MLB Sabermetric Primer

Posted on 2017-04-14 09:11 by Ray Flowers




You likely know most if not all of the below measures. They may not speak directly to the fantasy game, but they will allow you to understand player performances in a more complete way. Nothing fancy here, just quick hitting breakdowns of some of the main measures,



Sabermetrics is the analysis of baseball through objective evidence focusing specifically on statistical information. The term itself is derived from the acronym SABR (Society for American Baseball Research), which comes from the group of baseball historians who were the originators of most of these “fancy math” equations (the term was coined by the most famous sabermetrician, of them all, Bill James).



BASE OUT PERCENTAGE (created by Barry F. Codell)  

BOP = Bases / Outs

Base Out Percentage takes into account the two main pieces of the baseball landscape: outs and bases. If the theory of the game is to score without making outs, then why not have a measure that explains that relationship? This simple equation does just that.




BPA records how many bases a hitter earns per plate appearance (PA). Notice that this metric focuses on plate appearances and not the more traditionally referenced at-bat. The reason is that we should be concerned with every time a hitter comes to the plate regardless of the outcome of the event, so BPA considers such occurrences as walks and hit-by-pitch, events that are recorded in the PA column but not in the at-bat column.




BABIP, also referred to as a player's hit rate, is the rate at which batted balls end up as base hits. There is a caveat with BABIP – it removes home runs from the equation because technically the ball isn't in the field of play on a home run. The major league average is usually in the .290-.300 range year after year, but players establish their own levels so that some hitters consistently come in at the .270 range while others record marks in the .330's range year after year. The league leaders are usually above .380, a level that is nearly impossible to repeat year after year.


BATTING RUNS (created by Pete Palmer)


Batting Runs is the Linear Weights measurement of runs contributed beyond those of the “league average player.” Linear Weights, also called Total Player Wins, is Pete Palmer’s attempt to combine everything on the field into one measure (Bill James attempt is called Win Shares – discussed below – and most of you have also heard of WAR by this point, another such attempt at an all-encompassing measure). Batting runs is the batting section of the Linear Weights formula (for a full description of the measurement see: Total Baseball, 8th Edition, pp.2665-2666, 2674). 


EQUIVALENT AVERAGE, EqA (created by Clay Davenport) 

[(H+TB+1.5*(BB+HBP +SB)+SH+SF)] / [(AB+BB+HBP+SH+SF+CS+SB)]

This metric measures a player’s total offensive value per out earned. It takes into account a player’s home park as well as the team and leagues performance in the season under review. Therefore, it can be applied over various eras in order to help establish a baseline that can be used to compare players. The number that results represents an estimate of how many runs a player will contribute. A league average EqA is about .260.


ISOLATED POWER, or ISO (created by Branch Rickey and Allan Roth)

Slugging % - Batting Average

A sabermetric measure which attempts to describe a hitters overall effectiveness by measuring the players ability to generate extra base hits. Batting average measures all hits without any attention being paid to what type of knock they are. SLG measures all bases earned (including singles). ISO measures only extra base hits while excluding the other hits. The historical average for ISO is around .120, with .080 being roughly equivalent to a singles hitter, while anyone over .200 should be considered a power hitter. ISO was apparently created by baseball great Branch Rickey, along with Allan Roth in the 1950’s, though they termed it “Power Average.”


(Stolen Bases - Caught Stealing)

An extremely simple yet fun to play with measure designed to add value to players that don’t excel in the steals category but still produce positive results on the bases. It should replace steals in fantasy leagues.


POWER SPEED AVERAGE, or PWSA (created by Ray Flowers)

[(Personal HR x lgHR Ratio) + (Personal SB x lgSB Ratio)] x 1000

Not to be confused with Bill James' Power Speed Number, PWSA is the ratio of a player’s combined home runs and stolen bases compared to the league totals in those categories for the particular season under review. PWSA is limited in that it only deals with two of the five main categories used in fantasy baseball, so it is not a complete measure of player's overall effectiveness.

