In this final edition of The Blind Baseball Scout we’ll compare P-Sabr’s 2011 rankings with Baseball America’s, discuss the rankings of all Giants hitters in 2011, and take a close look at some trends in prospecting.

Baseball America and P-Sabr agree on the Arizona League top prospect Yoan Alcantara, who received the second highest P-Sabr score (77) of 2011, though it should be noted that the Arizona League had the highest of all P-Sabr scores in 3 of the 6 years that all 5 leagues were run, suggesting that further downward weight due to league level and duration could be added to the system. 5 of the top 15 BBA hitters make up the top 6 P-Sabr ranked players (Alcantara, D’Andre Toney, Marco Hernandez, Alberth Martinez and Gioskar Amaya) though there’s no overlap in the rest of list. Historically less than 10% of all the position players in the league will make it to the majors, so either list will do well to pick 5 players. Giants who made the top 15 include league MVP and relative old man, Jose Cuevas who has an outside chance to become a poor-mans Matt Downs, whatever that means. Cuevas ranked 7th by P-Sabr standards and his teammate Kelby Tomlinson ranked 8th in the league.

In the Northwest League BBA and P-Sabr agreed on the top 2 position players being Joe Panik and Cory Spangenberg, though they diverged on their order with BBA ranking Spangenberg ahead of Panik. Of the 11 position players who were ranked by BBA, 7 of them made the top 11 of P-Sabr’s rankings (Panik, Spangenberg, Pin-Chieh Chen, Zeke DeVoss, Jesus Galindo, Donavan Tate, Rougned Odor). Joining Galindo and Panik in the P-Sabr top 10 was teammate Mike Murray, so here’s to hoping he can find a position.

No obsessed Giants fan and prospect hound was surprised to find zero Giants on BBA’s top ranked Sally League position players. P-Sabr didn’t find any either. Five players who made BBA’s top 15 position players also made P-Sabr’s list (Jurickson Profar, Christian Yelich, Brandon Jacobs, Marcell Ozuna and Jacob Realmuto). This did not include BBA’s top two prospects, Bryce Harper and Manny Machado, who did not have enough At Bats to qualify for P-Sabr, though their third ranked prospect Jurickson Profar scored the highest P-Sabr score (83) of 2011.

Once again the top of both P-Sabr and BBA are similar, with both systems agreeing that Jedd Gyorko and Gary Brown were the number one and two position players in the league. Of the 15 position players ranked by BBA, 5 also made the P-Sabr top 15, Brown, Gyorko, Nolan Arenado, Michael Choice and Tommy Joseph. P-Sabr favored high contact hitters like Henry Roriguez (#3, P-Sabr score of 44) and Vincent Catricala (#5, P-S score of 41) to player like Chris Dominguez (#78, P-S score –77) who ranked as the 13th best position player in BBA’s rankings.

The Eastern league saw 1 Giant position player make the BBA top 12 position players (Francisco Peguero), but no Giant ranked above the Median in P-Sabr. The 2 systems agreed on 2 of the top three players in the league Anthony Gose and Travis D’Arnaud. Bryce Harper, who did not qualify for P-Sabr was replaced by Sterling Marte as the top player in the league.

Since this writing coincides with the McCoven Group community prospects list, I’ll post the P-Sabr top 35 hitters. This is a league-adjusted list, there’s nothing too scientific with this adjustment, so feel free to call B.S. I don’t care. The “adjustment” is I’ve given a bonus or penalty to the players league, and then another bonus or penalty depending on their quartile ranking in their overall league. All the information is there, and you’ve read this far in the third installment of this system, so you must be a geek…you figure it out.

League Rank	Player	P-Sabr Score	League	Adj. Score
2	Gary Brown	46	CAL	51
1	Joe Panik	68	NW	43
13	Tommy Joseph	16	CAL	21
7	Jose Cuevas	46	AZL	11
18	Adam Duvall	8	Sally	3
8	Kelby Tomlinson	34	AZL	-1
6	Jesus Galindo	23	NW	-2
51	Charlie Culberson	-40	EL	-5
51	Francisco Peguero	-40	EL	-5
26	Ehire Adrianza	-3	CAL	-8
10	Mike Murray	15	NW	-10
58	Roger Kieschnick	-46	EL	-11
18	Eric Sim	21	AZL	-16
15	Shawn Payne	5	NW	-20
14	Brett Krill	6	NW	-21
67	Juan Perez	-55	EL	-21
67	Johnny Monell	-55	EL	-21
23	Ben Thomas*	9	AZL	-24
40	Ryan Cavan	-22	CAL	-27
20	Joseph Staley	-6	NW	-31
39	Ryan Lollis*	-22	Sally	-37
46	Jarrett Parker*	-36	CAL	-41
91	Nick Noonan	-85	EL	-55
58	Carlos Willoughby	-41	Sally	-56
61	Josh Mazzola	-48	Sally	-63
93	Chris Dominguez	-93	EL	-63
68	Luke Anders*	-59	CAL	-64
50	Charles Jones	-43	NW	-68
48	Kaohi Downing	-41	NW	-76
74	Rafael Rodriguez	-62	Sally	-77
72	Nick Liles	-72	CAL	-77
56	Elliott Blair	-35	AZL	-80
81	Chris Dominguez	-77	CAL	-87
81	Chris Lofton	-69	Sally	-89
89	Devin Harris	-78	Sally	-98

Obviously the presence of Jose Cuevas at number four is a big “whaaaaaaa…?”, so there’s some work that needs to be done on grading the lower levels. But, aside from that, not so bad.

