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Thu, July 02, 2009
by KJOK
The latest version of the Seamheads (aka KJOK) Ballparks database has been loaded to the site (see http://seamheads.com/db/databases.htm, then click Ballpark Stat Splits. There is also a link for the documentation file.)
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Wed, April 08, 2009
One of the defining features of our national pastime—and what lends it to such close statistical scrutiny—is the head-to-head match-up between the batter and pitcher. It’s an intricate dance that, repeated hundreds of times over the course of a season, yields meaningful insight into the nature of the game. This inherent importance is only heightened when the two men involved are especially well equipped for the contest.
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Sat, December 27, 2008
by Mike Lynch
Last year I wrote an article about the top home run hitters and their chances of eclipsing Barry Bonds’ 762 circuit clouts. Here’s a look at their chances after putting up another season’s worth of four-baggers.
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Sat, September 13, 2008
by Brian Joseph
On Friday, on MVN Outsider (on a different site), I wrote an article called “Revisiting and reinventing the ‘Quality Start’”. In the article, I took a different approach to the definition of a Quality Start and established new criteria. Since then, after some other discussions, I have revised the criteria, making it a bit easier to follow and also took the data and applied it to historical data using Baseball-Reference’s Play Index to crunch the data from 1956-2008.
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Sat, August 16, 2008
by Brian Joseph
We’re no Elias Sports Bureau here at Seamheads but thanks to Baseball-Reference’s Play Index, this week, I decided to look at how rare hitting for the cycle is. On Thursday, Mark Kotsay became the 123rd player since 1956 to hit for the cycle and it was the 132nd time since 1956 someone has accomplished the feat.
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Sat, May 17, 2008
by Brian Joseph
Last week, Padres’ Greg Maddux registered his 350th win. There’s a good chance we won’t see that again in our lifetime. While I still hold to my earlier posts that 300-game winners are not dead yet, there is serious cause for concern with the disappearance of consistent, high quality starting pitching.
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Wed, April 23, 2008
by James Farris
Wed, March 12, 2008
Wed, March 05, 2008
Sat, January 19, 2008
by KJOK
Major League Equivalents (MLEs) are a series of calculations designed to take non-major league baseball performance and estimate what that performance’s results would look like statistically in the context of the Major Leagues. Bill James gets credit for being the inventor of MLEs, as he outlined his method for batters in the 1985 Baseball Abstract. James was only interested at the time in making sense of minor league statistics, but MLE’s theoretically can be used to evaluate ANY baseball performance, including minor league, Japanese or other foreign league, Negro League, NCAA league, etc. Not only those, but you can actually use the basic MLE procedure to evaluate the performance of an American League player relative to the National League, or vice versa (NLEs, ALEs?), or perhaps calculate what type of batting statistics Ty Cobb’s 1909 performance would look like in the 2007 AL.
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Fri, December 14, 2007
by KJOK
I never like to assume, so once again a quick clarification from the previous segment:
In Step #4, Leagues Park Difference, the 13% Park Difference will expand or contract depending on the SPECIFIC parks a player is coming from and going to. For example, if a Japanese player is moving from pitcher-friendly Koshien Kyujo in NPB to hitter-friendly Coors Field in MLB, that 13% difference would be much smaller – perhaps 6% instead of 13%.
Secondly the statistics that follow are Major League Equivalents and not actual NPB statistics. For the NPB stats check this site.
[more…]
Mon, November 26, 2007
by KJOK
Before moving on to Part III, there’s a clarification I need to make regarding Part II.
In addition to factoring in the difference in league parks between any two leagues when doing MLE’s, you must also factor in the difference between league SCORING environments. In the previous Part II, I ‘plugged’ a 7% difference into the formula and called it the park difference. In reality, the 7% difference is actually the ‘context’ difference - a combination of park differences AND league scoring differences. Since we can calculate the league scoring difference, we really should use it, and plug ONLY the park difference.
In terms of the final results, it won’t change anything, but in terms of understanding how to ‘properly’ do MLE’s, not just for Japanese League players but for AAA, or Negro Leagues, etc., and for understanding the interaction of parks, leagues, etc. it’s important to understand how this works.
Japanese league scoring historically is less than MLB. Japanese League managers traditionally have used more one run, ‘small ball’ strategies, and the league scoring environments definitely reflect this. For the period 1962 – 2007, NPB scoring per game was 94% of MLB scoring per game.