POWER SPEED NUMBER (created by Bill James)

(2 x HR x SB)/(HR + SB)

A metric used to determine how to best describe a player's combination of power and speed. This measure uses a fixed formula that combines home runs and stolen bases to produce a mark which relates how balanced a player's performance is/was. The reason we say "balanced" is that if a player is all about the homers and has no steals (a guy like Khris Davis), he won't score well at all in PSN. Conversely, if the player is all about speed but has little pop (i.e. Jarrod Dyson), he will also come out with a lower score according to this measure.


RUNS CREATED, or RC (created by Bill James)

[(H-+BB-CS) x (TB+.55SB) / (AB+BB)]

Runs Created, or RC, is an estimate of how many of each team's runs were scored by each of that team's hitters. RC takes into account three main factors: (A) the number of times a hitter reaches base, (B) the hitter's ability to advance runners and (C) the total opportunities of the hitter. RC assigns a value, in runs, of what a player's worth is, paying particular attention to his ability to get on base and move runners around the bases. There are more than a dozen variations to this formula. Here is a basic one.

((((C*2.4)+A)*((C*3)+B)) / (C*9)) - (C*.9)


B: ((BB-IBB+HBP)*.24)+(SB*.62)+((SH+SF)*.5)+TB-(SO*.03)



RUNS CREATED PER GAME, or RC/27 (created by Bill James)


An estimate of how many runs would be scored by a team made up of nine of the same hitter in a single game. Runs Created per Game, or RC/27 uses the number 25.5 in the equation because there are often not 27 batting outs per team per game. The reason for this is that batting outs do not count situations like a runner being thrown out trying to stretch a single into a double, a player caught stealing or if the home team doesn’t bat in the bottom of the ninth because they are leading and have won the game without needing to come to the plate. The historical average is actually about 25.5 batting outs per game, not 27. RC/27, allows us to see value of all players, particularly those who received less than a full season's worth of work but still performed admirably while on the field.


wRC+ = (((wRAA/PA + League R/PA) + (League R/PA – Park Factor* League R/PA))/ (AL or NL wRC/PA excluding pitchers))*100

wRC+ plays off the idea of wOBA, but takes things another step forward by building off it. wRC+ speaks to the offensive contributions of a player like wOBA but wRC+ also adjusts for the league average as well as park factors meaning that you can compare players from different seasons directly to one another. Practically speaking, you can compare a Rockies hitter to on the Giants on completely equal footing. It can be read like a lot of the other “+” measures meaning a mark of 100 is league average, a mark of 112 is 12 percent better than average and 93 being a mark that is seven percent worse than the league average. The mark relates to the runs scored prowess of the player. Ideally you would try to target players with a mark of 115 and above, with anything over 140 signaling all-star levels of work.  



[(TB - H + BB + SB - CS) / AB]

Used to gauge a player’s ability to produce extra bases independent of batting average (the total of a player's extra bases on hits, walks, and stolen bases expressed as a percentage of at-bats). This metric covers the three primary factors of offensive contribution outside of average; power (bases), eye (BB) and speed (SB).


STOLEN BASE RUNS (created by Pete Palmer)

([.22*SB] – [.45*CS])

Pete Palmer's Linear Weights, akin to Bill James' Win Shares or the now popular WAR (see below), attempts to measure a player's overall ability on the ball field by taking into account everything a player does in all phases of the game including pitching, hitting, fielding and base running. Linear Weights is an attempt to come up with a single measure to evaluate all players, regardless of position, on one scale. Stolen Base Runs is the base running component of the larger formula.



Triples + Doubles = Troubles

This measure gives credit to players who may not produce large home runs totals but still contribute heavily towards their team’s ability to win games by producing extra base hits. Fantasy leagues count homers & runs batted in for power guys, steals for speed guys, but what about the hitters in the middle who fall through the cracks? Troubles helps to give value to those that lack elite level skills.


VALUE OVER REPLACEMENT PLAYER, or VORP (created by Keith Woolner)

This measure attempts to describe the number of runs contributed by a player above what a replacement level player would produce at the same position on the field. This measure does not take into account defense. VORP can be used for hitters and pitchers.



