Glossary
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Batted Ball Statistics are fairly straightforward: they express the share of a pitcher’s balls in play that are line drives, ground balls, or fly balls. This includes balls that leave the park (home runs), so the sum of a pitcher’s batted ball statistics should be 100%. Major league pitchers feature a variety of pitches and approaches, resulting in different batted ball profiles. Some pitchers allow lots of fly balls, others allow lots of balls on the ground, and many others fall somewhere in between.
Calculation:
The statistics published on MatchupCenter are drawn from data from Baseball Info Solutions (BIS) and reflect the share of a pitcher’s total balls in play that are of a certain type, classified as line drives, fly balls, and ground balls. Fly balls are also divided up between infield fly balls and total fly balls. To wit, the following are the formulas to calculate the percentages you can find on the site:
Line Drive Percentage (LD%) = Line Drives / Balls in Play
Fly Ball Percentage (FB%) = Fly Balls / Balls in Play
Ground Ball Percentage (GB%) = Ground Balls / Balls in Play
Infield Fly Ball Percentage (IFFB%) = Infield Fly Balls / Fly Balls
Our batted ball data goes back to 2002, but it’s important to remember that there is no perfect way to define each type of batted ball so some balls that you might consider a fly balls might get classified as line drives and vice versa. In reality, batted balls exist on a continuous distribution from rolling perfectly on the ground to being launched straight up in the air. The cut points between the three classifications are somewhat arbitrary and imprecise, so do not treat the data as infallible.
Why Batted Ball Stats:
Batted ball stats are extremely useful for determining the type of pitcher at which you’re looking. There is no ideal batted ball distribution, but pitchers who allow a lot of line drives typically perform worse than pitchers who allow lots of fly balls or ground balls. Generally speaking, line drives go for hits most often, ground balls go for hits more often than fly balls, and fly balls are more productive than ground balls when they do go for hits (i.e. extra base hits). Additionally, infield fly balls are essentially strikeouts and almost never result in hits or runner advancement. Here are the numbers from 2014:
Type | AVG | ISO | wOBA |
GB | .239 | .020 | .220 |
LD | .685 | .190 | .684 |
FB | .207 | .378 | .335 |
We use these stats to tell us two things. First, we want to get a sense of a pitcher’s style of play. A ground ball pitcher is usually someone who doesn’t allow a lot of extra base hits, but gives up a fair share of singles and features a two-seam fastball or sinker quite often. A fly ball pitcher will often give up fewer hits, but that might include more extra base pop, including home runs. These pitchers tend to rely more on four-seam fastballs up in the zone.
Additionally, batted ball data can tell us something about a pitcher’s underlying performance. We often look at a pitcher’s Batting Average on Balls in Play (BABIP) to make determinations about the sustainability of their performance and batted ball data informs that analysis in an important way. If a pitcher allows a lot of line drives, a high BABIP is more likely a function of his true talent than a pitcher who allows a lot of fly balls, who has probably just been unlucky in running that higher BABIP. Generally, ground ball pitchers will run higher BABIPs than average and fly ball pitchers will run lower BABIPs than average.
We know that pitchers do not have complete control, or even much control at all, over what happens to a baseball once it’s put in play, but they do have some control over the type of batted ball they allow. If you allow ten ground balls, you can’t control if zero, three, or nine go for hits, but you did control the fact that none are leaving the park. On the other hand, fly ball pitchers can usually limit the number of hits they allow, but that also makes them more vulnerable to home runs.
Batted ball numbers allow us to dig deeper into the kind of pitcher a guy is and how responsible he is for the outcomes of balls in play. No pitcher is completely responsible for their BABIP, but we can get an impression of how legitimate the current number is by looking at their batted ball profile.
