replacement”-level player I’m generating is a guy whose line looks like this:

Generic Mixed League “Replacement Level” Guy: 5 HR, 25 RBI, 1 SB, 30 Runs, .269 BA

This assumption takes away less value from the guys at the bottom than other models do, which means that it adds less value to guys at the top. If you disagree with this assumption, that’s perfectly fine; just remember to add more money to guys at the top if you take more money away from the players at the bottom.

2. Steals are worth a lot less in mixed leagues and should be devalued considerably. Unless a player is a top stolen base option, if all he offers are stolen bases, he isn’t even worth being on your roster

There were a number of assumptions I made about mixed league pricing during this study that held up. But one of the assumptions that didn’t is that stolen bases lose a significant amount of value in mixed formats.

Table 8: Mixed League Hitters with Stolen Base Value Comparison

 Player SB Mixed \$ Only \$ Mixed Rank Jonathan Villar 18 \$0 \$7 229 Chris Getz 16 -\$1 \$7 253 Juan Pierre 23 \$2 \$11 205 Ben Revere 22 \$6 \$16 138 Elliot Johnson 22 -\$1 \$7 242 Jarrod Dyson 34 \$7 \$14 124 Jordan Schafer 22 \$3 \$12 181 Craig Gentry 24 \$6 \$14 140 DJ LeMahieu 18 \$5 \$14 148 Rajai Davis 45 \$13 \$21 69

Most of these players aren’t worth owning. But a few are and one of them—Rajai Davis—would have been quite valuable in mixed leagues last year.

Some of the reason for this is the formula modifications that I mentioned above. Some of this is because stolen bases aren’t as readily available as they were a generation ago. In 1986, for example, the average Major League team stole 127 bases. In 2013, the average big league team stole 90. While steals are worth less in mixed formats, the lack of stolen bases has made it harder to ignore the contributions of a 40-plus steal monster like Rajai Davis, even if he does absolutely nothing else.

3. Is any of this information useful for mixed league auctions?

The short answer to this question is “probably not.” The reasons why this information isn’t useful are complicated.

In another excerpt from Schechter’s book, he took a look at four sources of expert prices (magazines, web sites, etc.) and what the players actually were purchased for in the Tout Wars mixed league auction.

Table 9: Mixed League Suggested Bids and Actual Prices

 Mono #1 #2 #3 #4 Reality \$40 \$38 \$34 \$38 \$37 \$40 \$35 \$34 \$28 \$31 \$34 \$35 \$30 \$28 \$23 \$27 \$27 \$25 \$25 \$23 \$18 \$22 \$19 \$15 \$20 \$19 \$12 \$14 \$14 \$7 \$15 \$13 \$6 \$8 \$8 \$2 \$8 \$2 \$2 \$2 \$2 \$1 \$5 \$5 \$0 \$0 \$0 \$0

Without delving too deeply into the details in the chart, the higher the prices are in the mono leagues and the suggested expert prices, the more closely the suggested bids come close to resembling reality. The cheaper the expert prices get, the more variance there is in the price schemes. Does this mean that the experts are off base and have no idea how to price mixed league hitters?

No, this isn’t the case at all. Without even looking at the specific prices in Schechter’s chart, it is fairly simple to intuit what is happening.

In mono leagues, while there is some variance on player prices, nearly everyone has the same general idea of what a player “should” cost. In 2014, Russell Martin might go for anywhere between \$10-14, but it is extremely unlikely that he would go for \$20 or two dollars. There is a very narrow range of prices for Martin, and this is the case for most players in mono formats.

In mixed leagues, there is far more variance in player prices. This in part is because there is a much larger player pool to choose from and thus a far greater range of opinions. Someone might have had Matt Carpenter unranked in mixed league formats while someone else might have had an \$8 price tag on Carpenter. Neither of these rankings was unrealistic on Opening Day. The expectations for Carpenter were fairly broad and with all of the other players available for auction, there is greater latitude for a difference of opinion.

Pricing mixed league players is an interesting exercise in trying to come up with a valid yardstick for what players will earn. But even more than in only leagues, it is more important to try and observe the auction trends in your league than the hypothetical earnings of the players in your player pool. It is possible to use some of the information I have provided to your advantage, but for the most part, the idea that every auction is different takes on an even deeper meaning in mixed leagues.

This is a free article. If you enjoyed it, consider subscribing to Baseball Prospectus. Subscriptions support ongoing public baseball research and analysis in an increasingly proprietary environment.

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gpurcell
12/10
It's possible to get a bit more precise in determining replacement level, I think. The way I approach it is to get a set of player projections with playing time (thanks BP) and sort by PAs. I then fill all the slots in the league with the players that have the most projected playing time for each position. Replacement level then ends up being different for CI/MI/C/OF. Summing up the offensive statistics for this group then gives a predicted league total for each statistic while controlling for position variability.

