Properly Valuing Hitters with Injury Risk

Giancarlo Stanton has the highest projected value of the "Unlucky 13." (via Arturo Pardavila III & Joon Lee)

Giancarlo Stanton has the highest projected value of the “Unlucky 13.” (via Arturo Pardavila III & Joon Lee)

Editor’s Note: This is the fourth post of “Fantasy Baseball Preview Week!” For more info, click here.

Look at the Steamer projections for players like Giancarlo Stanton, Miguel Cabrera and Troy Tulowitzki and you’ll quickly see they’re not projected to play anywhere close to a full season. Certainly this is due to their increased likelihood of trip(s) to the disabled list. For fantasy players, most calculation systems convert those projected stat lines into rotisserie dollar values but don’t account for replacement level statistics accumulated when the injured player is on the DL.

In this article I’ll look at this adjustment using projections for the 2016 season.

Identifying the Players

I started this process with Steamer projections as of Feb. 1. Using a standings gain points approach, I then calculated dollar valuations for those players. I then scanned through that list and selected 13 players who appeared relatively high in value but who clearly had fewer projected plate appearances than those around them in the list. The unlucky players I selected are:

UNLUCKY 13, UNADJUSTED 2016 DOLLAR VALUE BASED ON STEAMER PROJECTIONS
PLAYER TEAM POS G R HR RBI SB AVG $VALUE
Giancarlo Stanton MIA OF 126 84 38 96  7 0.277 29.55
Miguel Cabrera DET 1B 134 86 26 93  2 0.313 26.88
Jose Bautista TOR OF 134 85 32 91  5 0.258 21.90
Ryan Braun MIL OF 131 73 23 78 15 0.276 20.40
George Springer HOU OF 129 79 26 75 16 0.255 19.83
Edwin Encarnacion TOR 1B 125 77 30 88  3 0.266 19.26
Yasiel Puig LAD OF 130 79 22 74 10 0.284 18.97
Carlos Gonzalez COL OF 123 71 28 84  4 0.276 17.94
Jacoby Ellsbury NYY OF 130 77 14 56 27 0.265 16.83
Albert Pujols LAA 1B 133 72 27 86  4 0.260 16.07
Lorenzo Cain KC OF 131 67 11 65 21 0.282 14.82
Carlos Gomez HOU OF 126 68 17 65 21 0.256 13.95
Troy Tulowitzki TOR SS 119 68 19 65  2 0.261  6.02

Assumptions Used

Before I get too far along, let me specify the assumptions I used in calculating the dollar values. These reflect a 12-team league using standard rotisserie rosters of 14 hitters, nine pitchers, and no bench. This means 168 hitters will be drafted. Teams are given a $260 budget and calculations reflect an allocation of 67 percent of the budget to hitting and 33 percent to pitching. I’ll also note that due to some issues with player IDs and missing projections, certain players like Byung-Ho Park and Hyung-Soo Kim were not included in this analysis.

Defining Replacement Level

Because projections are so volatile, and due to differences in opinion, it’s unrealistic to identify only one player as “replacement level.” Accordingly, I’ll use the average stat line for the first five undrafted players at each position to represent “replacement level.” For a 12-team league using the roster arrangements listed above, here’s what replacement level looks like:

FIRST FIVE UNDRAFTED PLAYERS (REPLACEMENT LEVEL)
NAME POS G PA AB H HR R RBI SB AVG
Adam Lind 1B 114 480 426 114 15 55 58  1 0.268
Ryan Zimmerman 1B  97 421 378  99 15 48 56  2 0.262
Matt Adams 1B  92 392 363  98 15 44 54  2 0.270
Logan Morrison 1B 110 456 405  97 15 50 52  6 0.240
Ryan Howard 1B 108 460 417  95 18 47 57  1 0.228
Howie Kendrick 2B 117 507 468 132  9 53 51  6 0.282
Brett Lawrie 2B 115 472 432 111 16 52 57  4 0.257
Joe Panik 2B 126 563 509 142  7 62 48  4 0.279
Javier Baez 2B  84 348 320  81 16 41 48  9 0.253
Devon Travis 2B  82 351 320  87  9 42 39  7 0.272
David Wright 3B 100 445 397 106 11 51 46  5 0.267
Martin Prado 3B 125 530 485 132  9 52 54  2 0.272
David Freese 3B 116 482 432 112 13 49 54  2 0.259
Danny Valencia 3B 106 462 425 106 14 49 55  3 0.249
Hector Olivera 3B 119 496 459 120 12 48 56  2 0.261
Michael Brantley OF  91 405 362 107  9 48 48  8 0.296
Leonys Martin OF 124 492 451 109  8 49 45 22 0.242
Corey Dickerson OF 103 448 414 107 15 50 56  5 0.258
Colby Rasmus OF 121 507 455 101 20 56 62  3 0.222
Rusney Castillo OF 101 414 382 104  9 45 47 10 0.272
Jose Reyes SS  87 389 358 107  6 48 36 15 0.299
Jose Iglesias SS 122 480 440 123  4 50 45 13 0.280
Didi Gregorius SS 138 539 488 125 10 56 55  5 0.256
Eugenio Suarez SS 112 458 416 106 14 46 51  6 0.255
Brad Miller SS 110 461 412 104 11 50 47  9 0.252

