Defense, UZR, and WAR

I want to start off in saying, I don't yet have a solution to this problem and may never have one.  I might be beating a dead horse in this post too.  We know the defense stat UZR is flawed.  Some of these points that follow are well based, some might be a bit unreasonable.  I found these comments off of saber sites like Fangraphs.  They make a good illustration of the problems with UZR.

"I think the more logical (and maybe more common) suggestion is to use multi-year and/or regressed fielding runs in the defense portion of things. I know Dave will counter with the argument that then we’re describing true talent rather than what actually *happened*, but I’ve never cared for that argument. And if you understand how UZR works, it’s not measuring what actually happened anyway (many plays are entirely thrown out of the data)."

This states two problems.

- One year UZR values are sketchy.  It's not a large enough sample size.  The creator of UZR has stated that we need to use multiple years to draw on conclusions from the data.  My current thought on this arguement is, could we regress these UZR values in half to get closer to the mean.  We would only do this for short samples.

Problem two stated in the comment was that plays are left out of the data.  This is true.   Defensive shifts are left out, any plays that involve them.  As we know over the last few years defensive shifts have been increasing, more and more are being out on.  This means more and more plays are being left out of UZR.  This is a problem for sure.

Now I want slightly alter the course of this topic to defense and WAR, which became a sticky subject on fangraphs in September.  Jeff Passan, a baseball conservationist, stated that he thought the defensive part of WAR was being weighed too much.  He stated Alex Gordon as an example of a player who despite is ok-good batting numbers was listed as one of the top players in the game by WAR.  There was a post about it on Fangraphs and everything.  I'm going to respond to Passan' arguement on how I see it.

- WAR weighing defense is not the problem in my eyes.  What's being fed into WAR from the defensive side is incorrect.  It's not the output, it's the input which of course is UZR.  The creator of UZR, like I mentioned at the beginning said you should use multi year samples.  WAR is using one year UZR data, feeding data into it which means very little takeaways from it are accurate.  I understand why they don't use multi year samples, but I think these numbers need to be regressed just a small bit closer to the mean (0).  The mean is average and since we're not making very good estimates using UZR the way it is now, why not get it closer to the mean?  I'm not advocating changing WAR, I'm pushing to change UZR, which leads me into my second point.

- Let's say we were to do what I mentioned above.  Change UZR.  I still don't know if I want it going into WAR.  I know it will continue too and it should.  If it's the best we have then we have to roll with it, but when we find a better option we need to input it into WAR.  I'm just saying with this point, that UZR is messing up WAR, WAR isn't messing up UZR.

Thanks for reading, I hope I painted an accurate picture of where we are at with Defense, UZR, and WAR.  I might make a second post on this leading into a new idea we'll see.

2014 Park Factors

Above 100: Hitters Park
Below 100: Pitchers Park
Exactly 100: Neutral

The reason for me posting these is because:

A) You might be interested in it.

B) These are the Park Factors I use for Total Runs.

In this article you can see how Park Factors are actually put into Total Runs (I have a new way of doing it)
http://regresstothemean.blogspot.com/2014/11/changes-to-total-runs.html?m=1


I'll be using just the team name, so Cubs of course means Wrigley Field.

D'Backs: 103
Braves: 98
Orioles: 99
Red Sox: 107
Cubs: 97
White Sox: 99
Reds: 97
Indians: 98
Rockies: 123
Tigers: 102
Astros: 100
Royals: 102
Angels: 97
Dodgers: 92
Marlins: 100
Brewers: 99
Twins: 105
Mets: 94
Yankees: 101
A's: 104
Phillies: 95
Pirates: 103
Padres: 92
Giants: 98
Mariners: 93
Cardinals: 100
Rays: 98
Rangers: 97
Blue Jays: 101
Nationals: 106

Changes to Total Runs

Some changes to my metric Total Runs have been implented.  The metric's formula is still
Runs Created+
Defensive Runs Saved+
Speed Runs+
League Adjustment+
Positional Adjustment+
Park Factors=
Total Runs

As you may have noticed my metric (Speed Runs) is also included.  I believe I made a blog post on it a while back.

The League Adjustments for 2014 have been determined and they are +5 for NL batters, +3 for AL batters.  That's all League Adjustments all for 2014.  (It's different for other years)

The way the Positional Adjustments and Park Factors were calculated has been changed.  The basis on the change is the same for both metrics.  I'll cover the Postional Adjustments first.

