Moe_Rahn
March 8th, 2009, 08:36 AM
1) What the fuck is sabermetrics and why should I care?
Stolen from Wikipedia, because I'm too lazy to write what would amount to the same thing:
Sabermetrics is the analysis of baseball through objective evidence, especially baseball statistics. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research. It was coined by Bill James, who was among its first proponents and has long been its most prominent and public advocate.
From David Grabiner's Sabermetric Manifesto:
Bill James defined sabermetrics as "the search for objective knowledge about baseball." Thus, sabermetrics attempts to answer objective questions about baseball, such as "which player on the Red Sox contributed the most to the team's offense?" or "How many home runs will Ken Griffey, Jr. hit next year?" It cannot deal with the subjective judgments which are also important to the game, such as "Who is your favorite player?"
Well, now that we know what that is, why the fuck does it matter?
Because the stats that everyone has been using to analyze baseball players for years are bad and wrong.
Okay, that's a bit harsh. But, with just a bit of looking, there are plenty of flaws to be found in “traditional” statistics. Just to name a couple:
-the amount of RBIs that any particular hitter gets is as much a function of the skill of the players batting in front of him, as it is of his own skill, since runs cannot be driven in if other hitters cannot get on base
-the ERA of a pitcher is often dependent on the defense behind him, as poor infielders may not be able to reach and make plays on batted balls that better defenders could easily field for an out
Sabermetrics seeks to, among other things, eliminate this kind of bias, and evaluate players on their own merits.
The basic goal of sabermetrics is to evaluate a measure for a given purpose. The most common uses of statistics are to evaluate past performance (such as to determine who should win the MVP award) and to predict future performance (such as to evaluate a trade that was just made). In both cases, we are interested in measuring contribution to games won and lost.
The reasons that such analysis is possible are the same reasons that make statistics more interesting in baseball than in other sports. Baseball statistics can measure individual performance, independent of what other players do. And while the importance of an individual event depends on the situation, the effect of the situations on the importance of the statistic over a large sample such as a season is not great. When a batter hits a single, this describes what he did; when a quarterback throws a ten-yard pass, the guard who took out a linebacker gets no statistical credit. And the batter who received a single is properly credited for a success; the ten-yard pass may have been a failure if it was third down with 13 yards to go. Thus it is reasonable for the goal of a baseball statistic to be to measure a player's
individual contribution to runs or wins.
Given the goal, it is possible to evaluate a statistic. Baseball statistics can be evaluated in the same way as non-baseball statistics; they can have the same types of flaws, or be misused or misinterpreted in the same ways.
I'm going to link the Sabermetric Manifesto at the tail end of all this, but if you care enough to read the whole thing (which you damn well should) you might as well just google it already.
2) Now that we know why sabermetrics are awesome, how about you tell me some of them?
O-fucking-kay! This section will cover a handful of fairly common sabermetric statistics, with information about them, and their formulas. This is far from a comprehensive list, and is focused on both the stats that I am most likely to cite in any given post about baseball, as well as stats that are fairly easily calculable by anybody. If you want more information, even a cursory glance at Wikipedia will give you reams of it. Lots of the descriptions here are stolen from Wikipedia, because, again, I'm too lazy to rewrite what would essentially amount to the same information. It is assumed that you are familiar with the abbreviations for the basic stats used in the formulas posted below, although uncommon ones will be explained. If you have any questions, post them.
2a) General
Pythagorean Win Percentage is a formula invented by Bill James to estimate how many games a baseball team "should" have won based on the number of runs they scored and allowed. Comparing a team's actual and Pythagorean winning percentage can be used to evaluate how lucky that team was (by examining the variation between the two winning percentages). The term is derived from the formula's resemblance to the Pythagorean theorem.
