By: Steve Guglielmo

In the time since Abner Doubleday invented baseball, nearly everything about the game has changed. Stadiums are now highly priced cathedrals. Players have gotten bigger and stronger, and their salaries have gotten bigger accordingly. The only thing that hasn’t changed since the Civil War is the box score. Ever since 1859 we have been judging players the same way. Today, the statistics exist to give a better indication of performance. These statistics are called sabremetrics.

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Table of ContentsAbstract iSection One: Flaws With Traditional Statistics 1Batting Average-AVG 2

Runs Batted In-RBI 3

Errors-ER 4

Win-Loss Record-W-L 5

Earned Run Average-ERA 6

Section two: Intro to sabremetrics 7On Base Percentage-OBP 8

Slugging Percentage-SLG 9

On Base Plus Slugging-OPS 9

Weighted On Base Average 10

-wOBA 10

Fielding Independent 11

Pitching-FIP 11

Walks And Hits Per Innings Pitched-WHIP 12

Section three: Sabremeterics For Comparison 14Runs Created-RC or RC/27 15

Value over replacement player-VORP 15

Wins Above Replacement- 17

WAR 17

Park Factor-pf 17

Adjusted OPS-OPS+ 18

Park adjusted fip-xfip 19

Glossary of Terms 20

Useful websites 21

Works cited 21

List of illustrations 21

“The problem is that baseball statistics are not pure accomplishments of men against other men, which is what we are in the habit of seeing them as.They are accomplishments of men in combination with their circumstances.”

-Bill James

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Batting Average-AVGBatting average has been the benchmark statistic for offensive players dating all

the way back to 1859, when Henry Chadwick published the first box score (Lewis, 70). According to Chadwick, “There is but one true criterion of skill at the bat, and that is the number of times bases are made on clean hits,” (Lewis, 71).

In the 150 years since Chadwick made this determination, Batting Average has survived as the media darling. To this day, players have their abilities judged first and foremost by their batting average. It continues to be the first thing on a player’s stat sheet, it is the first leg of the “Triple Crown”, and it is one of the biggest determinants in salary negotiations.

Flaws: Batting Average is also, basically useless. Using batting average alone it is impossible to determine a player’s success or failure as a hitter. A player’s success lies in his ability to avoid making outs more than his ability to get a hit. If a player gets 30 hits in 100 at bats, and walks 0 times, he is a .300 hitter, and was on base 30 percent of the time. If a player gets 20 hits in 100 at bats, but walks 25 times, he is only a .200 hitter but gets on base a staggering 45 percent of the time. The player who gets on base at a higher rate gives his team a better chance to score runs, which seems simple and logical, and yet the .300 hitter will be paid like a superstar and the .200 hitter may not even make a roster.

Another major problem with batting average is that it treat’s all hits as equal. Obviously, given the choice, every batter and manager on the planet would rather have a homerun than a single, but using batting average, these two are the same. A player with 30 singles in 100 at bats has the same exact batting average as a player with 30 homeruns in 100 at bats.

Suggested Alternatives: These flaws demonstrate the biggest problems with Batting Average, and show why it should never be used as a stand-alone statistic. Two stats that are much more accurate depictions of a players offensive prowess are On Base Percentage and Slugging Percentage.

Sandy Alderson, the General Manager of the Oakland Athletics in the 1990’s, figured out this critical flaw in Batting Average. “When baseball managers talked about scoring runs, they tended to focus on team batting average, but if you ran the analysis you could see that the number of runs a team scored bore little relation to that team’s batting average. It correlated much more exactly with that team’s on base and slugging percentages,” (Lewis, 57).

Alderson’s claims are verified in Table 1, Explained Variance from Individual Batting Statistics (Bradbury, 159). The table tells us what statistics account for runs scored most accurately. As you can see, in the National League, where the pitcher bats,

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Average is 9 percent less predictive than OBP and 10 percent less than Slugging. In the American League, where there is a Designated Hitter, Average is a whopping 21 percent less predictive than OBP and 16 percent less than Slugging.

Runs Batted In-RBIThe RBI is another statistic that is loved by the baseball community. Like Average,

the RBI has been around since the Reconstruction Era. In 1879, a Buffalo Newspaper, covering the Bison’s, began keeping track of the number of runs a player knocked in every game (Numbers, 2). Unlike Batting Average, however, the RBI was not immediately embraced. John Thorn, author of The Hidden Game of Baseball wrote, “Readers were unimpressed. Objections were that the men who led off, Dalrymple and Gore, did not have the same opportunities to knock in runs. The paper actually wound up almost apologizing for the computation,” (Numbers, 2).

