Catcher Simulation

	

by Mac Squibb

February 28, 2019

	

	
    Every year, fantasy owners have to get creative when dealing with a position that is particularly shallow. It used to be middle infield, specifically shortstop, until the most recent wave of young phenoms came along. Now, it’s the catcher position, which is particularly desolate this season. Owners use numerous different strategies including, drafting two catchers on the same team, waiting until the later rounds, and even avoiding the position altogether. In fact, this article wouldn’t exist if there was a straightforward way to approach the position. In a previous article, I proposed a potential strategy for the catcher position based on combining two players to create a serviceable catcher. In this article, I will propose a similar idea, but hope to also show just how viable of an option it is.

	
    Here are the top ten catchers based on NFBC ADP along with their Depth Charts projections, projected Player Rater rank, and Player Rater value.

	
Name NFBC ADP PR Rank Run HR RBI SB AVG PR Total
J.T. Realmuto 51.88 159 65 21 72 5 0.269 4.62
Gary Sanchez 56.92 139 74 31 85 2 0.245 4.92
Salvador Perez 110.95 215 60 25 74 2 0.252 3.71
Wilson Ramos 129.83 356 42 15 51 1 0.261 1.46
Yasmani Grandal 130.11 297 59 22 65 2 0.237 2.97
Willson Contreras 135.44 287 54 15 59 4 0.257 2.5
Yadier Molina 140.54 238 56 15 63 5 0.266 3.25
Buster Posey 143.17 206 62 12 62 4 0.287 3.91
Danny Jansen 205.02 333 52 14 51 3 0.256 1.77
Yan Gomes 223.9 571 31 11 37 1 0.241 -0.54

	
    For those that are unaware of what the Player Rater is, I recommend reading my article covering its importance here. In short, the Player Rater is an objective way to determine a players fantasy value based on their projections. Its validity is based on the fact that the goal of fantasy baseball is to accumulate the most and best statistics regardless of where they come from. It shouldn’t matter if a run, stolen base, or any other stat comes from your catcher or another position. It all counts the same. Thus, when comparing these catcher's ADP and Player Rater rank, it’s clear that none of the them are being drafted appropriately based on their projected stats. In fact, not a single catcher, including those not listed, is projected to produce at or above their projected Player Rater rank. Unfortunately, this means that we will all have to overpay at the catcher position unless you plan on leaving the spot open. I’m about to propose a strategy that I hope you consider when trying to maximize your value at the catcher position.

	
    The idea behind this strategy is that it’s possible to Frankenstein together two lower ranked catchers to produce a similar amount of value as a top catcher. In order to show that this is viable, I created a simulation of the upcoming season to predict just how the strategy would play out. In this simulation, I included the 48 catchers who are projected by Depth Charts to play at least 40 games in 2019. I ran this simulation 10,000 times for each pairing of catchers and then averaged the results. If you’re interested, the inner workings of the simulation are explained below, otherwise you can skip to the results.

	
Image #1
	

	
    As I mentioned before, the goal of this strategy is to avoid drafting a top catcher and instead draft two lower ranked players. There is a clear drop off in ADP after Buster Posey, the 8th ranked catcher, and thus we’ll only be considering the simulation pairings without those eight. Below, in the sortable table, I have listed the stats and Player Rater totals of the top 50 simulation pairings along with the top 8 individual catchers.

