How to correctly value catchers has always been up for debate. As a position, they are by far the worst hitters in the league. Last season, catchers posted a .713 OPS while second basemen were next with a .745 OPS. Most player calculations give catchers a valuation boost to attempt to even them out with the other hitters. This theoretical boost always exceeds the actual cost paid by draft picks or auction dollars. I assumed some experts knew more than me (safe bet) and the catcher projections have a wider range of outcomes. Owners don’t want to pay for these gambles. By examining historical values, I found the complete opposite. Catcher projections are the least volatile.

I’m not going to rehash why catchers need a positional adjustment. I’ve written a whole section on it in The Process. For anyone new to the subject or just wants a refresher, Ariel Cohen wrote an in-depth article on the subject.
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I was wrong. About seven years ago, I wrote on how hitters may overperform their projections since they played through an injury. The injury hampered their production in the season in question, lowered the future projection, and created a buying opportunity. For years, I believed this steadily until last season when I re-ran the numbers and found “jack squat”.

Earlier this week, I examined some of this past season’s hitters who fought through the pain and felt a deeper analysis was needed. I dove in and the results were backwards. I found no bounceback should be expected from hitters who played through injuries, but there is more. For those hitters who play through the discomfort, their future production will take a major hit.

The key to uncovering the following results was getting a usable dataset which is easier said than done. Many of the injuries I’m using for the analysis aren’t well documented, if at all. Real men play baseball and they play hurt because that is what real men do and most importantly, they don’t complain about. Besides the machismo, a player has every right to keep his medical data to himself so vagueness thrives. Simply, there is no good available data. Even with the hurdles, I dug into each of the hitters who were reported to have played through an injury the past three seasons (2017, 2018, 2019).

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In last week’s edition of this series, we uncovered several undervalued speedsters for 2020 drafts. We did so by comparing the Steamer projections to current NFBC Average Draft Position reports. Today, we will go through a similar exercise for potential power bargains.

For this article’s draft value comparisons, I look at:

• The player ranks as computed by the FanGraphs Auction Calculator with Steamer projections (standard NFBC 15 team roto league settings).
• The current NFBC ADP (of Draft Championship leagues from November 27, 2019 to present).

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I’ve had a semi-fixation on hitters playing through injuries and how the diminished production could hamper the next season’s projection. At first, I found some correlation. Then, I didn’t. One possible answer to there being no bounceback is that the injury becomes chronic and the hitter never improves. Or the dataset could be too small.

I want to dive further into the subject, but the information around injuries is sketchy at best. Most of the time, there are no usable details. The lack of an answer means that I should stop coming back to the subject but I’m stubborn.

Very.

I’m going to go through this past season’s hitters. The dive has a couple of goals. One is to create a better dataset for future reference. The second is to understand why some hitters may have struggled when creating a profile. And just maybe, I’ll find out if I can put to rest the notion that hitters who played through injuries are under projected.
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A new year and in turn, a new decade is now upon us! We have begun the Roaring Twenties once again. More importantly, fantasy baseball draft season is now upon us.

I am not one for paying large amounts of attention to positional rankings. Knowing who is valued as the 7^th best shortstop, or who the 11^th ranked second baseman is – is helpful no doubt, but that alone will not win you your fantasy leagues. We can spend time debating whether Rhys Hoskins is better than Trey Mancini, but since the duo have similar profiles, the discussion isn’t all that helpful on its own.

To me, the task that needs the higher priority – is the comparison of market prices to player valuations. The greater assignment is to determine the players which the market undervalues. One of the first exercises that I engage in – is to find the players which projections foretell a superior year as compared to where they are currently being priced.

The two needed components to complete this analysis are very generally:

• Projections
• Market

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I going to examine how both Hyun-Jin Ryu‘s and Dallas Keuchel’s fantasy value changed since both signed over the last few days.

Hyun-Jin Ryu signs with the Toronto Blue Jays

Ryu finally pieced together a great season by staying healthy and throwing more innings (182) than any time since 2013 (192). While his strikeout rate was acceptable (23%), he dominated (2.32 ERA) by walking almost no one (3%) and in the juiced ball era, he limited home runs (0.8 HR/9) with a surge in groundball rate (50%).

I hate this move for Ryu’s value with every aspect being a downgrade from the Dodgers. He moves to the AL where he’ll face a DH more often. He goes to the hyper-competitive AL East. He transitions from a pitcher’s park to one that is neutral overall but gives up more home runs than average. Finally, he goes from a nearly average defense to one in the bottom third.

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Over the years, I’ve been working on how to fine-tune my player evaluation process. The following are six datasets that I’ve found useful I’ll not go into detail on any of them since I provide a link to the original article. The following is basically a referenceable data dump.

Note: I know there is a lot of content and when questions arise, make sure the area in question is obvious in the comment. Also, I’ll only answer questions here and not in the original articles.

Voit/Muncy All-Stars (link)

These are older AAA hitters who have shown signs of a breakout.

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Introduction

I thought it would be an interesting exercise today to look at fantasy baseball in a different way – profitability at the major league team level. Certainly, the better real-life clubs consist of not only more fantasy relevant players, but also of the higher quality ones.

But what else can be said about the profitability of the fantasy player pool at the team level? Does a higher spend translate to more value for fantasy owners? Does winning ball games correlate with higher levels of fantasy profitably? What can we learn from looking at player investments from this unique ball club perspective?

Let’s dive in and see …

Definitions & Methodology

For today’s analysis, I have used the same pre-season pricing and full-season valuations as in my game theory projections comparison. There are three specific quantities that are relevant here:

• $Value – This is the 2019 full season rotisserie value that each player provided this season. I use my own auction calculator which employs a Z-score methodology to generate the $Values. Standard NFBC 15-team settings are assumed (Mixed AL/NL, $260 budget, standard NFBC positions). I assume that players are only eligible at their original 2019 positions + any positions that they were expected to gain in the first 2 weeks of the season.
• $AAV – Average Auction Value – This is the average of what NFBC owners paid to acquire players during the heart of the 2019 draft season. These values come from all NFBC auctions between March 15, 2019 and March 25, 2019.
• $Profit – The difference between the $Value and the $AAV per player.

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… probably not, but anyone probably already guessed that considering Stephen Strasburg’s injury history. The answer isn’t that far off after digging through some historic comps. Only once in his 10-year career has he topped 200 innings (215 in 2014) and only over 180 one other time. He’s thrown under 160 innings six times in ten seasons. Another issue besides the limited innings is that he’s going to be on the wrong side of 30 where pitcher breakdown faster. It’s time to look a little deeper and see what innings total should be expected.

I need to start with some guidelines. First, I’m only going to examine pitchers who throw the 200 innings between their ages 28 and 32 seasons. Also, the pitcher needs to be considered a starter with at least half of their games as a starter (GS/G >= 0.5). Finally, I rode the fine line of using recent data and having enough samples. With pitchers recently throwing fewer innings, I only used pitchers from the past 10 seasons.
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Historically, I’ve “corrected” hitter projections to my own liking and every time I’ve backtested them to the actual results, my adjustments have failed miserably. So why create more work when the end results make my final product worse? Am I a glutton for punishment? In all fairness, I’m sure a heavy dose of Dunning-Kruger is going on but I also believe there may be a sweet spot where personal scouting can come into play. Today, I’m going back to the well one more time to see if some injured hitters should have more encouraging projections because they may have played hurt.

First, I’ve always thought playing through an injury meant that the team and the player were accepting suboptimal production. Then the player could come back healthy and full productive the next season.
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