And here are those same players aggregated into a per-game average:

PER GAME AVERAGES BY POSITION
POS PA AB H HR R RBI SB AVG
1B 4.24 3.82 0.97 0.15 0.47 0.53 0.02 0.253
2B 4.28 3.91 1.06 0.11 0.48 0.46 0.06 0.270
3B 4.27 3.88 1.02 0.10 0.44 0.47 0.02 0.262
OF 4.20 3.82 0.98 0.11 0.46 0.48 0.09 0.256
SS 4.09 3.72 0.99 0.08 0.44 0.41 0.08 0.267

You might be wondering why I left catcher out of the mix. Feel free argue this in the comments, but I saw two issues with including catchers:

  • When I think about the actual logistics of how replacement level works, it goes something like this. Your player gets injured. You then go to the waiver wire, typically in search of a player you expect to receive regular playing time. For a 12-team league, if someone misses 30 games to the DL, you can probably find a replacement that will fill in for just about those same 30 games. I don’t believe that assumption holds true for catchers, particularly in the league setup I’m using for this analysis. In a two-catcher setup you’d be scraping the bottom of the catcher ranks for a replacement. These typically are not full-time players. If Buster Posey misses 30 games, you would be lucky to find a fill-in to play 20 games.
  • The other issue with including catchers is deciphering how much of their projected playing time is reduced due to injury versus lost to normal days off. If a catcher is projected for 130 games, is that because of injury? Or is it because that’s how much he’d play in a full season without being injured?

Not to mention that the replacement level stats you would add back would be putrid!

There’s too much mud in the water with catchers. For these reasons I concluded to leave catchers out. This decision did have some interesting ramifications which I discuss at the very end of this article.

An Example

Let’s start with a simple example using Giancarlo Stanton. As you see above, Stanton is projected to play in 126 games. Assuming he’d lose some time to the DL and some time to regular days off, let’s add 29 games of replacement level outfielder stats. That would suggest 155 games, leaving seven rest or nagging injury days off that we could not replace. Here’s how his projection and dollar value calculation transforms:

The Incompleat Starting Pitcher
The end of the nine-inning start and how we got here.

GIANCARLO STANTON
NAME G R HR RBI SB AVG VALUE
Giancarlo Stanton 126 84 38  96  7 0.277 29.55
Replacement  29 13  3  13  3 0.256  7.67
Total 155 97 41 110 10 0.273 37.22

Interesting. This adjustment was enough to move Stanton up from being the player with the 10th highest projected valuation to the second, behind only Mike Trout!

Not So Fast!

It would be a major error to do what I’m suggesting above with Stanton.

I can’t just do this for one player or even the small group of players who are “most likely to get injured.” Doing so would give those “adjusted players” an advantage not being offered to others and would result in artificially inflating their value. Yes, we want to incorporate the value of replacement level stats for the unlucky 13, but there are many other players who have lesser degrees of injury risk in their projection. To name a few: Freddie Freeman (140 projected games), Prince Fielder (144), Starling Marte (142), Justin Upton (144), Adam Jones (144), Nelson Cruz (140) and Joey Votto (138) are also projected to miss time. If we adjust only the unlucky 13 they’ll fly right past these other players in our rankings.

The highest projected games played I can find for any player in Steamer projections is 147 games played for Paul Goldschmidt, Jose Altuve and Evan Longoria. Seeing that makes my decision to bump Stanton up to a 155 game total look really poor. I’ve given him eight more games played than any other player AND he was originally projected for only 126.

