Link of the Day

As you have noticed, the Link of the Day feature comes and goes. Today though, I think we have a decent one. As a primer to Stotle's eventual draft write-ups and his 5 round shadow draft . . . I'll send this link out. I linked to some of Alex Eisenberg's stuff before when it appeared on the Hardball Times. I found that he also has a website called Baseball Intellect and he is on Orioles Hangout as NoVaO. It should also be noted that Kiley McDaniel at SaberScouting has mused that he thinks Eisenberg makes sweeping statements on limited video footage. Kiley was a scout for three years or so and is now employed by some unmentioned baseball organization, which lets him travel around and watch a lot of amateur games in the Southeast. I don't know Alex's background. So, take this as you will. I do think Eisenberg's analysis is good and instructional. He also seems to do well backing up him insights with others who have viewed the players in question. Anyway, with all of that out of the way . . .

Eisenberg looks at Brian Matusz today. Everyone and their momma are predicting Matusz being selected by the Orioles with the 4th pick of the 2008 amateur draft on June 5th. It will probably happen since everyone last year was having us take Ross Detwiler with our pick. Oh, wait, that didn't happen. I have little clue as to what will happen and that is why we have Stotle lurking around.

The basic run down on Matusz, which I agree with (if that means anything), is that he is a projectable lefty starter. That probably means that he has about a 50 or 60% chance to playing several years in the majors as a starter (1st round pitchers typically have a 1 in 3 chance of going to the big leagues). His most likely destination is league average or slightly above league average. It seems doubtful he will become a top tier pitcher. Eisenberg thinks that we should go with the best player available and that would be a position player.

That seems to be the growing consensus that Matusz and Aaron Crow are probably in the 6-9 pick range in terms of talent. I think Jonathan Mayo and John Sickells both have them slotted there. It just does not seem to be a draft full of high end pitching.

The Wieters Shift

A few posts back I discussed our catching. Along with what I wrote I brought up a possibility with Posey being drafted and Wieters shifted to 1B. The logic behind that goes that if Wieters hits well enough to be an all star 1B, he should be moved to a position that causes less duress. Catching is more likely to cause injury and also requires time off. I thought that we probably should go beyond the abstract and get a little bit more concrete.

Method

In order to determine production, I will use the generalized formula is used in previous exercises. I don't really know how the lineup would be, so I think it is more applicable if we just view differences from a more comprehensive metric. So, the numbers generated are basically from a team of potential Wieters and a team of a comp . . . which is then divided by 9 in order to put it all in a basic predicted runs saved or lost product.

I tested Wieters performance as restricted by 120, 110, and 100 games as catcher along with him being unable to DH. These numbers will give a conservative perspective as to what we can expect. As a first baseman, I used 150 games played as the benchmark. I paired Wieters with a replacement level first baseman (.333/.420) when he caught and with a league average catcher (.320/.403) when he played first base. Under this scenario, we would expect this to be a liberal predictor of Wieters worth as a first baseman. A league average catcher is not always the easiest thing to find and catcher tend to degrade (everyone wave to Ramon and Javy!). I added these pairs for combination of Wieters hitting ranging from .300 to .450 for obp and .350 to .600 for slg. In turn, I subtracted the 1B Wieters scenario from the C Wieters scenario. Simply put, a negative value is the number of runs you gain by switching Wieters to 1B. A positive value is the number of runs you gain by keeping him behind the plate.

Results

If Wieters can average 120 games as a catcher.

At 120 games, differences are just not very significant. Within the study range, there was no combination of OBP and SLG that would result in Wieters shift being worth more than a gain of 8 runs (0.8 wins). The break even line is about the type of player Russell Martin was last year. Victor Martinez is slightly above the line and Jorge Posada was worth about 0.5 wins above average if he was switched. As you can tell, you really have to rake to be worth the move at this level and a move does not result in as many wins as you may think.

If Wieters can average 110 games as a catcher.

Not much has changed with respect to group players from last year. Martin and Martinez are above the line more noticeably now, but Varitek is still below. Posada is now worth 0.9 wins above average. This is starting to be significant. I would probably think hard about moving a player if I could improve by a win. Of course, this scenario suggests that I can only find replacement level 1B and I can procure a league average catcher. It still does not seem viable. Although I does seem to suggest that the Yankees might be better off with Posada at first base and making a play for Varitek, Zaun, or Barajas.

If Wieters can average 100 games as a catcher.

2007 Jason Varitek is now the break even point. Martinez and Martion are clearly above the line. Shifting Posada-type performance would be worth 1.3 wins. This is probably where things get interesting. If Wieters would get injured so often catching that he is only averaging 100 games there each year . . . it may begin to make sense to shift him over to 1B. The most it seems to hurt a team would be about 1.5 wins.


Conclusion
Wieters should probably stay at catcher unless he shows a great propensity to get injured. Perhaps the ideal solution would be to sign some one like Teixeira to a seven year deal and at the end of the deal, shift Wieters to 1B, and bring in a catcher. Wieters bat looks pretty solid. It is the kind of bat where as he gets older . . . it may make sense to shift him so he stays healthy and potentially will hit better. Below is a chart that displays the break even points for the three game averages. That is probably the idea to take home.

Link of the Day

Catfish stew is a solid little blog focused on the Oakland A's. This is a little old, but I thought people might enjoy it.

