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A catcher strategy for leagues with daily transactions

MLB: New York Mets at Philadelphia Phillies Bill Streicher-USA TODAY Sports

A Catch-22 is a frustrating situation in which one is trapped by contradictory regulations or conditions. The regulation in this circumstance is that you must spend draft capital on a starting catcher. If you miss on the first 7-8 catchers, every time you watch someone draft a catcher before you do, you are profiting. They spend on a catcher, while you allocate resources elsewhere.

Wilson Ramos signed a two-year deal with the New York Mets on Sunday. He was the No. 5 ranked catcher in points leagues and roto leagues in 111 games played last season. He has a ADP of 137 on Fantrax and this is before he belonged to a team. Given the state of the catcher position, you might find that owners may reach even more to draft Ramos as the season nears. He is currently being selected ahead of Daniel Murphy, Andrew McCutchen, Brian Dozier, and Dallas Kuechel to name a few other players who are/were free agents. Is this too high?

Before you read any further, this piece is not applicable to weekly leagues, leagues with daily FAAB or capped transactions. It is reserved for leagues with unlimited daily roster moves.

Let’s start by using an example from a points league because it is simplest. We will move on to rotisserie afterwards. We also work under the assumption that it is not beneficial to roster more than one catcher due to roster constraints. We will work within the one-catcher league universe.

I ran the statistics of all catchers with 80 plate appearances or more in 2018. I have separated the following catchers likely not available on the waiver wire during the season:

Tier 1 – 330+ Points

1. JT Realmuto

2. Yasmani Grandal

3. Yadier Molina

4. Salvador Perez

Tier 2 – 300-329 Points

1. Wilson Ramos (111 games extrapolated using 120 games)

2. Gary Sanchez (89 games extrapolated using 120 games)

3. Buster Posey (105 games extrapolated using 120 games)

4. Willson Contreras

Tier 3 – 260-299 points

1. Tucker Barnhart

2. Francisco Cervelli

3. Kurt Suzuki

4. Robinson Chirinos

Tier 4 – 200-259 points

1. Yan Gomes

2. Danny Jansen (31 games extrapolated to 120 games)

3. Jonathan Lucroy

4. Wellington Castillo (49 games extrapolated to 120 games)

Tier 5 - Roto Specialists

1. Mike Zunino

**Francisco Mejia did not qualify for 80 PA, but he would likely fall into Tier 3.

I expect catching production to deteriorate in 2019 because:

1. Position durability issues will continue (see all of the extrapolations required)

2. The aging state of the catching landscape (Molina, Perez, Posey)

3. An increasingly even split of playing time (Gomes/Suzuki, McCann/Flowers, McCann/Castillo)

There are 52 other catchers that qualified for 80 plate appearances. Let’s refer to them as waiver wire catchers – catchers you can grab off the waiver wire at any time.

The average points per game among remaining catchers is 1.48, which, when extrapolated to 160 games, is 237 points. So, you could program a computer to play the catcher waiver wire and you’d see production of a Tier 4 starting catcher like Jonathan Lucroy. Remember, this waiver wire pool includes Jeff Mathis, Roberto Perez, John Ryan Murphy, etc.

Now, I’ve divided the waiver wire catchers into 3 tiers:

1. > 1.75 PPG (13 catchers)

2. 1.20-1.75 PPG (24 catchers)

3. < 1.20 PPG (15 catchers)

If you are skilled enough to avoid Tier 3 waiver wire catchers (like the ones I listed above), your projected points over 160 games increases to 262, which is what Suzuki and Chirinos produced in 2018.

If you can play the waiver wire with an average of Tier 1 waiver wire catcher production (average of 1.90 PPG), you will amass 305 points which is more than Willson Contreras and also more than Buster Posey’s 120-game pace.

For reference, the guest list for Tier 1 waiver wire catchers is as follows (in order of PPG):

1. Mitch Garver

2. Austin Hedges

3. Kevin Plawecki

4. Tyler Flowers

5. Isiah Kiner-Falefa

6. Russell Martin

7. Omar Narvaez

8. Manny Pina

9. Devin Mesoraco

10. Chris Iannetta

11. Brian McCann

12. Luke Maile

13. Michael Perez

So, if you think you can play the daily waiver wire streaming to a standard of the above 13 catchers for 160 games, you’ll equal the point production of a starting Tier 2 or Tier 3 catcher. You will also have the benefit of steaming pitcher matchups, ballparks, and hot streaks. Perhaps you are skilled enough to stream to a Tier 1 catcher value. If your games limit is more than 162, this strategy becomes even more enticing. Last year, I actually found myself able to stream catchers that were part of the 18 that I listed that would likely be drafted. In fact, at the conclusion of the 2018 season, only eight catchers were rostered in over 50% of roto leagues (this includes Evan Gattis, who should not qualify at catcher again but also excludes an injured Buster Posey).

For reference, in a roto league, these are the waiver wire catcher 160 game normalized stats (AVG/HR/R/RB/SB):

All waiver wire - .221/11/47/48/2

Tier 1 and 2 - .230/12/52/52/2

Tier 1 - .238/15/58/57/2

For perspective, in 2018, Tucker Barnhart went .248/10/50/54/0.

For those high on a catcher like Ramos this year and argue that that the alternatives are much worse despite Ramos and many other catchers likely to play in less than 120 games. I say, exactly. The alternatives are much worse. That is why Ramos is my #3 ranked catcher in a 5x5 league this season because of his opportunity to catch “most” games and hit for a high average with power. However, the alternatives do not get much worse right away. In fact, there are 5-6 catchers that project similarly. Relative to the rest of the field, I feel that 137th overall (Round 12 in a 12-team league) is still a steep price given the unlikelihood of playing more than 120 games and because you could wait and get one of the next five catchers several rounds later. I would expect at least one of Posey, Contreras, Grandal, Perez, or Molina to be available several rounds later and you will not be sacrificing much in terms of projectable stats.

When the quality is this bad, quantity trumps quality.