TRENDS

Now let’s take a look at some of P-Sabr’s historic trends and in prospecting trends in general.

First let’s look at top prospect Gary Brown. In the last 7 (2003-2010) years there have been 7 players that have had a P-Sabr score of 46 or more. 15 of those players have gone on to play in the majors, the best (by O-WAR) being Howie Kendrick, Pablo Sandoval, Erik Aybar and Billy Butler. So, the unscientific conclusion is that he’s got a 75% shot at being a major leaguer and a 20% shot at being a really good one – OK, I’ll take it.

P-Sabr Rank	Player	Score	Year/League	O-WAR
1	Josh Barfield	60	CAL 2003	1.3
2	Jason Bourgeois	54	CAL 2003	0.7
3	Josh Kroeger*	50	CAL 2003	-0.6
4	Jeff Mathis	47	CAL 2003	-1.9
1	Erick Aybar	67	CAL 2004	9.2
2	Jeff Salazar*	47	CAL 2004	-0.2
1	Billy Butler	61	CAL 2005	8.5
2	Howie Kendrick	61	CAL 2005	12.1
3	Eddy Martinez-Esteve	50	CAL 2005
1	Reid Brignac	57	CAL 2006	-0.3
1	Chris Nelson	50	CAL 2007	-0.6
2	Bubba Bell	49	CAL 2007
1	Pablo Sandoval	54	CAL 2008	11.6
2	Carlos Santana	47	CAL 2008	6.4
1	Alex Liddi	74	CAL 2009	0.4
2	Thomas Neal	70	CAL 2009
3	Tyson Gillies	66	CAL 2009
4	Trayvon Robinson	50	CAL 2009	-0.2
1	Kyle Seager	70	CAL 2010	0.8
2	Stephen Parker	57	CAL 2010

Now let’s take a look at some outliers, and their value as predictive measures.

The basic theory of P-Sabr is that what is good or acceptable in the sabermetric measurement of player value at the major league level, is not necessarily a good predictor at the minor league level. P-Sabr gives a bonus to age and penalizes low contact indicators like high K-rates an low BA. Here we’ll look at the outliers in K-rates and BB rates over three leagues, the AZL, the California and Eastern.

Here are the players with the outlying best BB% in the AZL (2003-2008) who have gone on to see ML action. That’s the top five ranked each year from 100 qualifying players.

P-Sabr Rank	Name	BB%	League	Year
2	Antoan Richardson	0.17460317	AZL	2005

Of the 35 outliers over a 5 year period only Richardson has seen Major League time. Since close to 10% of AZL hitters go on to see some ML time, this seems like a very small number and suggests that it is not valuable as a predictor of future value, though it is small sample size, so let’s just say that further investigation is warranted.

How about the other side of the BB% spectrum? Here are the future ML players who fell in the bottom (worst BB%) of the outlying spectrum.

P-Sabr Rank	Player	BB% (worst)	League	Year
3	Carlos Corporan	0.02380952	AZL	2003
3	Pablo Sandoval	0.02617801	AZL	2004

Not much better really, but the addition of Sandoval makes this list look a lot better in terms of value.

Now let’s look at K-Rates. Here are all the outliers of the players with the lowest K-Rates who have gone on to see ML time.

P-Sabr Rank	Player	K%	League	Year
1	Shane Costa	0.07954545	AZL	2003
1	Alexi Casilla	0.06134969	AZL	2004
3	Pablo Sandoval	0.0960452	AZL	2004
1	Michael Brantley*	0.07514451	AZL	2005
1	Matt Downs	0.05357143	AZL	2006
3	Alexi Amarista	0.0990099	AZL	2007

This is much more like it! 6 players of 35 who have gone on to see ML action, in a league where less than 10% of the hitters make it to the show, that’s a nice number. It looks as if at least at this lower level, the ability to make contact is a premium predictor. SSS of course, further investigation is required.

There have been zero players who have fallen in the bottom of the K% outliers who have gone on to see ML action, implying that the P-Sabr theory that high K-Rates (along with advanced age) are the biggest negative predictors in prospecting.

In the Cal league, where close to 30% of all the P-Sabr qualified players will go on to see Major League time the lists look a little fuller. Here are the outlying best BB-rates from 2003-2008.

Column1	Column2	BB%	Column4	Column5
1	Fred Lewis	0.15526802	CAL	2004
2	Mike Napoli	0.15068493	CAL	2004
2	Daric Barton	0.17174515	CAL	2005
4	Kila Ka'aihue	0.16033058	CAL	2005
2	Taylor Teagarden	0.17857143	CAL	2007
1	Carlos Santana	0.15898618	CAL	2008

The 2009 and 2010 season have seen four outliers thus far who have seen ML time, including Brandon Belt.

How about the bottom side of BB%?

P-Sabr Rank	Player	BB% (worst)	League	Year
5	Pablo Sandoval	0.03782506	CAL	2007
3	Carlos Peguero*	0.02597403	CAL	2008
4	Pedro Ciriaco	0.03202847	CAL	2008
5	Peter Bourjos	0.03486239	CAL	2008

I would expect as the sample grows larger to see more players on the higher end of the BB% spectrum to see ML time, but still this percentage does not imply that this outlier has great value in finding future major leaguers.

Now let’s look K-Rates.