Guthrie's aunt was one of those kindly old ladies who loves you no matter what, and everything you do is great, because you're trying your best. Her cheering, complete with anachronistic shouts of "Yay!" and "Yahoo!" and "Hooray!", was so charmingly optimistic--"C'mon Jer, you can do it, I know you can!", I began to fall under her spell. After about three or four innings, I had somehow come to believe that the worst possible outcome of this game would not be a loss for either team, but that Jeremy Guthrie might somehow end up with his feelings hurt.

So when Kurt Suzuki blasted this two-run homer, I didn't really have the heart to cheer very much.

Poor Jer. He must have felt so bad. Guthrie was on the hook for the loss until Andrew Brown entered the game in the eighth inning, and proceeded to give up twenty-nine consecutive grounders in the hole between Daric Barton and Mark Ellis. I'm sure Andrew Brown felt bad about turning a two-run lead into a 5-4 deficit, and perhaps even worse when walking off the mound to a round of boos. Aunt Guthrie was appalled. "That's just terrible, booing a player like that. I'm sure he was doing his best."

Moving On Up

Well . . . the numbers are all moving on up for the season ending number of wins. We are about a quarter of the way through the season and our odds stand at 1:147 (PECOTA) and 1:8 (ELO) for making the playoffs. The disparity is due to PECOTA's adherence to the season beginning predictions and ELO's use of a ranking system. The teams we have faced have a .503 winning percentage, so that explains the numbers being reported.

A quick run through:
PECOTA is the PECOTA based model.
ELO is the ranking based model.
Crawdaddy is the model I created originally based on ZiPS, but now based on 2008 PrOPS and xFIP.
Pythagorean Win Expectancy Model is kind of self explanatory.
Actual wins is also self explanatory.

Revisiting the Season Prediction

Several weeks back I predicted the number of runs the Orioles would give up and the number of runs the Orioles would score. The basis of this prediction depended on a few assumptions:

1) ZiPS/Morong Formula (my arrangement) would properly predict offensive and pitching performance.
2) Offensive replacements would cause a 10% reduction in run scoring while unearned runs would be ignored for pitchers.
3) Top 5 starters would start every game and provide an average of 6 innings pitched.
4) Relief pitchers would be league average.

1. ZiPS/Morong predicting performance.
ZiPS actually overpredicted the runs scored (with the run reduction application). ZiPS predicted that 193 runs would be scored. In actuality, 179 were scored. Even more of an issue was prediction of pitching performance. 222 runs were predicted, while 184 were actually scored. A major contribution to this error was the unexpected development of Daniel Cabrera and a bullpen that was much better than expected. It should be mentioned that my placeholder of a league average bullpen was actually somewhat optimistic. This formula under predicted the Orioles success.

2. Offensive reduction and static pitching.
My educated guess of a 10% reduction was pretty apt. Plugging in the actual OBP and SLG of each player resulted in a coefficient of 0.927 to reach the actual runs scored. The pitching prediction appeared a bit too kind. After plugging in the actual SP and RP era, the system predicted 177 runs, where there were actually 174 runs scored. The application of a coefficient (1.057) would have been appropriate to account for unearned runs.

3. Top 5 Starters would remain so and would average 6 IP.
It was to be expected that a starter or two would be injured. It was known this was a weak assumption. Loewen's injury made it so. The 6 IP prediction is actually almost right on the button.

4. RP would be league average.
Orioles RP are actually pitching 13% better than the league average bullpen.

New Adjustments

1. ZiPS is being replaced by PrOPS and xFIP.
As the season continues, in-season statistical methods may actually predict future performance better than season beginning predictions. The reason for this is that certain growth or degradation may not be apparent prior to the season. PrOPS takes in peripheral batting data to predict OBP and SLG. xFIP takes peripheral pitching performance data and predicts future ERA. Current relief pitching ERA will be multiplied by the coefficient factor mentioned in the next paragraph.

2. Performance Coefficients
The batting performance reduction coefficient will be changed from 0.9 to 0.927. The pitching performance coefficient will be changed from 1 to 1.057.

3. Record Calculation
The current record is considered a given, so the new predicted winning percentage will be applied to games yet to be played. The number of wins determined by the formula will then be added to the current total.

Team Used for Calculations
PrOPS/Morong
2 Roberts.........374obp/424slg
3 Mora............348/454
R Markakis........414/521
D Huff............324/441
L Scott...........328/395
1 Millar..........354/444
C Hernandez.......326/332
C Jones...........293/354
S Placeholder.....300/330

xFIP
S Olson...........3.75
S Guthrie.........4.19
S Cabrera.........4.19
S Trachsel........5.92
S Burress.........4.64
R Bullpen.........3.42

Results

In the games left, this method predicts the Orioles will score 542 runs and give up 537 runs. The season ending run totals would be 722 runs scored and 721 runs given up. In the remaining games, the winning percentage would be .505, which would end with us having a .518 winning percentage at the end of the year (Pythagorean Win Expectancy). This means that the current rendition of the model predicts we wind up with a 84-78 record, which is 2 wins above the PWE.

Discussion

The current model is placing a great amount of worth on the ability of PrOPS and xFIP to accurately predict future performance. In addition, the new coefficients are assumed to remain constant. Finally, expecting the current rotation to remain as the final rotation is, again, quite a weak assumption. Anyway, things look a lot brighter for the O's than it did a few months back.