How to Use Batted Ball Stats:
Batted ball statistics, like most statistics, should be used with caution for three key reasons. First, sample size is very important for line drive rate, and while you can get a good sense of fly ball and ground ball rate with a month or two of data, it takes more like a year and a half for line drive rate to “stabilize.” All this means is that six weeks of batted ball data shouldn’t change your opinion of a player’s talent level with respect to hard contact. Generally, we care more about grounders and fly balls for pitchers than line drives because we’re interested in the type of pitcher they seem to be. That stabilizes more quickly, but can still fool you in small samples.
Second, batted ball classification is tricky. What’s the difference between a fly ball and a line drive? At what angle does one become the other? While BIS has a great team scouting each major league game, video data only offers only a certain level of detail. Even the most diligent stringer can’t get it right 100% of the time because they just don’t always have the proper angle to distinguish between a fly ball and line drive. When StatCast becomes fully operational, this problem should disappear because we will be able to use a simple numeric cut point.
Finally, and most importantly, not all line drives/fly balls/ground balls are created equally. A pulled fly ball traveling at 105 mph to deep left field and one that lands harmlessly in the glove of the right fielder are extremely different. A screaming line drive up the gap and one that’s easily caught by the shortstop are different. This is essentially another example of the data being a continuous (in launch angle, direction, and velocity) but presented as discrete data. A ball isn’t a fly ball or a line drive, it is hit at X launch angle, Y degrees from center, at Z velocity. Essentially, use batted ball stats as a guide, not an anchor.
Our categorization is helpful, but it is far from perfect. For example, in 2014, Brandon Crawford and Anthony Rizzo had very similar batted ball statistics, but Rizzo was clearly the better hitter overall as the quality of his contact within those categories was much better than Crawford’s. This is true for pitchers as well. There are pitchers that allow weaker contact than others even if they have the same batted ball profile.
There are a few other interesting side effects to pitchers have extreme batted ball profiles. This is taken from the SIERA page, as SIERA uses batted ball data in its formula:
In general, ground balls go for hits more often than fly balls (although they don’t result in extra base hits as often). But the higher a pitcher’s ground ball rate, the easier it is for their defense to turn those ground balls into outs. In other words, a pitcher with a 55% ground ball rate will have a lower BABIP on grounders than a pitcher with a 45% ground ball rate. And if a pitcher walks a large number of batters and also has a high ground ball rate, their double-play rate will be higher as well.
As for fly balls, pitchers with a high fly ball rate will have a lower Home Run Per Fly Ball rate than other pitchers.
This serves as an important reminder that while we can get a general idea about a pitcher from their batted ball profile, a fly ball against Clayton Kershaw isn’t the same thing as one against Joe Blanton in most cases.
Context:
Please note that the following chart is meant as an estimate, and that league-average batted ball rates varies slightly on a year-by-year basis.
Type | League Average |
LD | 21% |
GB | 44% |
FB | 35% |
IFFB | 11% |
“Ground ball pitchers” generally have grounder rates over 50%, while “fly ball pitchers” have fly ball rates above (or approaching) 40%.
Things to Remember:
● Line drives are death to pitchers, while ground balls are the best for a pitcher. In numerical terms, line drives produce 1.26 runs/out, fly balls produce 0.13 R/O, and ground balls produce only 0.05 R/O.
● This data is tracked by Baseball Info. Solutions (BIS), which is why it’s only available for players back until 2002.
● A line drive produces 1.26 runs per out, while fly balls produce 0.13 runs per out and ground balls produce 0.05 runs per out. In other words, batters want to hit lots of line drives and fly balls, while pitchers generally want to cause batters to hit ground balls.
● Players that don’t allow many balls in the air (higher GB% with lower FB% and LD%) generally have higher BABIPs and batting averages against, but allow fewer extra base hits.
Weighted Runs Created (wRC) is an improved version of Bill James’ Runs Created (RC) statistic, which attempted to quantify a player’s total offensive value and measure it by runs. In Runs Created, instead of looking at a player’s line and listing out all the details (e.g. 23 2B, 15 HR, 55 BB, 110 K, 19 SB, 5 CS), the information is synthesized into one metric in order to say, “Player X was worth 24 runs to his team last year.”
wRC+ is park and league-adjusted, allowing one to to compare players who played in different years, parks, and leagues. Want to know how Ted Williams compares with Albert Pujols in terms of offensive abilities? This is your statistic. wRC+ is the most comprehensive rate statistic used to measure hitting performance because it takes into account the varying weights of each offensive action and then adjusts them for the park and league context in which they took place.