Many changes have been made to the formula in an attempt to make it more accurate; displayed here is the original incarnation:
Pythagorean Win % = 1 / 1+ (Runs Allowed / Runs Scored)^2
2b) Hitting
BABIP – Batting Average on Balls In Play is a statistic measuring the percentage of plate appearances ending with a batted ball in play (excluding home runs) for which the batter is credited with a hit. BABIP is commonly used as a red flag in sabermetric analysis, as a consistently high or low BABIP is hard to maintain. The formula for BABIP is:
BABIP = (H – HR) / (AB – K – HR)
SecA – Secondary Average is a complement to batting average, which is a simple ratio of base hits to at bats. Secondary average is a ratio of bases gained from other sources (extra base hits, walks and net bases gained through stolen bases) to at bats. Secondary averages have a higher variance than batting averages. In modern Major League Baseball, a secondary average higher than about .500 is considered outstanding, and one below .200 is considered very poor. The league average SecA is typically similar to the league average batting average, in the range of .250-280. The formula for SecA is:
SecA = (TB – H + BB + SB – CS) / AB
OPS – On-base Plus Slugging is a baseball statistic calculated as the sum of a player's on-base percentage and slugging percentage. The abilities of a player both to get on base and to hit for power, two important hitting skills, are represented, making it an effective way of measuring the player's offensive worth. An OPS of .900 or higher in Major League Baseball puts the player in the upper echelon of offensive ability. Typically, the league leader in OPS will score near, and not necessarily below, the 1.000 mark. Unlike many other statistics, a player's OPS does not have a simple intrinsic meaning, despite its usefulness as a comparative statistic. One fault of OPS is that it weighs on-base average and slugging percentage equally, although on-base average correlates better with scoring runs. Magnifying this fault is that the numerical parts of OPS are not themselves typically equal (league-average slugging percentages are usually 75-100 points higher than league-average on-base percentages). OPS has made its way into mainstream baseball coverage, due to its ease of calculation and reliance on two already-tracked statistics
OPS+ - Adjusted OPS is a closely related statistic. OPS+ is OPS adjusted for the park and the league in which the player played, but not for fielding position. An OPS+ of 100 is defined to be the league average. An OPS+ of 150 or more is excellent and 125 very good, while an OPS+ of 75 or below is poor. The formula for OPS+ is:
OPS+ = 100 ([OBP / *lgOBP] + [SLG / *lgSLG] – 1)
*lgOBP is the park adjusted OBP of the league; *lgSLG is the park adjusted SLG of the league
RC – Runs Created is a baseball statistic invented by Bill James to estimate the number of runs a hitter contributes to his team. James explains in his book, The Bill James Historical Baseball Abstract, why he believes runs created is an essential thing to measure:
With regard to an offensive player, the first key question is how many runs have resulted from what he has done with the bat and on the basepaths. Willie McCovey hit .270 in his career, with 353 doubles, 46 triples, 521 home runs and 1,345 walks -- but his job was not to hit doubles, nor to hit singles, nor to hit triples, nor to draw walks or even hit home runs, but rather to put runs on the scoreboard. How many runs resulted from all of these things?
Runs created is believed to be an accurate measure of an individual's offensive contribution because, when used on whole teams, the formula normally closely approximates how many runs the team actually scores. Even the basic version of runs created usually predicts a team's run total within a 5% margin of error. Other, more advanced versions are even more accurate.
The formula for Runs Created has undergone a great many revisions since its inception, and the most recent version is easily too complex to post here. The original, simplest (and, by extension, the least accurate) version of the formula can be expressed as:
OBP x TB
VORP – Value Over Replacement Player is a statistic invented by Keith Woolner that demonstrates how much a hitter contributes offensively or how much a pitcher contributes to his team in comparison to a fictitious "replacement player," who is an average fielder at his position and a below average hitter. A replacement player performs at "replacement level," which is the level of performance an average team can expect when trying to replace a player at minimal cost, also known as "freely available talent."
VORP is calculated with formulas too complex to try and post here, and I would recommend just reading the Wikipedia article on it instead.
2c) Pitching
BABIP was mentioned in the section on hitting stats, and it is also used to evaluate pitchers as well. BABIP can be used to spot fluky seasons by pitchers, as those whose BABIPs are extremely high can often be expected to improve in the following season, and those pitchers whose BABIPs are extremely low can often be expected to regress in the following season. BABIP for pitchers is calculated identically to BABIP for hitters.
ERA+ – Adjusted ERA adjusts a pitcher's ERA according to the pitcher's ballpark (does it favor batters or pitchers) and the ERA of the pitcher's league. Average is set to be 100; a score above 100 indicates the pitcher performed better than average, below 100 indicates worse than average. For instance, if the average ERA in the league is 4.00, and the pitcher is pitching in a ballpark that favors hitters, and his ERA is 4.00, then his ERA+ will be over 100. However, if the average ERA in the league is 3.00, and the pitcher is pitching in a ballpark favoring pitchers, and the pitcher's ERA is 3.50, then the pitcher's ERA+ will be (significantly) below 100. As a result, ERA+ can be used to compare pitchers across different run environments. In the above example, the first pitcher may have performed better than the second pitcher, but his ERA is higher. ERA+ can be used to correct this misleading impression.
