We here at The Athletic Philadelphia are thrilled to have our baseball coverage firing on all cylinders with spring training under way. Our staff is dedicated to providing you with the highest quality Phillies coverage available. One thing we will not shy away from is analytic content. In the 21st century baseball landscape — where top-down organizations and you, the fans, are more well-informed than ever before — it’s important for us to understand and embrace the sabermetric advancements impacting the game. Given both the Phillies front office and new coaching staff’s predilection for analytics, this will be even more crucial in covering the franchise this season and in the future.

The stories and human aspects of baseball are still as vital as ever. In addition to compelling reporting and insight, we aim to set our coverage apart with a keen eye toward analytics and an ability to mesh those numbers with stories that get to the heart of the game. In the current climate, many of baseball’s most interesting stories are found at the intersection of the game’s humanity and its vast statistical troves. Many fans no longer want to just know that a pitcher is pitching well, they want to know why he’s pitching well, and whether that success is sustainable, in addition to hearing the pitcher, coach and general manager’s opinions on the matter. These fans want to consume the game in a more in-depth and knowledgeable fashion.

Which brings me to the topic of this piece. The analytic baseball community continues to grow, as do the tools the community relies on. Here, we want to lay out many of the topics and statistics driving the analytic community that we will reference in stories. We won’t tailor our coverage only to those well-versed in sabermetrics and ostracize those who aren’t, but if you’re interested in these concepts, we want to help you understand them and grow your knowledge of the game. We’re not here to tell you that you’re dumb if you’re not a stathead; we’re here to provide you with information that will allow you to fully immerse yourself in and better appreciate some of our stories. If you have questions or feedback after reading, please let me know in the comments, where we can continue the discussion.

Without further ado …

Wins Above Replacement

Likely the most widely used sabermetric concept, Wins Above Replacement (WAR) is a metric that attempts to distill a player’s contributions into a single number. This number compares a player’s actions to the expected contributions of an easily available replacement-level player — think your typical minor-league free agent or quad-A player. While the basis of the stat is wins added compared to that of a replacement-level player, its calculation centers around the number of runs a player adds offensively and the number of runs he saves his team either defensively or on the mound. Since the universal goal of the game is to win games, these run totals are converted into wins. Every 10 runs added or saved equals 1.0 WAR — for example, 20 runs is 2.0 WAR.

As in life, it’s difficult to produce significant value without putting in the time necessary. Unlike, say, batting average, one solid game doesn’t produce a perfect WAR — 4-for-4 is a 1.000 average, but one incredible game of baseball won’t produce a high-caliber WAR. It is a stat that accumulates contributions over time. The longer the sustained contributions, the higher the WAR.

Different outlets use different stats and methods to calculate their versions of WAR, putting slightly different emphases on various aspects of the game. For example, Baseball Prospectus’ calculations include added value to catchers who frame pitches well and steal strikes. Fangraph’s WAR bases the pitching portion of their metric on Fielding Independent Pitching (FIP), a metric (explained later in this piece) that attempts to strip a team’s defensive contributions out of a pitcher’s stats.

The three main types of WAR are from Baseball Reference (bWAR or rWAR), Fangraphs (fWAR) and Baseball Prospectus’ Wins Above Replacement Player (WARP). They also include positional adjustments, because, for example, playing shortstop is more difficult than playing first base and more difficult positions should be rewarded in a metric that compares players of all positions.

As provided by Fangraphs, below is a rough WAR scale that tracks pretty well across the different calculations with an example of a player in each range from the 2017 season (by Fangraphs’ numbers):

<0 WAR: Below replacement level (Kendrys Morales)

0-1 WAR: Scrub (Tim Anderson)

1-2 WAR: Role player (Trevor Story)

2-3 WAR: Solid starter (Jackie Bradley Jr.)

