Introduction to Sabermetrics

[Electric City]

I'm very interested in learning about sabermetrics. I've found a lot of stuff on the internet through searches, but would like to cut down some time weeding through the pile. Can you please recommend some good sources that may be better suited for the basics and a beginner like myself? I plan to visit my local book store to sample the baseball section for info on sabermetrics. Thanks for any help you can provide in pointing me in the right direction.

Anthony

[Ravenlord]

Moneyball is a good start. as are articles by Rob Neyer at ESPN.com

www.Baseballprospectus.com is another good one.
as is www.baseballthingfactory.com.

then there's always just asking questions here or at www.RedsZone.com

[SABR Matt]

I would also recommend you read the articles posted at baseballthinkfactory.org and baseballprospectus.com and ask questions about what you don't understand here or within those sites...the best way to learn is to read a lot and talk to other baseball analysts...that's how I learned.

[Honus Wagner Rules]

Read the Bill James Annual Baseball Abstracts. There a bit hard to find but one can learn much from them.

[SABR Matt]

I owuld say read total baseball as well if they're still publishing.

[cubbieinexile]

Go down to your Library and head for the baseball section. If you truly live in Anderson, SC then your library has a Bill James 1984 Baseball Abstract check that out plus a host of other books your library system has.

Rob Neyer would have been a great start but his archives and current stuff is no longer free.

[SABR Matt]

Well that's pretty sucky...that Neyer has gone corporate...

But I understand it...I will eventually be corporate as well...sort of...I'll evenetually be selling what I have.

[cubbieinexile]

Neyer is great for beginners but outdated for anybody who has been reading his or really anybody elses stuff for awhile. Its odd most people who know Neyer and his work are vets at this and they are also the ones most likely not spend money for his articles. While on the flipside its the new guys who are most likely to enjoy his stuff yet they are not likely to buy his column since they have never had any experience with his work.

I like Neyer but his stuff stopped becoming really interesting a few years back for me.

[SABR Matt]

Agreed on that Cubbie.

Neyer is like a crutch for sabermetric newbies,

[Electric City]

Thanks to all for the help. My interest in sabermetrics started after I read Micheal Lewis' Moneyball last week. I have a general understanding of some basic metrics like OPS, K:BB, K/9, and BB/9. I'm just now starting to read about stuff like Defense Independent metrics and PECOTA projection systems on the Baseball Prospectus site. I've also been checking out the Baseball Think Factory, The Baseball Cube, and Baseball HQ.

Thanks Cubbieinexile about the tip of checking out the library. I do indeed live in Anderson, SC. That is an excellent idea. I will pay a visit to the library before the bookstore. A great way to keep the cost down.

Also, I plan to hang around the Baseball Fever site. I found this site after doing a search for sabermetrics.

[SABR Matt]

Go me...Go Cubbie...Go us for discussing sabermetrics often enough to get listed in online searches for that term. :)

[therealnod]

EC,

Willie Runquist (who I was not then, nor am now familiar with) wrote an excellent book titled Baseball by the Numbers: How Statistics Are Collected, What They Mean, and How They Reveal the Game. (http://www.amazon.com/exec/obidos/search-handle-url/index%3Dstripbooks%26field-keywords%3Dbaseball%20by%20the%20numbers%26results-process%3Ddefault%26dispatch%3Dsearch/ref%3Dpd%5Fsl%5Faw%5Ftops-1%5Fstripbooks%5F8034534%5F2/002-1536867-7532852) It may require some understanding of math, but in general Runquist explains the concepts very well. I would say it is recommended, if not required, reading. I was lucky enough to have found it at my college library. For baseball books, I usually just search the library catalog for any baseball book and then go to that section and look through all the books. Don't forget reference works. My college library has two editions of Total Baseball in the reference section.

In addition to that, Baseball Prospectus (www.baseballprospectus.com/library) has archived a series of articles for the newly interested. Then there are the HighBoskageHouse (www.highboskage.com) White Papers, which should give you a good idea of what, how, and why some things are done. HighBoskageHouse is Eric Walker's site, the same Eric Walker mentioned all too briefly in Moneyball as having written the "pamphlet" that Alderson gave to Beane.

I should not forget to mention Diamond Mind and Tom Tippet. Their site has a series of studies, as well. And Tangotiger, and a site called Sabernomics, which has a number of baseball studies. If you Google enough, you'll eventually find a helluva lot of sites dedicated to baseball research. Hard Ball Times, another one.