P-Sabr Rank	Player	K%	League	Year
1	Jesus Feliciano*	0.07968127	CAL	2004
2	Jeff Salazar*	0.10509554	CAL	2004
3	Joaquin Arias	0.106	CAL	2004
4	Luis Cruz	0.109375	CAL	2004
1	Kevin Frandsen	0.07560137	CAL	2005
3	Blake DeWitt	0.12389381	CAL	2007
5	Pablo Sandoval	0.12967581	CAL	2007
4	Julio Borbon*	0.10309278	CAL	2008
5	Eric Sogard	0.11567164	CAL	2008

This percentage (26%) is much closer to a number that would indicate a positive predictive value, though outside of DeWitt and Sandoval, player value is thin at best. Nick Liles was 5th among outliers in 2011, while Gary Brown just missed the list at 6th.

Here’s the bottom of K-Rate outliers.

P-Sabr Rank	Player	K% (worst)	League	Year
3	Mike Napoli	0.34439834	CAL	2004
3	Chris Carter	0.3083004	CAL	2008

So, it appears that high K-Rates even in high A are very difficult to overcome as one moves up through the ranks, though here Napoli clearly benefits from being a BB% outlier as well.

In the Eastern League approximately 45% of the P-Sabr qualified players will go on to see ML action, though for most it will only be a cup of coffee. Here are the BB% rate outliers.

Rank	Name	BB%	League	Year
1	Kevin Youkilis	0.20623501	EL	2003
2	Anderson Machado	0.19963031	EL	2003
4	Gabe Gross	0.14016173	EL	2003
1	Craig Wilson	0.16666667	EL	2004
3	Justin Huber	0.1559322	EL	2004
4	Curtis Granderson	0.14466546	EL	2004
2	Kevin Thompson	0.14058355	EL	2005
3	Ryan Roberts	0.13784461	EL	2005
3	Curtis Thigpen	0.13941019	EL	2006
4	Kory Casto	0.13728814	EL	2006
3	Jed Lowrie	0.15931373	EL	2007
4	Jeff Larish	0.15647482	EL	2007
1	Lou Marson	0.1721519	EL	2008

Here 37% of our outliers went on to see action in the show, with Youkilis and Granderson being legitimate stars. How about negative walk rates?

P-Sabr Rank	Player	BB% (worst)	League	Year
3	Eider Torres	0.03180915	EL	2005
4	Jesus Feliciano*	0.04034582	EL	2005
4	Argenis Reyes	0.04572565	EL	2007
5	Luis Hernandez	0.04580153	EL	2007

Finally, we see as we graduate to higher levels a more traditional Sabermetric valuation play out as predictive measure.

Let’s look at K-Rates. Here we see that even at the higher levels, low K-rates seem to be a consistent predictor in finding future major leaguers.

P-Sabr Rank	Player	K%	League	Year
2	Andy Cannizaro	0.06504065	EL	2003
4	Chris Heintz	0.08856089	EL	2003
5	Joe Mauer	0.09057971	EL	2003
1	Jeff Keppinger	0.05135135	EL	2004
4	Andy Cannizaro	0.0945122	EL	2004
5	Joe Inglett	0.10526316	EL	2004
2	Dustin Pedroia	0.1015625	EL	2005
3	Jesus Feliciano*	0.10869565	EL	2005
4	Melvin Dorta	0.1127451	EL	2005
5	Don Kelly	0.13069909	EL	2005
2	Melvin Dorta	0.08415842	EL	2006
1	Robinzon Diaz	0.05315615	EL	2007
3	Luis Cruz	0.09066667	EL	2008

As with the positive BB-rates, there were 13 players and 2 legitimate stars in this list, Mauer and Pedroia.

Surprisingly (to me) the outliers of negative K-Rates also produced 13 Major Leaguers, though realistically they amount to Ryan Howard and 12 cups of coffee.

P-Sabr Rank	Player	K% (worst)	League	Year
4	Anderson Machado	0.28368794	EL	2003
5	Mitch Jones	0.28293737	EL	2003
1	Ryan Howard*	0.34491979	EL	2004
3	Mitch Jones	0.30645161	EL	2004
4	Walter Young	0.29835391	EL	2004
1	Jonathan Van Every*	0.39845758	EL	2005
3	Brad Snyder*	0.30921053	EL	2005
3	Brent Clevlen	0.34936709	EL	2006
3	Matthew Cepicky	0.30939227	EL	2007
2	Dusty Ryan	0.32094595	EL	2008
3	Travis Snider*	0.32044199	EL	2008
4	Wilkin Ramirez	0.3187067	EL	2008
5	Brad Harman	0.31151242	EL	2008

Well that’s it. Thanks for humoring me. If I can get it together, next I'll do a pitching system.

In Part 2 of The Blind Baseball scout, we’ll look at how Giants prospects from 2009 & 2010 have stacked up according to P-Sabr with some league wide and BBA (Baseball America) comparisons. We’ll take a look at three leagues, The Sally, California and Eastern Leagues.

First the Sally League in 2009. Early returns are not good on the hitting prospects here, the league has produced only 7 Major League hitters (of those qualified for P-Sabr) and only one of those has a positive O-WAR (Ryan Lavarnway - #23 in P-Sabr rankings) as of yet. It’s far too early too pass judgment, but the eye test doesn’t seem to offer much hope either. Both P-Sabr and BBA agreed that the recently traded Derrick Norris was the best hitting prospect in the league, which even Billy Beane would probably admit has some major question marks. Two of P-Sabr’s top ten hitters have made it to the show (Jordan Pacheco and Steve Lombardozzi), while Tim Federowicz from BBA’s top list (DNQ in P-Sabr) has made it.