DIPS – Defense Independent Pitching Statistics measure a pitcher's effectiveness based only on plays that do not involve fielders: home runs allowed, strikeouts, hit batters, walks, and, more recently, fly ball percentage, ground ball percentage, and (to a lesser extent) line drive percentage. Those plays are under only the pitcher's control in the sense that fielders (not including the catcher) have no effect on their outcome. Several sabermetric methods use only these "defense-independent" pitching statistics to evaluate a pitcher's ability. The logic behind using only these statistics is that there is little to no difference in the abilities of Major League pitchers to influence the rate of hits against them on balls hit into the field of play. In other words, defense-independent statistics such as walks and strikeouts are determined almost entirely by the pitcher's ability level. But defense-dependent statistics, such as the rate of hits allowed on balls put into play (other than home runs), are almost entirely the result of luck and the skills of the defensive players on the field. The metric that I will probably use the most that falls under this umbrella is DICE – Defense Independent Component ERA, whose formula is:
DICE = ([13{HR} + 3{BB + HBP} - 2{K}] / IP) + 3
WHIP – Walks plus Hits per Inning Pitched is a sabermetric measurement of the number of baserunners a pitcher has allowed per inning pitched. It is a general measure of a pitcher's ability to prevent batters from reaching base. Where the earned run average (ERA) measures runs allowed, WHIP measures a pitcher's actual effectiveness against batters faced. If an error is committed with one out remaining in an inning, the pitcher's ERA stops at that point, and further runs aren't reflected by it. The WHIP will continue to accumulate as batters reach base. A WHIP of 1.0 or below will often rank among the best in Major League Baseball. The formula for WHIP is:
(BB + H) / IP (did I really need to bother typing that?)
VORP is also used to analyze pitchers; pretend I posted the same stuff from above again.
2d) Fielding
RF – Range Factor is a baseball statistic developed by Bill James. It is calculated by dividing putouts and assists by number of innings or games played at a given defense position. The statistic is premised on the notion that the total number of outs that a player participates in is more relevant in evaluating his defensive play than the percentage of cleanly handled chances as calculated by the conventional statistic fielding percentage. However, some positions (especially first baseman) may have substantially more putouts because of a superior infield around them, that commits fewer errors and turns many double plays, allowing them to receive credit for more putouts.
3) Who the fuck is Bill James and why does it sound like you would suck his dick for free if he asked?
Because I would! Also, since you asked, Bill James is a baseball writer, historian, and statistician whose work has been widely influential. Since 1977, James has written more than two dozen books devoted to baseball history and statistics. His approach, which he termed sabermetrics in reference to the Society for American Baseball Research (SABR), scientifically analyzes and studies baseball, often through the use of statistical data, in an attempt to determine why teams win and lose. In 2006, Time named him in the Time 100 as one of the most influential people in the world. He is currently a Senior Advisor on Baseball Operations for the Boston Red Sox. Just go read the rest of the goddamn Wikipedia article already and stop bothering me.
4) Resources and Recommended Reading
The Wikipedia article on sabermetrics (http://en.wikipedia.org/wiki/Sabermetrics) is a decent starting point, and has links to articles about all the stats I talked about here plus more.
David Grabner's Sabermetric Manifesto (http://www.baseball1.com/bb-data/grabiner/manifesto.html) has been mentioned multiple times, and gives a far better rundown of what sabermetrics are, why they're important and what they attempt to do than I probably did (that's why I quoted from it a whole lot).
Baseball Prospectus (http://www.baseballprospectus.com/), sometimes abbreviated as BP, is a think tank focusing on sabermetrics, the statistical analysis of the sport of baseball. Baseball Prospectus has fathered several popular new statistical tools which have become hallmarks of baseball analysis. (thanks again Wikipedia). Paying 5 bucks a month for a BP subscription is one of the smartest things I ever did.
SABR – the Society for American Baseball Research (http://sabr.org/) (also the namesake of sabermetrics, if you couldn't guess) actually has a lot more to offer besides their wonderful, massive online compendium of information. But who the fuck cares about all that when you can look at a listing of every single home run hit in the recorded history of major league baseball (I think a little bit of jizz came out when I first laid eyes on that data). Memberships are $65/yr, with discounts available for those under 30 and over 65 ($45/yr). Becoming a member of SABR is the other smartest thing I ever did.
