Player value through 7/23

[RedsManRick]

BP has improved their team report pages and now creates these handy summaries. This does NOT include defense, so you should adjust accordingly for overall value -- but it is insightful nonetheless. (and yes, I know VORP has it's issues, but fwiw...)

For those not familiar:
EqA: Equivalent Average. A measure of total offensive value per out, with corrections for league offensive level, home park, and team pitching. EQA considers batting as well as baserunning, but not the value of a position player's defense. The EqA adjusted for all-time also has a correction for league difficulty. The scale is deliberately set to approximate that of batting average. League average EqA is always equal to .260

VORP: Value Over Replacement Player. The number of runs contributed beyond what a replacement-level player at the same position would contribute if given the same percentage of team plate appearances. VORP scores do not consider the quality of a player's defense.

Remeber, 10 runs is approximately equal to 1 win. So replacing Taveras's bat with a replacement player (let's call him Dris Heisubbs) would have netted us about 1 win thus far.

Batting
Position Players: +51.4 runs
Pitchers: +2.1 runs
Total: +53.5 runs

Pitching
Starters: +22.9 runs
Relievers: +50.9 runs (included a -9.7 from Janish)
Total: +73.8 runs

Total: +127.3 runs or ~13 wins above replacement.

Save for the significant defense component of our run prevention, I think that tells the story of 2009 pretty darn well.

Rank 	Player			PA	AVG	OBP	SLG	SB	CS	EqA	 VORP
1	Joey Votto		260	.352	.435	.599	4	1	.346	31.1
2	Brandon Phillips	369	.271	.335	.465	14	7	.276	16.1
3	Jonny Gomes		123	.305	.398	.571	1	0	.326	12.6
4	Ryan Hanigan		189	.312	.406	.375	0	0	.285	10.4
5	Micah Owings		 50	.234	.245	.532	0	0	.256	 6.0
6	Chris Dickerson		246	.268	.365	.373	7	3	.268	 5.9
7	Laynce Nix		221	.240	.290	.461	0	0	.254	 3.5
8	Jerry Hairston		325	.250	.304	.387	7	3	.244	 2.8
9	Wilkin Castillo		  3	.667	.667	.667	0	0	.501	 1.1
10	Edwin Encarnacion	138	.209	.341	.365	1	1	.252	 0.7
11	Matt Maloney		  5	.250	.250	.250	0	0	.194	 0.2
12	Daniel Ray Herrera	  4	.000	.333	.000	0	0	.208	 0.1
13	Bronson Arroyo		 44	.147	.147	.206	0	0	.146	-0.1
14	Arthur Rhodes		  1	.000	.000	.000	0	0	-.226	-0.1
15	Jared Burton		  1	.000	.000	.000	0	0	-.226	-0.1
16	Ramon Ramirez		  1	.000	.000	.000	0	0	.273	-0.1
17	Aaron Harang		 40	.143	.167	.171	0	0	.083	-0.2
18	Johnny Cueto		 38	.125	.200	.125	0	0	.078	-0.2
19	Ramon Hernandez		316	.249	.330	.355	1	0	.247	-0.2
20	Danny Richar		  9	.250	.333	.250	0	0	.212	-0.2
21	Mike Lincoln		  2	.000	.000	.000	0	0	-.226	-0.3
22	Craig Tatum		  2	.000	.000	.000	0	0	.000	-0.5
23	Drew Sutton		  8	.125	.125	.250	0	0	-.058	-1.0
24	Homer Bailey	 	 10	.000	.000	.000	0	0	-.223	-1.4
25	Edinson Volquez	 	 19	.062	.062	.062	0	0	-.144	-1.7
26	Paul Janish		 92	.222	.308	.272	0	0	.208	-1.9
27	Jay Bruce		333	.207	.283	.441	3	2	.246	-1.9
28	Darnell McDonald	 44	.175	.250	.225	0	0	.154	-3.5
29	Alex Gonzalez		198	.214	.256	.302	0	1	.192	-7.3
30	Adam Rosales		161	.209	.289	.302	0	2	.210	-7.4
31	Willy Taveras		347	.247	.288	.301	19	6	.218	-8.9
									