	
Player 1 Player 2 Run HR RBI SB AVG PR Total
Gary Sanchez 74 31 85 2 0.245 4.92
J.T. Realmuto 65 21 72 5 0.269 4.62
Buster Posey 62 12 62 4 0.287 3.91
Salvador Perez 60 25 74 2 0.252 3.71
Yadier Molina 56 15 63 5 0.266 3.25
Yasmani Grandal 59 22 65 2 0.237 2.97
Danny Jansen Tucker Barnhart 62.6 15.8 62.3 3.7 0.254 2.96
Kurt Suzuki Danny Jansen 57.5 16.3 61.6 2.4 0.26 2.8
Danny Jansen Mike Zunino 64.3 19.7 65.7 3.1 0.244 2.76
Danny Jansen Welington Castillo 60.7 17.5 61.3 3.3 0.252 2.76
Danny Jansen Christian Vazquez 61.8 15 60.5 4.4 0.256 2.76
Danny Jansen Kurt Suzuki 58.5 16 59.1 3.1 0.258 2.69
Danny Jansen Francisco Cervelli 61 15.2 59.8 3.9 0.256 2.67
Danny Jansen Tyler Flowers 60.7 16.6 60.7 3.1 0.254 2.66
Danny Jansen Austin Wynns 60.9 16 59.6 3.5 0.252 2.55
Danny Jansen Grayson Greiner 62.1 16.2 61.5 3.4 0.251 2.54
Kurt Suzuki Welington Castillo 55.1 17.6 62.3 2 0.254 2.53
Danny Jansen John Hicks 58.9 15.6 59 3.7 0.254 2.52
Danny Jansen Chris Iannetta 58.7 16 58.1 3 0.255 2.5
Willson Contreras 54 15 59 4 0.257 2.5
Welington Castillo Danny Jansen 58.8 19.3 63 2.9 0.246 2.49
Christian Vazquez Danny Jansen 59.9 11.4 58.5 5.9 0.256 2.49
Danny Jansen Brian McCann 57.2 16.3 57.5 3.1 0.252 2.47
Danny Jansen Omar Narvaez 60.2 14.9 58.7 3.3 0.254 2.45
Danny Jansen Austin Barnes 59.2 15.2 57.7 4.6 0.251 2.45
Danny Jansen Francisco Mejia 58.4 16 58.1 3.4 0.254 2.45
Danny Jansen Yan Gomes 57.5 16.3 58.5 3.1 0.253 2.43
Chris Iannetta Danny Jansen 59.3 16.7 59.7 2.4 0.252 2.43
Kurt Suzuki Francisco Cervelli 54.8 13.6 58.9 2.9 0.26 2.42
Danny Jansen Jorge Alfaro 61.2 16.8 62.4 3.7 0.251 2.4
Danny Jansen Austin Hedges 57.2 16.6 58.5 3.4 0.25 2.4
Danny Jansen Jonathan Lucroy 59.4 15 58.5 3 0.256 2.39
Danny Jansen Jason Castro 61.5 16.2 59.7 3.4 0.247 2.37
Welington Castillo Francisco Cervelli 58 17.7 62.2 3.2 0.246 2.37
Welington Castillo Mike Zunino 59.3 22.3 66.4 2.2 0.232 2.36
Tyler Flowers Danny Jansen 58.2 16.4 60.5 2.3 0.251 2.35
Kurt Suzuki Mike Zunino 57.7 20.2 66 1.5 0.24 2.3
Welington Castillo Kurt Suzuki 54.6 18.6 61.1 2.2 0.246 2.29
Danny Jansen Kevin Plawecki 58.6 15.4 58 3.1 0.253 2.27
Yan Gomes Danny Jansen 54.8 17.2 59.8 2.4 0.248 2.27
Mike Zunino Danny Jansen 63.1 24 69.3 1.9 0.222 2.26
Welington Castillo Grayson Greiner 58.2 18.6 63.1 2.6 0.239 2.26
Danny Jansen Robinson Chirinos 60 17.2 59.7 3.3 0.243 2.23
Danny Jansen Josh Phegley 58.2 15.4 57.3 3.2 0.25 2.23
Francisco Cervelli Danny Jansen 57.9 12.1 56.7 4.4 0.256 2.21
Danny Jansen Roberto Perez 57.7 15.1 56.5 3.4 0.248 2.2
Chris Iannetta Mike Zunino 59.9 20.6 64.3 1.4 0.232 2.2
Danny Jansen Chance Sisco 56.2 14.7 55.3 3.1 0.253 2.19
Danny Jansen Isiah Kiner-Falefa 55.3 13.6 53.8 4.3 0.257 2.18
Welington Castillo Tucker Barnhart 57.4 17.8 62.6 2.9 0.243 2.17
Welington Castillo Christian Vazquez 55.3 16.6 59.4 3.6 0.245 2.17
Welington Castillo John Hicks 54.5 17.9 60.2 2.9 0.243 2.17
Chris Iannetta Welington Castillo 56.6 17.9 60 1.9 0.246 2.17
Francisco Cervelli Welington Castillo 56.9 13 57.5 4.2 0.252 2.15
Danny Jansen Alex Avila 58.4 15.6 56.9 3 0.246 2.15
John Hicks Danny Jansen 55.5 14.1 57.8 4 0.251 2.15
Welington Castillo Chris Iannetta 54.7 18.6 59.6 2.2 0.243 2.14
Wilson Ramos 42 15 51 1 0.261 1.46

