HEAD-TO-HEAD: Building a homogeneous team in the pitching era

Introduction

In 2013, we introduced the concept of drafting a homogenous team in head-to-head leagues.  The Fantasy Sports Writers' Association named the article the fantasy baseball print article of the year after a version of it was published in the 2014 Baseball Forecaster. However, with offensive numbers in decline, this approach has quickly become more difficult to implement. In order to survive, we must adapt and make adjustments that continue to allow us to draft a team that consistently does the same things well.

The Principle of Homogeneity in Head-to-Head Leagues

In a head-to-head league, drafting players that do the same thing well increases your team’s consistency on a week-to-week basis. Our standard filters for a homogenous squad (built on power) are:

ct% greater than or equal to 80%
xBA greater than or equal to .280
PX  greater than or equal to 120
RC/G greater than or equal to 5

Applying BaseballHQ.com’s 2015 projections, only 12 players meet the standard.  This number is just under half of players that qualified in 2013.

Name              Team   Ct%   xBA   PX   RC/G   2014 QC
==========        ====   ===   ===   ==   ====   =======
Tulowitzki, T.    COL    84    299   148  8.18   100
Cabrera, M.       DET    82    309   154  8.16    74
Abreu, J.         CHW    80    307   150  7.54    50
McCutchen, A.     PIT    80    282   145  7.49    62
Ramirez, H.       BOS    83    303   140  6.94    32
Bautista, J.      TOR    82    283   151  6.87   126
Encarnacion, E.   TOR    85    296   152  6.83    82
Zimmerman, R.     WAS    82    288   130  6.33    77
Ortiz, D.         BOS    81    292   152  6.25   100
Gonzalez, A.      LAD    82    287   125  6.03    79
Cuddyer, M.       NYM    80    297   139  5.69   100
Arenado, N.       COL    86    296   122  5.50   120

Absent a modification, it will not be possible to assemble a team core of 6-8 homogeneous players.  The goal of this article is to implement a strategy that creates a template to follow to draft more consistent players.

The players listed above would qualify for elite consistency status.  However, the results created cannot be used as part of a straight draft ranking system.  Michael Cuddyer (OF, NYM) will obviously not be a top 12 pick in your draft.  Instead, these results must be considered in the context of ADPs, and will help show where you can find consistent values in your draft. So, if Cuddyer is going in the tenth round of drafts, you might consider him a round or so early given the consistency he provides.

Approaching Consistency From a Second Angle: Quality-Consistency Scores

The purpose of drafting a homogeneous team is to help generate a more consistent lineup.  In order to widen the player pool, but nonetheless continue to draft players that consistently perform well, we can incorporate quality-consistency scores (QC scores) into our analysis.

QC scores provide us with a convenient measurement of how consistent a player is on a week-to-week basis. The formula is: (DOM% - (2 x DIS%)) x 2. A player earns a DOMinant week if his BPV is greater than or equal to 50. A player achieves a DISaster if his BPV is less than 0 for a given week. A week where a player’s BPV is between 0 and 49 is neutral. Players are scored on a scale of 200 (perfect) to -400.  For more information on QC scores, please check out our 2014 consistency series

Our "top tier" of homogenous players all have very good QC scores.  In 2014, only 2.6% of batters had a QC score of 100 or greater (13 players). Five of them appear on our list. Only 8.8% of batters had a QC score of 50 or higher (44 players). Eleven of these players appear on our list of 12, with Hanley Ramirez (OF, BOS) being the lone player to have a 2014 score below 50.  This suggests that there is a correlation between one or more of homogenous filters and QC scores.

In order to expand our player pool, we need to adjust our filters. The most significant problem, however, is when the ct% filter is lowered (even to 75%), we see a precipitous drop-off in QC scores.  This is concerning, as the purpose behind building a homogenous team is to make it more consistent.

ct% greater than or equal to 75%
PX greater than or equal to 120
RC/G greater than or equal to 5        
Name           Team   Ct%   PX    RCG   QC    
============   ====   ===   ===   ===   ===
Rizzo, A.      CHC    78    152   6.27  128    
Werth, J.      WAS    78    129   6.56   77      
Cruz, N.       SEA    75    151   5.19   67      
Votto, J.      CIN    77    141   7.73   55      
LaRoche, A.    CHW    76    142   5.48   40      
Dickerson, C.  COL    79    152   6.51   24      
Santana, C.    CLE    78    128   5.60   22      
Freeman, F.    ATL    76    126   6.27   22      
Braun, R.      MIL    78    139   6.00   16      
Mesoraco, D.   CIN    77    145   5.19    8        
Adams, M.      STL    76    129   5.07    8        
Davis, K.      MIL    75    159   5.00    0        
Soler, J.      CHC    76    155   5.61    0        
Donaldson, J.  TOR    77    145   5.57   -7       
Gomez, C.      MIL    75    139   5.60   -8       
Jones, A.      BAL    79    125   5.11  -15     
Puig, Y.       LAD    77    135   6.07  -43     
Pearce, S.     BAL    78    137   5.78  -49     
Marte, S.      PIT    75    124   5.21  -54     
Harper, B.     WAS    75    125   5.40  -67     
Jones, G.      NYY    77    147   5.00  -81     
Martin, R.     TOR    78    138   5.94  -96
Souza, S.      TAM    75    135   5.43   NA