Flaws: 125 years ago baseball fans understood something that owners, general managers, and media members still can’t understand today. RBI is flawed, because it is context dependant. If Barry Bonds (Figure 2) had hit all solo homeruns in his record setting 2001 season, he would have finished with 73 RBIs. That is the problem; RBIs are dependant on what your teammates do in front of you. The readers in 1879 understood that leadoff hitters are at a distinct disadvantage. In their first at bat the best that they could hope for was one RBI, and for the rest of the game they had to follow the weakest hitters in the lineup. The fourth batter in the lineup, however, gets the privilege of following some of the best hitters on the team, and has many more RBI opportunities because of it.

Suggested Alternatives: The RBI was created with good intentions. The creators understood that

Figure 1:Expanded Variance From Individual Batting Statistics

Figure 2: Barry Bonds hitting a Home Run.

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the team that scores the most runs wins, and attempted to come up with a statistic to measure a players run producing ability. Today there are much better indicators of a players run producing ability and value. Some of these statistics are: Runs Created, WAR (Wins Above Replacement), and VORP (Value Above Replacement Player).

Errors-ERFor as many flaws as traditional batting statistics have, fielding and pitching

statistics are still miles behind. The error is currently the cornerstone for all fielding and pitching statistics. A player’s fielding percentage is determined by the number of balls he successfully converts without making an error and a pitcher’s Earned Run Average (ERA) is determined by the number of runs that he gives up without an error. Unfortunately, the error is arguably the most flawed statistic in use today, and proxy flaws multiple other stats.

Flaws: “Errors are the most commonly used statistics to judge fielders and it’s the third line in the box score after runs and hits. But the difference between an error and a hit is a highly subjective decision which an official game score keeper determines…A line drive to the gap that bounces off a fast outfielders glove may be scored an error, while that same ball played by a slow outfielder, who doesn’t come within ten feet of the ball may be scored a hit,” said J.C. Bradbury (Bradbury, 164).

The problem with errors are that often a player is penalized for not making a play on a ball that other players would not have even gotten to. The poster child for the flaw with errors is Derek Jeter. Jeter (Figure 3) has three gold gloves, awarded to him in large part because of his sparkling fielding percentages. The untold story, however, is that Jeter’s range is consistently amongst the worst in the majors. To put it another way, Jeter doesn’t make errors, because he doesn’t get to enough balls to make the play in the first place. His patented jump throw is compensation for his slow first step, and limited range. By contrast an excellent defensive shortstop like Omar Vizquel will often have a worse fielding percentage, and more errors, than Jeter because he got so many more opportunities to make errors.

Bill James, the father of sabremetrics, said it best. “It is, without exception, the only major statistic in sports which is a record of what an observer thinks should have been accomplished…A talent for avoiding obvious failure is no great trait in a big league baseball player; the easiest way to not make an error is to be too slow to reach the ball in the first place. You have to do something right to get an error, even if the ball is hit right at you, then you were standing in the right place to begin with,” (Lewis, 67).

Suggested Alternatives: While fielding statistics are still not perfected, there are several marked improvements over errors and fielding percentages.

Figure 3:Derek Jeter’s jump throw.

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Bill James’ formula, Ultimate Zone Rating (UZR and UZR/150) are enormous steps in the right direction

A starting pitcher’s goal, whenever he takes the mound, is to get his team a win that day. When a pitcher successfully holds his opponent to less runs than his team scored that day, he is credited with a win. When he is unsuccessful, he is credited with a loss. To many people it is cut and dry; when a pitcher wins he was successful and when he loses he was a failure.

Flaws: The problem with this logic is that it assumes that a pitcher is the only player capable of affecting the outcome of the game. Keith Woolner, of the Baseball Prospectus team of experts, describes the problem, “To many fans, then, the primary way to measure a starting pitcher’s success is his won-lost record. Any pitcher worth his salt should win more than he loses, and a 20-win season is the hallmark of excellence. Except that there are two parts to winning a game: having your team score runs and preventing your opponent from doing so. In theory, pitchers can affect only half the equation by preventing runs. But since defense makes up a significant portion of run prevention, pitchers actually influence a fair bit less than half the equation,” (Numbers, 49-50).

Win totals on their own do not paint an accurate picture of performance. A pitcher on the Yankees, with their juggernaut offense, is often afforded more latitude, knowing that his team will score plenty of runs. In 2009, CC Sabathia (Figure 4) won 19 games for the Yankees with a 3.37 ERA. Zack Greinke, on the other hand, notched only 16 wins for the hapless Kansas City Royals but did so sporting an amazing 2.16 ERA. Wins, like RBIs, are far too context dependant to be meaningful.

Another problem with the win-loss record is what happens after a starting pitcher exits the game. In today’s game very few pitchers can throw 9 innings with any kind of regularity, meaning that for a period of time the game is out of their hands. A pitcher may exit a game after a masterful 7 innings with 0 earned runs and in line for a win, when the bullpen comes out and blows the game. Instead of picking up a win, through no fault of their own the pitcher now has a no decision.