Baseball-Reference (http://www.baseball-reference.com/) is a massive collection of traditional statistics and is great for quick reference. B-R (or BP) is the place that I get those stat lines that I love to post.
tl;dr version
I AM WAY SMARTER AT BASEBALL THAN YOU
Stolen from Wikipedia, because I'm too lazy to write what would amount to the same thing:
Sabermetrics is the analysis of baseball through objective evidence, especially baseball statistics. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research. It was coined by Bill James, who was among its first proponents and has long been its most prominent and public advocate.
From David Grabiner's Sabermetric Manifesto:
Bill James defined sabermetrics as "the search for objective knowledge about baseball." Thus, sabermetrics attempts to answer objective questions about baseball, such as "which player on the Red Sox contributed the most to the team's offense?" or "How many home runs will Ken Griffey, Jr. hit next year?" It cannot deal with the subjective judgments which are also important to the game, such as "Who is your favorite player?"
Well, now that we know what that is, why the fuck does it matter?
Because the stats that everyone has been using to analyze baseball players for years are bad and wrong.
Okay, that's a bit harsh. But, with just a bit of looking, there are plenty of flaws to be found in “traditional” statistics. Just to name a couple:
-the amount of RBIs that any particular hitter gets is as much a function of the skill of the players batting in front of him, as it is of his own skill, since runs cannot be driven in if other hitters cannot get on base
-the ERA of a pitcher is often dependent on the defense behind him, as poor infielders may not be able to reach and make plays on batted balls that better defenders could easily field for an out
Sabermetrics seeks to, among other things, eliminate this kind of bias, and evaluate players on their own merits.
The basic goal of sabermetrics is to evaluate a measure for a given purpose. The most common uses of statistics are to evaluate past performance (such as to determine who should win the MVP award) and to predict future performance (such as to evaluate a trade that was just made). In both cases, we are interested in measuring contribution to games won and lost.
The reasons that such analysis is possible are the same reasons that make statistics more interesting in baseball than in other sports. Baseball statistics can measure individual performance, independent of what other players do. And while the importance of an individual event depends on the situation, the effect of the situations on the importance of the statistic over a large sample such as a season is not great. When a batter hits a single, this describes what he did; when a quarterback throws a ten-yard pass, the guard who took out a linebacker gets no statistical credit. And the batter who received a single is properly credited for a success; the ten-yard pass may have been a failure if it was third down with 13 yards to go. Thus it is reasonable for the goal of a baseball statistic to be to measure a player's
individual contribution to runs or wins.
Given the goal, it is possible to evaluate a statistic. Baseball statistics can be evaluated in the same way as non-baseball statistics; they can have the same types of flaws, or be misused or misinterpreted in the same ways.
I'm going to link the Sabermetric Manifesto at the tail end of all this, but if you care enough to read the whole thing (which you damn well should) you might as well just google it already.
2) Now that we know why sabermetrics are awesome, how about you tell me some of them?
O-fucking-kay! This section will cover a handful of fairly common sabermetric statistics, with information about them, and their formulas. This is far from a comprehensive list, and is focused on both the stats that I am most likely to cite in any given post about baseball, as well as stats that are fairly easily calculable by anybody. If you want more information, even a cursory glance at Wikipedia will give you reams of it. Lots of the descriptions here are stolen from Wikipedia, because, again, I'm too lazy to rewrite what would essentially amount to the same information. It is assumed that you are familiar with the abbreviations for the basic stats used in the formulas posted below, although uncommon ones will be explained. If you have any questions, post them.
2a) General
Pythagorean Win Percentage is a formula invented by Bill James to estimate how many games a baseball team "should" have won based on the number of runs they scored and allowed. Comparing a team's actual and Pythagorean winning percentage can be used to evaluate how lucky that team was (by examining the variation between the two winning percentages). The term is derived from the formula's resemblance to the Pythagorean theorem.