	P-SABR		P-SABR Score
1	Derek Norris		42
2	Travis d'Arnaud		29
3	Anthony Gose*		29
4	Corban Joseph		28
5	Jordan Pacheco		27
6	Steve Lombardozzi		27
7	Scott Robinson		20
8	Jose Pirela		16
9	Jay Austin*		14

It wasn’t a pretty year for Giants hitters either. Only one current Giants prospect (Ehire Adrianza) had a P-Sabr score (-4) above the mean of –31.38. His score was good, for an overall ranking of #21. Other current prospects Charlie Culberson (P-Sabr score –38, ranking #60) and Wendell Fairley (-78, #88) placed below the mean.

Lg/Year	Giants	P-Sabr Score
Sally-2009	Charlie Culberson	-38
Sally-2009	Ehire Adrianza	-4
Sally-2009	Wendell Fairley	-78

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In the Sally League 2010 rankings, there is a consensus on 2 of the top three prospects in P-Sabr and BBA rankings. They both agreed that Jonathan Singleton and Nolan Arenado were among the three best hitting prospects in the league, but P-Sabr pulled off a short-term coup finding Jose Altuve (one of only three eligible players to make the show thus far) as the second best prospect in the league. Altuve missed BBA’s rankings altogether. This top ten represents the only players with positive P-Sabr Scores in the league.

	P-SABR		P-Sabr Score
1	Jonathan Singleton*		68
2	Jose Altuve		51
3	Nolan Arenado		47
4	J.P. Ramirez*		32
5	J.D. Martinez		32
6	Jake Goebbert*		13
7	Cesar Puello		13
8	Chris McGuiness*		13
9	Eury Perez		9
10	Jefry Marte		5

As for Giants prospects, all four relevant players scored above the P-Sabr mean score of –36.19. Of note, Ryan Cavan just missed out on the top ten, tied for eleventh in the rankings, and the only player to make BBA’s top list had the worst P-Sabr score of all Giants, Chris Dominguez. Liles ranked #20, Joseph #43 and Dominguez #45.

Lg/Year	Giants	P-Sabr Score
Sally 2010	Ryan Cavan	0
Sally 2010	Nick Liles	-12
Sally 2010	Tommy Joseph	-24
Sally 2010	Chris Dominguez	-25

Now for a look at the Cal league 2009. 20 of all qualified players have gone to play MLB, 8 of those players were members of BBA’s top 12 Cal League hitting prospects, and 6 of those made the P-Sabr top 13. BBA liked the Giants prospects this year who placed four players in the BBA top list, while P-Sabr only had 2. This is also the year that Buster Posey ranked at the top of BBA’s chart, and to this point he’s clearly the best player to come out of the league in 2009. In this instance showed P-Sabr once again showed its weakness in projecting catchers and ranked Buster as only the 6th best prospect in the league.

	P-SABR		P-Sabr score
1	Alex Liddi		74
2	Thomas Neal		70
3	Tyson Gillies		66
4	Trayvon Robinson		50
5	Koby Clemens		35
6	Buster Posey		31
7	Matthew Sweeney		27
8	Logan Forsythe		20
9	James Darnell		12
10	Grant Desme		12
11	Scott Van Slyke		11
12	James McOwen		10
13	Jason Castro		10

Of the 8 Giants ranked, 6 ranked above the P-Sabr mean of -41.94. I’ve included Brian Bocock, because hey – He’s Brian Bocock! He’s the man with the 94th worst offensive performance in the Cal League 2009 according to P-Sabr.

Lg/Year	Giants	P-Sabr Score
Cal 2009	Thomas Neal	70
Cal 2009	Buster Posey	31
Cal 2009	Conor Gillaspie	6
Cal 2009	Nick Noonan	-6
Cal 2009	Roger Kieschnick	-11
Cal 2009	Darren Ford	-11
Cal 2009	Angel Villalona	-42
Cal 2009	Brian Bocock	-110

From the 2010 Cal league, 8 qualifying players have made the show thus far, but this does not include Mike Trout, who BBA considered the best prospect in the league as well as all of baseball. Three of BBA’s top hitters, including Trout have made the Majors as have three of P-Sabr’s. On both lists are Paul Goldschmidt and Brandon Belt, but P-Sabr concluded that the best prospect was Kyle Seager, who didn’t appear in BBA’s rankings.

Rank	P-SABR		P-Sabr Score
1	Kyle Seager		70
2	Stephen Parker		57
3	Johermyn Chavez		42
4	Brandon Belt*		42
5	Grant Green		41
6	Daniel Robertson		38
7	Cole Figueroa		31
8	Charlie Culberson		28
9	Vincent Belnome		25
10	Paul Goldschmidt		25
11	Rich Poythress		25
12	Marc Krauss		19
13	Cody Decker		17
14	Davis Stoneburner		17
15	Juan Perez		16

It was a good year for the San Jose Giants in 2010 and 6 of the 8 players who qualified finished with a P-Sabr score above the Mean of -39.71.

Lg/Year	Giants	P-Sabr Score
Cal 2010	Brandon Belt*	42
Cal 2010	Charlie Culberson	28
Cal 2010	Juan Perez	16
Cal 2010	Francisco Peguero	2
Cal 2010	Johnny Monell	-6
Cal 2010	Jose Flores	-20
Cal 2010	Ehire Adrianza	-42
Cal 2010	Wendell Fairley	-65

The Eastern League of 2009 has produced 30 players who have gone on to play in the majors. This does not include 8 of the 12 players in BBA’s top rankings who did not qualify for the P-Sabr system. This included some very good young players including Pedro Alvarez, Jesus Montero, Dominic Brown, Wilson Ramos, Ike Davis, Jose Tabata, Scott Sizemore, and Brandon Snyder. Nevertheless, the top 6 players found by P-Sabr have gone on to see time in the majors.

