PRS revision for pitchers

I’ve been working on a proper method for adapting PRS in a way that makes sense for pitchers. What makes sense to me is that wins, saves and strikeouts should be graded on the same scale, because they all count equally, whether they come from a “starter” or a “reliever”. However, ERA and WHIP are very different for starters and relievers, i.e. a starter with a 3.20 ERA is a Cy Young candidate, but a reliever with a 3.20 ERA is merely better than average. So I’ve developed two separate scales for those two categories.

As with the last hitters post, I’ve now consolidated the past three seasons into the percentiles scales, and generated rankings based on that. I’ve had to arbitrarily set 10 games started as the cutoff between a SP and a RP; if anyone has suggestions about that, please let me know. I’ve used the 2008 CHONE v2 pitchers projections, and simply used the Marcels 2008 numbers for wins and saves (I adjusted wins by 1 for a couple of pitchers who are coming over to the NL). I know Marcels is not the best way to do this, but I couldn’t find any other databases in spreadsheet format. Any help there would be appreciated, too.

The starters:

1. Johan Santana
2. Jake Peavy
3. C.C. Sabathia
4. Scott Kazmir
5. Dan Haren
6. Cole Hamels
7. Josh Beckett
8. Brandon Webb
9. Erik Bedard
10. Aaron Harang

That looks pretty reasonable to me.

And relievers:

1. J.J. Putz
2. Takashi Saito
3. Huston Street
4. Jonathan Broxton
5. Joe Nathan
6. Jonathan Papelbon
7. Francisco Rodriguez
8. Billy Wagner
9. Matt Capps
10. Rafael Betancourt

It’s interesting to see Jonathan Broxton and Rafael Betancourt up so high even without the saves (yet), but they should contribute in a number of categories, which makes them valuable relievers. The wins and saves definitely need adjustment so this kind of thing doesn’t happen. At least in this model, Jonathan Papelbon’s value is diminished somewhat by the Red Sox limiting his innings due to that shoulder. And judging by how high Huston Street’s been coming out on this list, I’d suggest that the CHONE projection for him might be a bit bullish, especially coming off an injury.

Trade: Mets reportedly land Santana

A winter of discontent finally looks to be over. The New York Mets have reportedly landed Johan Santana in a trade with the Minnesota Twins. The Twins will receive outfielder Carlos Gomez in addition to pitchers Deolis Guerra, Philip Humber and Kevin Mulvey. New York is now within a 3-day negotiation window to come to an extension agreement with the dominant left-hander.

Amazingly, I think this is a pretty fair trade for both sides. You have got to read Aaron Gleeman’s take on this trade, it’s much better than any info I’d have given you. Wow, that’s pretty cool to be able to pitch Santana and Pedro Martinez in the same rotation. The Mets should be very good this year. As for the Twins, they now have a legitimate CF of the near future and have restocked their pitching staff for a good while. They could spin off another starter or two to bolster their offense this offseason.

This trade should open the floodgates for other moves that have been sort of pending. Now teams will be able to get their rotations in order, now that they know who’s available. The Red Sox, who were also in the bidding for Santana, will focus on trading Coco Crisp to the highest bidder, but they won’t move him unless they get some value back. The Yankees will probably stand pat for their part.

Free Agents: Toronto finally gets Rod Barajas

Last year, J.P. Ricciardi thought he had Rod Barajas, but he slipped to the Phillies at the last moment. The then 31-year old Barajas played backup catcher, and struggled through nagging injuries. He appeared in just 48 games. Now he has signed a one-year deal with Toronto worth $1.2M with an option for 2009.

Barajas will begin in a backup role, but he will provide valuable insurance for the 37-year old Gregg Zaun. Zaun can still hit a little bit, but one has to wonder if his body will let him catch 100 games this year. Barajas has some decent power, so if he takes over the starting job, he could become a decent fantasy catcher. A good option if your catcher tanks or gets injured.

Trade: David Aardsma to Red Sox

The Chicago White Sox, having a full 40-man roster, had to designate David Aardsma for assignment this weekend, and the Boston Red Sox pounced, sending nondescript minor leaguers Willy Mota and Miguel Socolovich in a deal.