Rank 	Player			IP	H9	BB9	SO9	HR9	ERA	WHIP	 VORP
1	Johnny Cueto		115.3	8.4	3.0	6.9	1.2	3.67	1.27	18.7
2	Francisco Cordero	 38.0	6.2	3.8	7.6	0.2	1.66	1.11	15.2
3	Aaron Harang		121.0	10.6	2.2	7.7	1.3	4.17	1.42	12.9
4	Arthur Rhodes		 32.3	5.0	3.9	7.8	0.6	1.67	0.99	12.7
5	Nick Masset		 39.7	5.5	3.0	8.2	0.7	2.50	0.93	11.2
6	David Weathers		 33.3	6.2	4.1	5.9	1.1	2.97	1.14	 9.1
7	Carlos Fisher	 	 25.3	8.2	5.7	8.5	0.0	2.84	1.54	 7.3
8	Edinson Volquez		 49.7	6.2	5.8	8.5	1.1	4.35	1.33	 5.6
9	Daniel Ray Herrera	 38.7	9.8	3.3	6.8	0.7	2.56	1.45	 5.6
10	Josh Roenicke		 12.3	8.8	2.9	8.8	0.0	2.92	1.30	 3.4
11	Robert Manuel		  4.3	10.4	2.1	4.2	0.0	0.00	1.38	 2.6
12	Jared Burton		 36.0	10.5	4.3	6.5	0.5	5.25	1.64	 0.8
13	Ramon Ramirez		  2.3	3.9	3.9	11.6	3.9	7.71	0.86	-0.5
14	Matt Maloney		 17.7	9.2	2.6	7.1	3.1	6.11	1.30	-0.8
15	Micah Owings		 99.7	10.0	4.5	5.3	1.4	5.33	1.62	-1.4
16	Bronson Arroyo		124.3	9.8	3.4	5.3	1.6	5.21	1.47	-2.2
17	Mike Lincoln		 23.0	11.4	7.4	3.5	2.7	8.22	2.09	-6.8
18	Paul Janish		  2.0	40.5	9.0	13.5	9.0	49.50	5.50	-9.7
19	Homer Bailey		 30.7	8.8	6.2	5.6	1.2	7.63	1.66	-9.9

[nate]

Great. So we just trade everyone in the red, right?

[RedsManRick]

Great. So we just trade everyone in the red, right?

Well... not giving them so much playing time would be a good start.

Replacing Taveras, Rosales, Gonzalez, McDonald, Bailey, Janish (pitching and hitting), and Lincoln wtih replacement level production would have netted us 55.4 runs (5.5 wins). Basically, they're the difference between us and the Cardinals thus far.

[LoganBuck]

I can't help but laugh every time I see Paul Janish's pitching stats. Seeing them in that context is just hilarious. The next time the clown next to you at the game starts talking about "how hard could it possibly be", point this out.

[nate]

Well... not giving them so much playing time would be a good start.

Replacing Taveras, Rosales, Gonzalez, McDonald, Bailey, Janish (pitching and hitting), and Lincoln wtih replacement level production would have netted us 55.4 runs (5.5 wins). Basically, they're the difference between us and the Cardinals thus far.

So it's a re-run from last year?

[bucksfan2]

I can't help but laugh every time I see Paul Janish's pitching stats. Seeing them in that context is just hilarious. The next time the clown next to you at the game starts talking about "how hard could it possibly be", point this out.

I heard that had he stayed at Rice for his Sr. year he would have been converted to a P. It makes you wonder how good of a pitcher he could have been because he has a live arm. His fastball is as straight as an arrow, I wonder if he had a little more movement if he would be more successful.

[flyer85]

Arroyo (-2.2), amazing how meaningless wins are in revealing pitching performance.

[RedsManRick]

I got asked in a PM why the Reds keep having so much negative production... I thought I'd share my response.