Picking and choosing the individual players to adjust for opens us up to bias. To be fair and accurate, I should run this same exercise on all players and push them all to an equivalent number of games played.

A Better Example

In this example I pushed most players to 147 games played, using replacement level stats to get them to 147. For example, Mike Trout‘s original projection shows 143 games played. I added four games of replacement level outfield performance to his totals.

You might ask why I concluded to do this for Trout. If he’s only four games less than 147 it doesn’t represent a true DL stint and I couldn’t replace him. Right?

My reasoning goes back to the way projections work. We know they’re a weighted average calculation of all possibilities. Maybe there’s a 20 percent chance Trout plays in 160 games, but there’s also likely a 20 percent chance he ends up on the DL for 30 days. When you mash all those outcomes together into one projection, you might come out with 143 games. And again, I can’t ignore Trout and make adjustments to all other players.

Yes, Trout’s only four games from the 147 mark while some are more than 15 games, or a full-DL stint, away. But Trout’s 143 reflects the possibility of DL stints too. They’re just less likely to happen. So I’ll still adjust for the four games.

There’s still one significant elephant in the room. And that is how to deal with players who simply are not “full-time” starters. Matt Adams is projected for 92 games played, Ryan Zimmerman 97. Zimmerman’s projection is likely due to potential injury. Adams’ is likely due to injury and some platooning. Giving him the full credit for 147 games wouldn’t be appropriate. I’ll discuss this more in a bit. But for this example I decided to proceed with the 147.

After making this 147-game adjustment to players (except catchers), here’s Giancarlo Stanton‘s revised dollar value:

REVISED GIANCARLO STANTON
NAME G R HR RBI SB AVG VALUE
Giancarlo Stanton 126 84 38  96 7 0.277 29.55
Replacement  21 10  2  10 2 0.256  6.44
Total 147 94 40 106 9 0.275 35.99

To summarize, his original unadjusted value was $29.55. My faulty calculation put him at $37.22. And my next iteration put him at $35.97. He still goes up when we add his replacement stats. But not by as much when we include the effects of on all players.

What Happens to the Rest of the Unlucky 13?

Let’s take a closer look at all the players I singled out earlier. Each player’s original, or unadjusted, value is presented in the “$ORIG” column. The “$ADJ” column represents the player’s value after the entire player population gets replacement level adjustments.

UNLUCKY 13, ADJUSTED 2016 DOLLAR VALUE BASED ON STEAMER PROJECTIONS
PLAYER POS $ORIG R HR RBI SB AVG $ADJ CHG
Giancarlo Stanton OF $29.55 93.6 40.4 106.0  8.9 0.264 $35.99   $6.44
Miguel Cabrera 1B $26.88 92.1 27.9  99.9  2.3 0.259 $31.65   $4.76
Jose Bautista OF $21.90 91.0 33.5  97.2  6.2 0.264 $22.64   $0.74
Ryan Braun OF $20.40 80.3 24.8  85.6 16.4 0.243 $21.68   $1.28
George Springer OF $19.83 87.3 28.0  83.6 17.6 0.234 $21.67   $1.85
Edwin Encarnacion 1B $19.26 87.3 33.3  99.7  3.5 0.280 $24.56   $5.30
Yasiel Puig OF $18.97 86.8 23.9  82.1 11.5 0.260 $20.09   $1.11
Carlos Gonzalez OF $17.94 82.0 30.7  95.5  6.1 0.278 $21.26   $3.33
Jacoby Ellsbury OF $16.83 84.8 15.9  64.1 28.5 0.260 $17.21   $0.37
Albert Pujols 1B $16.07 78.6 29.1  93.4  4.3 0.271 $17.33   $1.27
Lorenzo Cain OF $14.82 74.3 12.8  72.6 22.4 0.265 $14.06 $(0.76)
Carlos Gomez OF $13.95 77.6 19.4  75.0 22.9 0.271 $14.77   $0.82
Troy Tulowitzki SS  $6.02 80.3 21.2  76.5  4.4 0.260  $9.71   $3.69

Well, that’s interesting. Most players went up, but not all. And not by similar amounts. Cain actually lost value because he was passed by players projected to play fewer games than him, Carlos Gomez and Ben Revere, and therefore received less of a bonus than those around him.

Let’s Think About the Dynamics

To understand the full situation, I should also tell you that Mike Trout‘s value went from $42.44 in the unadjusted scenario to $47.28 after the adjustment. For giving him credit for only four more games? How could that be?!? Replacement level drives everything when calculating dollar values.