Everyone seems to know of WAR at this point. No one knows how to figure it out (no one, even genius level professors at M.I.T have been heard to utter explicatives under their breadth in the hallways). WAR takes into account everything a player does on the field (hitting, fielding, pitching) and attempts to provide one number to represent a players total value on the field. There is no standardized calculation for this measure. WAR is a more complete way to look at a player than VORP because it also takes into account fielding. For more on WAR reference this link which attempts to simplify the often confusing situation. 

We don’t recommend you try to figure out WAR on your own. If you do, make sure you have a bottle of Aspirin laying around, and I would also recommend a nice bottle of whiskey (Maker’s Mark if you’re a Jeff Mans fan or Jameson if you’re Team Ray). It’s going to hurt your head. Two places where you can get a full rundown of WAR are BaseballReference and Fangraphs.


WIN SHARES (created by Bill James)

Win Shares, or WS, is the attempt by Bill James to create an all-encompassing measure of a players’ overall performance. WS attempts to take into account everything a player does on the field, whether he is a hitter or a pitcher. Accordingly, WS measures all players, regardless of position, on the same scale, and in that sense it attempts to be the "holy grail" of statistical analysis.

1- WS are divided between offense and defense with defense being a combination of fielding and pitching. WS are divided amongst a team's hitters based on Runs Created and outs made.

2- WS assigned to defense are divided between fielding and pitching.

3- WS assigned to pitching are determined by runs allowed and innings pitched.

4- WS assigned to fielding are broken down by position played (1B, 2B, SS, etc.).

5- OVERALL: 48 percent of WS are assigned to hitters/base runners, 35 percent are assigned to pitchers and 17 percent are assigned to fielders. However, it should be noted that these percentages are not set in stone. Slight adjustments can be made based upon the era in which one is reviewing (i.e. did the era lean more toward pitching or hitting?).


1- Figure out Runs Created.
2- Figure out Outs Made.
3- Divide Outs Made by 12 and subtract that number from Runs Created.
4- Divide this number by three. This is the hitter's WS.
5- For Pitchers, do the same, just don't subtract the Outs Made.
6- Multiply the pitchers ERA by 1.50 and then subtract 1.00.
7- Find out how many earned runs the ERA in Step #6 would have produced.
8- Subtract his actual earned runs allowed total.
9- Add his saves total.
10- Divide by three. This is the pitcher's WS.
11- For Fielders: add one WS for every 24 games at C, on for every 76 games at 1B, one for every 28 games at 2B, one for every 38 games at 3B, one for every 25 games at SS and one for every 48 games in the OF.
12- Find the team total and then adjust it so that the team total matches the teams win total times three.
13- Round off the numbers. Generally, this "short form" of WS will give you numbers very close to the overall, and more in depth, WS number. This "short form" overestimates hitters in a hitter's park, overestimates poor defensive players and underestimates good defensive players. In a pitcher's park, the opposite happens; WS will overestimate the value of a pitcher but underestimate the hitters.


wOBA – WEIGHTED ON-BASE AVERAGE (Created by Tom Tango)

wOBA is based around an older idea created by Pete Palmer called Linear Weights (Linear Weights was the precursor to WAR - an attempt to rank all players, regardless of skill or position played, on the same continuum). Each event on the field is given a value and the individual events are combined to get one measurement. wOBA attempts to quantify the value of hits much the same way that OPS does. It "weights," or weighs, the aspects of the offensive game in a way that is relative to the actual run value of the event (i.e. a homer is worth more than double the value of a single). In this respect wOBA is more accurate than OPS since OPS gives slightly more value for hitting for extra bases than it does for getting on base. wOBA is figured out on the same scale as regular on-base percentage. That means if you understand the value assigned to OBP you will understand how to read wOBA.