Calculation:
The formula for wRC is:
wRC = (((wOBA-League wOBA)/wOBA Scale)+(League R/PA))*PA
To calculate a player’s wRC, find their wOBA on their player page, in the leaderboards, or calculate it yourself and then plug it into this equation with the necessary weights and number of plate appearances. For example in 2013, Miguel Cabrera had a .455 wOBA in 652 PA. Using the weights from 2013 we arrive at the following:
(((.455-.314)/1.277)+.11)*652 = 143.7
In order to park and league adjust wRC, it takes a few more steps, but it’s nothing you can’t do on your own with basic calculator or Excel spreadsheet. You may notice that there are shortcuts to arriving at some of the numbers below depending on what statistics you already have in front of you, but we’ve provided full details if you’re looking for a very thorough breakdown.
wRC+ = (((wRAA/PA + League R/PA) + (League R/PA – Park Factor* League R/PA))/ (AL or NL wRC/PA excluding pitchers))*100
The best way to explain how this works is to walk through each of the steps, starting from left to right. First we have wRAA/PA, which measures the number of runs above average a player contributes to his team at the plate per plate appearance. Another way to arrive at wRAA/PA is to simply take a player’s wOBA minus the League wOBA and divide it by the wOBA Scale.
After that we have the park adjustment, which we arrive at using the additive method.
We’ll use 2012 Mike Trout as an example.
((((48.2/639) + 0.114) + (.114-(0.95*.114)))/(10032/85797))*100 = 167
If you attempt these calculations by hand, you will occasionally wind up with a value that is one point off due to where we choose to round decimals places, but otherwise this equation will allow you to match our wRC+ calculations exactly.
Why wRC and wRC+:
If you’ve looking to measure a batter’s value using a cumulative statistic that credits a player for total production rather than on an at bat by at bat basis, then wRC is extremely useful. It combines the virtues of a weighted statistic like wOBA, which credits a hitter for how valuable each particular action truly is, with the virtues of counting stats that give players credit for producing at a given level over a great number of plate appearances. wRC isn’t necessarily better or worse than wRAA, it’s simply the same statistic communicated differently. Both provide you with a measure of how many runs a player contributed to his team with their bat.
If you want a rate statistic for hitters that weights each offensive action and controls for league and park effects, wRC+ is for you. While wOBA is a huge step forward from stats like batting average and slugging percentage, it doesn’t credit hitters who play in difficult parks or deduct points for hitters who play in smaller ones. wRC+ brings all the virtues of wOBA plus two added benefits; park and league adjustments. A .400 wOBA at Coors is much less impressive than one at Petco, for example. Additionally, wOBA tracks with overall league offense, so you can’t use it to compare players of different eras very effectively. A .400 wOBA in 2000 is much less impressive than one in 2014, but a 140 wRC+ in 2000 means essentially the same thing in 2014.
How To Use wRC and wRC+:
Both wRC and wRC+ are easy to use once you learn their scales. Since wRC is a counting stat, you want to be very aware of the number of plate appearances the batter in question currently has. A player with 10 wRC in 50 PA is very good, but a player with 10 wRC in 200 PA is very bad, just like 50 RBI in 100 PA would be considering excellent and 50 RBI in 700 PA would be considered poor. wRC is a measure of raw production and should be used as such, but remember it is not park, league, or position adjusted.
Using wRC+ is even easier because league average for position players is always 100. If a player has a 110 wRC+, you know they are ten percentage points better than league average offensively. This is a great tool for comparing the at bat by at bat offensive performance of any two players in the league. However, you should note that wRC+ does not control for position.