Suggested Alternatives: Instead of examining win-loss records, which are largely a matter of luck, it is far better to observe a pitchers peripheral statistics. A much better observation of a pitcher’s success would be his FIP (Fielding Independent Pitching), his WHIP (Walks/Hits per Innings Pitched) or even his ERA.

Win-Loss Record-W-L

Figure 4: CC Sabathia, pitcher for the Yankees.

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ERA is probably the least flawed statistic being employed today. It is certainly more indicative of performance than wins and losses. A pitcher with an ERA of 5 has not pitched as well as a pitcher with an ERA of 3. ERA isn’t perfect, however.

Flaws: The main problem with ERA can be tied to the problem with errors. Once a ball is put in play the pitcher can no longer control what happens to it, unless the ball is hit back to him. A pitcher’s ERA is as much a reflection on the player’s around him, as it is a reflection on him. In Moneyball, Michael Lewis sums up the problem with ERA, as it relates to errors. “If you don’t know how to credit the fielder for what happens after a ball is put into play, you also, by definition don’t know how to debit the pitcher. And therefore, you would never be able to say with real certainty how good any pitcher was.” (Lewis, 236)

It is also possible, although rare, for a pitcher to have a great win-loss record, and a sparkling ERA and still not have had a good season. In 2008, Boston Red Sox pitcher Daisuke Matsuzaka (Figure 5) had an 18-3 record with a 2.9 ERA. Both of these numbers indicate that he had a Cy Young caliber season. However a deeper look at his numbers that don’t show up on a traditional stat sheet, his peripherals, shows a much different story. His walk rate of 5.05 is incredibly high, meaning he was pitching far too often with men on base. His batting average on balls in play (BABIP) of .267 was unsustainably low. Often a pitcher can sustain a stretch of lucky play like this where he gets by on smoke and mirrors. However, inevitably, his peripherals regress

towards the mean. Those balls in play that were outs will start dunking in for hits and many of the men who he issued free passes to will come around to score. Incredibly, however, Matsuzaka never had his regression. He managed to ride his luck out all season. His ERA in 2008 was not indicative of his actual performance, and certainly not predicative of his future performance, a forgettable 2009 campaign where his regression hit hard.

Suggested Alternatives: A much more revealing statistic is DIPS (Defensive Independent Pitching Statistics) which shows a pitcher’s performance only measuring those things that he has control over. Another good alternative is xFIP.

Figure 5: Daisuke Matsuzaka.

Earned Run Average-ERA

“A hitter should be measured by his success in that which he is trying to do, and that which he is trying to do is create runs. It is startling, when you think about it, how much confusion there is about this.”

-Bill James

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On Base Percentage-OBPWhen a hitter steps into the batters box his main goal is not to get a hit. That is

certainly a goal, but it is not the primary focus of a hitter. The main goal of every at-bat is to avoid making an out. While a seemingly small difference, it is important to differentiate between getting a hit and avoiding an out. In a regulation 9-inning game, each team is only afforded 27 outs to score more than their opponent. These 27 outs are precious and cannot be wasted. OBP is basically how well the hitter avoided giving away an out.

There is an adage that baseball player’s use, “A walk is as good as a hit.” According to the prevailing statistic of the day, batting average, that is untrue. A walk is as good as not coming to the plate at all.

Why it is better: A walk is, however, as valuable as a hit for many reasons. If two players both have a .300 batting average, but the first player’s OBP is .300 and the second player’s is .400 the second player has given his team a better chance to score runs, thereby giving his team a better chance to win the game. In Figure 6 (Bradbury, 32) we see the run expectancies tied to each possible situation in an inning. In the example we used, player two will be at first ten percent more of the time, which increases his teams expected runs that inning from .461 to .813.

Another reason that a walk is just as good as a hit is that it has forced the pitcher to throw more pitches. As an offensive unit, an integral part of the team’s game plan is to force the starting pitcher to throw many pitches early so that he can tire and the team can face the bullpen. In Moneyball, Michael Lewis explains why this is important. “The more pitches the opposing starting pitcher throws, the earlier he’ll be relieved. Relief pitchers aren’t starting pitchers for a reason: they aren’t as good. When a team wades into the opponent’s bullpen in the first game of a series, it feasts, in games two and three, on pitching that is not merely inferior but exhausted.” (Lewis, 144)

Flaws: OBP, while demonstrably better than batting average, is not perfect. Like Batting Average, it falls into the trap of treating all hits as equal. A scrappy singles hitter may get on base at the same rate as a homerun hitter, but they did not contribute

Figure 6:Expected runs per inning based on outs and runner configuration

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the same amount to the team’s pursuit of runs.

