OSU BB: Season Underway

The Buckeyes beat James Madison 8-6 today in the first game of the Buckeye Baseball Classic. Dan DeLucia was solid...6 IP, 3 R, 0 W, 6K

True freshman Theron Minium had some control issues, walking 2 and hitting one, and he gave up 3 runs in 1 1/3, but Rory Meister got the save with 1 2/3 scoreless IP, 1 walk, 2 Ks.

Just about everyone was in on the act offensively...Angle 2-4 with a walk, Howell 2-5, Fryer 2-4 with a walk, Shuck 1-4, Miller 2-3 with a walk, Dew 1-4 with a 3 run double, Zoeller 0-3 with a sac fly, Kennedy 3-4, Rupert 0-2 with 2 sac bunts.

It was interesting to see Dew starting in RF and batting 6th and Kennedy getting the nod at third. Tomorrow the Bucks have a doubleheader versus Kansas State; Corey Luebke and Josh Barerra will get the starts.

Career WAT Data for Recent Pitchers

Here we will look at pitchers who have retired in the last decade or so or who remain active. For active pitchers, I looked for guys who are on the back end of their careers and have won at least 150 games. So you won’t see pitchers like Roy Oswalt, Johan Santana, or Roy Halladay here. Their time may come in another five years or so.

Now, the raw records for these pitchers:



One thing that jumped out at me from this chart is just how good Kenny Roger’s W-L record is. It’s not historical by any means, but I was surprised that it was similar to those of Brown and Cone. Then you consider that he has pitched for .500 teams more or less and it’s even more impressive.

I have reservations about including a guy who spent so much time in the bullpen (the Eck) on this list, but he is a HOF pitcher who won nearly 200 games, so I decided not to exclude him. Just take his record with a bigger grain of salt then you do the others.

Now the neutralized records, which include some historically great figures:



As teased in the Lefty Grove comment, there are three active pitchers who currently sport a NW% higher then Grove’s record .650--Martinez at .680, Johnson at .661, and Clemens at .654. These figures are subject to change as they are still active, but I think it’s a good bet that at least one of them holds on and takes that record.

Clemens’ incredible +138.9 WCR ranks behind only Young, Johnson, and Alexander all-time. He trails the same three in WAT, but is within striking distance of Johnson and Alexander. The Rocket of course is brilliant in run-based measures as well, and if you apply any kind of “timeline” adjustment at all, you can very easily come to the conclusion that he is the greatest pitcher in the history of baseball.

The Braves trio is probably underrated here, as our assumption of team balance between offense and defense is certainly off base in this case. The Braves didn’t have bad offenses, but of course they were a great team primarily because of the aforementioned pitchers. However, they all do better in the Wood approach then the Oliver or Deane approach because at least the Wood approach recognizes that Mate includes the contributions of the team’s other pitchers and does not compare the pitcher to the full force of Mate.

OSU BB: Preview Updates

The best OSU sports site on the net, The-Ozone, has checked in with their preview of the baseball team. They have the benefit of being able to talk to Coach Todd, and so they have some new information that changes the projected roles of a few players.

For one thing, Brian DeLucia, as I speculated, is the front-runner at third base, but Tony Kennedy is also being looked at there, with no mention of Chris Macke. Eric Fryer will not be used at first base this year, apparently, although I still would not be surprised if he is used at DH every once in a while in order to get a break from catching. Also, true freshman outfielder Ryan Dew has impressed and should be in the mix for playing time, and redshirt freshman outfielder Zach Hurley has apparently taken a big step forward.

On the pitching side, it appears as if Jake Hale could be moved to the bullpen, as the true freshman lefties Theron Minium and Josh Edgin have earned mound time. One of those two or Josh Barerra could take Hale's rotation spot. Hale and Meister would make a pretty strong 1-2 punch out of the pen, although this move would create an all-lefthanded starting rotation. However, Hale's move to the pen is far from certain, with Coach Todd describing it as "an experiment early to see whether that could happen".

The season will begin Friday as the Buckeyes "host" James Madison at the Buckeye Baseball Classic, held at the Yankees' spring training site in Tampa. They will play a doubleheader with Kansas State on Saturday and a single game against Seaton Hall on Sunday. I do not believe that it is a coincidence that the Bucks are hosting their tournament at the Yankees facility, as George Steinbrenner is a well-known friend of the Ohio State Athletic Department.

OSU BB: Bill Davis Stadium PF

I intend to be doing a little more blogging on the Ohio State baseball program this year, and I will mark these posts with the "OSU BB" prefix before the title. I will be blogging on OSU baseball whenever I feel like it, as well as general baseball news when I feel like it. I also intend to post at least one sabermetric-related piece around Monday each week. That will continue next week with the conclusion of the WAT series.

Anyway, today I decided to look at the park factor for Bill Davis Stadium. I have never actually sat down and figured it out, so I looked at 2002-2006 data. I only looked at OSU home v. road games in the Big Ten.

In 79 B10 home games, OSU scored 421 runs and allowed 362, for 9.91 RPG. In 69 road games, OSU scored 454 runs and allowed 334, for 11.42 RPG. Interestingly, OSU scored more on the road and allowed more at home, giving the Buckeyes a .573 Pythpat record at home v. .651 on the road, which is very strange. In fact, OSU was 50-29 (.633) at home and 41-28 (.594) on the road. I don't know what to make of that.