Do you remember the names on the replacement level list much earlier in this article? Logan Morrison, Brett Lawrie, Jose Reyes, David Wright, Devon Travis…. Notice anything about them? They’re all injury-prone themselves.

There’s a circular calculation effect that I should probably think more about. But these players also received the bonus we’re talking about. By adding replacement level stats to these players, replacement level rises.

When replacement level rises, there are fewer resources (runs, homers, stolen bases, etc.) to divide the league budget over.This means more of the league budget gets allocated to the top players. Even if I take the four replacement level games away from Trout, his value still rises to nearly $46.

Before an adjustment, replacement level was full of players who are fairly productive when they play, but injury prone. After adjustment, replacement level looks more like players who just aren’t productive and do play a fair amount. For example, some notable players who fell below replacement level are Joe Mauer, Starlin Castro, Alcides Escobar, Trevor Plouffe, Chase Headley, Nick Markakis and Nori Aoki.

You might also be wondering why most of the outfielders in the unlucky 13 didn’t change significantly in value. This again goes back to replacement level. After the adjustment, replacement level for outfielders is noticeably higher than it is for any other position. It appears this happened because a number of outfielders benefited greatly from the adjustment. Michael Brantley, Jarrod Dyson (not an injury risk, so we’ll revisit him later), Avisail Garcia, Corey Dickerson, Rusney Castillo, Hanley Ramirez and Byron Buxton all moved up over 20 places. You can see these are very productive players who have been riddled with injuries.

The same can’t really be said about a position like first base, where most of the interesting players are already owned and there aren’t as many injury risks toward the bottom of the rankings. Bumping Justin Smoak, Mike Napoli and Yonder Alonso to 147 games played doesn’t really get intriguing.

I Said I Wouldn’t Single Players Out, But…

Let’s revisit the topic of platoon players and those who otherwise would not reach 147 games played, even if healthy. Instead of applying a blanket limit of 147 games, I could spend forever refining this model for each player. “I think Matt Adams will sit 20 games against tough lefties, Billy Burns 25, and I think Javier Baez will only play 115 games in a utility role….”

Another point to ponder is that not all injuries or injury-prone players are created equal. Take Yasiel Puig for example. The incredibly useful Pro Sports Transactions suggests Puig missed as many as 13 games in 2015 with “day-to-day” injuries. Peruse Troy Tulowitzki‘s recent history and you’ll see a similar story (granted, that rib injury in 2015 would have put him on the DL had it occurred outside of September). Day-to-day injuries like this don’t fit neatly into our “you replace the player with a free agent or waiver wire claim” scenario. If the player doesn’t hit the DL, there’s no mechanism to replace him with another player.

Some playing time projections are low because of DL risk. Some are because of platooning, days off, and projected “nagging” injuries. These can’t be replaced.

It’s all about balance. I shouldn’t adjust only Giancarlo Stanton. And I also should assume everyone plays the same number of games. Ideally, one would think more critically about the actual circumstances of each player and adjust only for games missed that can be replaced according to your league’s rules.

Use Caution, Apply Your League’s Rules

Speaking of league rules: I am relying on a significant underlying assumption for this analysis. That assumption is that your league rules allow you to freely and easily replace a DL-ed player. Any experienced fantasy baseball player knows this is not always the case. With the rising frequency of injuries in major league baseball, fantasy leagues that only offer one or two DL spots don’t necessarily offer this luxury.

With that said, the players most likely to benefit from the exercise of adding in these replacement level stats are higher valued players like the unlucky 13 above. If the 150th best player in the league is injured, you’re not that upset if you have to cut him to land a replacement.

Conclusions

As with anything complicated, it’s difficult to pull out broad generalizations that can be applied to everyone. There are a lot of moving parts to this analysis. Here are the main points I’d like to remind you of as you head away.