Going through all of that would see you off to a running start, I'd imagine.

Edit: Yikes! Right on the heels of SABR Matt and I forget to mention At Home Plate (www.athomeplate.com), which has a series of articles under the name Serious Baseball that are archived, among other things.

[SABR Matt]

Most of that is pretty advanced...start with the basics first. :)

But yes, therealnod has some very good ideas for where to go when you feel comfortable with the Jamesian sabermetrics...the basic stuff.

[cubbieinexile]

Also that book is in the library in South Carolina.

[538280]

Bowling Green University has a good explanation of what sabermetics is. You can read the whole article to find out about some basic metrics, or just read the intro to get an idea of what exactly sabermetrics is. It seems that a link won't take you to the correct place, so I'll just give you the whole article right here:

An Introduction to Sabermetrics

by Jim Albert

What is Sabermetrics?

Sabermetrics is the mathematical and statistical analysis of baseball records. To understand the field of sabermetrics, one first should be familiar with the game of baseball. This sport is one of the most popular games in the United States; it is often called the {\it national pastime}. Baseball began in the eastern United States in the mid-1800's. Professional baseball started near the end of the 18th century; the National League was founded in 1876 and the American League in 1900. Currently in the United States, there are 28 professional teams in the American and National Leagues and millions of people watch games in ballparks and on television.

The game of baseball

The game of baseball is played between two teams, each consisting of nine players. The nine players are a pitcher, a catcher, first baseman, second baseman, shortstop, third baseman, left fielder, center fielder and right fielder. A game of baseball consists of nine innings. One inning is divided into two halves; in the top half of the inning, one team plays in the field and the second team comes to bat, and in the bottom half, the teams reverse roles. The team that is batting during a particular half-inning is trying to score runs. The team with the higher number of runs at the end of the nine innings is the winner of the game.

During an inning, a player on the team in the field, called a pitcher, throws a baseball toward a player of the team at-bat, called the batter. The batter will try to hit the ball using a wooden stick (called a bat) in a location out of the reach of the players in the field. By hitting the ball, the batter has the opportunity to run around four bases that lie in the field. If a player advances around all of the bases, he has scored a run. If a batter hits a ball that can be caught, or that can be thrown to first base before he runs to that base, then he is said to be out, and cannot score a run. A batter is also out if he fails to hit the baseball three times or if three good pitches (called strikes) have been thrown. The objective for the batting team during an inning is to score as many runs as possible before obtaining three outs.

The basic batting statistics

One notable aspect of the game of baseball is the wealth of numerical information that is recorded about the game. The effectiveness of batters and pitchers is typically assessed by particular numerical measures. The usual measure of hitting effectiveness for a player is the batting average which is computed by dividing the number of hits by the number of at-bats. This statistic gives the proportion of opportunities (at-bats) in which the batter succeeds (gets a hit). The batter with the highest batting average during a baseball season is called the best hitter that year. Batters are also evaluated on their ability to reach one, two, three, or four bases on a single hit; these hits are called respectively singles, doubles, triples, and home runs. The slugging average is computed by dividing the total number of bases (in short, total bases) by the number of opportunities. Since it weights hits by the number of bases reached, this measure reflects the ability of a batter to hit a long ball for distance. The most valued hit in baseball is the home run where a player advances four bases on one hit. The number of home runs is recorded for all players and the batter with the largest number of home runs at the end of the season is given special recognition.

The basic pitching statistics

A number of statistics are also used in the evaluation of pitchers. For a particular pitcher, one counts the number of games in which he was declared the winner or loser and the number of runs allowed. Pitchers are usually rated in terms of the average number of ``earned" runs allowed for a nine inning game. Other statistics are useful in understanding pitching ability. A pitcher records a strikeout when the batter fails to hit the ball in the field and records a walk when he throws four inaccurate pitches (balls) to the batter. A pitcher who can throw the ball very fast can record a high number of strikeouts. A pitcher who is ``wild" or relatively inaccurate will record a large number of walks.

Better measure of hitting ability -- runs created

One goal of sabermetrics is to find good measures of hitting and pitching performance. Bill James (1982) compares the batting records of two players, Johnny Pesky and Dick Stuart, who played in the 1960's. Pesky was a batter who hit for a high batting average but hit few home runs. Stuart, in contrast, had a modest batting average, but hit a high number of home runs. Who was the more valuable hitter? James argues that a hitter should be evaluated by his ability to create runs for his team. From an empirical study of a large collection of team hitting data, he established the following formula for predicting the number of runs scored in a season based on the number of hits, walks, at-bats, and total bases recorded in a season.