I would say you have to first differentiate the negative producers in to four categories:

1) Good players having bad seasons: There's not much you can do about these guys. It happens. Usually they'll come back around to the positive by the end of the season -- at minimum they'll likely rebound the following year. Example: Arroyo (yes, he's in decline, but he still should be a positive contributor)

2) Legit prospects getting a shot and not doing well: These guys are tough because they're harder to predict and you've got to give them an extended chance to be solid contributors. I put these guys in the risks-worth-taking category, so long as you cut the cord at a certain point. At least these guys are cheap. Example: Bailey, Rosales, Janish (though less so given his track record).

3) Non-prospects getting a shot and not doing well: When you're 28 and haven't reached the majors, there's usually a good reason for it. A good run in AAA or Spring Training is most often a string of good luck, not a jump in ability. This one comes down to your scouts -- is this guy really a better ball player? Usually the answer is no, but optimism and confirmation bias can be a powerful combination. Example: McDonald.

4) Established major leaguers who just aren't good: This is the biggest problem group and the worst mistake GMs tend to make. Often, this is the result of being blind-sided by one good year that was a perfect storm of peak performance and good luck -- and wholly unrepeatable. Occasionally it's just wishcasting on a prospect who never panned out. Often, it results in committing an inappropriate amount of resources (talent or treasure) given the production you should have expected. Examples: Gonzalez, Lincoln, Taveras.

The sabermetric approach is most valuable with group #4. While new school GMs might miss out on a guy who has put up bad numbers in the past but has great skills, it's an error of omission that doesn't end up hurting the team badly. (Type II error -- excess conservatism). Not signing a good player or promoting a good prospect doesn't help your team, but it doesn't do long term damage.

Meanwhile, old school GMs are more prone to misevaluating players which require significant talent or treasure to acquire. They give Eric Milton a 3 year deal or Gary Matthews Jr. 5. Of course, the classic case is Barry Zito. The signs were all there if you knew where to look. Beane knew, Sabean didn't.

The Reds have cycled through old school GM after old school GM. While they've made good moves like acquiring Phillips for peanuts and selling high on Hamilton, every year the Reds commit excess money and playing time to at least one guy who clearly didn't deserve it from the very start. They think that Alex Gonzalez inability to get on base doesn't matter. They bet (literally) that Willy Taveras is a better bet to produce than Chris Dickerson. They think Josh Fogg belongs in a major league rotation.

Don't get me wrong, scouting and other qualitative evaluations are an enormous part of building a winning team. But in addition to creating opportunity for upside, you have to manage your downside as well. And that's where quantitative analysis is perhaps most useful. It helps you differentiate between flash* and substance** so you can do a better job avoiding big mistakes.

*Flash: A combination of small sample success resulting from "luck", aka variance, and aesthetically pleasing skills/actions that might not be terribly valuable in winning baseball games.

**Substance: A skill base which will support sustained success, including attributes which are undervalued such as plate discipline and defensive range.

[GOYA]

Whatever the equation is to figure VORP, it has to be a joke. Look at Janish's pitching stats and tell me that guy is better than Homer.

[I(heart)Freel]

I got asked in a PM why the Reds keep having so much negative production... I thought I'd share my response.

Thanks. Spot-on commentary and thoughtful (ie not kneejerk hyperbole) analysis. Appreciate it.

[BuckeyeRedleg]

Whatever the equation is to figure VORP, it has to be a joke. Look at Janish's pitching stats and tell me that guy is better than Homer.

He's only thrown 2 innings. Homer has gone 30. If Janish had gone 30 (at the same rate) I'm sure is VORP would be 15 times worse (-130 something).

VORP accumulates (negative and positive) like a counting stat as the season runs it's course.

[RedsManRick]

Whatever the equation is to figure VORP, it has to be a joke. Look at Janish's pitching stats and tell me that guy is better than Homer.