Other stats to consider: OBP is most often used along side slugging percentage, because they correct each other’s flaws. Another good statistic to use is weighted on-base average (wOBA) because it gives the necessary weight to each hit, but also considers walks and hit by pitches.

Another superior alternative to batting average and RBI’s is slugging percentage. SLG is basically batting average with a kick. You still count up the number of hits a player has and divide it by the number of at-bats (without including walks) but in between those two steps you assign a value to each hit. The formula for calculating SLG is: (Singles)+(2x doubles)+(3x triples)+(4x home runs)/At-Bats, or (total bases/at-bats).

Why it is better: Slugging Percentage is a better determinant of success than batting average because of the values it assigns for each hit. A single and a homerun both count as one hit in batting average, even though a homerun is a more important and more desirable result. In slugging percentage, however, a player who is one for one with a single has a SLG of 1.000, while a player who is one for one with a home run has a SLG of 4.000.

Flaws: SLG, like OBP, is not without it’s flaws. Like AVG, it treats a hit as the only means to avoid making an out. It also fails to consider a player’s home field when weighting each hit. Baseball is not played in a vacuum and, therefore, should not treat each environment as equal. A player with a high slugging percentage at Fenway Park may see a drastic drop in slugging when playing in a less hitter friendly environment, where balls that may have been home runs at Fenway are now outs.

Other stats to consider: Whenever a person analyzes SLG to determine a player’s performance it should always be done alongside OBP, to consider walks. An easier alternative would be to simply look at OPS, because it combines the two statistics. Other, more accurate statistics to consider are wOBA and OPS+, which does adjust for park and league.

OPS is a solution to the flaws with OBP and SLG. By combining the two formulas it accounts for the successful ways of getting on base (Hit, Walk, Hit by Pitch) but also treats each hit individually instead of applying the same value to each of them.

Why it is better: OPS is a better determinant of success than batting average for all of the same reasons that SLG and OBP are better than batting average. It does not fall into the trap of treating a single the same as a home run, like average and OBP. Nor does it discount the value of a walk, like SLG and average do. It combines the best

Slugging Percentage-SLG

On Base Plus Slugging-OPS

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parts of two statistics that are each better than average and makes one statistic that is much better than average.

Flaws: OPS would seem, on the surface, to be the perfect hitting statistic. It does successfully account for the value of each hit and the importance of walks, but it does so in a way that implies that SLG and OBP are of equal importance. By simply adding the two percentages together, an extra point of OBP is equal to an extra point of SLG. Oakland A’s assistant General Manager, Paul DePodesta, had trouble believing this assumption. Michael Lewis describes DePodesta’s thought process in his book Moneyball. “Not long after he arrived in Oakland, Paul asked himself a question: what was the relative importance of on-base and slugging percentage? His answer began with a thought experiment: if a team had an on-base percentage of 1.000 (referred to as “a thousand”)--that is, every hitter got on base-how may runs would it score? An infinite number of runs, since the team would never make an out. If a team had a slugging percentage of 1.000--meaning, it gained a base for each hitter that came to the plate—how many runs would it score? That depended on how it was achieved, but it would typically be a lot less than an infinite number, a team might send four hitters to the plate in an inning, for instance. The first man hits a home run, the next three make outs. Four plate appearances have produced four total bases and thus a slugging percentage of 1.000 and yet have scored only one run in the inning.” (Lewis, 127)

Other stats to consider: OPS is generally a very accurate depiction of talent at the bat. It is nearly impossible to fool OPS, or have a good OPS without being a good player. Its flaws are minor. Other statistics to look at in addition to OPS are wOBA, OPS+, WAR, and VORP.

Weighted on base average serves a similar function as OPS. The goal of wOBA is to paint as accurate a picture of batting talent as possible. Weighted on base average takes every event that the hitter can be credited for (Walk, Hit, Hit by Pitch, Reached on Error) and weights them according to their run predictability. A home run, therefore, is weighted more heavily than a single.

Why it is better: According to Dave Cameron, of the website fangraphs.com, wOBA is a better statistic than OPS because, “OPS significantly undervalues the ability of a hitter to get on base. It treats a .330 OBP/.470 slug as equal to a .400 OBP/.400 slug, when the latter is more conducive to scoring runs. wOBA gives proper weight to all the things a hitter can do to produce value, and is a more accurate reflection of a hitter’s value.” (Cameron)

wOBA is also convenient because it is scaled to match up with OBP. The formula for wOBA is (.72 x Walks + .75 x Hit by Pitches + .9 x Singles + .92 x Reached base

Weighted On Base Average

-wOBA

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on error + 1.24 x Doubles + 1.56 x Triples + 1.95 x Home runs)/Plate appearances. The league average OBP for a season will equal the league average wOBA, and the numbers are comparable. A .400 OBP and wOBA are both considered excellent seasons. In 2009, Albert Pujols (Figure 7) led Major League Baseball in wOBA with a .449. He was second in OBP to Joe Mauer, with a .443 compared to Mauer’s .444. However he dwarfed Mauer in Slugging, .658 to .587, which explains how he had a higher wOBA.