I’ll walk thru complete examples for a hypothetical batter and pitcher again, this time doing it completely the ‘right’ way:
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Mon, November 19, 2007
by KJOK
As was mentioned in Part I, over 600 (664 by my count) players have played in MLB and NPB. Out of that 664, 32 of those have been native born Japanese players. If we break out the numbers by batters and pitchers, we get:
Japanese Pitchers to MLB - 23
Japanese Position Players to MLB - 9
Foreign Pitchers to NPB - 213
Foreign Position Players to NPB - 419
We have a good sample size of players moving to NPB, but rather limited data for players moving from NPB to MLB, especially for position players. Several studies have been done to estimate the difference in ‘quality’ between the two leagues, such as:
http://baseballguru.com/jalbright/analysisjalbright19.html
and
http://www.baseballprospectus.com/article.php?articleid=1330
and also William McNeil, in his books “Baseball’s Other All-Stars” and “The King of Swat”. All seem to reach the same general conclusions:
1. NPB baseball quality is better than AAA baseball, but less than MLB.
2. Home Runs for batters going from MLB to NPB increase dramatically, and decrease dramatically for batters going from NPB to MLB.
While I plan to have a ‘super-duper’ study completed within the year on NPB quality that will also use U.S. minor league statistics to give an even better sample, for rough estimates I use a “90% of MLB runs” quality factor when creating MLE (Major League Equivalent) statistics for players moving to and from MLB. However, one point that is often missed is that ‘difference in quality’ is not the only factor to consider. Ballpark differences between MLB and NPB are a HUGE factor – so much so that they probably shape the perceptions of Japanese players coming to the U. S. more than any single aspect. The average/neutral NPB stadium is more offense friendly than its MLB counterpart, and MUCH more HR friendly. The average NPB park is 318-319 ft. down the lines, 371-372 ft. in the alleys, and 396 ft to dead center. The averages for MLB are 329, 378, and 402 ft. By my preliminary calculations for the ‘super-duper’ study, a neutral NPB park favors offense by 7% over a neutral MLB park, and favors home runs by a whopping 150%.
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by Mike Lynch
I have a list of articles I intend to write, most about players who toiled long before any of us were a gleam in our parents’ eye (hell, long before our parents were gleams in their parents’ eyes), but sometimes events collide and new ideas crop up and articles seemingly write themselves. With the recent indictment of Barry Bonds for perjury and obstruction of justice, it seems Bonds’ career is over and his home run total of 762 is the new standard (assuming it’s allowed to stand). Frankly, I was overjoyed when I heard of Bonds’ indictment because I can’t stand him and it’s always been clear to me (and I’m assuming a majority of baseball fans) that Bonds had been taking performance enhancing drugs. I’m also saddened and a little outraged that the indictment didn’t come BEFORE Bonds broke Hank Aaron’s home run record, but I’ll save that rant for another day.
Prior to Bonds’ indictment I obtained a copy of the 1999 edition of a book called Diamond Chronicles (There were three editions from 1998 to 2000) that features commentary from Bill James, Rob Neyer, David Pinto, Mat Olkin, John Dewan, Craig Wright, and Don Zminda, among many others. Last night while watching the Patriots beat the snot out of the Bills, I ran across an interesting article written by Jim Henzler on September 11, 1998 called “The Ultimate Home-Run Race,” in which he uses James’ Favorite Toy formula to determine which player had the best chance of surpassing Aaron. Now that we’re all but certain that Bonds’ 762 is the new mark to beat, I thought it would be interesting to review what Henzler wrote and rerun those Favorite Toy predictions almost 10 years later.
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Wed, November 14, 2007
by KJOK
Here on seamheads.com at “What’s On Second?”, we’ll be doing various ‘statistorian’ types of analysis, looking at the history and past statistics of baseball, hopefully with the goal of understanding the present a little differently.
Japanese baseball continues to have a bigger impact on U. S. baseball each passing year, with this year being the first time a Japanese born pitcher won a Wold Series game. This influx of Japanese players is of course relatively new, mostly occuring since Hideo Nomo debuted in 1995. But U. S. baseball impacting NPB (Nippon Professional Baseball) has been going on for quite some time – over 600 players have played in both MLB and NPB. The influx of
Because more Japanese players are coming to America, the question this time of year becomes ‘Who is coming?” and “How well will they do?” By looking at the history of players moving between MLB and NPB, we’ll try to answer those questions intelligently in Part II.
Mon, October 22, 2007
by Mike Lynch
About 25 years ago, Bill James introduced a prediction system that picked the World Series winner with 70% accuracy. He wrote about the system for Inside Sports magazine in 1982, then expounded on it in his 1984 Baseball Abstract. He developed the system in 1972 and it accurately predicted the World Series winner at a 68% clip for 12 years. Then he went back and used it to predict all of the postseason series of the 20th century and it returned a success rate of 73%. So I’ve decided to apply the system to this year’s Fall Classic to see which team is expected to win.
The system hasn’t been updated since 1984 (as far as I know), so the point distribution may not be as precise as it should be. For example, James awards 19 points to the team that threw more shutouts during the regular season because at the time of the original research, teams with more shutouts had won 19 more postseason series than teams with fewer shutouts. There have been 35 years of postseason games since he developed the system and I have no doubt the point distribution should be adjusted based on the last three-and-a-half decades of playoff games, but for now we’ll stick with the old system and see how it does. Maybe someday I’ll update it and see how it works moving forward (or maybe James will).
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