Context:
Please note that the following chart is meant as an estimate, and that league-average wRC will vary from year to year. But as a general breakdown, this distribution works fine with wRC listed per 600 plate appearances. League average wRC+ will always be 100.
Ratings | wRC | wRC+ |
Excellent | 105 | 160 |
Great | 90 | 140 |
Above Average | 75 | 115 |
Average | 65 | 100 |
Below Average | 60 | 80 |
Poor | 50 | 75 |
Awful | 40 | 60 |
Things to Remember:
● Both wRC and wRC+ are context neutral, meaning that a hit with men on base and a hit with no one on are weighed equally and the score of the game or inning in which the event occurred does not matter.
Wins Above Replacement (WAR) is an attempt by the sabermetric baseball community to summarize a player’s total contributions to their team in one statistic. You should always use more than one metric at a time when evaluating players, but WAR is all-inclusive and provides a useful reference point for comparing players. WAR offers an estimate to answer the question, “If this player got injured and their team had to replace them with a freely available minor leaguer or a AAAA player from their bench, how much value would the team be losing?” This value is expressed in a wins format, so we could say that Player X is worth +6.3 wins to their team while Player Y is only worth +3.5 wins, which means it is highly likely that Player X has been more valuable than Player Y.
WAR is not meant to be a perfectly precise indicator of a player’s contribution, but rather an estimate of their value to date. Given the imperfections of some of the available data and the assumptions made to calculate other components, WAR works best as an approximation. A 6 WAR player might be worth between 5.0 and 7.0 WAR, but it is pretty safe to say they are at least an All-Star level player and potentially an MVP.
While WAR is not as complicated as some might think, it does require a good bit of information to calculate and understand. Below you can find general information about WAR and links to specific information about position players and pitchers, as WAR is obviously calculated differently for each.
Calculation:
● Position players – To calculate WAR for position players you want to take their Batting Runs, Base Running Runs, and Fielding Runs above average and then add in a positional adjustment, a small adjustment for their league, and then add in replacement runs so that we are comparing their performance to replacement level rather than the average player. After that, you simply take that sum and divide it by the runs per win value of that season to find WAR. The simple equation looks something like this:
WAR = (Batting Runs + Base Running Runs +Fielding Runs + Positional Adjustment + League Adjustment +Replacement Runs) / (Runs Per Win)
● Pitchers – While position player WAR is based on Batting Runs and Fielding Runs, pitching WAR uses FIP (with infield fly balls), adjusted for park, and scaled to how many innings the pitcher threw. FIP is translated into runs, converted to represent value above replacement level, and is then converted from runs to wins. This is a slightly more complicated process than for position players, so you should click over to the pitcher WAR page if you want the details.
Why WAR:
WAR is trying to answer the time-honored question: How valuable is each player to his team? Baseball is the sum of many different parts and players can help their teams win through hitting, base running, defensive play, or pitching. Comparing two players offensively is useful, but it discounts the potential contribution a player can make by saving runs on defense. WAR is a simple attempt to combine a player’s total contribution into a single value.
The goal of WAR is to provide a holistic metric of player value that allows for comparisons across team, league, year, and era and a framework for player evaluation. While there will likely be improvements to the process by which we calculate the inputs of WAR, the basic idea is something fans and analysts have desired for decades. WAR estimates a player’s total value and allows us to make comparisons among players with vastly different skill sets. Who is better, a slugging first baseman or a superlative defensive shortstop? WAR gives you a method for answering that question.
How to Use WAR:
Perhaps one of the most controversial aspects of sabermetrics is the way in which WAR is used. Given the nature of the calculation and potential measurement errors, WAR should be used as a guide for separating groups of players and not as a precise estimate. For example, a player that has been worth 6.4 WAR and a player that has been worth 6.1 WAR over the course of a season cannot be distinguished from one another using WAR. It is simply too close for this particular tool to tell them apart. WAR can tell you that these two players are likely about equal in value, but you need to dig deeper to separate them.
However, a 6.4 WAR player and a 4.1 WAR player are different enough that you can have a high level of confidence that the first player has been more valuable to their team over the given season.