3-4 WAR: Good player (Andrew McCutchen)

4-5 WAR: All-Star (George Springer)

5-6 WAR: Superstar (Nolan Arenado)

6+ WAR: MVP (Giancarlo Stanton)

wOBA: Weighted On-Base Average

Weighted on-base average is a catch-all offensive metric that addresses deficiencies in other offensive stats. While we will often use slashline (Batting average/On-base percentage/Slugging percentage), wOBA provides one number that is easier on the eyes and saves time. Instead of saying Rhys Hoskins hit .259/.396/.618 in 2017, we can say he had a .417 wOBA. In terms of scale, wOBA is formatted to a similar scale as on-base percentage. Last year, league average OBP was .324, while league average wOBA was .321.

wOBA separates itself from slashline metrics or OPS (On-base plus slugging), for example, by assigning unique values to different methods of reaching base. This is intuitive, and a major reason some metrics oversimplify things — homers are more valuable than triples, which are more valuable than doubles, etc. It also credits players for non-intentional walks. A hitter who is 3-for-10 with three home runs is hitting .300, because batting average treats all hits as equal. However, that same hitter holds a massive .585 wOBA.

wRC+: Weighted Runs Created Plus

Like wOBA, this is a highly useful all-in-one offensive metric. What makes wRC+ even more useful is its scale. wRC+, and all other statistics that end with a +, are scaled around a league average of 100, with every point above or below 100 corresponding to a percentage above or below league average. Here are a few examples of guys close to each of the following wRC+ benchmarks in 2017:

180: 80 percent above average (Mike Trout)

160: 60 percent above average (Jose Altuve)

140: 40 percent above average (Paul Goldschmidt)

120: 20 percent above average (Carlos Santana)

100: League average (Ender Inciarte)

90: 10 percent below average (Jordy Mercer)

80: 20 percent below average (Freddy Galvis)



The metric also accounts for park factors to even the playing field, as some stadiums are more pitcher or hitter friendly than others. However, it does not apply positional adjustments like WAR.

OPS+: On-Base Plus Slugging Plus

If you’re familiar with OPS, and just read the above paragraph explaining wRC+, then OPS+ should be simple. OPS is the sum of a player’s on-base and slugging percentages, scaled so that league average is 100. While OPS isn’t a perfect stat because it treats OBP and SLG as equals (they’re not), it’s not wildly inaccurate.

The main shortcoming with OPS is it adds together those two stats, OBP and SLG, which have two different denominators. Instead of adjusting for that difference, the two numbers are just mashed together. Furthermore, research by Tom Tango has shown that OBP is nearly two times more important to run production than SLG.

BB% and K%

A focal point of a large portion of sabermetric analysis is an aversion to traditional counting stats (think RBIs or hits) in favor of rate-based stats (like OBP) or in this case, walk rate (BB%) and strikeout rate (K%). Counting stats, like simple walk and strikeout totals, can be misleading because they strip away vital context. For instance: Last year, Rich Hill struck out 166 batters, while Marcus Stroman struck out 164. While their strikeout totals are about identical, Hill faced 552 hitters and struck out more than one per inning. Stroman, on the other hand, faced over 800 hitters and struck out fewer than one per inning.

By dividing a player’s strikeout total by his total batter’s faced (or plate appearances, if he’s a hitter), we find K%. Hill’s strikeout rate of 30.1% was about 50 percent above league average, while Stroman’s 19.7 K% was a tick below average, despite their strikeout totals being virtually identical. It’s not as important to know how many guys a pitcher strikes out as it is to know how likely he is to strike someone out.

BABIP: Batting Average on Balls In Play

A staple Sabermetric concept, BABIP measures how often a ball put in play falls for a hit (excluding strikeouts, walks and homers because they are not technically hit into play and affected by the defense). The stat is typically used to parse out the luck involved when a pitcher or hitter is hitting/allowing a ball in play. Drastic BABIP outliers typically regress to the mean. While it is often a good indicator of luck, it is not authoritative. Many things can impact a hitter or pitcher’s BABIP. For example, hitters with elite speed are more likely to leg out groundballs than the average hitter, and their BABIP should benefit from that. If a pitcher is surrounded by a dismal defense or routinely allows hard contact, his fielders are less likely to get to the spot where the ball will land, thus increasing the pitcher’s BABIP.