  • You can’t treat individual players to the replacement level bonus. You need to attempt to apply it globally and then adjust where it doesn’t make sense (Seth Smith’s missed games aren’t going to all be due to injury, meaning you can’t bump him up to the same level of games played).
  • It’s really hard to draw conclusions from how this would affect your league. You would need to calculate dollar values and playing time adjustments based on your league’s rules, settings, and DL limitations. These settings could have a dramatic effect on how your player values change. Those factors would adjust who the replacement players are, how freely you can replace injured players, and if replacement level players are likely to be full-time players (in an AL- or NL-only league you can’t assume you can replace 30 days on the DL with someone from the waiver wire).
  • The best and most valuable players can benefit from this adjustment even if they’re not injury prone! Don’t just assume those suffering injuries get a bump here.
  • I didn’t point this out above, but catchers received a huge bump in value even though I didn’t adjust them. That’s because the catcher replacement level stayed the same while it rose for all other positions. The same reason Mike Trout’s value increased is why catchers rose even higher in value. Every catcher’s “SGP over replacement level” stayed the same while all other player’s fell.
  • All of these points keep going back to how important replacement level is in calculating a player’s value. Player value is determined by how much better a player is than the replacement level player at his position. Value changes when a player’s production changes (if you add replacement level stats) AND when replacement level changes. Catchers become more valuable in this scenario because the position is so weak. It has a compounding effect.
  • There are other applications to this analysis. The most notable example would be minor league players. Take Kris Bryant or Byron Buxton during the 2015 season. There was a lot of speculation about when they would be promoted. If your league rules allow for you to freely replace minor leaguers, adding replacement level stats to their projections would have been warranted.
  • Players like Jarrod Dyson and C.J. Cron benefited immensely from this adjustment. These are guys that are destined not to see full-time action, but are more productive than average when they do play. You don’t want to clog your bench up with players like this, but their value increases dramatically at the hint of them seeing increased playing time.
  • I noticed a similar phenomenon with Michael Brantley. I like knowing that Brantley is likely to miss the beginning of the season. This allows you access to the waiver wire early in the year, when the quality of players there may be its highest. This is due to playing time battles shaking out and an increased likelihood someone will make a mistake and drop a player of value after a slow start.
  • As you might expect, the players who decline in value and fall down the rankings are the “compilers” who aren’t highly productive but earn their value by staying healthy and playing full-time.


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Tanner writes for Fangraphs as well as his own site, Smart Fantasy Baseball . He's written two e-books, Using SGP to Rank and Value Fantasy Baseball Players and How to Rank and Value Players for Points Leagues, and worked with Mike Podhorzer developing a spreadsheet to accompany Projecting X 2.0. Much of his writings focus on instructional "how to" topics, Excel, and strategy. Follow him on Twitter @smartfantasybb.
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HappyFunBall
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HappyFunBall

I don’t know. I feel like with costs incorporating playing time the notion of replacement level …replacements … is sort of implicitly included.

What I like to do is run two valuations. The first is playing time based and the second normalized to 600 PAs (450 for catchers). I can compare a player’s value in both scenarios and get an idea of their upside with greater playing time. Strategically what I will often do is lean on the PT-informed values while I’m drafting my starting lineup, and pull my backups and long shots from the 600-PA values.

Mike610
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Mike610

Yup. Looking at the Steamer600 provides enough of a proxy for me.

Tanner Bell
Guest

HappFunBall, I like your idea of one valuation with playing time and another playing time neutral.

That’s probably an interesting exercise.

I’m not sure I understand how a replacement level adjustment is already included in the playing time valuation though.

If player x is projected to play 130 games and player y is projected for 145, shouldn’t we try to account for that and value it?

HappyFunBall
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HappyFunBall

Look at it this way. If you build in the replacement value and add that cost to the unlucky player, you’re making him more expensive. AND you still have to buy a more capable (read: expensive) backup to account for your starter’s unluckiness. You’ve now made the position terribly costly.

Tanner Bell
Guest
It might help to think of both “cost” and “value”. And to think of them separately. In the exercise above, I’m trying to determine the value of an injury-prone player by measuring the full effect they’ll have on the team, both from their own stats and the stats of any replacement. That concept of value has nothing to do with the player’s cost or the cost to replace them. Stanton might be valued or worth $35, but he may only cost $30 in an auction. If I had Stanton valued at $29.55 (without adjusting for replacement stats), I might not… Read more »
PATRICK
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PATRICK

Another factor with injury-prone players is that you will have to use a roster spot while the player is day-to-day.

Klubot3000
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Klubot3000

Looks like there’s a typo in M.Cabrera’s average column in the final table. Without doing the math, .313 to .259 because of 13 games of someone else? Maybe supposed to read .310?

Tanner Bell
Guest

Thanks, Klubot. Nice catch. It should be .309. I double-checked my original calculations and the dollar value is still correct. It reflects the .309 average and not a .259 average.

Klubot3000
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Klubot3000

No problem. Interesting work as always, Tanner.