As you may know Positional Adjustments used to be given to all players, on a fixed amount based on the position they played.  Here is the chart.

Catcher: + 42 runs
Shortstop: + 36 runs
Second Base: +32 runs
Center Fielder: +29 runs
Third Base: + 25 runs
Right Fielder: + 20 runs
Left Fielder: + 19 runs
First Base: + 13 runs
DH: - 7 runs

I used to give the full credit depending based on this chart which is based off the assumption that they played 162 games.  The change is now, you only get credit for the games you played.  The way to determine the Positonal Adjustment Value is now the following:

Games Played/162 * Postional Adjustment Value based on Position.

This only gives them credit for the games they played rather then giving them a credit for 162 games.  My old approach was just lazy but now it's been fixed.

Now to the revamped Park Factors Value.  Like the Positional Adjustment, I used to give credit to the players for playing 162 games now that's been adjusted.

Above 100 Hitters park
Below 100 pitchers park
100=Neutral

Games Played/162 * + or - the player's park above or below 100.

That's the new way we do Park Factors and Postional Adjustments.  Only giving then credit for the games they actually played.  The Positional Adjustment will have a larger impact on the player's Total Runs over Park Factors most of the time.

Total Runs is hopefully done with new parts.  And by parts I mean the 6 "values" that go into it.  The way we figure these values will continue to be changed.  I've already had some new ideas to fix Speed Runs a bit.

Just an update on the Database for 2014 stats.  I'm currently on the last names starting with P so making steady progress.

Fielding Metric for Total Runs

As you may or may not know I have now came out with a fielding metric.  I think I’ve only posted results/formulas for outfielders and middle infielders.  I’m working on expanding to other positions as well but for now all positions have not been completed.  Meanwhile, I have a bit of a problem on my hands for Total Runs. 

About a year ago I introduced Total Runs and made adjustments like Park Factors to it.  This summer I made yet another adjustment and that was based on the leagues offensive power.  My metric I used at the time for it was DRS, also known as Defensive Runs Saved.  As I mentioned in the first paragraph I have invented my first defensive metric and it’s reliable, kind of.  You can’t tell me DRS is reliable too though.  You know that’s the problem with defensive stats, throughout building my Fielding Linear Weights metric I was constantly comparing it to what DRS and UZR said while I should be comparing it to my subjective knowledge.  So, to make this long story short I think I’ve made up my mind on this regarding whether to use DRS or FLW and that is I’ll be using DRS.  There is one main reason for this and that is:

1)    If I choose FLW, I would have to figure all of the Fielding Linear Weights before I could do any Total Runs values. 

 

There is also a secondary reason and that is, I think I might need another year of tinkering with FLW before I insert it into Total Runs.  The trustworthy and common acceptance might be a bit lower on FLW than DRS too. 

 

That’s all I have for today.  Thanks for reading, some results on 2014 data regarding Total Runs, FLW, and other stats I created should start to appear on here in about 1-2 weeks.  

Thoughts on Pitching

You might have noticed that I have not done much with pitching stats on this blog.  And I really haven't.  Almost a year ago I attempted to make a stat called Total Runs for Pitchers which didn't really work at all.  I've had a few posts on Wins and Losses and the shortcomings of them.  But no stat that I believe can measure a pitcher accurately.  

 

When I startup my database on recording 2014 stats for players, as of now I only have these stats.  

-Total Runs

-Fielding Linear Weights

-Speed Runs

-Estimated Runs Created

-Maybe Total Wins (Based off of Total Runs)

And then a few other metrics that can relate to the ones I invented above.  But no pitching stat as of now.

 

There is a lot of work in looking at making a pitching metric and it has to adjust for a lot of things like...

-Defense behind pitcher

-Luck controlled variables

 

Those two are probably some of the hardest.  

 

 

Sticking with pitching here, last night in the World Series Jeremy Guthrie was doing ok.  I believe it was the MLB twitter feed who tweeted out that Guthrie was flying and doing very well around the 4^th inning or so.  I had two thoughts about this.

 

A)    Guthrie had not recorded a strikeout yet and was a mediocre pitcher throughout 2014. 

 

B)    With no strikeouts he was placing his fate in the hands of the Royals defense which luckily for him is historically excellent. 