For position players, the largest point of contention comes in measuring defense and estimating the positional adjustment. Our measures of both are more uncertain than our measures of offense, so players who get a good amount of their value through their defensive ratings likely have more uncertainty around their WAR value than players who have defensive value closer to average. This does not mean that WAR is wrong or biased, but rather that it is not yet capable of perfect accuracy and should be used as such.
For pitchers, the biggest open question is how much credit a pitcher should receive for the result of a ball in play. At MatchupCenter, we use FIP which assumes average results on batted balls. We know that there is some skill involved in suppressing hits on balls in play, but we have no idea exactly how much. Therefore, WAR will sell short players with certain FIP-beating skills and oversell those pitchers whose results fall short of their FIP for reasons within their control. At this point, we don’t have a good way of assigning credit more accurately for balls in play.
However, we also house RA9-WAR, which is WAR based on runs allowed instead of FIP. This allows you to use one to inform the other however you like.
Context:
League-average WAR rates vary. An average full-time position player is worth about 2 WAR, while average bench players contribute much less (typically between 0 and 1 WAR). Average starting pitchers also are worth around 2 WAR, while relief pitchers are considered superb if they crack +1 WAR.
For position players and starting pitchers, here is a good rule-of-thumb chart:
Scrub | 0-1 WAR |
Role Player | 1-2 WAR |
Solid Starter | 2-3 WAR |
Good Player | 3-4 WAR |
All-Star | 4-5 WAR |
Superstar | 5-6 WAR |
MVP | 6+ WAR |
On-base Plus Slugging (OPS) is exactly what it sounds like: the sum of a player’s on-base percentage and their slugging percentage. Many sabermetricians don’t like OPS because it treats OBP as equal in value with SLG, while OBP is roughly twice as important as SLG in terms of its effect on run scoring (x1.8 to be exact). However, OPS has value as a metric because it is accepted and used more widely than other, more accurate statistics while also being a relatively accurate representations of offense. You can find OPS on baseball cards and in broadcasts, and it’s a simple statistic that has made its way into the main stream,
On-base Plus Slugging Plus (OPS+) has not gained as much widespread acceptance, but is a more informative metric than OPS. This statistic normalizes a player’s OPS — it adjusts for small variables that might affect OPS scores (e.g. park effects) and puts the statistic on an easy-to-understand scale. A 100 OPS+ is league average, and each point up or down is one percentage point above or below league average. In other words, if a player had a 90 OPS+ last season, that means their OPS was 10% below league average. Since OPS+ adjusts for league and park effects, it’s possible to use OPS+ to compare players from different years and on different teams.
Calculation:
OPS’ name tells you how to calculate it. On-base plus slugging:
OPS = OBP + SLG
If you’re looking for a more technical understanding, here is how you calculate OBP and SLG individually:
OBP = (H + BB + HBP) / (AB + BB + HBP + SF)
SLG = (1B + 2*2B + 3*3B + 4*HR) / AB
We don’t house OPS+ on the site, but the calculation can be found at Baseball-Reference.
Why OPS:
In general, OPS is better than something like batting average or RBI because it captures a player’s ability to get on base and their ability to hit for extra bases. For the most part, those two factors capture more of what hitters are trying to do. Generally speaking, if you sort hitters by OPS, you are sorting them based on their production to date with some minor exceptions.
The problem with OPS is that one point of OBP and one point of SLG are not equal. OBP is about twice as valuable as SLG, meaning that OPS overrates power hitters and underrates high-OBP guys. It’s a rather mild issue overall, but we have a better statistic, wOBA, which does not do that. OPS has the benefit of being very easy to calculate in a pinch, and it is more widely understood, but there is really no reason to choose OPS over wOBA if you have the choice.
How to Use OPS:
OPS is used to determine how well a hitter has performed over a given period of time. It is not as precise as wOBA or wRC+, but it is a reasonably good estimator if it’s the only thing available. The key to using OPS is understanding the current offensive climate. A .900 OPS is much more impressive in 2015 than it was in 2000. These days, league average is about .710 or so, with the best hitters around 1.000.