	
    So the top handful of catchers are still far and away the best, but several of the simulation pairings actually performed as well as, if not better than, the other three top catchers. A common factor of nearly all of the simulation pairings is Danny Jansen who, by no coincidence, has the 9th highest ADP. There are, however, numerous simulation pairings that don’t contain Jansen which are worth highlighting. In those pairings, Welington Castillo appeared 11 times, Kurt Suzuki, Francisco Cervelli, and Chris Iannetta appeared three times, Mike Zunino appeared twice, and Grayson Greiner, Tucker Barnhart, Christian Vazquez, and John Hicks appeared once. The most important part of these results is that all of those catchers are being drafted outside the top 200, including 3 outside the top 400. This means that you’ll be able to construct a playable catcher with players who are available late in the draft or even on the waiver wire.

	
Name NFBC ADP
Danny Jansen 205.02
Yan Gomes 223.9
Welington Castillo 230.13
Mike Zunino 239.16
Jorge Alfaro 240.26
Robinson Chirinos 253.39
Francisco Mejia 254.86
Tucker Barnhart 271.5
Francisco Cervelli 275.21
Isiah Kiner-Falefa 275.73
Omar Narvaez 303.65
Austin Hedges 305.41
Jonathan Lucroy 311.99
Kurt Suzuki 330.52
John Hicks 358.01
Austin Barnes 366.48
Chris Iannetta 409.45
Tyler Flowers 424.47
Brian McCann 453.32
Christian Vazquez 536.02
Grayson Greiner 552.02
Chance Sisco 599.74
Kevin Plawecki 611.21
Jason Castro 641.83
Austin Wynns 662.34
Josh Phegley 710.72
Alex Avila 716.79

	
    Hopefully the results of this simulation, along with the players ADP, is enough to convince you of the validity of this strategy.

	
    There are some limitations worth noting with both the simulation and the strategy in general. The simulation makes several assumptions the we need to be aware of, first of which is that the simulation uses a per game average of a players stats. Obviously, players can’t accumulate a fraction of a stat, and thus, the totals that the simulation produces are imperfect. The simulation also uses a random number generator to determine whether or not a player is playing on a given day, which can produce unreasonable results. It is possible, but highly unlikely, that the simulation allows a player to play all 162 games. Gary Sanchez, who is projected for the highest number of games played, would have less than 1 in 1,000,000,000,000 chance of it happening. Despite these potential issues, averaging the results of 10,000 simulations should even out any potential irregularities.

	
    The simulation also makes the assumption that the league allows daily moves and only has a single catcher spot. Leagues that set their lineups several times per week might still be able to adopt this strategy, but weekly lock leagues and leagues with two catchers likely can not. This strategy also doesn’t consider the value of the bench spot that the second catcher is occupying. It is entirely possible that the bench spot would be better used for something other than a second catcher: streaming pitchers, stashing a prospect, or another position player.

	
    Ultimately, the results of this simulation were very promising. We were able to show that, for the most part, two catchers are better than one while also coming at a steep discount. At the very least, this should be a strategy worth considering when drafting.

	
	

Download all 2352 simulation pairings here.

	
	
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