Janish has allowed 11 ER in 2 IP. How many runs would a guy with a 5.50 ERA (a roughly replacement level pitcher) allow? On average, he'd allow 1.2 runs every 2 IP, or 9.8 runs less than Janish. I'd say the VORP formula gets it pretty close.

Bailey has 26 ER in 30.2 IP. The replacement level pitcher would allow, on average, would allow 18.8, a 7.2 difference.

Is VORP perfect? Of course not, but it's a pretty solid guide.

[traderumor]

I can't help but laugh every time I see Paul Janish's pitching stats. Seeing them in that context is just hilarious. The next time the clown next to you at the game starts talking about "how hard could it possibly be", point this out.

But he is above Homer

[LoganBuck]

But he is above Homer

In fairness to Homer nearly every baserunner that he has left on base for the bullpen has been allowed to score. Sure he put them on base, but the bullpen could pick him up a little.

[*BaseClogger*]

I got asked in a PM why the Reds keep having so much negative production... I thought I'd share my response.

I would say you have to first differentiate the negative producers in to four categories:

1) Good players having bad seasons: There's not much you can do about these guys. It happens. Usually they'll come back around to the positive by the end of the season -- at minimum they'll likely rebound the following year. Example: Arroyo (yes, he's in decline, but he still should be a positive contributor)

2) Legit prospects getting a shot and not doing well: These guys are tough because they're harder to predict and you've got to give them an extended chance to be solid contributors. I put these guys in the risks-worth-taking category, so long as you cut the cord at a certain point. At least these guys are cheap. Example: Bailey, Rosales, Janish (though less so given his track record).

3) Non-prospects getting a shot and not doing well: When you're 28 and haven't reached the majors, there's usually a good reason for it. A good run in AAA or Spring Training is most often a string of good luck, not a jump in ability. This one comes down to your scouts -- is this guy really a better ball player? Usually the answer is no, but optimism and confirmation bias can be a powerful combination. Example: McDonald.

4) Established major leaguers who just aren't good: This is the biggest problem group and the worst mistake GMs tend to make. Often, this is the result of being blind-sided by one good year that was a perfect storm of peak performance and good luck -- and wholly unrepeatable. Occasionally it's just wishcasting on a prospect who never panned out. Often, it results in committing an inappropriate amount of resources (talent or treasure) given the production you should have expected. Examples: Gonzalez, Lincoln, Taveras.

The sabermetric approach is most valuable with group #4. While new school GMs might miss out on a guy who has put up bad numbers in the past but has great skills, it's an error of omission that doesn't end up hurting the team badly. (Type II error -- excess conservatism). Not signing a good player or promoting a good prospect doesn't help your team, but it doesn't do long term damage.

Meanwhile, old school GMs are more prone to misevaluating players which require significant talent or treasure to acquire. They give Eric Milton a 3 year deal or Gary Matthews Jr. 5. Of course, the classic case is Barry Zito. The signs were all there if you knew where to look. Beane knew, Sabean didn't.

The Reds have cycled through old school GM after old school GM. While they've made good moves like acquiring Phillips for peanuts and selling high on Hamilton, every year the Reds commit excess money and playing time to at least one guy who clearly didn't deserve it from the very start. They think that Alex Gonzalez inability to get on base doesn't matter. They bet (literally) that Willy Taveras is a better bet to produce than Chris Dickerson. They think Josh Fogg belongs in a major league rotation.

Don't get me wrong, scouting and other qualitative evaluations are an enormous part of building a winning team. But in addition to creating opportunity for upside, you have to manage your downside as well. And that's where quantitative analysis is perhaps most useful. It helps you differentiate between flash* and substance** so you can do a better job avoiding big mistakes.

*Flash: A combination of small sample success resulting from "luck", aka variance, and aesthetically pleasing skills/actions that might not be terribly valuable in winning baseball games.

**Substance: A skill base which will support sustained success, including attributes which are undervalued such as plate discipline and defensive range.

Normally, I wouldn't bump a thread that is 24 hours old. But I just read through this thread and I had to point out what an awesome post this is...