Since Roto is a zero sum game the relative value of each player may be determined simply through an analysis of the SHARES of each of the offense category contributed by the player. (I generally underweight SB in this procedure.) The proof of this (and the process of determining dollar valuations) is left as an exercise for the student.
ravenight
12/10
Even there, though, how do you account for players with multiple eligibility? Also, is it really useful to say that replacement level is the value of the best guy who doesn't play and calculate value off that?

Just for a simple example, let's say that you've got 2 teams playing one hitter at each of 2 positions (C and F) and there are 5 available hitters at each position. All you care about is steals, with projected numbers for the Cs of 5, 3, 2, 2, 2 and for the Fs of 9, 7, 5, 3, 2. If you call "replacement" level 2 for C and 3 for F, and each team has \$10 to spend, then you are saying that there's \$20 to spend on 10 "steals above replacement", so each one is worth \$2, and thus the value of the players are \$6, \$2, \$0, \$0, \$0 for Cs and \$8, \$4, \$0, \$0, \$0 for Fs. If you discover that the 3rd-best F is really projected at 6, does it make sense for that to reduce the value of the best F?

Now I know that's a contrived example, and the whole concept of replacement level is that it's the "readily available" talent level, but I think it's a fundamental flaw to assign so little value to the field. Almost no one drafted guys like Domonic Brown, Josh Donaldson, etc and projections had them solidly below replacement level entering the season, yet they put up huge numbers. I would argue, therefore, that they and any other players with a similar shot at a breakout (which is basically anyone near the top of the depth charts, in some sense) had value greater than 0 prior to the season. Their value if they were a bust was zero because no one would just toss them in the lineup and let them muddle through, but their value if they had a breakout was high, so the weighted average including the odds of those things was greater than 0.

PECOTA gives the weighted mean performance, but that isn't really helpful for fantasy - a player with values for 90/75/50/25/10 of \$5/\$0/-\$2/-\$4/-\$8 isn't worth -\$1.90, he's worth closer to \$0.50 (though not exactly since you won't know in advance where his performance will end up, so there's some chance of missing upside or including downside when you end up playing him). The PFM should use the different percentile projections directly - at the very least, it could establish a replacement level based on the weighted means, and then evaluate each player relative to that level using his own percentiles (and then normalize so the cash values still add up).
gpurcell
12/11
I think I may not have communicated clearly: determining the league point totals my rank ordering through projected playing time into each position slot merely has the EFFECT of imposing any needed control for position scarcity and so positions can then be ignored in the draft/auction outside the mechanical requirement of filling the roster--it doesn't imply there is any great weight attached to it and indeed I generally find that the back end of the ranking is mixed in with catchers and MI who are barely above replacement level and deserve no great consideration for the small increase in production they can provide.
MikeGianella
12/10
Yes, this is true. However, something I should have mentioned in the article (which is another piece of the research Schechter did) is that positional differences in value - even in mixed leagues - are overblown. There is definitively a difference for catcher in mixed leagues; however, this is true in two catcher leagues, not one catcher formats. With more and more leagues moving to one catcher formats, this difference has been muted as well. My model assumed a mix of one and two catcher mixed formats, which resulted in 16 catchers included among the top 168 players. If you are in a two catcher league, it is definitely important to make an adjustment for scarcity in your draft or auction!

You can analyze the shares of each offensive category, but that is not an SGP model, and that is what I'm working with. If you disagree with SGP theory (and if you do you are certainly not alone), then when you run the PFM leave this option as OFF. My issue with doing this is I have found that I wind up with a steals/saves heavy team that is deficient elsewhere but - as I always say - your experience in your leagues might differ from my experience in my leagues.
gpurcell
12/11
Yeah, I agree with almost all of that--particularly the danger in ending up with a steals-heavy team if you weight that category similar to the others (I don't use a specific valuation system for pitchers; rather I just do a modified LIMA approach and target a handful of guys at various draft points). What I do for steals is not include them in the sum of shares of the other four categories I use to rank order hitters but rather have the projection alongside them on my draft sheet and just keep a running tally of projected steals through the draft.
brooksp
12/10
Per the last comment, how does all this relate to the PFM? Is it of any use for a mixed league?
SJLedet
12/11
I normally enjoy these type of articles, but, I'm recovering from minor surgery and the pain meds are dulling my comprehension (luckily). So as brooksp asks above, is all this factored into PFM? Am I being prudent using it as a base?
MikeGianella
12/11
You can use PFM as a base, although make sure to set the SGP option to yes.
Hodiggity2001
12/11
If you can analyze past performance what would Ty Cobb's 1915 season look like?
MikeGianella
12/12
It's difficult to go back and figure out who would have been purchased in a Roto auction in 1915, but using a similar model Ty Cobb would have been worth \$57 in an AL only league in 1915. Steals were worth a lot less in 1915 because there were so many of them, but Cobb's 96 still would have given him a great deal of value. Where he really would have stood out, though, would have been in his batting average. His ..369 batting average was over 100 points over the league average and even accounting for adjustments for players not taken at auction, Cobb's batting average would have provided more value than in any other category.
Hodiggity2001
12/12
That is just sick. The runs scored also stood out to me when I read his line. Appreciate you taking the time to look at this Mike.
MikeGianella
12/12
Peter Kreutzer (a.k.a. Rotoman) ran through this exercise years ago in one of Peter Goldenbock's "How to Win at Rotisserie Baseball" books for a number of players. I believe 1921 Babe Ruth was a \$90-100 player or something absurd like that.
Hodiggity2001
12/13
Doesn't surprise me. It's always interesting to look at some of the stat lines early last century. Babe was the man. Although there's been recent rumors of him being tied to performance enhancing Budweiser. Would have been interesting to see the griping about missed pitcher ab's when he was pitching in Boston. One can only imagine.
Hodiggity2001
12/13
Oh, forgot to mention. My great great grandfather had babe in a dynasty league. He always complained about lack of protection until Gehrig got there.
brianjenner
12/28
I'm late to the party, but I just read Schechter's book and this particular chapter was the most ... strange, for me. As somebody who has been familiar with player valuation since Todd Zola opened my eyes in 2004, I find it very odd to see the best Mixed fantasy player in the world uses adjusted mono-league values. I feel like I would have been less shocked if I discovered he used a Ouija Board.