(League ERA / Personal ERA)*Park Factor

This is ERA+ (see below) adjusted for the pitcher’s Park Effects (also see below). Let’s look at Pedro Martinez’s 1999 season. Martinez had a 2.07 ERA, while the American League ERA was 4.86. The Red Sox Park Factor was 1.02. Therefore, Pedro’s AERA was: (4.86/2.07*1.02) = 2.40. The league average performance in a season is 1.00, so Pedro’s 2.40 mark means he was produced a 1.40 mark according to AERA, or 140 percent better than the average AL pitcher in 1999. 



(Innings Pitched divided by 9) x (League ERA – ERA)

A metric which measures how many runs a pitcher prevents from scoring as compared to what a “average” pitcher would have allowed. It is similar to ERA+ (see below).


AVERAGE BASES, or ABA (created by Ray Flowers)

(TBA + BB / IP)

An innovative way to look at a pitcher’s effectiveness designed to replace WHIP (Walks + Hits / IP). Instead of using hits and walks, ABA uses total bases allowed and walks. The reason for this is simple. Is it more important to know how many batters were allowed to reach base or is it more important to know how many bases they received when they reached base? Does it not stand to reason that the pitcher who allows fewer bases to those who do reach base would have a better chance of limiting the amount of runs that score? Take this example. Two batters hit solo homers in two innings. According to WHIP, that pitcher's mark is an excellent 1.00. Still, he's actually allowed two runs leading to an ERA of 9.00, an atrocious number. ABA would put this performance under the microscope more directly to let you know what WHIP doesn't – what type of hits and damage was done with those hits. For more see the article devoted to ABA in this Draft Guide.



(ER while catcher was behind the plate*9) / IP)

The ERA of a club's pitchers with a particular catcher behind the plate. To figure this metric simply multiply the earned runs allowed by pitchers while that specific catcher was behind the plate, multiply that number by nine, and then divide that number by the innings caught. 



ERC represents the expected ERA of a pitcher based upon an overall reading of all performance. ERC represents the expected ERA of a pitcher based upon a reading of his entire pitching performance. In essence, ERC is a metric which attempts to establish if a pitcher pitched in "good luck" or "bad luck" by letting you know what his ERA should have been based upon a more complete reading of his production on the hill.

ERC Equation

In order to come up with ERC, a two-part equation is necessary. 


PTB = 0.89 x [(1.255 x (H-HR) + 4 x HR)] + 0.56 x (BB+HBP-IBB)

*PTB (Pitchers Total Base Estimate)

Let’s use Jason Schmidt’s 20004 season.

Hits: 165  HR: 18  BB: 77  IBB: 3  HBP: 3  BF: 907 (batters faced)  IP: 225

PTB = 0.89 x [1.255 x (165-18) + 4(18)] + 0.56 x (77+3-3)

PTB = 0.89 x [1.255 x (147)+72] + 0.56(77)

PTB = 0.89 x [184.485+72] + 43.12

PTB = 0.89 x [256.485] + 43.12

PTB = 228.272 + 43.12

PTB = 271.392


ERC = [(H+BB+HBP) x PTB / (BFP x IP)] x 9 – 0.56

ERC = [(165+77+3) x 271.392 / (907 x 225)] x 9 – 0.56

ERC = [245 x 271.392 / 204075] x 9 – 0.56

ERC = [66491.04 / 204075] x 9 – 0.56

ERC = [.326] x 9 – 0.56

ERC = 2.934 – 0.56

ERC = 2.374

Therefore, Jason Schmidt’s ERC in 2004 was 2.37.


DIPS ERA (created by Voros McCracken)

Voros McCracken’s Defense Independent Pitching Stat, or DIPS, says that a pitcher’s skill level has little to no bearing on whether or not a batted ball becomes a hit. This means the batting average a pitcher allows on balls put in play is random and the outcome of the batted ball is not in the control of the pitcher no matter how talented he is. Therefore measures such as ERA and WHIP, which depend on defense dependent events (a single, double or triple, a ball put in play that results in an error or a ball put in play resulting in an out of some type), are really fairly useless when it comes to predicting the performance of a pitcher. The reason for this is that those measures are tracking randomly occurring events that have nothing to do with a pitchers skill level (some subsequent studies do suggest that pitcher's talent can have some bearing on the outcome of the batted ball, though still much less than one would suspect).

