Category Archives: Fielding Ranking

Best and Worst Team Fielding

We haven’t listed a sort of best and worst team fielding since the end of May so it’s about time.   As before we will sort on unearned runs (UR).  The best teams at fielding will have given up the fewest unearned runs and the worst the most unearned runs.   There are a lot of other theories that make fielding calculations.  Unearned runs is the only metric we know for sure because we can count it and they do exist.   Unearned runs do not count as an earned run against a pitcher but they do count to determine the outcome of a game.

Note: The UR column in the following two tables represents runs above or below team MLB average which stands at this moment at 44.47 URs/Team.

Top 5 Teams

BAT PITCH R RA W L UR LR Team Name
19.1 27.6 558 494 75 56 20.5 -4.1 BAL
-41.9 29.5 502 497 64 69 16.5 0.9 CIN
-43.9 22.6 496 508 71 61 12.5 -3.1 SLN
-16.4 103.5 528 427 72 60 12.5 1.4 SEA
24.6 -82.4 572 614 58 74 11.5 4.4 MIN

Bottom 5 Teams

BAT PITCH R RA W L UR LR Team Name
24.1 16.6 564 547 67 64 -22.5 -3.1 CLE
23.6 2.6 563 552 69 64 -14.5 -3.6 PIT
-23.9 -31.4 520 585 61 72 -12.5 0.9 PHI
-28.4 -64.4 517 620 55 78 -12.5 2.4 ARI
-36.4 5.5 501 546 59 73 -10.5 -5.6 CHN

What about Fielding?

The PLAYER class has three types; BATter, PITCHer, and FIELDer.  So far we’ve covered the former two with this model but haven’t addressed the FIELDER PLAYER type.  I haven’t figured out how to do a value ranking for fielding.  Sabermetrics tried to calculate this but I have issues with their methods and soon I’ll be running some numbers to demonstrate how inaccurate their measurements can be.

Baseball games are won and lost based upon runs given up and runs scored.  Runs given up are the purview of pitching and runs scored are the responsibility of batting.  So where does fielding fit into this?   A great fielder can rob hits from opposing batters.  As we calculated in a previous post,  every two hits is equivalent to a single run.  Thus, theoretically, for every hit a fielder saves his team means 0.5 runs didn’t score.  The problem here is that runs that didn’t score cannot be reliably counted like a run that did score.

“Advanced” Sabermetrics use zone data to determine a fielder’s worth.  How valuable is a great fielder and exactly how many opportunities does your average position player get where their superior fielding turned a hit into an out.   My assumption is that these opportunities are negligible but I’ll run some studies using the newly released 2013 event data from retrosheet.org in future posts.

For now I divide fielders into 3 categories; Great, OK, and Liability.  A GM should be concerned about players who fall into the latter category.  If that player generates a lot of BATting or PITCHing value (pitchers are FIELDers too) maybe having them as a fielding liability is acceptable.  The ERROR stat has been maintained in box scores since the beginning of baseball.  An official score keeper will determine whether a fielder should have made an out but didn’t.

The following table lists  MLB fielders sorted by the most errors showing how many unearned runs they gave up because of those errors.  This table was derived from event data.  A script goes through and notes an error, tabulates that against a player, then counts all associated  unearned runs.

Name_tm #Errors #Uruns
Pedro_Alvarez_PIT 27 13
Alexei_Ramirez_CHA 22 23
Starlin_Castro_CHN 22 11
Ryan_Zimmerman_WAS 21 15
Ian_Desmond_WAS 20 10
Pablo_Sandoval_SFN 18 4
Pedro_Florimon_MIN 18 17
Daniel_Murphy_NYN 18 19
Jed_Lowrie_OAK 18 9
Conor_Gillaspie_CHA 17 11
Chase_Utley_PHI 17 12
Juan_Francisco_ATL 3 1
Juan_Francisco_MIL 14 6
Anthony_Rendon_WAS 16 4
Alberto_Callaspo_ANA 11 9
Alberto_Callaspo_OAK 5 4
Josh_Donaldson_OAK 16 3
Jonathan_Villar_HOU 16 8
Mike_Moustakas_KCA 16 15
Matt_Dominguez_HOU 16 12
Erick_Aybar_ANA 15 4
Jordy_Mercer_PIT 15 8
Brandon_Crawford_SFN 15 7
Jean_Segura_MIL 15 15
Adeiny_Hechavarria_MIA 15 6

From this table you can probably figure out fielders that pose a liability to a team.  Normally every error results in around 0.6 runs.  I’ll get more into this ratio and how it varied throughout history in a future post.  Alexei Ramirez for the Chicago White Sox led the league by letting up 23 runs through his 22 errors.  These are real runs that scored against the White Sox.  Either Alexei was unlucky (possible) and this is a statistical anomaly or he has panic issues where he commits errors in the worst possible situations.  You can’t make any conclusion from this number other than perhaps the top players in the above list pose a liability.  Considering Pedro Alverez posted a +4.2 WAA perhaps that makes up for the 13 runs he let score.

I don’t have a good handle on how to model fielding for OK and great fielders and to what extent fielding should impact their overall value.  Sabermetric and WAR people do lump fielding into their overall rating which I think is wrong.  Since fielding is its own class type it should have its own value and it can’t be tabulated like pitchers and batters.    Throughout the winter I’ll be doing some more counting studies on the 2013 event data to see what is the best way to represent this and try and determine exactly what value a great fielder provides over an OK or Liability fielder.

Post Season Leaders in Errors

After posting the careers of top home grown St. Louis Cardinals I decided to present minor leagues differently and automate this more and hopefully make it easier to find and analyze top prospects.  Players with the best WAA in a minor league season are not necessarily top prospects. They may be players who can excel in the minors and not higher. More code is required to try out and implement some more and different sorts.

In the meantime here’s a fun table generated from my data model showing top 15 players in post season play who gave up the most runs due to errors.  These stats were derived from modified play by play event files curated and made available by retrosheet.org. The general rule of thumb average throughout history is that an unearned run is scored for every two errors. Individual results will vary.  Conclusions shouldn’t be made from this data other than these players played a lot in post season.

Name Errors Unearned Runs
Derek_Jeter 16 5
Art_Fletcher 12 9
George_Brett 11 9
Chipper_Jones 11 10
Davey_Lopes 11 3
Bill_Russell 10 7
Frankie_Crosetti 10 5
Honus_Wagner 9 9
Peewee_Reese 9 1
Edgar_Renteria 9 3
Larry_Doyle 9 5
Roger_Peckinpaugh 9 7
Frankie_Frisch 9 4
Doug_DeCinces 8 6
Tony_Kubek 8 4

This type of data is part of the way this data model ranks fielding.