A Sabermetric Primer

It's fair to suggest that none of what follows matters much in fantasy baseball, at least not directly, but I believe that being exposed to new ways of analyzing player performance is never a bad thing. Get your spectacles and abacus ready as we explore the world of advanced metrics. 

SABERMETRICS DEFINED

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 sabermatrician, of them all, Bill James).

HITTING  

BASE OUT PERCENTAGE (created by Barry F. Codell)   
BOP = Bases / Outs 

Base Out Percentage takes into account 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? 

BASES PER PLATE APPEARANCE, or BPA
(TB+BB+HBP+SB-CS-GIDP) / (AB+BB+HBP+SF) 

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 turned to usage of at-bats. 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. 

BATTING AVERAGE ON BALLS IN PLAY, or BABIP 
(H-HR / TBF-HR-HBP-K-BB)

BABIP, also referred to as a player's hit rate, is the rate at which batted balls end up as base hits. There is one 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 but players establish their own levels so that some hitters consistently come in at the .270 range while others seem to record marks in the .330's etc. The league leaders are usually above .380, a level that is nearly impossible to repeat year-to-year.

BATTING RUNS (created by Pete Palmer) 
[1B(.47)+2B(.78)+3B(1.09)+HR(1.40)+BB(.33)+HBP(.33)+SB(.22)-CS(.45)-[.25(AB-Hits)]

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 – see below, and most have also hear of WAR by this point, another such attempt). 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.”

NET SB
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 (see below). 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. See the article on this measure in the Guide.

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 Adrian Beltre), he won't score well at all in PSN. Conversely, if the player is all about speed but has little pop (i.e. Ben Revere), 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)
A: H+BB+HBP-CS-GIDP
B: ((BB-IBB+HBP)*.24)+(SB*.62)+((SH+SF)*.5)+TB-(SO*.03)
C: AB+BB+HBP+SF+SH

RUNS CREATED PER GAME, or RC/27 (created by Bill James)
[(RC*25.5)/(AB-H+CS+GIDP)]

An estimate of how many runs would be scored by a team made up of nine of the same hitter. Runs Created per Game, or RC/27, allows us to see value in a player who received less than a full season's worth of work but still performed admirably while on the field. The number 25.5 is used 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.  

SECONDARY AVERAGE
 [(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, 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.

TROUBLES
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 HR & RBI for power guys, and steals for speed guys, but what about the hitters in the middle who fall through the cracks?  

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. 

WINS ABOVE REPLACEMENT, or WAR

Everyone seems to know of WAR at this point. No one knows how to figure it out. 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.  

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?).

BASIC WIN SHARES FORMULA 
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.

PITCHING 

ADJUSTED ERA, or AERA
(League ERA / Personal ERA)*Park Factor

This is ERA+ (see below) adjusted for the pitcher’s Park Effects (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.  

ADJUSTED PITCHING RUNS, or APR 
(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 pitchers 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 Guide.

CATCHER’s ERA, or CERA
(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.  

COMPONENT ERA, or ERC

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.  

A) 
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

B)
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 pitchers 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 pitchers 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.

Numerator 
(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
683.745

Denominator 
(225*.712) + (165*.244) + (251*.096) – (18*.244)
160.2 + 40.26 + 24.096 – 4.392
224.556 - 4.392
220.164

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).

EARNED RUN AVERAGE PLUS, or ERA+
[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.  

FIELDING INDEPENDENT PITCHING, or FIP
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 takes into account 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 pitchers 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, 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 one another. 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 it actually was. 

QUALITY STARTS, or QS

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). However, like many other measures, it 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.

RUNS AGAINST AVERAGE, or RAA
(Runs*9)/IP

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).

SKILL INTERACTIVE ERA, or SIERA
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 pitchers 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. 

SWIP (created by Ray Flowers)
(K-BB/IP)

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.

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 teams 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.

MISCELLANEOUS METRICS

FIELDING AVERAGE
(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

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 because they will always be balanced out by the opposing team’s performance. 

PYTHAGOREAN EXPECTATION, or PYTHAGOREAN THEORY (created by Bill James)
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.

RANGE FACTOR
(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.

ZONE RATING

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.  

Finally, just a review of some of the basics...