Aardsma is a hard-throwing righty who was once considered a closer candidate. Scouts have loved his stuff for a long time, but he hasn’t had much success at the big league level (though he has still struck out almost a batter per inning over 96 Major League innings). He’s still only 26, and could land a spot in Boston’s already strong bullpen, if they can straighten him out.

The 21-year old Mota is a young outfielder who is being converted to a pitcher. He throws hard, and has had some good success at low-A ball. Socolovich, also 21, has good strikeout ratios in low- and single-A, but has struggled with consistency.

Free Agents: Rangers sign Jason Jennings

Sorry, I’m a little backlogged on signings and trades…

Jason Jennings has signed a one-year, $4M deal with the Rangers. The deal includes another $4M in incentives, should Jennings come back healthy from tendinitis and a torn tendon in his throwing elbow from last season. The 29-year old is a native Texan from the Dallas area.

Jennings is an above-average pitcher when healthy. I thought he was going to surprise a lot of people last year, but he didn’t really have the chance to. This is a pretty significant risk, but it could actually pay dividends for the pitching-poor Rangers. I’m a little surprised that he didn’t sign with a team that has a chance to contend, but maybe his injury is worse than I thought.

Percentile Ranking System (PRS) development

I am continuing to develop my PRS system, outlined in a previous post here. The basic logic is that your fantasy score comes from 5 offensive and 5 pitching categories; if you draft the available player who is rarest over all five categories, you will have made the best pick.

The changelog for this version is pretty significant. I’ve expanded the percentile-generating algorithm to include the years 2005-2007 and limited the number of fantasy hitters to 200 per season, seeing as only about 180 get drafted every year anyway. I’ve also upgraded the percentile system, making it finer. Players now get points for every 5th percentile, and I’ve also included the 99%, 97% and 1% data points as well, which should give us finer resolution of the top fantasy players.

Using these changes, the new list for top 20 hitters looks like this:

1. Alex Rodriguez
2. David Wright
3. Matt Holliday
4. Albert Pujols
5. Miguel Cabrera
6. Vladimir Guerrero
7. Chase Utley
8. Hanley Ramirez
9. Prince Fielder
10. David Ortiz
11. Alfonso Soriano
12. Carlos Lee
13. Jimmy Rollins
14. Grady Sizemore
15. Ryan Braun
16. Mark Teixeira
17. Carlos Beltran
18. Ryan Howard
19. Chipper Jones
20. Lance Berkman

I have to say I like this order a lot better than what came out of the first try. Keep in mind, this is based solely on the CHONE ‘08 revised hitters projections from Dec 2007. If someone has other projections in spreadsheet format I’d be happy to run those numbers as well.

Ranking: Playing the percentages

This is an idea I’ve been mulling over for some time now. How do you determine the overall fantasy value of a player? Is there an objective measure of their value in the 5×5 categories that could allow you to value them correctly? It’s not as simple as adding up runs, HR, RBI and stolen bases. What I mean is this: because stolen bases are relatively rare compared to the other categories, each one is more valuable than just one of each of the other stats. If there were a way to weight each category, you could judge whether getting player A, who has a great batting average, is worth the tradeoff over player B, who has more balanced production.

One solution I had to this conundrum was to use percentile ratings for each scoring category. Because 2007 was a very different looking statistical year from 2005 or 2006, I used every player who had at least 250 AB in 2007 (there were 306 of them) to generate percentiles (broken down into every 10%) for each category, which you can see below:

Percentile	BA	R	HR	RBI	SB
90		.320	100	28	101	24
80		.297	87	22	87	13
70		.290	80	19	74	9
60		.283	71	16	66	5
50		.275	60	12	60	4
40		.266	54	10	51	3
30		.259	47	8	45	2
20		.251	40	6	38	1
10		.236	33	3	31	0