Rank	P-SABR		P-Sabr Score
1	Michael Taylor		55
2	Carlos Santana		44
3	Eduardo Nunez		40
4	Ruben Tejada		38
5	Josh Thole		38
6	Ryan Kalish*		32
7	Reegie Corona		25
8	Nick Weglarz*		24
9	Rene Tosoni		20
10	Alex Avila		19
11	Josh Reddick		18
12	David Cooper*		15
13	Ryan Strieby		12
14	Brock Bond		7
15	Brett Pill		3

It was a good year for Major league prospects, but not necessarily Giants prospects. Of the 5 relevant Giants, three had a P-Sabr score above the League mean of –40.9, but the shine is all but gone on all save 2 of them.

Lg/Year	Giants	P-Sabr Score
EL 2009	Brock Bond	7
EL 2009	Brett Pill	3
EL 2009	Mike McBryde	-11
EL 2009	Eddy Martinez-Esteve	-15
EL 2009	Brandon Crawford	-50

The Eastern League of 2010 thus far has seen an incredible 35 P-Sabr qualified players who have gone on to see Major League time. That says something of the depth of the league, but the jury is still out on the top tier talent. Nine of the top ten players ranked by BBA have gone on to see Major League time, most notably Brandon Belt, who did not qualify for P-Sabr and Dominic Brown was ranked number 1 by both systems. Eight of the top Eleven P-Sabr ranked players have seen action in the show and at this point the best player found by P-Sabr not ranked by BBA seems to be Ben Revere.

Rank	P-SABR		P-Sabr Score
1	Domonic Brown*		58
2	Brandon Laird		33
3	Chris Marrero		31
4	Thomas Neal		29
5	Che-Hsuan Lin		26
6	Lonnie Chisenhall		26
7	Anthony Rizzo*		25
8	Yamaico Navarro		25
9	Kirk Nieuwenhuis		20
10	Ben Revere		20
11	Jason Kipnis		19
12	Matt Rizzotti*		19

Only two Giants ranked above the league mean P-Sabr score of –40.94, and the best rated Giant is now an Indian.

Lg/Year	Giants	P-Sabr Score
EL 2010	Thomas Neal	29
EL 2010	Conor Gillaspie	-6
EL 2010	Darren Ford	-57
EL 2010	Brandon Crawford	-60
EL 2010	Nick Noonan	-73

Next week – 2011.

One of my favorite topics on the McCovey Chronicles is Minorlines and the debate on the potential of Giants prospects, but since I live on the east coast and nowhere near Richmond or Augusta I’m limited in my ability to assess prospects via the old-fashioned eye test. Aside from a few college At-Bats and the scouting videos on the internet, I suspect I’m like most of us, I get my information from the typical sources, Baseball America, John Sickels et. al. I’ve often thought it would be cool to have a sabermetric system of grading prospects, so I finally got off my butt and did what any industrious McCoven would do an made one.

So, here’s The Blind Baseball Scout, which we’ll refer to as P-SABR (if you can think of something better, please let me know). Today we’ll look at some historical comparisons of P-SABR versus Baseball America’s top hitters (there’s no P-SABR for Pitchers yet) by league (AZ, NW, SALLY, CAL and EL) from 2003 to 2008.

I should note that P-SABR is meant to be a quick and dirty analysis tool, and has some obvious drawbacks. It’s compiled using players with the top 100 plate appearances in the league, so some “hot” prospects who are promoted mid-season don’t qualify for P-SABR due to its sample size constraints. An example of this in 2011 would be Bryce Harper, who made two lists for Baseball America, but did not qualify for P-SABR due to too few plate appearances in both the Sally and Eastern League.

Here are some significant players selected by Baseball America between 2003-2008 who did not qualify for P-SABR due to too few plate appearances.

Non-Qualified players found by BBA Year League Asdrubal Cabrera 2005 Cal Chris Coughlin 2007 Sally David Wright 2004 EL Ian Desmond 2005 Sally Jacoby Ellsbury 2006 EL Jarrod Saltalamacchia 2004 Sally Jason Kubel 2004 EL Justin Upton 2007 Cal Matt Wieters 2008 EL Nate Schierholtz 2004 Sally Nick Markakis 2005 EL Nick Swisher 2003 Cal Nolan Reimold 2007 EL Pablo Sandoval 2008 EL Ryan Braun 2005 Sally Ryan Zimmerman 2005 EL Stephen Drew 2005 Cal Yunel Escobar 2005 Sally

While this sample size demand appears to be a considerable limitation, P-SABR seems to be adept at finding players that Baseball America overlooks as well. Here are some players that appear on P-SABR’s rankings, but did not make Baseball America’s top lists (by league).

Players found by P-Sabr, Not BBA	Year	League
Carlos Gomez	2005	Sally
Dan Uggla	2003	Cal
David Murphy*	2005	EL
Kevin Kouzmanoff	2004	Sally
Logan Morrison*	2007	Sally
Martin Prado	2004	Sally
Matt Downs	2007	NWL
Matt Kemp	2004	Sally
Melky Cabrera*	2005	EL
Michael Brantley	2006	Sally
Michael Brantley*	2005	AZL
Mike Fontenot	2003	EL
Nate McLouth	2004	EL
Nick Markakis	2004	Sally
Ryan Raburn	2004	EL

There are also players that P-SABR just did not rate high enough to make its list. The first thing you’ll notice about these eleven players is that 5 of them are (or in the case of Pablo, were) catchers, others like Span, Bourne and Bourjos owe a good to significant proportion of their value to their defensive skills, which represent two areas of potential improvement to the P-SABR system, Positional and defensive valuations. As it stands now, P-SABR has no way of accounting for a prospect potential value based on position or defensive skill.