Flaws: The only flaw with wOBA is that it fails to adjust for league or park factors. Hitters in home run friendly parks like Yankee Stadium or Coors Field will often have a higher home wOBA than their road wOBA. wOBA can be adjusted to include these weights, however.

Other stats to consider: Weighted on base average is one of the best offensive statistics available today. When it is adjusted for park and league factors, it is arguably the most accurate, and predictive statistic there is. OPS+ and Equivalent Average (EqA) are similar statistics to wOBA.

Fielding independent pitching is an alternative pitching statistic to ERA. Because ERA relies so much on the quality of fielding behind the pitcher, and on the scorekeeper’s objective opinion about what constitutes an error and what does not, it is not the best stat available to evaluate pitching performance. In 1999, Voros McCracken attempted to determine why Greg Maddux’s (Figure 8) ERA ballooned from 2.22 in 1998 to 3.57 in 1999. McCracken figured out that ERA is not predicative year in and year out, because batting average on balls in play (BABIP) fluctuate wildly from year to year, even

Figure 7: Albert Pujols

Fielding Independent

Pitching-FIP

Figure 8: Greg Maddux

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when the pitcher is pitching the same way. Balls that may have been caught the year before may start dunking in for hits the next year, and inevitably some of those base runners will come around to score.

Why it is better: What McCracken determined was that once a ball was put into play, a pitcher no longer had control over what happened to it. Because of this, it is unfair to reward or penalize the pitcher for what happens after the ball is put into play. McCracken developed a formula that credits the pitcher with only the events that he can reasonably influence. FIP is determined by using the formula: (Home Runsx13 +(walks + hit by pitches-intentional walks)x3-Strikeoutsx2/Innings Pitched + 3.2)

This formula takes away anything that could reasonably be determined to be a factor of luck, and credits or debits the pitcher with only what he could control (walks, strikeouts, home runs). J.C Bradbury sums up McCracken’s findings, “The noise of earned runs generated on balls put in play, which were randomly turned into hits or outs by the fielders, actually hindered the identification of the pitcher’s true ability. It turns out that the real reason Greg Maddux is so good is that, though he is not an overpowering strikeout pitcher, he rarely walks batters or gives up home runs. This makes DIPS (FIP) a valuable tool for disentangling responsibility for preventing runs.” (Bradbury, 170)

Flaws: FIP and Defensive Independent Pitching (DIPS, another name for FIP) fail to account for park factors. A homerun over the Green Monster in Fenway Park could be a routine fly ball in 29 other stadiums. Since not every ballpark has the exact same dimensions, it is unwise to use FIP to compare pitchers across different teams, as their home runs allowed may be attributed to their ballpark.

Other stats to consider: FIP is an ideal statistic to compare two pitchers from the same team. When attempting to compare pitchers from different teams or different eras, however, you would be better-suited using xFIP which ball park adjusts or total runs allowed (tRA) which gives each possible pitching outcome a weight

Another stat to consider when attempting to quantify pitching success is WHIP. WHIP is a measurement of the amount of base runners the pitcher allows per inning. Like on-base percentage for batters, WHIP takes into account all of the events that factor into scoring runs. As a pitcher allows more base runners, the likelihood of surrendering runs goes up, and the likelihood that your team will lose the game also goes up.

Why it is better: WHIP is a more predicative statistic than ERA because it indicates what should have happened, not what ultimately did happen. If a pitcher loads the bases and manages to work out of the inning without surrendering a run, he was tremendously lucky. His ERA for the inning may have been 0, but his WHIP was 3.00.

Walks And Hits Per Innings Pitched-WHIP

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Pitchers with high WHIPS can sustain short periods of luck, where they do not surrender many runs, like we talked about with Daisuke Matsuzaka’s 2008 campaign, but eventually the numbers will regress toward the mean, and those base runners will come around to score. Mariano Rivera (Figure 9) has been so successful, in part, because he has been able to prevent batters from reaching base. The probability of scoring runs goes down as outs are recorded without base runners.

Flaws: WHIP, like, ERA relies heavily on the use of errors and fielding percentage. A sharp ground ball into the hole between third base and shortstop that a good defender will get to will not count against your WHIP, but if that ball cannot be fielded, it will be counted as a hit.

Other stats to consider: WHIP is a helpful tool for judging performance, but probably should not be utilized as a standalone statistic. More accurate reflections of pitching performance are tRA, and xFIP.