Many changes have been made to the formula in an attempt to make it more accurate; displayed here is the original incarnation:
Pythagorean Win % = 1 / 1+ (Runs Allowed / Runs Scored)^2
2b) Hitting
BABIP – Batting Average on Balls In Play is a statistic measuring the percentage of plate appearances ending with a batted ball in play (excluding home runs) for which the batter is credited with a hit. BABIP is commonly used as a red flag in sabermetric analysis, as a consistently high or low BABIP is hard to maintain. The formula for BABIP is:
BABIP = (H – HR) / (AB – K – HR)
SecA – Secondary Average is a complement to batting average, which is a simple ratio of base hits to at bats. Secondary average is a ratio of bases gained from other sources (extra base hits, walks and net bases gained through stolen bases) to at bats. Secondary averages have a higher variance than batting averages. In modern Major League Baseball, a secondary average higher than about .500 is considered outstanding, and one below .200 is considered very poor. The league average SecA is typically similar to the league average batting average, in the range of .250-280. The formula for SecA is:
SecA = (TB – H + BB + SB – CS) / AB
OPS – On-base Plus Slugging is a baseball statistic calculated as the sum of a player's on-base percentage and slugging percentage. The abilities of a player both to get on base and to hit for power, two important hitting skills, are represented, making it an effective way of measuring the player's offensive worth. An OPS of .900 or higher in Major League Baseball puts the player in the upper echelon of offensive ability. Typically, the league leader in OPS will score near, and not necessarily below, the 1.000 mark. Unlike many other statistics, a player's OPS does not have a simple intrinsic meaning, despite its usefulness as a comparative statistic. One fault of OPS is that it weighs on-base average and slugging percentage equally, although on-base average correlates better with scoring runs. Magnifying this fault is that the numerical parts of OPS are not themselves typically equal (league-average slugging percentages are usually 75-100 points higher than league-average on-base percentages). OPS has made its way into mainstream baseball coverage, due to its ease of calculation and reliance on two already-tracked statistics
OPS+ - Adjusted OPS is a closely related statistic. OPS+ is OPS adjusted for the park and the league in which the player played, but not for fielding position. An OPS+ of 100 is defined to be the league average. An OPS+ of 150 or more is excellent and 125 very good, while an OPS+ of 75 or below is poor. The formula for OPS+ is:
OPS+ = 100 ([OBP / *lgOBP] + [SLG / *lgSLG] – 1)
*lgOBP is the park adjusted OBP of the league; *lgSLG is the park adjusted SLG of the league
RC – Runs Created is a baseball statistic invented by Bill James to estimate the number of runs a hitter contributes to his team. James explains in his book, The Bill James Historical Baseball Abstract, why he believes runs created is an essential thing to measure:
With regard to an offensive player, the first key question is how many runs have resulted from what he has done with the bat and on the basepaths. Willie McCovey hit .270 in his career, with 353 doubles, 46 triples, 521 home runs and 1,345 walks -- but his job was not to hit doubles, nor to hit singles, nor to hit triples, nor to draw walks or even hit home runs, but rather to put runs on the scoreboard. How many runs resulted from all of these things?
Runs created is believed to be an accurate measure of an individual's offensive contribution because, when used on whole teams, the formula normally closely approximates how many runs the team actually scores. Even the basic version of runs created usually predicts a team's run total within a 5% margin of error. Other, more advanced versions are even more accurate.
The formula for Runs Created has undergone a great many revisions since its inception, and the most recent version is easily too complex to post here. The original, simplest (and, by extension, the least accurate) version of the formula can be expressed as:
OBP x TB
VORP – Value Over Replacement Player is a statistic invented by Keith Woolner that demonstrates how much a hitter contributes offensively or how much a pitcher contributes to his team in comparison to a fictitious "replacement player," who is an average fielder at his position and a below average hitter. A replacement player performs at "replacement level," which is the level of performance an average team can expect when trying to replace a player at minimal cost, also known as "freely available talent."
VORP is calculated with formulas too complex to try and post here, and I would recommend just reading the Wikipedia article on it instead.
2c) Pitching
BABIP was mentioned in the section on hitting stats, and it is also used to evaluate pitchers as well. BABIP can be used to spot fluky seasons by pitchers, as those whose BABIPs are extremely high can often be expected to improve in the following season, and those pitchers whose BABIPs are extremely low can often be expected to regress in the following season. BABIP for pitchers is calculated identically to BABIP for hitters.
ERA+ – Adjusted ERA adjusts a pitcher's ERA according to the pitcher's ballpark (does it favor batters or pitchers) and the ERA of the pitcher's league. Average is set to be 100; a score above 100 indicates the pitcher performed better than average, below 100 indicates worse than average. For instance, if the average ERA in the league is 4.00, and the pitcher is pitching in a ballpark that favors hitters, and his ERA is 4.00, then his ERA+ will be over 100. However, if the average ERA in the league is 3.00, and the pitcher is pitching in a ballpark favoring pitchers, and the pitcher's ERA is 3.50, then the pitcher's ERA+ will be (significantly) below 100. As a result, ERA+ can be used to compare pitchers across different run environments. In the above example, the first pitcher may have performed better than the second pitcher, but his ERA is higher. ERA+ can be used to correct this misleading impression.