Adam Jones	2003	AZL
Brian McCann	2003	Sally
Chase Headley	2005	NWL
Chris Ianetta	2005	Cal
Denard Span	2005	EL
Michael Bourne	2005	EL
Miguel Montero	2005	Cal
Nick Hundley	2005	NWL
Pablo Sandoval	2004	AZL
Peter Bourjos	2008	Cal
Ryan Howard	2004	EL

Over the next couple of days we’ll look at comparisons of Giants prospects over the last few years, and then take a look at what P-SABR thinks of Giants prospects of 2011. But first here’s the statistical paper that analyzes P-SABR, complete with hypothesis testing and explanations of the grading system – so be warned –Geeks only!

The Blind Baseball Scout

Abstract

The Blind Baseball Scout (P-SABR) is a Sabermetric model that measures minor league production with the goal of predicting a players future value as, and likelihood to become a Major League Ballplayer. The P-SABR assigns points to players across 9 different statistical areas, the sum of which produces a total score for the entire season. The top players represent the players with the highest scores. In order to test this model the results have been compared against the top hitting prospects from Baseball America (BBA), the nations leading scouting publication, using the proportion of players who make the Major Leagues and the population of “top players” mean WAR value as measurements of value from both systems. P-SABR was run from 2003 to 2008 in five select leagues, the Arizona Rookie league, The Northwest Rookie League, The Sally League, The California League and the Eastern League and compared to BBA’s results. The results of both systems were analyzed statistically on the basis of proportion of players who made the major leagues. The populations of both methods were then tested based on their mean “WAR” value (based on WAR {Wins over replacement}, a Sabermetric valuation of individual players). Both comparisons were tested by the hypothesis that P-SABR would be equal to BBA in finding future major leaguers and forecasting the value of those players at a 98% confidence level, with the alternative hypothesis being that they will not be equal.

Introduction

The goal of this project is to determine a viable process in applying the use of sabermetric data in projecting the future performance and value of minor league prospects.

Sabermetrics is generally considered an effective and objective method in the valuation of Major League Baseball player performance, however because of a number of factors it is not nearly as reliable in predicting the future performance of Minor League players. This process will attempt to address the factors that make the application of sabermetrics to minor league performance ineffective by giving specific skills, flaws and factors greater weight in order to bring equilibrium to the players performance.

In order to measure the effectiveness of this system, I will test the hypothesis that this system is equal Baseball America, the nations leading scouting publication, by comparing their top hitting prospects from 2003 to 2008 in five select leagues, the Arizona Rookie league, The Northwest Rookie League, The Sally League, The California League and the Eastern League to this system. I will compare proportions of ranked players who go on to see Major League playing time in both systems, as a test of proportion. I will also compare the value of those players who made it to the major leagues based on their Offensive WAR (Wins Above Replacement) value as a test of means. In this later test, the mean (WAR value) of both systems will be taken for both systems.

The aforementioned variables that make Minor League predictions so difficult are age, where “very young players, as a whole, return 25 percent more value than expected by their draft slots” (Rany Jazayerli), Strike-out rates , where “it appears as though the success rates for prospect development drop sharply when strikeout rates hit about 22%.” (Minorleagueball.com). Then there are the more traditionally acknowledged variables like Walk Rates, Batting Average, Extra-Base Hit percentage, Home Run Percentage, On Base Percentage. There’s also my own metric Stolen Base Efficiency, which attempts to project the value of a hitters speed and running ability. The best performers in each category are assigned a score, which decreases towards zero incrementally as we reach the median, once the median is reached scores progress negatively incrementally towards the worst performers.

The data will be obtained from Baseball-reference minor league data sorted by Plate Appearances. The top 100 plates appearances will be each sample. This will be used to ensure the largest possible sample sizes. This has the advantage of eliminating top performers who do it over relatively short periods of time, the disadvantage is that sometimes really good prospects spend a short time in a given league because of good performance, are promoted to another league and (consequently) will be undefined under this system.

Notes on the data compilation

Page 1 is the source data compiled from Baseball-reference. Com

Page 2 is the age of the players. As mentioned above age is one of the two primary factors in this approach. Previous research from Bill James and Rany Jazayerli are central to the theory, which states, “The younger the player, the greater the slope of the curve—meaning the greater the rate at which he improves” (Javeri, Rani. taken from the web) It is for this reason that age is weighted so heavily in this model.

AGE	Score
17	40
18	35
19	30
20	20
21	-3
22	-15
23	-20
24	-30
25	-35

As you see here players that who are younger than the mean are given points strictly on the basis of the basis of their age. The farther below the mean the greater the positive score, and conversely the greater the age above the mean the greater the negative score.

Players who are at the mean are given a small negative score. This is primarily because they are often (in this league) college players who have experience that is potentially at or above the level of the league.

This weighting system based on age and the weighting system based on K% are the only pieces that will change from league to league. At the lower levels (NW league and AZ league) older players are given a greater disadvantage (negative score), this is based on the theory that their advanced age and experience is an even greater advantage when the competition is so young and inexperienced. Players with higher strike out rates at lower levels are also given a greater disadvantage (negative score) at the lower levels, based on the assumption that the inability to make contact will be a further hindrance at higher levels when the competition is stiffer.

Page 3 is a simple effort to manage sample size. Sample size plays a very important role in this experiment, as it is the basis of the population selected (the top 100 players based on # of Plate Appearances) and it potentially a major weakness in the hypothesis. It is common for organizations to promote their players who are performing well to a higher league, which often mean that those players (good performers) won’t eligible for this method. We will see many players in Baseball America’s (BBA) top rankings who do not qualify for this method.