Figure 9: Mariano Rivera

“People who think they know what they are talking about when they talk about baseball include the announcers and all of the sports press. No matter how much evidence you present them to the contrary they will continue to think that what they think is right.”-Michael Lewis

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The goal of any team is to win. You win by scoring more runs than your opponent. You do not automatically win because you had more hits than your opponent, or because you walked more times than your opponent. The only way to win the game is to cross home plate more times than they did. Using this philosophy, why aren’t players judged the same way? Why aren’t player’s judged by their ability to create runs, thereby contributing to the overall goal of the team? Bill James, creator of RC, had this to say, “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 base paths. Willie McCovey (Figure 10) 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.” (James, 273-74)

Why it is better: OBP, SLG, wOBA, OPS, and other advanced offensive metrics are all great indicators about how well a player contributed to his team. Runs created, however, measures how well he contributed to his team through the most important lens, the run. The run is the most important part of the game. It is the goal of every single at-bat. Runs created can quantify how many runs a player is worth, and when it is all boiled down, that is all that matters. RC is calculated by multiplying OBP by total bases. RC/27 takes the number of runs a player creates and divides it by 27 (the number of outs in a regulation game) to determine how many runs a player is worth per game.

Other statistics to consider: Runs Created should be the crown jewel of offensive statistics. It considers players based on the most important aspect of baseball. RC and RC/27 are also integral parts of VORP and WAR, which are useful for comparing players.

Value above replacement player, and it’s sister stat, wins above replacement (WAR) are the best statistics available today to compare players. These numbers take some long calculations, but once they are determined, they can illustrate which players

Runs Created-RC or RC/27

Figure 10: Willie McCovey

Value over replacement player-VORP

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have the most value to a team or who contributed most to a team’s success. General Manager’s who are considering which free agents to sign or which player’s to trade for should be using these calculations.

Why it is better: Keith Woolner, of the baseball prospectus team of experts, defines replacement level as, “The expected level of performance a major league team will receive from one or more of the best available players who can be obtained with minimal expenditure of team resources to substitute for a suddenly unavailable starting player at the same position.” (Numbers, 161)

Replacement level determines the performance a team would get if their normal starter at a given position were injured or traded. VORP determines exactly how much more valuable a starter is than the typical player that would replace him. In order to determine a player’s value above replacement, we must first determine what replacement level is. Keith Woolner gives a step-by-step demonstration, “For a position with a replacement level of R percent (R=80 percent for most positions), subtract P points from the position’s average AVG/OBP/SLG, using the following formula (Figure 11). Let’s simplify the math with an example. Suppose we want to find replacement level for left field, where the league-average LF hits .270/.340/.430. Left field is an 80 percent replacement-level position, so we plug R=80 percent into the formula and find that P is equal to 33 points. Left field in this hypothetical league would have a replacement level of .237/.307/.397, which is 33 points below the position’s average AVG, OBP, and SLG.” (Numbers, 164)

Comparing an established player’s statistics with their replacement level, General Manger’s can make informed decisions about free agents and trades. The Replacement level statistics can be converted into runs created (RC/27) to determine runs created over replacement level. For every 10 runs created over replacement, a team gains about 1 win.

Flaws: The biggest flaw that VORP encounters is that it only accounts for a player’s offensive value above replacement, and ignores defense. This is ok if the two player’s in question are both designated hitters, but to compare two position players, you should look at WAR because it does account for fielding.

Figure 11: Replacement Level Formula

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WAR is a statistic that is closely tied with VORP. Instead of measuring the number of runs a player contributes over a replacement level player, it measures the amount of wins contributed above replacement level. The general rule of thumb is that for every 10 runs a player contributes over replacement level, they produce one win. Because VORP does not account for defense, which is, after all, half the game, we cannot simply divide VORP by 10 to find WAR.

The formula for WAR is more complicated than VORP. Tom Tango breaks down the formula on his website, insidethebook.com. “To convert wOBA for a hitter into wins: (wOBA - .338) / 1.15 * 700 / 10.5 will give you wins above average. (The .338 is whatever the league average wOBA is, which is EXACTLY equal to whatever the league average OBP is; 1.15 is the relationship between wOBA and runs; 700 is the number of PA per 162 games; 10.5 is the relationship between runs and wins.) Adding in the wins above average at the position plus the positional adjustment gives you wins above average per 162 games. Add in the replacement level, and that gives you WAR per 162 games. Simply multiply that number by the percentage playing time you expect (no more than 90%, typically at 85% for regulars), and you have your WAR.” (Tango)

The positional adjustment he describes is the average amount of defense necessary to play the position in question. It is hardest to replace defense at catcher, so their positional adjustment is higher, whereas first base is relatively easy to replace defensively. The positional adjustments can be seen in Figure 12.

Flaws: The only problem with WAR is that it does not account for playing time. A player may have one great game, get injured, and have a WAR that is through the roof. When using WAR, it is important to look at a player with a big enough sample size.