(HITS + WALKS) (TOTAL BASES)
RUNS = ----------------------------
AT-BATS + WALKS

This formula reflects two important aspects in scoring runs in baseball. The number of hits and walks of a team reflects the team's ability to get runners on base. The number of total bases of a team reflects the team's ability to move runners that are already on base. This runs created formula can be used at an individual level to compute the number of runs that a player creates for his team. In 1942, Johnny Pesky had 620 at-bats, 205 hits, 42 walks, and 258 total bases; using the formula, he created 96 runs for his team. Dick Stuart in 1960 had 532 at-bats with 160 at-bats, 34 walks, and 309 total bases for 106 runs created. The conclusion is that Stuart in 1960 was a slightly better hitter than Pesky in 1942 since he created a few more runs for his team.
Linear weights
An alternative approach to evaluating batting performance is based on a linear weights formula. George Lindsey (1963) was the first person to assign run values to each event that could occur while a team was batting. By the use of recorded data from baseball games and probability theory, he developed the formula


RUNS = (.41) 1B + (.82) 2B + (1.06) 3B + (1.42) HR

where 1B, 2B, 3B, and HR are respectively the number of singles, doubles, triples, and home runs hit in a game. One notable aspect of this formula is that it recognizes that a batter creates a run three ways. There is a direct run potential when a batter gets a hit and gets on base. In addition, the batter can advance runners that are already on base. Also, by not getting an out, the hitter allows a new batter a chance of getting a hit, and this produces an indirect run potential. Thorn and Palmer (1993) present a more sophisticated version of the linear weights formula which predicts the number of runs produced by an average baseball team based on all of the offensive events recorded during the game. Like James' runs created formula, the linear weights rule can be used to evaluate a player's batting performance.
Runs to wins
Although scoring runs is important in baseball, the basic objective is for a team to score more runs than its opponent. To learn about the relationship between runs scored and the number of wins, James (1982) looked at the number of runs produced, the number of runs allowed, the number of wins and the number of losses during a season for a large number of recent major league teams. James noted that the ratio of a team's wins to losses was approximately equal to the square of the ratio of runs scored to the runs allowed. Equivalently,


WINS RUNS^2
RUNS = -------------- = --------------------------- .
WINS + LOSSES RUNS^2 + OPPOSITION RUNS^2

This relationship can be used to measure a batter's performance in terms of the number of wins that he creates for his team.
Better measure of pitching ability
Sabermetrics has also developed better ways of evaluating pitching ability. The standard pitching statistics, the number of wins and the earned runs per game (ERA) are flawed. The number of wins of a pitcher can just reflect the fact that he pitches for a good offensive (run scoring) team. The ERA does measure the rate of a pitcher's efficiency, but it does not tell you about the actual benefit of this pitcher over an entire season. Thorn and Palmer (1993) developed the pitching runs formula

League ERA
PITCHING RUNS = Innings Pitched x ----------- - ER.
9

The factor (League ERA/9) measures the average runs allowed per inning for all teams in the league. This value is multiplied by the number of innings pitched by that pitcher --- this product represents the number of runs that pitcher would allow over the season if he was average. Last, one subtracts the actual earned runs (ER) the pitcher allowed for that season. If the pitching runs is larger than 0, then this pitcher is better than average. This new measure appears to be useful in measuring the efficiency and durability of a pitcher.
Player game percentage
Good measures of hitting, pitching, and fielding performance of baseball players have been developed. However, these statistics do not directly measure a player's contribution to a win for his team. Bennett and Flueck (1984) used data from two baseball seasons to estimate the probability the home team wins a game given the run differential (the home team runs minus visiting team runs), the half inning (top or bottom of the inning), the number of outs, and the on-base situation. Using these estimated probabilities, one can see how the probability of winning changes for each game event. One can measure a player's contribution to winning a game by summing the changes in win probabilities for each play in which the player has participated. This statistic, called the Player Game Percentage, was used by Bennett (1993) to evaluate the batting performance of Joe Jackson. This player was banished from baseball for allegedly throwing the 1919 World Series. A statistical analysis using the Player Game Percentage showed that Jackson played to his full potential during this series.