Mean PA	468.33
Plate App.	Score
x-525	7
524-475	3
474-425	0
424-375	-5
374-x	-10

A small positive is given for players who have the most PA’s and a small deduction is given to those who have the fewest. This theorizes that the more PA’s one has the better one’s performance will represent ones talent. The mean should fall somewhere in the middle and will be given no bonus or deduction.

Pages 4, 5 and 8 are measurements of one very important measure of value, the ability to not make an out. As Bill James said “the difference between winning teams….is the difference between ‘outs’ and ‘runners on base’ “(James, Bill, The New Bill James Historical Baseball Abstract, 2001, The Free Press. Page 642), these three statistics attempt to measure this skill. First is Batting Average (BA).

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Mean BA *	=	Score
1.25	0.3297125	17
1.2	0.316524	13
1.175	0.30992975	10
1.15	0.3033355	7
1.1	0.290147	3
1	0.26377	0
0.95	0.2505815	-5
0.9	0.237393	-10
0.85	0.2242045	-15
0.8	0.211016	-20
0.75	0.1978275	-25

Averages that are at the mean are credited with 0 and are progressively (i.e. Mu + 10% =3) awarded point as players are above the mean.

The next measurement is on base percentage (OBP), where we see a similar grading system where the highest points are gained above Mu + 20%.

	Mu ( x) + Mu
X	=	Score
0.2	0.3987	15
0.17	0.3887325	13
0.13	0.3754425	10
0.1	0.365475	7
0.07	0.3555075	5
0.03	0.3422175	3
Mu	0.332	0
-0.03	0.3222825	-5
-0.07	0.3089925	-10
-0.1	0.299025	-15
-0.13	0.2890575	-20
-0.17	0.2757675	-25

And lastly, there is BB%, which measures the percentage of walks a player takes per plate appearance. “1” represent Mu * 1.

Mu (x)	=	Score
1.65	0.13645191	10
1.4	0.11577738	7
1.25	0.10337266	5
1.1	0.09096794	3
1	0.08269813	0
0.9	0.07442832	-3
0.75	0.0620236	-5
0.6	0.04961888	-7
0.45	0.03721416	-10
0.25	0.02067453	-15

Next on page 6 and 7 we have two measurements of power hitting ability. Extra-base hit percentage (XBH%) and Home Run percentage (HR%).

XBH%

	Mu (x) + Mu
X	=	Score
0.5	0.124798452	15
0.45	0.120638504	13
0.3	0.108158658	10
0.15	0.095678813	7
0.05	0.087358916	3
Mu	0.0832	0
-0.05	0.07903902	-3
-0.15	0.070719123	-7
-0.25	0.062399226	-10
-0.35	0.054079329	-15
-0.45	0.045759432	-20
-0.5	0.041599484	-25

HR%

X	Mu (x) =	Score
3.5	0.06884643	15
2.75	0.05409362	13
2	0.03934082	10
1.5	0.02950561	7
1.25	0.02458801	3
1	0.01967041	0
0.9	0.01770337	-3
0.75	0.01475281	-7
0.6	0.01180224	-10
0.5	0.0098352	-15
0	0	-20

On page nine we have Strike-out percentage (K%). Thanks to the work of RedSoxFaithful we have a sense that as K-rates in prospects increase, their chance for major leagues success goes down. ( Minorleagueball.com ) This is how we’ll weigh Strike Outs in our model in the higher leagues (Sally, Cal & Eastern).

X	Mu (x) =	Score
0.45	0.10074501	15
0.55	0.12313279	13
0.65	0.14552057	10
0.75	0.16790835	5
0.95	0.21268391	0
1	0.2238778	-3
1.1	0.24626558	-10
1.2	0.26865336	-20
1.35	0.30223503	-30
1.5	0.3358167	-35

On Page ten we have an attempt to measure speed as a tool for success. This is not really attempt to measure how well one can steal bases, it works on the theory that a player who has potentially valuable speed will be asked to steal often and will have some measured success (Stolen Bases), so this is not the percentage of successful stolen bases, this stolen bases divided by Plate Appearance. The negative grading is significantly less in this instance because, while speed can be a valuable tool, its absence is not necessarily equally a detriment.

X	Mu (x) =	Score
4	0.11559657	25
3.5	0.101147	20
2.75	0.07947264	10
1.75	0.0505735	7
1.35	0.03901384	3
1	0.02889914	0
0.9	0.02600923	-3
0.75	0.02167436	-5
0.5	0.01444957	-7
0.25	0.00650231	-10

You’ll notice that in virtually all of these points systems the distribution is weighted heavier on the negative side, or one gets penalized more performing below or at the mean than one gets rewarded for performing above the mean. This is meant to help create a negative linear (or logistical?) model which results in markedly fewer players having a positive rating than players with a negative rating, based on the simple fact that more players will fail to make to the next level than players who move on and eventually become major leaguers. This chart is exemplary of this principle.

(For some reason I am unable to insert my graph here- sorry!)