 

  

He really wasn’t flying just getting pretty lucky.  In the top of the sixth inning Ned Yost turned some heads when he allowed Guthrie to bat for himself.  Many assumed he would pinch hit for him and go the bullpen.  Ned didn’t, and the Royals went to go get two more runs in the inning for a 3-0 lead. 

Measuring pitcher dominance is easy for me and that’s you measure dominance by strikeouts they get.  If a pitcher is not getting a lot of strikeouts then he is skating on thin ice and placing his fate in the often untrustworthy hands of BABIP. 

 

I might write more about pitchers soon on the blog.  I just wanted to get this post out to show you what I think on the topic of pitching.  Thanks for reading.

The Offseason Plan

As you might know I'm currently working on a study involving whether good speed means good fielding ability.  I am actually going to move back that study for about two weeks.  There are two reasons for this.

- I get a lot of my data from the Bill James Handbook and that comes out November 1st, 2014. 

- If I wait until November 1st, I can get 2014 data from the Handbook instead of using outdated 2013 stats. 

I'm mostly just trying to make the study more current in your minds and I think pushing that back will do that.

Next, I'm going to be getting 2014 data finalized quicker.  You might have noticed I'm just wrapping up 2013 data, but this year I hope to be done with 2014 data by April 1st, 2015.  That way the blog can actually focus on the 2015 season. 

Finishing the 2014 data will include doing totals on these things:

Total Runs, Fielding Linear Weights, Estimated Runs Created, Speed Runs, and if I feel the need to create another stat then another one.  And of course you're get a reminder on how each of these stats are calculated.  Then the results will of course be posted here.

For this offseason, I'm also introducing something a bit new.  To promote other people's baseball research, I will be posting other people's links to articles that I find interesting. 

Also whenever I have something that doesn't exactly fit in with anything mentioned above, I'll use a bullets type post, covering a few topics that are just interesting.   

Thanks for reading.  I hope to actually post more then other off seasons so far. 

Intro to: Fast players, Good fielders?

Developing my Fielding Linear Weights was hard, but well worth it.  I can use it to study a few issues including the one I'm going to start over the next week or two.  Do fast outfielders make good defensive outfielders.  Most people's thought process would think something like this. 

More Speed=More Range=More Putouts/Assists=Good defensive outfielder.

Obviously I needed to capture the value of speed.  And that's where my newest metric comes in, Speed Runs like it's name suggests measures speed.  To know what goes into this metric, here is my blog post on it here.  http://regresstothemean.blogspot.com/2014/10/speed-runs.html

When looking at two metrics and forming a conclusion off of them you need to make sure these two metrics work.  If they don't work then you will draw bad conclusions.  I have continued to tinker with each, and by the time the results come out on this the formulas for my fielding and base running metric could be slightly different.  I already know the Speed Runs will be different from the one in that blog post. 

So armed with these two new formulas, I want some questions answered.  These are:

Are players with good speed, good defensive outfielders?

Guess:  My gut tells me yes.  One potential drawback of having good speed would be overrunning the ball.  I would think that affect would be small though.

Do slower outfielders make bad defensive outfielders?

Guess:  I think the correlation here is not good.  I know quite a few bad defensive outfielders. 

Using the data we have, by the end of this study we will have the above two questions answered and maybe even a few more questions that we discover throughout the research of this.  If we don't get these questions answered we can always go back multiple years and add those to the study along with 2013/2014. 

Thanks for reading.  Remember to follow me on Twitter @CastroRizzo      

2014 Total Runs Leaders

Player	Runs Created	Pitching RunsCreated	Runs Saved	Baserunning Runs	Position	Total Runs
Rendon, Anthony	101	0	16	7	25	149
Trout, Mike	127	0	-9	2	26	146
Lucroy, Jonathan	97	0	11	1	36	145
Stanton, Giancarlo	112	0	7	5	18	142
Kinsler, Ian	83	0	20	6	31	140
Donaldson, Josh	93	0	20	1	23	137
Altuve, Jose	110	0	-7	2	30	135
Brantley, Michael	115	0	-3	2	20	134
Heyward, Jason	83	0	32	1	18	134
Gordon, Alex	86	0	27	2	18	133
McCutchen, Andrew	120	0	-11	-4	26	131
Kluber, Corey	0	128	0	0	2	130
Cano, Robinson	97	0	0	3	29	129
Dozier, Brian	90	0	0	7	30	127
Beltre, Adrian	99	0	9	-2	20	126
Jones, Adam	91	0	2	5	28	126
Peralta, Jhonny	79	0	17	-3	33	126
Hernandez, Felix	0	124	-2	0	2	124
Cueto, Johnny	-1	114	6	0	3	122
Gomez, Carlos	95	0	2	-1	26	122
Puig, Yasiel	100	0	2	0	20	122

Speed Runs

The name for this statistic isn't really good but I think the content put into it gives you some good results.  The reason I went about trying to measure how many runs a player gained on the bases was I need for some research I'm doing.  I'll eventually study the issue:

How well does speed correlate with outfield defense?