Making adjustments

People are often interested in comparing batters or pitchers from different eras. In making these comparisons, it is important to view batting or pitching statistics in the context in which they were achieved. For example, Bill Terry led the National League in 1930 with a batting average of .401, a mark that has been surpassed since by only one hitter. In 1968 Carl Yastrzemski led the American League in hitting with an average of .301. It appears on the surface that Terry was the clearly superior hitter. However, when viewed relative to the hitters that played during the same time, both hitters were approximately 27 percent better than the average hitter (Thorn and Palmer, 1993). The hitting accomplishments of Terry in 1930 and Yastrzemski in 1968 were actually very similar. Likewise, there are significant differences in hitting in different ball parks, and hitting statistics need to be adjusted for the ball park played to make accurate comparisons between players.

Learning from selected data

Watching a baseball game raises questions that motivate interesting statistical analyses. During the broadcast of a game, a baseball announcer will typically report selected hitting data for a player. For example, it may be reported that Barry Bonds has 10 hits in his most recent 20 at-bats. What have you learned about Bonds' batting average on the basis of this information? Clearly, Bonds' batting average can't be as large as 10/20 = .500 since this data was chosen to maximize the reported percentage. Casella and Berger (1994) construct the likelihood function for a player's true batting average on the basis of this selected information and find the maximum likelihood estimate. They conclude that this selected data only provides a little insight into the ``complete data" batting average that is obtained from batting records over the entire season.

Streakiness?

Another interesting question is on the existence of streakiness in hitting data. During a season it is observed that some ballplayers will experience periods of ``hot" hitting where they will get a high proportion of hits. Other hitters will go through slumps or periods of hitting with very few hits. But these periods of hot and cold hitting may be just a reflection of the natural variability observed in coin tossing. Is there statistical evidence for a ``hot hand" among baseball hitters where the probability of obtain a hit is dependent on recent at-bats? Albright (1993) looked at a large collection of baseball hitting data and used a number of statistics such as the number of runs to detect streakiness in hitting data. His main conclusion was that there little statistical evidence generally for a hot hand in baseball hitting.

Situational data

Currently there is great interest among fans and the media in situational baseball data. The hitting performance of batters is recorded for a number of different situations, such as day versus night games, on grass fields and artificial turf fields, against pitchers who throw right-handed and left-handed, and during home and away games. There are two basic questions in the statistical analysis of this type of data. First, are there particular situations that can explain a significant amount of variation in the hitting data? Second, are there ballplayers that perform particularly well or poorly in a given situation? Albert (1994) analyzed a large body of published situational data and used Bayesian hierarchical models to combine data from a large group of players. His basic conclusion is that there do exist some important situations. For example, batters hit on average 20 points higher when facing a pitcher of the opposite arm, and hit 8 points higher when they are playing in their home ballpark. However, there is generally little statistical evidence for individual differences in these situational effects.

Prediction

Major league baseball is currently divided into six divisions and one goal of any team is to finish first in its division. Suppose that part of the season has been completed. Using the teams' records from this partial season, is it possible to predict accurately the winners of the divisions? Barry and Hartigan (1993) use a choice model for the probability that a team wins an individual game. This model allows for different strengths between the teams, different home advantages, and team strengths that can change randomly with time. The authors use this model to simulate the results of future baseball games and estimate the probabilities that each team will win its respective divisions.

Currently, major league baseball games are recorded in very fine detail. Information about every single ball pitched, fielded and hit during a game are noted, creating a large database of baseball statistics. This database is used in a number of ways. Public relations departments of teams use the data to publish special statistics about their players. The statistics are used to help determine the salaries of major league ballplayers. Specifically, statistical information is used as evidence in salary arbitration, a legal proceeding which sets salaries. A number of teams have employed full-time professional statistical analysts and some managers use statistical information in deciding on strategy during a game. Bill James and other baseball statisticians have shown that it is possible to answer a variety of questions about the game of baseball by means of statistical analyses.


References

Albert, J. (1994), "`Exploring baseball hitting data: what about those breakdown statistics?", Journal of the American Statistical Association , 89, 1066-1074.


Albright, S. C. (1993), "A statistical analysis of hitting streaks in baseball," Journal of the American Statistical Association , 88, 1175-1183.

Barry, D., and Hartigan, J. A. (1993), "Choice Models for Predicting Divisional Winners in Major League Baseball," Journal of the American Statistical Association , 88, 766-774.