When looking at statistics across Major League Baseball it is vital to bear in mind that every stadium is unique. Unlike in football or basketball, baseball does not have rigidly defined boundaries. Fenway Park (Figure 13), for instance, has a 45-foot high wall only 310 feet from home plate standing in left field. Because of its small dimensions, Fenway Park consistently yields more home runs than the typical ballpark does. It is unfair then to say that a player who gets to play 81 games per season at a home run haven like Fenway has the same opportunities to hit home runs as a player

Wins Above Replacement-

WAR

Figure 12: Positional Adjustments

Park Factor-pf

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stuck playing their home games in the cavernous Petco Park. Because of this, it is unwise to compare home run totals that have been compiled in different ballparks.

Why it is better: Park Factor is the solution to this problem. By calculating the role that a ballpark plays in determining home run totals, and adjusting accordingly, statistics can now more accurately compare players from different teams and different eras. Figure 14 shows how Park Factor is calculated. A ballpark with a park factor of 100 is considered neutral. It does not lend an advantage to the pitcher or the hitter. Any park with a PF over 100 is hitter friendly, and anything under 100 is pitcher friendly.

Flaws: The main problem with park factor is that it treats all teams as equal. Some teams have better pitching which may make their home park have a lower PF because they can contain home runs. Some teams have prodigious power hitters, who would hit home runs regardless of their stadiums, and cause an artificially high PF.

Other stats to consider: While PF is not perfect, it is undeniably an improvement over comparing offensive statistics as if they were compiled in a vacuum. When

looking at any offensive statistic it is important to be sure that it has been park adjusted first.

OPS+ is OPS that is adjusted for league and park factors. Because baseball is not played in a vacuum and every park is different, it is important to look at adjusted stats when comparing players. Traditionally, the American League is a stronger offensive league than the National League because of the Designated Hitter rule. Because of this, when comparing players from different leagues it is imperative to look at OPS+ instead of simply OPS. OPS is a great statistic for comparing two players from the same team because they play in the same stadium and generally face the same pitchers. However, outside of single team comparisons, OPS+ should be the statistic of choice. Figure 15 shows the formula for calculating OPS+. It is important to note when calculating OPS+ that the league average OBP and SLG must also be park adjusted before plugging them

Figure 13: Fenway Park

Figure 14: Park Factor

Adjusted OPS-OPS+

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into the equation.

Flaws: The main issue with OPS+ is the same problem that OPS has, it gives equal value to OBP and SLG, when in reality OBP is the more important statistic. Another problem with OPS+ is that it does not adjust for position. A first basemen is expected to have a high OPS+, whereas a shortstop or second basemen is traditionally more of a table setter in the lineup. OPS+ is best used to compare players of the same position.

Other stats to consider: Since OPS+ is exclusively an offensive statistic, a better alternative is RC/27, which measures a player’s offensive success through the scope of runs.

Fielding independent pitching is a statistic that attempts to take any factors that a pitcher can’t control out of consideration to rate performance. It is a great improvement over traditional statistics such as ERA.

Why it is better: However, when attempting to take uncontrollable circumstances out of consideration, FIP failed to include home ballpark. A pitcher can’t control the dimensions of his home park any more than he can control balls put into play. Considering that home runs allowed are such an integral part of FIP, this is a major problem. Park adjusted FIP (or expected FIP) takes care of this problem. It adds a step to the FIP formula. xFIP’s formula is: ((Fly balls x.11) x13 +(walks + hit by pitches –intentional walks) x3- (strikeouts) x2/innings pitched).

By replacing home runs allowed with fly balls allowed (and the rate at which they leave the ball park) xFIP has taken a great stride in park adjusting a pitching statistic. xFIP is a far more accurate depiction of a pitcher’s performance than ERA and should be used accordingly.

Figure 15:OPS+

Park adjusted fip-xfip

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Batting Average (AVG)- Shows what percentage of time a batter is safe on a batter ball that was not an error.

Runs Batted In (RBI)- The number of runs that scored on a hit by the batter that was not due to an error.

Error (ER)- A batted ball that the official score keeper determines should have been converted into an out, but was not.

Earned Run Average (ERA)- The amount of runs scored without the benefit of an error that a pitcher allows on average per 9 innings pitched.

On-Base Percentage (OBP)- The percentage of times a hitter does not make an out, or reach base by an error.

Slugging Percentage (SLG)- The average of the hitter taking into consideration the amount of bases each hit yielded.

On-Base Plus Slugging (OPS)- A player’s on-base percentage plus his slugging percentage

Weighted On Base Average (wOBA)- A batting statistic designed to give every possible event at-bat a weight to determine a player’s true offensive success. wOBA can be compared with OBP.

Fielding Independent Pitching (FIP)- A pitching statistic that takes balls put in play out of the equation, because once a ball is put in play a pitcher cannot control what happens to it. Considers only things within the pitcher’s control.