Anyway, if we convert the RPG ratio into a PF, we get .879. However, this is just a factor for application to home stats, not to composite stats. In a major league situation, I’d just take the average of .879 and 1 to account for this. However, thanks to the fact that you can’t play baseball in Ohio in February and March, the Buckeyes over the same five year period have played 123 games at home and 179 on the road (40.7% home).

So assuming that the road park is average, the park factor is now .407*.879 + .593 = .951. I also regress this at 40% toward the mean, since the number of games in the sample is about the same as one major league season, and 40% is the factor I would use for that. So we end up with a PF of 97.

So it appears that Bill Davis Stadium is a fairly strong pitching park that has its influence on the Buckeyes’ statistics made fairly moderate by the extremely unbalanced schedule.

Historical Park Factors

I have just posted a new spreadsheet with park factors for all teams, 1901-2004, as a Google Spreadsheet. These are five-year park factors, calculated in the same manner I describe on this page.

The guiding philosophy was to try to include as much data as possible. If there are five possible years of data to be used for a park, they will all be used, even if four of the seasons were in the past or in the future. The source of the raw data was KJOK’s excellent park database.

I treat a park as new if there are major changes to the dimensions, but I did not by any means do a complete historical survey to find out when those changes have taken place, so some that probably should have been treated differently are not. If you have specific data on when a change should have (or shouldn’t have) been made, feel free to leave a comment and I will try to incorporate these changes when I update the chart some time in the future.

Additionally, when a team moves, and a new team immediately moves in (for example, the Senators of ’60 and ’61), this is treated as a new team. Also, in cases in which teams have played a significant (which I defined as around ten or more) number of games in a different stadium in the same year, those years are treated as being a new park (an example is the Dodgers playing games in New Jersey the two years before they moved from Brooklyn). Whenever a “new park” of this sort is established, when the old order is restarted it is treated as another new park.

The reason the park factors are only shown through 2004 is that my ideal data set is two previous years, the year in question, and two future years. For most of the parks active in 2005, we will after 2007 be able to fill this dataset, and so I don’t want to publish a park factor now and change it later. However, there are a few parks where the 2004 or 2003 factors are not yet settled because they are new and there are not yet five years of data available. In these cases, I have listed a PF but marked it as one that will change in the future.

Now I will give an example of how I chose the years to be considered in figuring the PF. Suppose we look at the Diamondbacks, who have played in Bank One Ballpark since 1998. In 1998, we have no previous data, but we do have four future years of data we can use, so the sample is 1998-2002. For 1999, we have one previous year, so we take three future years, and get 1998-2002. For 2000, we have two previous years and two future years, so we use 1998-2000. In 2001, we use the two previous years (1999 and 2000), and two future years (2002 and 2003), making the total sample 1999-2003.

Let’s also consider the end of the Braves’ tenure in Fulton-County Stadium. The last season there was 1996. For 1994, we have two previous years (92 and 93) as well as two future years (95 and 96), so we use 1992-1996. For 1995, we have just one future year, so we use three previous years, and also use 1992-1996, and the same for 1996.

For a few random facts, the highest PF is for Coors Field in 1998, 124. The highest non-Coors PF is 114 for Mile High in 1993-94. The highest non-Colorado PF is 113 for the Baker Bowl in a number of years (1923-26, 1933-1937). The lowest PF is 91 for Braves Field in 1936, Dodger Stadium in 1966 and County Stadium in 1959.

Career WAT Data, 1973-Recent

As I mentioned in the first installment, I sort of lumped pitchers from the final Neft/Cohen era into groups as I saw fit, without caring too much if Dave Stieb wound up as “recent” and Jack Morris as “1973-Recent”, and it shows. However, only the margins are confused, and I don’t present the eras as a prism through which you must view a pitcher, just a way to group dozens of pitchers into workable groups.

Here are the actual records and other approach WATs for these pitchers:



Steve Rogers may be the worst pitcher I chose to list, but I was interested in seeing how he shook out, and it’s my blog, so he’s here. Rick Reuschel is a guy you don’t hear discussed much, but he pitched for terrible teams. Ron Guidry’s unadjusted record is fairly similar to another lefty, Sandy Koufax. Guidry won five more and lost four more then Koufax, with a percentage four points lower. But the surprise for me was that Guidry pitched for even better teams then Koufax did (.555 to .544 advantage in Mate). Jenkins is the first pitcher we’ve considered who comes out on a .500 team, with Gaylord Perry just a point off.

Now the Neutralized stats:



The case of Bert Blyleven has been discussed ad nauseam on the blogosphere, but it’s true that his W-L record isn’t that impressive when compared to average. However, compared to replacement, he’s pretty strong. And of course all of the research that has been done on Blyleven’s actual run support, his record when supported by X runs, etc., should be allowed to take precedence over the data here.

Nolan Ryan, even with the favorable comparison to replacement, does not stand out above a number of pitchers who he dwarfs in the general public’s assessment, like Perry, Sutton, Palmer, and Jenkins. Of course, there are other factors, and Ryan probably relied less on his defense then any other pitcher ever. Of course, that can cut both ways. If you pitch for a poor team with poor defense, and you rely on them to make plays behind you, then that is a significant disadvantage. But if you pitch for a poor fielding team and you strike everybody out anyway, there is less extra credit available for you to pick up. Our simple model doesn’t account for this, and I don’t think that W-L records are reliable enough to be subject to that kind of scrutiny, but I suppose if you wanted to do a lot of work, you could do some accounting for those things.