Bennett, J. M. (1993), "Did Shoeless Joe Jackson Throw the 1919 World Series?", The American Statistician, 47, 241-250.

Bennett, J. M. and Flueck, J. A. (1984), "Player Game Percentage", in Proceedings of the Social Statistics Section, American Statistical Association, 378-380.

Casella, G. and Berger, R. (1993), "Estimation With Selected Binomial Information or Do You Really believe that Dave Winfield is Batting .471?", Journal of the American Statistical Association , 89, 1080-1090.

James, B. (1982), The Bill James Baseball Abstract, New York: Ballantine Books.

Lindsey, G. (1963) "An Investigation of Strategies in Baseball," Operations Research, 11, 447-501.

Thorn, J. and Palmer, P. (1993), Total Baseball, New York: Harper Collins.

The books listed in the bibliography would all be good purchases if you're interested in the field of sabermetrics.

[Electric City]

Thanks, everybody for all the great tips and advice. I was able to get The New Bill James Historical Baseball Abstract from the my local library. Also, the the Basic stuff on the Baseball Prospectus site is great reading.

Anthony

[SABR Matt]

Always happy to help another aspiring sabermetrician along.

[nolanryan5714]

Hi all,

I have "stickied" this thread because I believe it is important to have easy access to. Just as the case is with many others, I'm a huge baseball fan, but know almost nothing about sabermetrics.

Thanks, and happy learning! :)

[Honus Wagner Rules]

Perhaps we can start a list of sabermetrically inclined books:

1. The Bill James Annual Baseball Abtract (1977-1988)
2. The Bill James Historical Abtract (1984)
3. The Bill James New Historical Abtract (2003 paperback)

4. The Diamond Appraised (1989)
by Craig Wright and Tom House

http://www.angelfire.com/tx2/dickiethon/diamapp.html

I love this forgotten treasure. It's the first sabermetrics book I ever read. This is one of my favorite quotes about Joe Morgan in the book

Going back to the idea that old scouting reports die hard, I came across another report on Joe Morgan written in 1977 after he won his back-to-back MVP awards. Under Player's Weaknesses the scout put, "Somewhat limited because of small body." Fortunately, the Hall of Fame does not have size requirements.

5. All the Stats Inc. Annual Baseball Scoreboard books are good

6. The Hidden Game, by Pete Palmer and John Thorn

Groundbreaking book, Linear Weights, etc

7. Baseball Prospectus books

[538280]

Here's probably the first ever sabermetric article ever written. It was written in 1954 by Branch Rickey (even though I suspect most of the work was done by his statistician, Allan Roth). Here is the link:

http://www.baseballthinkfactory.org/btf/pages/essays/rickey/goodby_to_old_idea.htm

[buckthis]

Thank you for the article link, 538280. Do you happen to know Babe Ruth's Extra Base Power and Batting Rating for his entire career? What's listed is just for when he was with the Yankees.

[538280]

Thank you for the article link, 538280. Do you happen to know Babe Ruth's Extra Base Power and Batting Rating for his entire career? What's listed is just for when he was with the Yankees.

I don't know. Can anyone else help us here?

P.S.:Sorry for the late response. I haven't viewed this thread in a long time.

[Ubiquitous]

extra base power is (total bases-hits) divided by at bats.

batting rating is simply OBP plus extra base power. Basically a modified OPS in which SLG is replaced by a metric in which the first base is removed from the tally.

[Dasperp]

I wanted to add beyondtheboxscore.com as a great sabermetrics blog. I had a moderate interest in this stuff after reading Moneyball, but reading this blog got me more onto sites like BP and Hardball Times.

[Chisox]

Hi all,

I have "stickied" this thread because I believe it is important to have easy access to. Just as the case is with many others, I'm a huge baseball fan, but know almost nothing about sabermetrics.

Thanks, and happy learning! :)

I'm not sure whether or not to start a new thread or leave it here since it's pretty much the same question.
Can someone post a post with all the advanced formulas on for a quick link? I thought that's what I was going to find here, but I was partially mistaken. I found some from 53820's post, but I would like to have like a notecard with the formulas together so I can look them up when I here you talk about them. WARP, EQA, WS, ect. Anything like that would be much appreciated so I know what you are talking about on here.

[Mr. Red]

The new Baseball Between the Numbers by the Baseball Prospectus team is good.

[SABR Matt]

Anyone wanting to actually do research needs "Baseball Hacks" by Oreilly Publishing...