So, I needed a speed metric and this is the result of that.  I think this metric works well enough to study the question I want to look at.  Without farther ado here is the formula.  All the values for Stolen bases, Caught stealing, and Triples are based off of Linear Weights values to run scoring. 

A Factor:

Stolen Bases times .18
Caught Stealing times -.48
SB-CS values

The result is the A factor.

B Factor:

Triples times 1.07

The result if the B factor.

C Factor:

Grounded into Double Play %  (GDP/GDP opportunities)

5 to 10%: +6 runs
11 to 12%: +1 run
13 to 14%: 0 runs
15 to 17%: -1 run
18 to 20%: -3 runs
21 to 26%: -6 runs

The result is the C factor

Then with those three factors the formula would be:

A+B-C minus 5 at the end

I think this formula works pretty well for evaluating a player's speed in more ways then just base stealing.

Walking you through an example...

Mike Trout will be our example

Stolen Bases: 33 times .18
Caught Stealing: 7 times -.48

5.94-3.36: 2.58

A Factor Value: 2.58



9 triples times 1.07: 9.63

B Factor Value: 9.63



8/127: About 7%

C Factor Value: 6


2.58 + 9.63 + 6= 18.21
18.21-5=13.2

Mike Trout had 13.2 Speed Runs in 2013.

Thanks for reading.  I'll start to get a post or two ready on if speed correlates with outfield defense.


 

Corner Outfielders in 2013 and their Fielding

 

This is the Corner Outfield part in my quest to make a reliable defensive metric.  I have currently done shortstops and now corner outfielders.  Fielding Linear Weights is the name of my metric. 

 

Name	FLW	DRS
Starling Marte	-3	20
Marlon Byrd	6	12
Giancarlo Stanton	-4	-7
Jay Bruce	29	18
Yasiel Puig	3	10
Jose Bautista	-1	4
Alfonso Soriano	5	1
Drew Stubbs	0	-6
Nelson Cruz	-2	-3
Torii Hunter	8	-10
Shane Victrino	28	24
Nori Aoki	6	13
Josh Reddick	8	13
Ichiro	9	7
Hunter Pence	9	-7
Yoenis Cespedes	5	4
Alex Gordon	29	16
Dominic Brown	-12	-7
Justin Upton	-2	-8
Bryce Harper	1	4
Carl Crawford	2	1
David Murphy	14	8
J.B. Shuck	2	0
Dayan Viciedo	-10	-5

 

So, these are the results.  Over the last few weeks I’ve been working on measuring Left and Right fielders defense.  Using putouts, assists, errors, double plays, and strikeout data for teams, and Groundball/Fly ball adjustments I’ve developed a formula that has given me good results.  You might be wondering why I didn’t lump CF in with these two positions and there are two reasons for this.   

 

A)    I don’t know quite how to do them yet.

 

B)     The formula couldn’t be the same as LF and RF

 

 

I’ll probably come back for centerfielders soon but for now I’ll be sticking with corner outfielders.  The formula is pretty easy to understand.  You can be the judge of its accuracy.

 

Putouts (* .25) + Assists (* 6) + Double Plays (* 8) / Errors ( +1 *1.5) minus 7

 

This is essentially the ‘A’ factor.  The B factor is Range Factor. 