DIPS – Defense Independent Pitching Statistics measure a pitcher's effectiveness based only on plays that do not involve fielders: home runs allowed, strikeouts, hit batters, walks, and, more recently, fly ball percentage, ground ball percentage, and (to a lesser extent) line drive percentage. Those plays are under only the pitcher's control in the sense that fielders (not including the catcher) have no effect on their outcome. Several sabermetric methods use only these "defense-independent" pitching statistics to evaluate a pitcher's ability. The logic behind using only these statistics is that there is little to no difference in the abilities of Major League pitchers to influence the rate of hits against them on balls hit into the field of play. In other words, defense-independent statistics such as walks and strikeouts are determined almost entirely by the pitcher's ability level. But defense-dependent statistics, such as the rate of hits allowed on balls put into play (other than home runs), are almost entirely the result of luck and the skills of the defensive players on the field. The metric that I will probably use the most that falls under this umbrella is DICE – Defense Independent Component ERA, whose formula is:
DICE = ([13{HR} + 3{BB + HBP} - 2{K}] / IP) + 3
WHIP – Walks plus Hits per Inning Pitched is a sabermetric measurement of the number of baserunners a pitcher has allowed per inning pitched. It is a general measure of a pitcher's ability to prevent batters from reaching base. Where the earned run average (ERA) measures runs allowed, WHIP measures a pitcher's actual effectiveness against batters faced. If an error is committed with one out remaining in an inning, the pitcher's ERA stops at that point, and further runs aren't reflected by it. The WHIP will continue to accumulate as batters reach base. A WHIP of 1.0 or below will often rank among the best in Major League Baseball. The formula for WHIP is:
(BB + H) / IP (did I really need to bother typing that?)
VORP is also used to analyze pitchers; pretend I posted the same stuff from above again.
2d) Fielding
RF – Range Factor is a baseball statistic developed by Bill James. It is calculated by dividing putouts and assists by number of innings or games played at a given defense position. The statistic is premised on the notion that the total number of outs that a player participates in is more relevant in evaluating his defensive play than the percentage of cleanly handled chances as calculated by the conventional statistic fielding percentage. However, some positions (especially first baseman) may have substantially more putouts because of a superior infield around them, that commits fewer errors and turns many double plays, allowing them to receive credit for more putouts.
3) Who the fuck is Bill James and why does it sound like you would suck his dick for free if he asked?
Because I would! Also, since you asked, Bill James is a baseball writer, historian, and statistician whose work has been widely influential. Since 1977, James has written more than two dozen books devoted to baseball history and statistics. His approach, which he termed sabermetrics in reference to the Society for American Baseball Research (SABR), scientifically analyzes and studies baseball, often through the use of statistical data, in an attempt to determine why teams win and lose. In 2006, Time named him in the Time 100 as one of the most influential people in the world. He is currently a Senior Advisor on Baseball Operations for the Boston Red Sox. Just go read the rest of the goddamn Wikipedia article already and stop bothering me.
4) Resources and Recommended Reading
The Wikipedia article on sabermetrics (http://en.wikipedia.org/wiki/Sabermetrics) is a decent starting point, and has links to articles about all the stats I talked about here plus more.
David Grabner's Sabermetric Manifesto (http://www.baseball1.com/bb-data/grabiner/manifesto.html) has been mentioned multiple times, and gives a far better rundown of what sabermetrics are, why they're important and what they attempt to do than I probably did (that's why I quoted from it a whole lot).
Baseball Prospectus (http://www.baseballprospectus.com/), sometimes abbreviated as BP, is a think tank focusing on sabermetrics, the statistical analysis of the sport of baseball. Baseball Prospectus has fathered several popular new statistical tools which have become hallmarks of baseball analysis. (thanks again Wikipedia). Paying 5 bucks a month for a BP subscription is one of the smartest things I ever did.
SABR – the Society for American Baseball Research (http://sabr.org/) (also the namesake of sabermetrics, if you couldn't guess) actually has a lot more to offer besides their wonderful, massive online compendium of information. But who the fuck cares about all that when you can look at a listing of every single home run hit in the recorded history of major league baseball (I think a little bit of jizz came out when I first laid eyes on that data). Memberships are $65/yr, with discounts available for those under 30 and over 65 ($45/yr). Becoming a member of SABR is the other smartest thing I ever did.
Baseball-Reference (http://www.baseball-reference.com/) is a massive collection of traditional statistics and is great for quick reference. B-R (or BP) is the place that I get those stat lines that I love to post.
tl;dr version
I AM WAY SMARTER AT BASEBALL THAN YOU