It's been mentioned here...it's a very important primer in the fundamentals in database management, baseball analysis methods and the tools available to aspiring sabermetricians.

[Ubiquitous]

I broke down and bought it the other day, it should arrive sometime next week. Hopefully it can answer some of my questions.

[SABR Matt]

It's answering mine...that's for sure.

Freeing me up to start at least looking at PBP data (the problem continues to be the missing years. :( )...

Learning a few new programming languages (R and Perl are the main ones since I already am familiar with MySQL), and learning to do things I always wanted to do (like get in-season data).

[skyking162]

I find myself reading Tango's series on run-creation every few months. Each time I read it, I pick up on new details and concepts. The rest of the articles at his site are great, too -- don't get frustrated if some go over your head the first few times. Here's the link to the three part article:
http://tangotiger.net/#Baseruns

Baseball Prospectus has a great series of primer articles. They go in somewhat of an order, starting at the bottom.
http://www.baseballprospectus.com/news/index.php?column=31

I think I've read them all, but the one that sticks in my mind is this one by Nate Silver on forecasting:
http://www.baseballprospectus.com/article.php?articleid=2659

[SABR Matt]

Agreed SK162...Tango successfully convinced me to stear away from regression and toward a BaseRuns model with some of his better articles on the subject, though it took a few reads to be sure I understood everything being said.

[Revilo]

Read Fantasyland: a season on baseballs lunitic fringe by sam walker
It's not very scientific but it has a plot and still has good amount of general baseball math that may not be as complex as sabermetics but is still good

good if you play fantasy baseball

[Sports Fan 07]

I'm very interested in learning about sabermetrics. I've found a lot of stuff on the internet through searches, but would like to cut down some time weeding through the pile. Can you please recommend some good sources that may be better suited for the basics and a beginner like myself? I plan to visit my local book store to sample the baseball section for info on sabermetrics. Thanks for any help you can provide in pointing me in the right direction.

Anthony

Join SABR (if you haven't already) and go back and buy every book they've ever published. Also, if you haven't already, I'd buy all of Bill James' books. Also, SABR's online newsletter is awesome source of information - you get immediate feedback on questions, comments and ideas from SABR members.

[#4 - The Dude]

This might be worthy of a new thread, but...

I found this thread useful and informative. Do all of you sabermetricians (correct spelling?) just do this for fun? I'm very interested in the concept, love the game (no matter how badly the Local 9 tries to extinguish the fire), and young enough that if I totally immersed myself I could see it as a career opportunity.

I'm curious as to what's out there... if anything.

[SABR Matt]

You have to take a lot of joy out of handling the data and searching for answers. You can't become a good sabermetrician if you're just seeking some dough (heh!), but I have ambitions toward a career in sabermetrics. There are three routes to go if you're interested in immersing yourself in the study of baseball statistics. You can try to get a job working for a specific major league team as a statistical consultant, you can try to land a position with STATS Inc. or equivalent consulting with every major league team (and this extends to other sports as well) or you can try to establish a career as a sports writer specializing in the information that can be gleaned from the statistics.

I guess a fourth option would be attending a college like Rutgers that has a professor who is interested in sabermetrics and getting a graduate degree in applied math where your research is baseball-focused...from there you can work as a statistician almost enywhere and get your baseball work in on the side.

[Ubiquitous]

If you want to work for a sports team you are going to have to go to college and get a degree. Probably even minor in sports management so that you can intern at a pro team. The sports management degree is a joke, a gimmick to get kids but it does open doors to pro teams. I knew a kid who got an internship job with the New York Knicks last season because he was in the sports management program. The second thing you will have to do is be able to bring something to the data that they don't have and cannot get. Which means telling them player X's win shares or WARP isn't going to get you a job.

[SABR Matt]

Yep...you definitely need to show an original approach to your analysis and an ability to think on your feet. the guys landing jobs with teams invented new ideas like DIPS, DRA, UZR, and Win Shares when it was new.

[Ubiquitous]

Bill James landed a job with the Red Sox because of who he is and what he has done not because of Win Shares. I"m not 100% sure on it but I'm willing to bet that win shares is not used in any kind of meaningful way by any baseball teams. To a team win shares is useless.

[SABR Matt]

Well that's perhaps true...if you just use one uberstat that explains absolutely everything and have no way of breaking it down and explaining why things are the way they are, it's not going to add that much. But James' ability to come up with an idea that had not previously been thought of, and the many individual pieces of win shares that were themselves revolutionary at the time (marginal analysis, top-down defensive analysis...things like that) are indeed useful to teams and probably a part of their statistical analysis now.