The effect of DIPS is basically not to “blame” a pitcher for events that are out of his control by focusing on the events which are in his control, namely, Defense Independent events. Those events are strikeouts, walks, hit by pitch and home runs. The resulting DIPS totals therefore are a more precise tool that can be used to gauge a pitcher’s overall effectiveness from year to year than ERA which deals with too much white noise.

DIPS Equation

DIPS ERA is complicated in its full form. Therefore, we will present to you what is known as the “Down and Dirty” version of DIPS which you might actually be able to follow. The D&D version is much easier to compute and is an excellent representation of the more complex full-version of the DIPS number.

Let’s use Jason Schmidt’s 2004 season.

IP: 225  H: 165  HR: 18  BB: 77  K: 251

[((IP*2.35) + (H*0.805) + (HR*10.76) + (BB*2.76) – (K*1.53))] / [((IP*0.712) + (H*.244) + (K*0.096) – (HR*0.244))]

Yes. This is the simple version.


(225*2.35) + (165*.805) + (18*10.76) + (77*2.76) – (251*1.53)

528.75 + 132.825 + 193.68 + 212.52 – 384.03

1067.775 - 384.03



(225*.712) + (165*.244) + (251*.096) – (18*.244)

160.2 + 40.26 + 24.096 – 4.392

224.556 - 4.392


6837.745 / 220.164 = 3.106

Therefore, Jason Schmidt’s DIPS, Down and Dirty style, was 3.11 in 2004 (his “official” DIPS mark was 3.03).

For more on the strength and weaknesses of DIPS see this SABR report.



[ERA+ or RA] League ERA (divided by) ERA

This metric measures how successful a pitcher was in the ERA category compared to what the league average was for a particular season. As an example, Pedro Martinez had a 2.07 ERA in 1999 while the American League had an overall mark of 4.68. Therefore, Pedro’s ERA+ was 2.26 (4.68/2.07). With 1.00 being the league average (4.68/4.68), any number above 1.00 is good, and any number below 1.00 is poor. Pedro’s mark of 2.26 was 1.26 above the league average of 1.00, so, Pedro’s ERA was 126 percent better than the league average pitcher in 1999. 



FIP = ((13*HR)+(3*(BB+HBP))-(2*K))/IP + constant
* The constant is generally around 3.20.

A pitching measure that is more accurate at depicting the performance of a pitcher than ERA. FIP only considers the events that are directly in the control of the pitcher (K, BB, HR, HBP). In effect, FIP builds off the work of Voros McCracken in DIPS ERA by trying to allow the FIP number to be representative of the events that are directly in a pitcher's control versus those that he cannot such as (a) how effective are his fielders? (b) where are those players being positioned by coaches etc.

If you have time, check out this great little video.

More often than not, when Ray Flowers refers to FIP the variation of the measure referenced is xFIP or Expected Fielding Independent Pitching. This mark is recorded the same way as FIP with one variation – it normalizes the pitchers homer rate to what it should have been. In essence, you take a pitcher’s fly ball rate, multiply that by the league HR/F ratio (generally in the 9-10 percent range), and arrive at a number that is more reflective of what the HR portion of the equation should look like. Here is the basic formula for xFIP:

xFIP = ((13*(FB% * League-average HR/FB rate))+(3*(BB+HBP))-(2*K))/IP + constant


GAME SCORE (created by Bill James)

50 + Outs + 2(IP after the 4th) + K – 2(hits) – 4(ER) – 2 (UER) - BB

*UER = Unearned Runs

Game Score, through a simple process listed above, quantifies what it means to pitch a great game by taking out the subjective conjecture and replacing it with a tangible formulaic equation. Basically, Game Score places all attributes of a pitchers outing on one scale in order to present an objective total to quantify that performance. If we are going to bother to record individual Game Score, why shouldn't we try to do the same thing for the whole season? Average Game Score Season (AGSS) is nothing more than just what it says; AGSS measures what each pitcher's average GSC was for each start he made over the entire year. Here is the formula for AGSS.

