WAR vs WAA Batters 2013

In past posts we highlighted certain teams that were either under or over rated according to WAR or WAA. This post will highlight the largest differences between WAR and WAA with respect to batting. The tables below will pick the top 5 differences of under and over rated players not mentioned in previous posts. Thus, SLN, DET, PIT, and LAN players will be shown in the rankings but not in the pertinent stats table.

WAR underrate or WAA overrate?

Unlike in previous posts on this topic only players with greater than 100 PAs were included in the rank.  This cleans up a lot of noise around zero for WAA.  Since all players start at WAA=0 a player with a couple of plate appearances will rank higher than a player who goes into negative territory.  This skew is confusing.  Eliminating short time batters, including most all pitchers, cleans up the ranking numbers.

This sort is based on WAA so only WAA ranked players less than 50 were considered.  The next section, will sort based on WAR ranking.  The table following each ranking table will list the player’s pertinent stats including their WAR and WAA values.  The actual WAR and WAA are included for informational purposes only as there is no direct comparison between the two numbers.  We are concerned with how each system ranks players with each other.  That is the only way to compare the two systems.

Diff WAR WAA Name_tm
144 164 20 Brandon_Phillips_CIN
131 157 26 Prince_Fielder_DET
116 139 23 Michael_Cuddyer_COL
115 158 43 Yoenis_Cespedes_OAK
115 125 10 Brandon_Moss_OAK
109 126 17 Allen_Craig_SLN
106 128 22 Mark_Trumbo_ANA
1.6 4.8 0.261 0.310 666 103 80 Brandon_Phillips_CIN BAT
2.0 4.7 0.331 0.389 540 84 74 Michael_Cuddyer_COL BAT
1.7 3.5 0.240 0.294 574 80 74 Yoenis_Cespedes_OAK BAT
2.2 5.8 0.256 0.337 505 87 73 Brandon_Moss_OAK BAT
2.2 4.7 0.234 0.294 678 100 85 Mark_Trumbo_ANA BAT

WAR overrate or WAA underrate?

This table sorts the top 5 largest differences of less than 50 ranked WAR players.  Either WAR overrated these players or WAA underrated them.  You make the call.

Welington Castillo is a catcher for CHN.  Since WAR folds defense into their number it’s quite possible that led to their high rating for Wellington.  The catcher is the most important defensive asset in fielding so perhaps they have a point.  This model treats defense as a separate class with its own rating.

Diff WAR WAA Name_tm
431 37 468 Welington_Castillo_CHN
379 16 395 Gerardo_Parra_ARI
311 8 319 Andrelton_Simmons_ATL
263 24 287 Starling_Marte_PIT
224 44 268 Russell_Martin_PIT
186 29 215 Ben_Zobrist_TBA
185 25 210 Joe_Mauer_MIN
4.4 -2.2 0.274 0.349 428 32 41 Welington_Castillo_CHN BAT
6.1 -1.2 0.268 0.323 663 48 79 Gerardo_Parra_ARI BAT
6.8 -0.7 0.248 0.296 658 59 76 Andrelton_Simmons_ATL BAT
5.1 0.1 0.275 0.355 693 71 77 Ben_Zobrist_TBA BAT
5.4 0.1 0.324 0.404 508 47 62 Joe_Mauer_MIN BAT

Update 2/8/2014

I had noticed an anomaly with Joe Mauer’s stats in the last table above.  How did a player batting 0.324 with a 0.404 OBP end up in the top either overrated WAR or underrated WAA list?  Isn’t this a clear case of WAA underrating a player?

All tables in this model get spit out automatically based upon certain guidelines I define.  It  surprises me when something doesn’t seem right so I  trace back and check to make sure there isn’t a software bug somewhere (that happens).  First I check RISP tables.  RISP tables are described here.  We have shown in this post that 3/4 of all runs occur in Runners In Scoring Position situations.  The fundamental difference between the WAR and WAA systems is WAR focuses on hits and walks whereas this model focuses on run production.  Below is Joe Mauer’s RISP record.

RAA BA OBP PA Name_team
0.9 0.272 0.405 126 Joe_Mauer_MIN

Although Mauer maintained his OBP in RISP situations, his BA is 50 points lower.  He still is above league average with a 0.9 runs above average with runners in scoring position.  A batter brings value to his team by knocking in runs and/or scampering around the bases so others can hit him in easier.  Mauer’s WAA=0.1 places him slightly above an average MLB player which is appropriate here.  Had Mauer hit 0.324 in RISP situations he would have produced a lot more wins for his team and scored a higher WAA value.  In other words; the data here indicates many of Mauer’s hits came in situations that didn’t matter. The WAR math does not have the capability to discern this.

In the end, baseball rules dictate that the team with the most runs wins a ball game — not the team with the most hits and walks.