Walks and Hits per Innings Pitched (WHIP)-The amount of base runners who haven’t reached base via an error that a pitcher allows on average per inning.

Runs Created (RC or RC/27)- The average amount of runs a hitter is directly responsible for during the course of the season or the course of a regulation 9-inning game.

Value Over Replacement Player (VORP)- The amount of runs a batter creates more than a fringe major leaguer of the same position would.

Wins Above Replacement (WAR)- The amount wins a player is directly responsible for over a fringe major leaguer of the same position would.

Park Factor (PF)- A statistic which normalizes all offensive statistics to simulate games all taking place on the same field under the same condition.

Adjusted OPS (OPS+)- A player’s OPS after being adjusted to a context neutral environment.

Park Adjusted FIP (xFIP)- A pitcher’s FIP after being adjusted to a context neutral environment.

Glossary of Terms

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Below is a list of websites that employ sabremetrics, or give a more in depth tutorial about how to use sabremetrics.

http://www.fangraphs.com/

http://www.sabernomics.com/

http://home.comcast.net/~briankaat/statsite.html (WAR calculator)

http://espn.go.com/blog/sweetspot

http://www.prosportsdaily.com/forums/forumdisplay.php?f=436

http://www.baseball-reference.com/

http://baseballprospectus.com/

Bradbury, J.C. The Baseball Economist. 1st ed. New York: Penguin Group, 2007. Print

Cameron, Dave. “The Joy of wOBA.” Fangraphs. 25 NOV 2008. fangraphs, Web. 10 Nov 2009.

James, Bill. The Bill James Historical Baseball Abstract (1st ed.), Del Rey: Ballantine Books, 1985

Lewis, Michael. Moneyball. 1st ed. New York: W.W. Norton & Company, 2004. Print.

Tango, Tom. “Weighted on Base Average or wOBA.” Inside the Book. 10 MAR 2007. Potomac Books Inc., Web. 9 Nov 2009.

Woolner, Keith, et al. Baseball Between The Numbers. New York: Basic Books, 2007. Print.

Figure 1: Expanded Variance From Individual Batting Statistics. Bradbury, J.C. The Baseball Economist. 1st ed. New York: Penguin Group, 2007. Print (159)

Figure 2: Barry Bonds Hitting a Home Run. Barry Bonds Hits Home Run 754. San Francisco Sentinel. July 28, 2007.

Figure 3: Derek Jeter’s Jump Throw. Derek Jeter. Reuters. July 8, 2008

Figure 4: CC Sabathia, Pitcher for the Yankees. CC Sabathia got off to a horrendous start as a Yankee, giving up six earned runs. Associated Press. 6 APR 2009

USeful websites

Works cited

List of illustrations

PAGE 22

Figure 5: Daisuke Matsuzaka. Daisuke Matsuzaka finished tied for fourth in the AL with 18 wins but was just 31st in innings pitched. Associated Press. October 3, 2008.

Figure 6: Expected Runs Per Inning Based on Outs and Runner Configuration. Bradbury, J.C. The Baseball Economist. 1st ed. New York: Penguin Group, 2007. Print (32)

Figure 7: Albert Pujols. Brad Lidge Tried to Get Albert Pujols to Bite on a Second Slider, Only the Cardinal Turned it into a Three-Run Homer. Ronald Martinez. Getty Images. October 18, 2005.

Figure 8: Greg Maddux. Greg Maddux Dominated Hitters Throughout His Hall of Fame Career. Associated Press. December 8, 2008.

Figure 9: Mariano Rivera. Researchers Predict the AL Cy Young Award Will Go to Mariano Rivera for his Extraordinary 2005 Season. Nick Laham. Getty Images. November 7, 2005.

Figure 10: Willie McCovey. Back in Time: June 30. Lane Stewart. Sports Illustrated. June 30, 2008.

Figure 11: Replacement Level Formula. Woolner, Keith, et al. Baseball Between The Numbers. New York: Basic Books, 2007. Print. (164)

Figure 12: Positional Adjustments. Tango, Tom. “Weighted on Base Average or wOBA.” Inside the Book. 10 MAR 2007. Potomac Books Inc., Web. 9 Nov 2009.

Figure 13: Fenway Park. Fenway Park. ESPN.com. November 10, 2009.

Figure 14: Park Factor. Park Factor Formula. Wikipedia.com. September 12, 2009. Web. 10 Nov 2009.

Figure 15: OPS+. Adjusted OPS. Wikipedia.com. November 5, 2009. Web. 10 Nov 2009.

Cover Image: Andy Pettite. Web. http://ownersedge.fanball.com/media/video/f50/splash/apet.jpg

Section Cover: Home Plate. Web. http://www.kieranchapman.net/images/weblog/homeplate.jpg