Sample Size

As mentioned before, sample size weighs heavily in this system. Without the benefit eyes to see (a players movement, physicality and swing) and ears to hear (a coaches or scouts observations or fears) P-SABR is at significant disadvantage. Sample size represents a factor that we can attempt to account for. As an example, here are the top hitters from the Eastern League 2006 –

	BBA Top Hitters			WAR		P-SABR		WAR
1	Adam Lind			4.7		Carlos Gomez		1.3
2	Jacoby Ellsbury		DNQ	12		Brandon Moss		-1.2
3	Carlos Gomez			1.3		Kevin Kouzmanoff		5.1
4	Trevor Crowe		DNQ	-1		Adam Lind*		4.7
5	Kevin Kouzmanoff			5.1		Gary Burnham*		0
6	Kory Casto			-1		Nate Schierholtz		1.7
7	Alexi Casilla		DNQ	2.6		Jeff Fiorentino		0.5
8						Kory Casto		-1.4
9						Luis Antonio Jimenez*		0
10						Chad Spann		0

In this example, only 7 hitters this year made BBA rankings for the top 20 players of the league and 3 of those players (Elsbury, Crowe and Casila) did not qualify for the P-SABR system due to an insufficient amount of Plate Appearances. There are two ways we can deal with this, one, remove the players who would not qualify for P-SABR and compare only those top players who qualify (4 players), or two, expand the P-SABR sample to account for this sample size factor. The later was chosen because it is potentially a better test of P-SABR’s value, where potentially P-Sabr can find a greater proportion of MLB players (n=10) or lesser proportion, as in this case, where BBA had all (7) of their selections make it to the major leagues and P-SABR had only 70%. It also expands the population in determining mean value (WAR), where in this case BBA selections have a mean value of 3.36 (WAR) and P-SABR a 1.07 (WAR) thus far in their respective careers.

Conclusion

After testing the data it’s evident that P-SABR has some real value in finding and projecting the value of minor league players. Two sample Hypothesis tests for proportion and mean were run on each league at a 95% confidence intervals, then run on all the data combined at a 98% confidence interval, where the final P values were .0004 for proportion (rejected proportion) and .0217 (failed to reject mean), suggesting that while BBA is better at finding players who will become MLB players, P-SABR finds enough players who have value as future MLB players to suggest that the system has potential.

The null hypothesis of proportional equality was rejected in 2 out of 5 leagues, the California League and the Northwest league as well as in the overall conclusion. Clearly, BBA has an advantage in selecting players who are most likely to become Major league ball players, finding 226 future major leaguers out of a sample of 351, while P-SABR found only 213 out of 412.

In the testing of mean, the null hypothesis of equality was rejected once in the Eastern league, but failed to reject overall. This suggests that P-SABR has some value in identifying players who may have been overlooked by BBA and their system. The Mean WAR for all players selected by BBA was 2.75, while it was 1.8 for P-SABR, suggesting that there’s also room for improvement in the system.

It is also clear that there are areas that P-SABR could be improved, most notably a positional ranking, as the players that were most likely overlooked by P-SABR had positional values (i.e. catcher or middle infielder) that should have raised their overall value when compared to their peers. The most illustrative example of this would be Brian McCann (Sally league 2003), who ranked 11th in P-SABR’s system and 8th in BBA’s, but has gone on to be a 21.7 WAR player thus far in his career.

Since P-SABR is limited to analyzing a players potential value as a hitter, the expansion to a more rounded approach is recommendable. One way to attempt to account for a players future defensive value is to include positional rankings, by providing a scale of value for a players defensive position, such as 20 points for a catcher, 10 points for a Shortstop and Center fielder, 5 points for a second baseman, 0 for a Third baseman, -5 Left and Right Fielders and –10 for First baseman.

References

1-James, Bill, The New Bill James Historical Baseball Abstract, 2001, The Free Press. Page 642

2(http://www.minorleagueball.com/2011/4/22/2123847/the-significance-of-minor-league-k-rates. By Redsoxfaithful

3- http://www.baseballprospectus.com/article.php?articleid=15295#commentMessage

by Rany Jazeyleri.

4- Baseball America, The Baseball America Prospect Handbook 2004, 2004, Simon & Schuster

5- Baseball America, The Baseball America Prospect Handbook 2005, 2005, Simon & Schuster

6- Baseball America, The Baseball America Prospect Handbook 2006, 2006, Simon & Schuster

7- Baseball America, The Baseball America Prospect Handbook 2007, 2007, Simon & Schuster

8- Baseball America, The Baseball America Prospect Handbook 2008, 2008, Simon & Schuster

9- Baseball America, The Baseball America Prospect Handbook 2009, 2009, Simon & Schuster

Hypothesis test of 2 means

	Mu 1	Mu 2
	P-SABR WAR	BBA WAR
Mu	1.79782082	2.75156695
StDev	5.0269922	6.11363269
Claim µ1 = µ2
Test Statistic, t: -2.3287
Critical t: ±2.33186
P-Value: 0.0202
Degrees of freedom: 677.7948
98% Confidence interval:
-1.90931 < µ1-µ2 < 0.0013097
Fail to Reject the Null Hypothesis			<
Sample does not provide enough evidence to reject the claim

In honor of BA's Eastern league top twenty tomorrow....Let's play the prospect comparison game!

 

I suspect it's a long shot Brock Bond makes the list, but if he does it would be in the 15-20 range. I was looking for historic comps by age and league, here's one I found. He was 23 like Bond, but the numbers are over three different leagues, the highest level being 200 PA's in the Eastern league.

 

135 games/ 481 AB's/ 149 H's / 31 2B / 8 HR / 93 BB / 63 SO / .310/.436/.424

 

Here's a link to Brock Bond's numbers.

Brock Bond

 

 

Answer Friday.

http://www.sjgiants.com/playerlist.aspx?SecID=43

 

 

Rehab has begun. In the Dominican League.

http://www.mlb.com/milb/stats/stats.jsp?n=Merkin%20Valdez&pos=P&sid=l131&t=p_pbp&pid =429723