For example, regardless the identity of the players, the top 10% of players in the HR category hit at least 28 HR, and the top 20% of base stealers swiped at least 13 bases. Now, using projections for 2008 (I used the freely available CHONE 2008 v2 projections) and using this scale, we effectively get a “rating” for each player in each category. These ratings in each category can be totaled, and the result is a list of hitters’ projected overall fantasy value. I think you’ll agree that the method works pretty well, based on the top 20 players I got from this first run:

Tier 1	Albert Pujols
	Alex Rodriguez
 	David Wright
 	Matt Holliday
Tier 2	Prince Fielder
 	Miguel Cabrera
 	Vladimir Guerrero
 	Chase Utley
	Ryan Braun
 	Hanley Ramirez
	Carlos Lee
Tier 3	G Sizemore
 	A Soriano
	J Rollins
Tier 4	D Ortiz
	C Jones
	C Beltran
 	N Markakis
Tier 5	L Berkman
 	M Teixeira

Of course, everything rides on the quality of the projections you use. You would get a different ordering by using another projection system. The results lend themselves well to a closely tiered system. I think that generally reflects reality, so that you could say that any player within a certain tier could easily outplay the others with a good year.

Pitchers are a little bit trickier, because starters sometimes become relievers, and relievers often make spot starts. It’s difficult to decide where the cutoff should be. Secondly, predicting wins and saves is notoriously difficult. Still, here are the derived percentiles for starters and relievers from 2007 (SPs had at least 10 GS and pitched 100IP, relievers had to pitch at least 30 innings to qualify):

Percentile	W	K	ERA	WHIP
90		16	183	3.33	1.19
80		14	163	3.70	1.24
70		13	141	3.88	1.30
60		11	131	4.12	1.34
50		10	114	4.40	1.38
40		9	104	4.63	1.41
30		8	93	4.94	1.48
20		7	78	5.21	1.54
10		5	64	5.70	1.60

CHONE does not even try to predict wins, because it’s so hard to do. So what I did is I took the mean of the Bill James and Marcels projections for wins, and rounded to the nearest whole number. So without further ado, here are the top 15 starters, ranked:

1. Jake Peavy
2. Johan Santana
3. C.C. Sabathia
4. Dan Haren
5. Cole Hamels
6. Scott Kazmir
7. Josh Beckett
8. Aaron Harang
9. Brandon Webb
10. John Smoltz
11. John Lackey
12. Roy Oswalt
13. James Shields
14. Chris Young
15. Javier Vazquez

The picture is slightly different for the relievers:

Percentile	W	Sv	K	ERA	WHIP
90		6	21	78	2.35	1.02
80		5	6	64	2.88	1.15
70		4	2	57	3.10	1.24
60		3	1	51	3.54	1.31
50		2	0	46	3.88	1.36
40		1	0	41	4.28	1.44
30		0	0	36	4.72	1.52
20		0	0	30	5.16	1.61
10		0	0	26	5.83	1.74

I had to do the same kind of deal for wins and saves with the bullpen guys using Bill James and Marcels projections. Top 10 relievers:

1. J.J. Putz
2. Huston Street
3. Takashi Saito
4. Jonathan Papelbon
5. Francisco Rodriguez
6. Matt Capps
7. Billy Wagner
8. Joe Nathan
9. Mariano Rivera
10. Rafael Betancourt

This was a huge list of pitchers, 140 “starters” and 213 “relievers”. In applying the rating system, I tried to value all pitchers on the same scale, not separately, so I used the starters’ scale for wins and strikeouts, because the contributions of a relief pitcher in those categories must approach those of a starter to be truly useful. The combined list seems to favor relievers a little too much, but all in all, not a bad first pass. Because this percentile system is done for each category individually, I think I could even combine the hitters and pitchers list to get an overall ranking eventually that wouldn’t be too out of whack with reality (fantasy reality…uh, yeah).

I’ll be the first to admit it needs some pretty major tuning, and ideally we would use a blend of projections for the most accurate rankings, but you get the idea. Please tell me what you think!

