(*) HEAD-TO-HEAD: Building a homogeneous team in the pitching era
Feb 10 2015
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 Ramírez (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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