Scooter
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Scooter

There’s a lot about this article that I didn’t understand — probably because I’m not that into fantasy ball — but I really enjoyed reading it. Very well written.

Tanner Bell
Guest

Thanks, Scooter. If I can help elaborate on or clarify a topic, please let me know.

Scooter
Guest
Scooter

That’s a very kind offer.

From your bio, I see that you’ve written a ton about valuing players for fantasy ball, and I suspect that I just haven’t studied the prerequisites. This article wasn’t meant as a primer, so I guess I kind of came in halfway though.

I did mean my comment purely as a compliment. I hope that that came through.

Tanner Bell
Guest

Most definitely! Thank you again. It is good to hear as I never thought I’d be writing content for THT!

Czern
Guest
Czern

Your $/game for the replacement OF are higher than for Stanton, which suggests something is off somewhere. And Carlos Gomez presumably gets identical replacement stats as Stanton, since both are projected for 126 games, yet his replacement games are good for less than $1, whereas Stanton’s add $6.50?

Encarnacion & Pujols have similar projections, including 266 & 260 for AVG. Add in some replacement 1B games with a 253 AVG, and their revised AVG projections are 280 & 271.

I think you’ve fubared something in your methodology.

Tanner Bell
Guest
Hi Czern, thanks for the disczerning eye… Bad joke? Sorry. Reading your comment, I see that the Stanton table doesn’t present the information very well. It’s misleading. I should have presented Stanton’s replacement level stats as being worth the same as Gomez (because they both played 126). But the value of Stanton’s OWN stats are what changed during the exercise. So instead of $29.55 + $6.44 = $35.99 It probably should be something like $34.50 + $1.49. (I don’t actually value what 1 RBI is worth, etc. So I’m speculating on the $1.49). But I do comment above on why… Read more »
Tanner Bell
Guest

In regards to Encarnacion and Pujols’ averages, it’s hard to say what went wrong in me creating the tables above. But those tables are independent of the actual spreadsheet used to perform the calculations.

In that spreadsheet I have Encarnacion for a 0.263 average after the replacement stats are added (down from .266) and Pujols for .2597 (down from .2604).

While I realize it’s disconcerting to see errors in the tables above. Nothing changes in the end dollar values. It’s just an error in me translating things from the spreadsheet.

Jason B
Guest
Jason B
Love the article Tanner, really did provide some interesting food for thought. Adding on to what Czern said, Stanton’s replacement is worth more on a per-game basis than Stanton is? Looking into the issue more closely, it may have to do with your base assumption of drafting only 23 players and no bench. I don’t know of any league that has 0 bench spots, so this assumption is artificially raising the caliber of players that you are likely to find on waivers. If you re-ran the analysis assuming, say, a 4-player bench, that’s an extra 48 players drafted, of which… Read more »
Tanner Bell
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Hi Jason, see my reply above to Czern. I chose a poor method of displaying the value of the replacement level stats. It isn’t those stats that are worth more. It is Stanton’s OWN base stats that changed in value during this process. And you are correct. If I had included additional reserve/bench spots, replacement level would have been further down. But I think we’d still see similar results. Those bench players would also need to be included in Stanton’s initial value calculation. So his value there would be different. And the exercise of adding back replacement level stats is… Read more »
Jason B
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Jason B

Fair points all! Thanks for the article and all of the thoughtful responses to all the comments.

Roger Rocket
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Roger Rocket
Thanks for the thought-provoking article, Tanner. I’m struggling a bit with the following: Most value calculators/methodologies subtract out replacement level so that value is effectively value-above-replacement, and a perfect replacement player is worth $0.0. If so, how does adding $0.0 to, say, Stanton’s value increase his worth? Put differently, replacement-level players have no value, and an injury-prone player’s missed time is a reasonable deduct from his value, as even in a perfect world (immediate DL, no day-to-day etc.) you would plug in a player who, by definition, is not adding value. A possible answer is one you hint at in… Read more »
Tanner Bell
Guest
Hi Roger, Let me frame your thinking in a different way. This is rather simplistic, but we can likely agree that if Stanton’s projection of 36 HR suddenly gets raised to 37 HR his value should rise. He becomes more valuable the more he produces. That simplistic example is all that’s happening. We are taking a player like Stanton and adding in additional stats. I think it’s appropriate to add these stats because we truly would earn them if Stanton is injured. Any responsible owner would replace him when injured. Even if it’s a super deep NL-only league, some replacement… Read more »
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