This analysis suggests that ct% and xBA function as the consistency "controls" of a homogenous team, while RC/G and PX provide us with our counting statistics. Here, however, we need to incorporate a new consistency metric to expand our player pool. If we replace ct% and xBA with QC scores, we can still identify the more consistent options that provide power and counting statistics as well.  The analysis below uses average QC scores over a 3-year period:

QC greater than or equal to 50
PX  greater than or equal to 117
RC/G greater than or equal to 5         

Name            QC  PX  RC/G
==============  ==  ==  ==== 
Pujols, A.      110 118 5.78
Votto, J.       101 141 7.73
Trout, M.       89  187 7.96
Rizzo, A.       88  151 6.27
Holliday, M.    86  117 5.53
Werth, J.       79  129 6.56
Fielder, P.     54  117 6.24
Goldschmidt, P. 50  180 7.90
Braun, R.       50  138 6.00
Rendon, A.      50  117 5.81

We now have a player pool with 22 targets, which is the exact number of homogenous players our 2013 exercise generated. Due to our stringent consistency requirements and the dearth of power available, we slightly lowered the PX filter from 120 to 117 to capture additional draft candidates.

This “additional” level of players generated is not meant to suggest that sure-fire first round picks, Mike Trout (OF, LAA) and Paul Goldschmidt (1b, ARI), should not be considered in the first round of your head-to-head drafts, or with any of the players above that met the traditional homogenous filter requirements. The best approach is to print-out a list of players’ ADPs and highlight those that make our consistency cut. This will help you determine when is appropriate to select a given player.

Finalizing your 2015 Homogeneous Draft Board

To round out your 2015 homogenous targets, let’s first look strictly at the list of remaining players with the highest remaining QC scores (on average) over the past 3 years:

Name          QC   PX
============  ===  ===
Martinez, V.  127  110
Brantley, M.  106  101
Beltre, A.    105  116
Betts, M.     100  117
Lucroy, J.     94  117
Kinsler, I.    88   96
Aoki, N.       81   62
Cano, R.       80  116
Posey, B.      77  117
Markakis, N.   74   78
Ramirez, A.    70  112
Reyes, J.      70   83
Prado, M.      67   93
Pedroia, D.    66   84
Altuve, J.     64   75
Pagan, A.      61   77
Zobrist, B.    61   99
Molina, Y.     59   91
Span, D.       55   76
Lowrie, J.     55  104
Utley, C.      54   97
Cabrera, M.    50   96

Some of the players above are better suited for a homogenous team utilizing the Spd metric versus the PX metric. If you are using the PX metric (as this article does) you should target those players above with above-average PX grades.

Finally, as explained last year, often times hitters with low QC scores but high HctX scores have room for their QC score to grow.  In 2014, this was true of Anthony Rendon (3b, WAS), A.J. Pollock (OF, ARI) and Ryan Zimmerman (OF, WAS), among others.  Here, if we go back to our chart containing those hitters with 75% ct%, 120 PX and 5 RC/G, we find that the following players with relatively low or low 2014 QC Scores also had above average HctX rates:

Name            Team  Ct%  PX   RCG   QC   HctX
==============  ====  ===  ===  ====  ===  ====
LaRoche, A.     CHW   76   142  5.48   40   126
Dickerson, C.   COL   79   152  6.51   24   123
Santana, C.     CLE   78   128  5.60   22   117
Freeman, F.     ATL   76   126  6.27   22   131
Mesoraco, D.    CIN   77   145  5.19    8   123
Adams, M.       STL   76   129  5.07    8   109
Davis, K.       MIL   75   159  5.00    0   132
Soler, J.       CHC   76   155  5.61    0   129
Donaldson, J.   TOR   77   145  5.57   -7   118
Gomez, C.       MIL   75   139  5.60   -8   117
Jones, A.       BAL   79   125  5.11  -15   113
Puig, Y.        LAD   77   135  6.07  -43   116
Pearce, S.      BAL   78   137  5.78  -49   116
Marte, S.       PIT   75   124  5.21  -54   105
Jones, G.       NYY   77   147  5.00  -81   124
Martin, R.      TOR   78   138  5.94  -96   109
Souza, S.       TAM   75   135  5.43   NA   113

Your best targets in this final group are those players with HctX scores in excess of 120. These players are most likely to see their QC scores increase.  Jorge Soler (OF, CHC), Freddie Freeman (1b, ATL), Corey Dickerson (OF, CHC) Devin Mesoraco (C, CIN) and Adam LaRoche (1b, CHW) all fall into this category.

Conclusion

Even in an era of offensive decline, we can still build a homogeneous team by incorporating QC scores in lieu of ct% and xBA. If you can roster 6-8 of these players, you will once again generate an advantage in your head-to-head leagues.

 

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  For more information about the terms used in this article, see our Glossary Primer.