AGSS = (GS x 50)+Outs+2(IP after the 4th)+K– 2(Hits) – 4(ER) – 2(UER) – BB


Normalized Winning Percentage, or NWP  (created by Bill Deane)

NWP places all pitchers on the same hypothetical .500 winning percentage team and expresses how each pitcher would have done in this hypothetical situation based upon his actual accumulated winning percentage compared to that of his team.

There are two formulas that we must employ when attempting to figure NWP. The reason for this is that we are trying to put all pitchers on equal footing so that we can equitably measure their performance against another hurler. A crucial point to remember is that the Teams Winning Percentage that will be used in the formula for NWP is not the raw Win% total of the team but the team’s winning percentage when the pitcher under discussion did not record a decision. 

I.) If the pitchers winning percentage is lower than that of his team, then the following formula should be used:

NWP: (.500) – [(Team Win% -  Pitcher Win%) / (2 x Team Win%)]

Here is an example. Jason Marquis went 11-9 for a .550 Win% in 2008. The Cubs went 97-64 overall. If we subtract the games in which Marquis earned a decision, the Cubs went 86-55 for a Win% of .610. Therefore Marquis’ personal Win% was below that of his teams. Here is how we figure out his NWP.

.500 – ((.610 - .550) / (2 x .610))

.500 – (0.06/1.22)

.500 – (0.0161) = .0492

NWP = .492

In order to find out what his adjusted win total should have been, we multiply his NWP by his decision mark to get his adjusted win total.

.492 x 20 = 9.84

This says that Marquis, according to NWP, should have won 10 games in 2008. Since he had 20 decisions, this means his overall record should have been 10-10 and not the 11-9 that it actually was. That may not change his record that much, but it still makes a difference doesn’t it? 

II.) On the other hand, if the pitchers winning percentage exceeds that of his team, then the following formula should be used:                                     

NWP = (.500) + [(Pitcher Win% - Team Win%) / (2 x (1.000 – Team Win%)]

Here is an example. Tim Lincecum went 18-5 for a .783 Win% in 2008. The Giants went 72-90 overall. If we subtract the games in which he earned a decision, the Giants went 54-85 for a Win% of .388.

.500 + ((.783 -.388) / (2 x (1.000 - .388)))

.500 + .395 / (2 x .612)

.500 + .395 / (1.224)

.500 +  .323

NWP = .823

Lincecum’s NWP for 2008 was .823 and had 23 decisions on the season (22-10). So in order to find out what his adjusted win total should have been, we multiply his NWP by his decision mark to get his adjusted win total.

.823 x 23 = 18.93

This means that Lincecum, according to NWP, should have won 19 games in 2008. Since he had 23 decisions, this means his overall record should have been 19-4 and not the 18-5 mark he ended the year with.



A Quality Start, which is not a sabermetric measurement, is defined as six or more innings pitched with three or fewer runs allowed (3 ER in 6 IP is a 4.50 ERA by the way which, yes, kinda stinks). Like many other measures, QS is limited in that it is just a number with no context attached. Quality Start Percentage isn't concerned with the overall number of quality starts produced but the percentage of starts that were actually converted into quality starts (quality starts / total starts).




RAA is calculated the same as ERA but whereas ERA counts only earned runs, RAA counts all the runs the pitcher gave up (meaning it also considers unearned runs).



SIERA = 6.145 - 16.986*(SO/PA) + 11.434*(BB/PA) - 1.858*((GB-FB-PU)/PA) + 7.653*((SO/PA)^2) +/- 6.664*(((GB-FB-PU)/PA)^2) + 10.130*(SO/PA)*((GB-FB-PU)/PA) - 5.195*(BB/PA)*((GB-FB-PU)/PA)
The formula hurts to look at, so just ignore it and don't bother trying to work it out yourself. Focus on what SIERA is attempting to record. SIERA operates within the same world as FIP and xFIP in that it attempts to report on a pitcher’s performance based upon the events that are in his control. SIERA takes this line of thought even further as it attempts to ascertain why some hurlers or more effective than others. SIERA is park adjusted for ballpark and defense, while focusing on walk/strikeout/ground ball rates. It is slightly more predictive than measures like FIP.