ISO: Isolated Power

Isolated power is a different measure of a player’s slugging ability that differentiates it from slugging percentage in one major way. ISO isolates the extra-base hits that make up slugging percentage. While slugging percentage is a measure of total bases (coming from hits) per at-bat, ISO looks at extra bases per at-bat. The calculation is simply SLG-AVG.

For example, a player who is 6-for-10 with six singles holds a .600 slugging percentage. That’s enormous, and would suggest the player is a premier power hitter. What ISO does is strip singles from the equation. That same player, who is 6-for-10 with six singles, has a .000 ISO, because Isolated Power deals with extra bases only. While this surely is an extreme example, it demonstrates ISO’s usefulness in separating the signal from the noise when trying to identify players with good raw power.

FIP: Fielding Independent Pitching

While ERA has long been the mainstream king of pitching metrics, FIP was created to capture what ERA largely ignores. FIP looks just at a pitcher’s strikeout, walk and allowed home run rates, three main areas that the pitcher has the most control over. Going back to the BABIP explainer above, you’ll remember that defense can greatly impact a pitcher’s run prevention. Historically, pitchers BABIP allowed often fluctuates from year to year, pointing to the luck and randomness of balls in play turning into hits.

FIP aims to decipher a pitcher’s value based on those things he has most control over, hence the Fielding Independent moniker, while negating the effects of the defense behind him. It is used on an ERA scale, so anything you’d typically think is a good ERA is a good FIP.

Fangraphs’ Pitch Values

In an effort to identify successful or struggling pitches in one’s repertoire, Fangraphs has numbers that assign values to each pitcher’s individual offerings. They are also available to see how a hitter fared against a certain type of pitch across all plate appearances.

Every pitch changes the count, and every ball in play alters the game state. Each of these alters what we call the Run Expectancy. In essence, decades of game data have determined the expected increases/decreases in runs scoring as the count, number of outs and number of baserunners change. Pitch Values add up all the changes in run expectancy from before a pitch is thrown to after, creating a cumulative value for a certain pitch. For a pitcher, a pitch that routinely leaves the offense in a worse position to score will accumulate a higher value than one that is often hit hard or thrown outside the zone.

Plate Discipline

Fangraphs has phenomenal plate discipline metrics that are highly useful for learning what is actually happening in batter-pitcher matchups. When used for batters, the metrics detail when they swing and how often they make contact. For pitchers, they detail the swings their opponents take. Here are important concepts.

Swing%: Percentage of swings at all pitches

O-Swing%: Percentage of swings on pitches outside the zone

Z-Swing%: Percentage of swings on pitches inside the zone

Contact%: Percent of swings resulting in fair contact

O-Contact%: Percentage of pitches swung at outside the zone that result in fair contact

Z-Contact%: Percentage of pitches swung at inside the zone that result in fair contact

Zone%: Percent of pitches in the strike zone

F-Strike%: Percentage of first pitches that are strikes

SwStr%: Percent of pitches resulting in swings and misses (different from whiff rate, which calculates the percentage of swings in which a batter whiffs)

Many of these are highly complex stats. For the uninitiated who were previously unfamiliar with these metrics, these definitions will suffice for now. If this whet your appetite and you are interested in digging deeper, there is plenty more to learn about all of these statistics. Fangraphs has phenomenal, in-depth descriptions on these and many more metrics.

For the majority of the stats above, the working definitions provided will give you what you need to understand their general use and importance. For the more complex statistics (like WAR, BABIP, or FIP), which have hidden calculations under the hood or greater implications on broader sabermetric concepts like luck, there is always more research to be done and discussions to be had.

Top photo: Jonathan Dyer/USA TODAY Sports