Now, in table 4 there in this article, there are mixed league values. Why do you have the 170th ranked hitter worth \$8? How did you come up with these values? If the values were done properly, the 170th player in terms of value is worth \$1 and Miggy's value relative to this player, the "replacement player", goes up significantly. Rather than doing an ad-hoc adjustment to get his value from \$32 to \$38, it may very well be closer to \$40 simply by using sound valuation methods. Maybe I'm just misunderstanding, but I've never had an issue with mixed league valuation and it's never been far off of what the "experts" value players at, just with a proper method and no fiddling around with arbitrary adjustments.
MikeGianella
12/28
The explanation of how the values were derived was explained in painstaking detail within the body of the article.

The short answer to why the 170th hitter was worth \$8 is that as with nearly every SGP model I have come across, values are spun off of the final auction or draft rosters, not off of the best 168 hitters at the end of the season. If you used this methodology, you are correct that the 168th hitter would indeed be worth \$1 (in fact, he might be worth slightly less depending upon how you valued batting average) but the best hitter would be worth significantly less than \$32. If you are having a retrospective auction, this information would be of great use to you, but if you were auctioning before the season started, this method wouldn't be practical in any way, shape, or form.
brianjenner
12/28
Maybe this is just my unfamiliarity with SGP but I don't see the benefit of using it when it takes a lot of arbitrary massaging of the numbers to get them to make sense. I just don't see how any valid system can say the last player is worth \$4 or \$8, or anything other than \$1, and that it needs to be further adjusted. This could just be my ignorance, but reading the entire article twice did not do anything to persuade me toward this method.

Using Z-scores, the top player in a 15 team mixed is typically worth around \$44 and the last players picked are always worth \$1. The curve of values almost exactly mirrors the actual auction results of leagues like Tout. There is a bit of cash shifted to the elite players from the guys in the \$5-\$15 range but the gap is only about \$2 in that range.

If I'm missing something I'd be glad to have it pointed out, and seeing the success of the people using methods like these I'm sure there are some things that I'm missing.
MikeGianella
12/29
I would recommend doing some fundamental reading on SGP. Unfortunately, the industry standard on the subject is not a cheap book to buy. However, without a basic understanding of SGP on your end, I could offer more explanations and your likely response is going to be along similar lines of the "I don't see the benefit" or "I don't understand" comments you have provided earlier. Addressing the origins and validity of SGP is beyond the scope of a 3,000 word article, let alone a response to a comment.

Here is a link for options to buy this book (this is not an endorsement of this book nor do I receive any compensation): https://www.google.com/search?q=art+mcgee+how+to+value+players&oq=art+mcgee+&aqs=chrome.1.69i59l2j69i57j0l3.5097j0j7&sourceid=chrome&es_sm=93&ie=UTF-8#q=art+mcgee+how+to+value+players&tbm=shop

SGP is a 25-30 year old industry standard for player valuation. It doesn't make it "right" (and I'd cringe at anyone who offered a "that's the way it has always been done" argument), but it does put the burden or proof for saying that it is wrong or misguided on those who don't believe in its validity.
brianjenner
12/29
Well I think just by virtue of the fact that it is widely used is enough of a reason to get familiar with it. Even if it's not the best objective measure, or "right", if people use it then it must be accounted for. Just like knowing about the hitter/pitcher split, or how much to value closers, etc. Being "right" stops being so important sometimes. I guess I've never given it enough credence despite being vaguely aware of it for the past decade. Personally I blame the fact that I've learned most of what I know about valuation from Todd Zola for the reason I haven't given a lot of thought to SGP, haha. Thanks for your patience.