SWIP (created by Ray Flowers)


What does SWIP stand for?

S- Strikeouts (also abbreviated as K)
W- Walks (also abbreviated as BB)
IP- Innings Pitched

Numerically speaking, the formula for SWIP works along the same lines as WHIP. Another way to look at this is to say that for each positive result, the recording of an out by K the pitcher receives a +1, and for each negative encounter (BB) he receives a (-1). Though SWIP is recorded in the same manner as WHIP, the way to read the results is a bit different. Whereas the lower the WHIP the better one has performed, SWIP works in the opposite direction; the higher the SWIP the better. For more see the article on SWIP in this Guide.

TRIPLE ERA (created by Ray Flowers)

 (ERA + ERC + DIPS) / 3

Triple ERA is the average of three different ERA measures totaled together and divided by three. Since each metric has its own pluses and minuses, if we add all three figures together we would be able to hopefully minimize the "weaknesses" that each of them possess. With TERA, we are also able to take three ideas and combine them in a way that does not favor any method over the other as they are all weighted equally. See the article in the Guide for more on this measure.


WINS ABOVE TEAM, or WAT  (created by Pete Palmer)

WAT = [((Decisions x (Pitchers Win% - Team%)) / (2 x (1.000 – Team%))]

WAT is a metric that attempts to measure the number of wins a pitcher contributes over what an “average pitcher” on the same team would have in the same situation. To state it another way, WAT attempts to determine how effective or ineffective a pitcher was in relation to his team’s overall performance. The resulting WAT number will represent the amount of wins a pitcher either won or lost in relation to what an “average pitcher” would have been expected to produce on that team in the same number of decisions.

Tim Lincecum went 18-5 for the Giants in 2008.

Lincecum’s personal Win% was .783 while the Giants’ Win% in games where he didn’t earn a decision was .388.

((23 x (.783 - .388)) / (2 x (1.000 - .388)))

(23 x .395) / (2 x .612)

9.085 / 1.224

WAT = 7.42

That means that Lincecum produced seven wins more than what an average pitcher would have been expected to produce over the course of 23 decisions for the Giants in 2008.


Expected Fielding Independent Pitching, or xFIP

(HR*13+(BB+HBP-IBB)*3-K*2)/IP, + a league-specific factor (usually around 3.2)

FIP, read like ERA, helps you to gain a handle on how a pitcher performed irrespective of his fielders. It helps to paint a better picture of how a hurler actually performed than does ERA by focusing more on the events that are in the pitchers control. This analysis is taken to the next level with xFIP where the pitchers personal home run rate is replaced with the league average mark.




(Putouts + Assists) divided by (Putouts + Assists + Errors)

This metric measures the rate of success that the fielder has in fielding the ball cleanly. However, if a player has a limited amount of range and doesn’t get to a ball he is “rewarded” by not gaining an opportunity to fail, whereas a player with “extra” range might get to the ball and record an error.


MAJOR LEAGUE EQUIVALENCY, or MLE (created by Bill James)

A “secret” formula that James has kept pretty much hidden away, MLE is used to convert a player’s minor league stats into an estimation of how he would have performed in the major leagues. Generally speaking, the conversion works best when using Triple-A numbers (it is less accurate for the lower levels of the minors). 



Park Factors put the stadium under the microscope to analyze whether or not the ballpark favors pitchers or hitters from a variety of angles (also referred to as Park Factors or Park Indices).  These factors take into account the performance of both the home and the road team at each specific ballpark so that the team that plays its home games in a certain park is only responsible for 50 percent of the final number. That way a team with a poor pitching staff, or a great one, can’t skew the final numbers dramatically because they will always be balanced out by the opposing team’s performance.



Win % = [RSx1.83] / [(RSx1.83 + RAx1.83)]

RS = Runs Scored

RA = Runs Allowed

Pythagorean Expectation is a formula used to estimate how many games a team “should” have won based on the number of runs they scored and allowed.