[#4 - The Dude]

You have to take a lot of joy out of handling the data and searching for answers. You can't become a good sabermetrician if you're just seeking some dough (heh!), .

Nah, dude. I'm an accountant. I like my job, and its the eternally anal process of sifting through data and number and number relations.

My dream job has been owning the Phillies since I was oh, about 15. Figuring, how could anyone trade Ryne Sandberg, Jorge Bell, Julio Franco, Curt Schilling, Scott Rolen, and on and on and on and get NOTHING! Well, owning the Phil's won't be happening anytime soon, but maybe I could parlay my skills in numbers into a front office job somewhere down the road...

Thanks for the knowledge, fellas.

[SABR Matt]

I've been a Mariner fan all my life and I can relate to being frustrated by bad choices in a front office. LOL

What do you think of the Abreu trade? :)

[Mariano_Rivera]

I've been a Mariner fan all my life and I can relate to being frustrated by bad choices in a front office. LOL

What do you think of the Abreu trade? :)
I LOVE that trade :D (view my avatar)

[Mariano_Rivera]

The new Baseball Between the Numbers by the Baseball Prospectus team is good.
Yeah I definitely reccomend that book, it's what got me hooked on Sabermetrics in the first place.

[Zagi-CRO]

Please,
which parameters shows actual scored runs and allowed runs /team stats/?

For inst. for Detroit ?
Is it R=686 ? What is H=1320?

[Sports Fan 07]

This might be worthy of a new thread, but...

I found this thread useful and informative. Do all of you sabermetricians (correct spelling?) just do this for fun? I'm very interested in the concept, love the game (no matter how badly the Local 9 tries to extinguish the fire), and young enough that if I totally immersed myself I could see it as a career opportunity.

I'm curious as to what's out there... if anything.

For me it's for fun (I have a succesful business that thankfully allows me a lot of time to do a lot of reading and a lot of baseball research) and always has been. I got the original MacMillan Baseball Encyclopedia in 1969 and it wasn't long after that I was a Sabermetician before the term was ever invented, lol. I became totally emersed in that book. I also loved playing the game (good enough to play at a small college but not good enough to go any further.) I also coached baseball for many years, and have announced it on radio here locally for our high school and college teams. My first 15 years after college I worked as a sportswriter which afforded me to meet many people associated with baseball. And since it was near the St. Louis area, I spent a gazillion hours doing research at The Sporting News, going through their archives. Met some of the greatest people ever while doing research at TSN.

One thing, you have to balance statistics with common sense and an overall deep knowledge and understanding of the game. Albert Einstein once you you can create a formula to prove or disprove anything. Just creating a list does very little. To be able to explain exactly why all the reasons are certain people appeared on that list is an entirely different matter. You have to know what ballparks a player played in and how that ball park played. You have to know what era a guy played in and how the game was played in that era. You have to know where in the lineup a batter hit. So many other factors. As an example I saw a guy create a list of the "all-time greatest fielding pitchers" and almost the entire list consisted of pitchers who played in the 1920s and on back. The list was obviously a joke, and I know exactly why it came out that way, but I'm not sure the person who did it knows.

Also, it was obvious to anyone with a brain when the first Total Baseball came out, that they didn't factor in where a player batted in the lineup when they came up with their formula for "Clutch Hitters." First is all, it was based entirely on RBIs alone which is so stupid for many reasons. But anyway, when it was all said and done, according to Total Baseball, practically every leadoff man who ever played the game was a horrible clutch hitter according to their stats, LOL. They didn't factor in at all that a leadoff man isn't going to get near the RBI opportunties that hitters in the 3-4-5 sports are going to get for instance.

I have so much more on this but it's late and I have to get up early. More later.

[zink]

Does anyone know any good sites to learn the equations for sabermetrics?

[weskelton]

Zink,

Here's a link to some useful links...

http://www.netshrine.com/statglossary.html

I would also suggest that you visit the Sabermetrics Wiki at http://www.tangotiger.net/wiki/index.php?title=Main_Page. It's not so much a list of formulas, but it covers most of the key concepts and has a good number of the related metrics.

[Tango Tiger]

On that wiki page, if you go to the Websites page, there's a section called "Glossary". That'll point you to Hardball Times and Baseball Prospectus glossary pages.