 

Range Factor RF Chart

 

 

Range Factor	Runs (+,-)
2.00-2.10	0 runs
2.11-2.20	+ 3 runs
2.21-2.30	+ 7 runs
2.31-2.50	+ 10 runs
2.51-3.00	+ 13 runs
1.90-1.99	- 3 runs
1.89-1.80	- 7 runs
1.79-1.70	- 10 runs
1.69-1.60	- 12 runs
1.59-1.40	- 16 runs

 

Range Factor LF Chart

 

Range Factor	Runs (+,-)
1.88-1.95	0 runs
1.96-2.00	+ 3 runs
2.07-2.20	+ 7 runs
2.21-2.30	+ 10 runs
1.88-1.78	- 4 runs
1.77-1.67	- 8 runs
1.66-1.58	- 12 runs
1.57-1.41	- 15 runs

 

Then the C factor is how many batters each team strikes out.  The thinking behind this is if a team strikes out a lot of batters then the fielders will get less chances with the ball.  This is adjusted for by the following chart.  There are no set standards for the amount of runs + or – just me making guesses based on the average in the MLB strikeouts per team.  (The Twins had the lowest with 985 SO, so they get runs subtracted from their FLW totals, The Tigers had the most with 1428 so they get runs added to their FLW totals) 

 

Teams	Strikeouts	Runs (+,-)
Royals	1208	-1
Tigers	1428	+5
A’s	1183	-2
Rangers	1309	+ 3
Rays	1310	+3
Red Sox	1294	+2
Indians	1379	+4
Yankees	1233	0
Orioles	1169	-2
White Sox	1249	+1
Angels	1200	-1
Mariners	1297	+2
Blue Jays	1208	-1
Twins	985	-8
Astros	1084	-6
Braves	1232	0
Pirates	1261	+2
Dodgers	1292	+2
Reds	1296	+2
Cardinals	1254	+1
Nationals	1236	0
Marlins	1177	-2
Mets	1209	-1
Brewers	1125	-3
Cubs	1184	-2
Giants	1256	+1
Diamondbacks	1218	0
Padres	1171	-2
Phillies	1199	-1
Rockies	1064	-2

 

 

 

  The D factor has Groundball/Flyball adjustments inserted. 

 

Groundball %	Runs (+,-)
44-45%	0 runs
46-47%	+3 runs
48-50%	+7 runs
51-55%	+10 runs
42-43%	-4 runs
41-40%	-8 runs
39-35	-10 runs

 

And that’s all the formula.  The results follow this formula.  For 2012 and back results go to my blog at regresstothemean.blogspot.com

 

Now looking over the results gives some interesting finds. 

 

Starling Marte.  His runs saved in Fielding Linear Weights is -3.  DRS says +20.  Let’s get into the numbers. 

 

Starling Marte	Putouts	Assists	Double Plays	Errors	FLW	DRS
	176	5	0	6	-3	20

 

He did not have a good defensive season by any of those stats expect DRS.  Why does DRS rate him so high?  As you probably know Defensive Runs Saved uses actual video and bins to determine how many runs they saved.  Not anything to do with putouts, assists, errors or any number.  I could be wrong on that but that’s how I interoperated the methodology behind it.  I don’t understand how somebody could come to the conclusion he was the best LF in baseball but I say he’s one of the worst.  If you think -3 is low, before the Strikeout/Groundball/Fly ball adjustment he was rated -15.  The point of the matter is he isn’t very good at defense.  At least in 2013. 

 

Jay Bruce is a good fielder.  I had actually before constructing this system, never knew anything about Bruce as a fielder.  And if I don’t know anything about a player’s defense then you would assume they are about an average defender.  That is far from the case with Bruce.  Here is a look at Bruce’s 2013 defensive stats.

Name	Putouts	Assists	Double Plays	Errors	FLW	DRS
Jay Bruce	330	13	3	3	29	18

 

Yasiel Puig before Groundball/Fly ball I believe was rated as -2.  I think that was a pretty accurate rating on him.  Some of the younger defensive outfielders that have burst on to the scene like (Trout, Puig, Harper, Stanton, Marte,) are rated quite poorly by my metric and DRS.  You would not get that type of feeling if you just watched games however.  The reason I think that is because all the players I listed are so exciting and flashy.  Other guys like Jay Bruce aren’t very exciting.  Those guys I believe would be overrated by the casual fan with no knowledge of defensive metrics. 

 

The largest fly ball and the largest groundball staffs in 2013 were surprisingly the A’s and Pirates.  This affected Cespedes, Reddick, and Marte quite a bit.  Before those adjustments you would have a got lot different messages about these players then you do now.  It amazes me how some people have attempted to make defensive metrics without adjusting for these things.  They are very important.     

 

All the other players I think are pretty straight ford I believe.  Thanks for reading.