(Putouts + Assists) x 9 divided by Defensive Innings Played

RF records the numbers of plays that a fielder makes per game. The measure should only be used to compare fielders at the same position. This metric “rewards” players with greater range since it gives credit for balls reached that the “average” player at the position does not get to. 


VALUE OVER REPLACEMENT PLAYER, or VORP (created by Keith Woolner)

Basically, VORP records the number of runs produced by a player beyond what an “average” replacement player would generate. This “replacement” level player is deemed as the expected level of performance of a bench player taking over for an injured starter.



Attempts to deal with the shortcomings of Fielding Average. Zone Rating does this by taking into an account the area or “zone” that the average fielder is responsible for. 


Baseball Basics: Abbreviations


2B - Doubles

3B - Triples

AB - At Bats

AB/GIDP - At-Bats per Grounded Into Double Play

AB/HR - At-Bats per Home Run

AB/RBI - At-Bats per Runs Batted In

AO - Fly Outs

AVG - Batting Average

BB - Bases on Balls (Walks)

CS - Caught Stealing

G - Games Played

GIDP - Ground into Double Plays

GO - Ground Outs

GO/AO - Ground Outs/Fly Outs

GSH - Grand Slam Home Runs

H - Hits

HBP - Hit by Pitch

HR - Home Runs

IBB - Intentional Walks

LIPS - Late Inning Pressure Situations

LOB - Left On Base

NP - Number of Pitches

OBP - On-base Percentage

OPS - On-base Plus Slugging Percentage

PA/SO - Plate Appearances per Strikeout

R - Runs Scored

RBI - Runs Batted In

SAC - Sacrifice Bunts

SB% - Stolen Base Percentage

SB - Stolen Bases

SF - Sacrifice Flies

SLG - Slugging Percentage

SO - Strikeouts

TB - Total Bases

TP - Triple Play

TPA - Total Plate Appearances

XBH - Extra Base Hits


A - Assists

CS - Caught Stealing

DER - Defensive Efficiency Rating

DP - Double Plays

E - Errors

FPCT - Fielding Percentage

G - Games Played

INN - Innings Played

OFA - Outfield Assists

PB - Passed Balls

PO - Putouts

RF - Range Factor

SB - Stolen Bases (allowed)

TC - Total Chances

TP - Triple Plays


AO - Fly Outs

APP - Appearances

AVG - Opponents’ Batting Average

BB - Bases on Balls (Walks)

BB/9 - Walks per Nine Innings

BF - Batters Faced

BK - Balks

BS - Blown Save

CG - Complete Games

CGL - Complete Game Losses

CS - Caught Stealing

ER - Earned Runs

ERA - Earned Run Average

G - Games Played

GF - Games Finished

GIDP - Grounded Into Double Plays

GO - Ground Outs

GO/AO - Ground Outs/ Fly Outs Ratio

GS - Games Started

GSH - Grand Slams

H - Hits

H/9 - Hits per Nine Innings

HB - Hit Batsmen

HLD - Hold

HR - Home Runs

I/GS - Innings Per Games Started

IBB - Intentional Walks

IP - Innings Pitched

IRA - Inherited Runs Allowed

K/9 - Strikeouts per Nine Innings

K/BB - Strikeout/Walk Ratio

L - Losses

LIPS - Late Inning Pressure Situations

LOB - Left on Base

MB/9 - Baserunners per 9 Innings

NP - Number of Pitches Thrown

OBA - On-base Against

PA - Plate Appearances

P/GS - Pitches per Start

P/IP - Pitches per Innings Pitched

PK - Pick-offs

R - Runs

RW - Relief Wins

SB - Stolen Bases

SHO - Shutouts

SLG - Slugging Percentage Allowed

SO - Strikeouts

SV - Saves

SVO - Save Opportunities

TB - Total Bases

TP - Triple Plays

UR - Unearned Runs

W - Wins

WHIP - Walks + Hits/Innings Pitched

WP - Wild Pitches

WPCT - Winning Percentage

XBA - Extra Base Hits Allowed