Free Agents: White Sox sign Octavio Dotel

The Chicago White Sox signed yet another free agent reliever in Octavio Dotel yesterday. The contract is reportedly for $11M and runs for two years. Dotel pitched reasonably well for Kansas City and Atlanta last year, and his velocity and stuff are reportedly back. He can even fill in at closer if needed. If he can stay healthy, this contract could be worthwhile, if a bit pricey. If he gets hurt again, well, this is like flushing $11M down the toilet.

Kenny Williams is still in shock from last year’s bullpen experiment. He completely dismantled his bullpen from 2006 and filled it full of young, unproven arms. The result? A horrid, stinky mess out of which only one pitcher with at least 30 IP had an ERA under 4.79 (that would be Bobby Jenks, their closer). Not that those guys can’t pitch, but you just can’t turn over a whole bullpen like that and expect it to work.

How to optimize a lineup, sabermetric style

Remember when I looked at lineup order on this blog? Baseball Musings has posted an online lineup analysis tool which allows you to predict the strength of an offense based on OBP and SLG numbers for each player. Even more interesting is that it gives sabermetrics-optimized alternative batting orders which would actually yield more runs than the lineup you put in.

Of course, this is kind of a naive projection, because you don’t have anything in there about left-right splits, how often players might be injured, the quality of your reserves, etc. But it still provides you with the overall quality of an offense, and is interesting to think about, right? If there’s a platoon at a position, you can plug in composite numbers, due to the freeform fields on the page. I think the coolest application is to plug in players to see what their effect might be if they were signed by or traded to your team. BM is already using it to project some 2008 offenses, like the Detroit Tigers.

Here’s what the prediction was for some offenses using 2007 numbers, to test how accurate it is:

2007 New York Yankees (best in the AL with 968 RS)

1. Damon
2. Jeter
3. Abreu
4. Rodriguez
5. Matsui
6. Posada
7. Cano
8. Giambi
9. Cabrera

Plugging in this batting order gave a result of 6.049 R/G. Their actual performance was 5.98 R/G, down due to at-bats by subs such as Doug Mientkiewicz and Andy Phillips. Interestingly enough, the optimized lineup, according to the calculation, would’ve looked like this:

1. Posada
2. Matsui
3. Abreu
4. Rodriguez
5. Jeter
6. Cano
7. Giambi
8. Cabrera
9. Damon

Some obvious problems here. Posada gets that many at bats, he wears out as a catcher. I don’t see a lot of players going for that, and Joe Torre would have been fired a lot earlier if he posted that sort of lineup. But that’s the traditional bias in baseball, isn’t it?

2007 Chicago White Sox (worst in the AL with 693 RS)

1. Owens
2. Iguchi
3. Thome
4. Konerko
5. Dye
6. Fields
7. Pierzynski
8. Podsednik
9. Uribe

With this lineup, we get 4.793 R/G. Actual output was 4.28 R/G, due to injuries and the trade of Tadahito Iguchi. My feeling is that bad lineups will move players around and substitute more during a season, which hurts production (if the subs are so great, why aren’t they starting?).

The optimal lineup given by the calculation was:

1. Thome
2. Konerko
3. Iguchi
4. Dye
5. Pierzynski
6. Fields
7. Uribe
8. Podsednik
9. Owens

That would have yielded approximately 4.960 R/G. The general strategy seems to be simple: get your highest OPS hitters the most ABs, and cluster the sluggers early-middle in the lineup. The Chicago offense suffered by leading off the very player they should have been batting 9th.

Free Agents: Royals sign Tomko

Dayton Moore likes his pitching depth, and improved it recently by reportedly adding veteran Brett Tomko to his staff. Last year, Tomko went 4-10 as a starter (in 19 starts) with a 5.40 ERA. Tomko will earn $3M this season, with another $1.5M in potential incentives. He will likely have a shot at a rotation spot in Kansas City this spring.

Tomko has been an on-again, off-again starter the last few years in the NL. He has been consistently a few percentage points below average, but has pretty decent stuff and has had streaks where he’s looked good. Not a terrible risk for the Royals, but I’m not optimistic, with him coming over to the tougher league.