In general, OPS needs a decent sample size to be reflective of true talent, as it is very easy for a good hitter to have a rough couple of weeks and produce a .500 OPS over 50 PA, even though they are one of the best hitters on their team. When using OPS, make sure you understand the context and sample size involved.
Context:
Please note that the following chart is meant as an estimate, and that league-average OPS varies on a year-by-year basis.
Rating | OPS |
Excellent | 1.000 |
Great | 0.900 |
Above Average | 0.800 |
Average | 0.710 |
Below Average | 0.670 |
Poor | 0.600 |
Awful | 0.570 |
Things to Remember:
● If you’re looking to evaluate a player’s offense, OPS is a better metric to use than batting average, but should always be used in conjunction with other statistics as well. It’s a good gateway statistic to get people thinking beyond the traditional statistics.
● Since it provides context and adjusts for park and league effects, OPS+ is better to use than straight OPS, especially if you’re comparing statistics between seasons.
Weighted Runs Above Average (wRAA) measures the number of offensive runs a player contributes to their team compared to the average player. How much offensive value did Evan Longoria contribute to his team in 2009? With wRAA, we can answer that question: 28.3 runs above average. A wRAA of zero is league-average, so a positive wRAA value denotes above-average performance and a negative wRAA denotes below-average performance. This is also a counting statistic (like RBIs), so players accrue more (or fewer) runs as they play.
Calculating wRAA is simple if you have a player’s wOBA value: subtract the league average wOBA from your player’s wOBA, divide by the wOBA scale coefficient (1.26 for 2011), and multiply that result by how many plate appearances the player received.
wRAA = ((wOBA – league wOBA) / wOBA scale) × PA
Context:
Please note that the following chart is meant as an estimate. No matter the year, this statistic will always have 0 wRAA as league-average.
Rating | wRAA |
Excellent | 40 |
Great | 20 |
Above Average | 10 |
Average | 0 |
Below Average | -5 |
Poor | -10 |
Awful | -20 |
Things to Remember:
● wRAA is league adjusted, meaning you can use it to compare players from different leagues and years.
Quality of Contact Stats (Soft%, Med%, and Hard%) represent the percentage of a hitter or pitcher’s batted balls that have been hit with a certain amount of authority. The percentages will sum to 100%, totaling all of a player’s batted balls hit or allowed. While a lot of statistics are based on the outcome of the play (i.e. hit or not), quality of contact stats are more like pitch velocity in that they define a process that occurred en route to an outcome.
Soft%, Med%, and Hard% are based on data from Baseball Info Solutions (BIS) which attempt to capture how well each baseball was hit. The data goes back to 2002, but the methodology for calculating the stats changed in 2010. Quality of contact doesn’t perfectly correlate with success on the field, but in general, hitting the ball hard or allowing weak contact is better than the alternative.
Calculation:
For the early years of quality of contact stats, the BIS video scouts had to make judgments, but since 2010, the video scouts recorded the amount of time the ball was in the air, the landing spot, and the type of batted ball (fly ball, ground ball, liner, etc) and the BIS algorithm determines if the ball was soft, medium, or hard hit.
Unfortunately, the exact algorithm (the exact cut points/methodology) are proprietary to BIS and we can’t share exactly what constitutes hard contact, but the calculation is made based on hang time, location, and general trajectory. It’s not perfectly analogous to exit velocity, but until we have more complete StatCast data, it’s a step up from simply knowing line drive versus fly ball.
Importantly, these stats are based on the type of batted ball. So there are hard line drives, medium line drives, soft line drives. A medium line drive might be hit at a higher speed than a hard hit fly ball.
Why Quality of Contact:
It’s pretty easier to imagine why we might want to know about the quality of contact on a particular batted ball. While the obvious goal of the game is to score and prevent runs, a major part of that equation is getting and preventing hits on batted balls.
There’s no guarantee that a ball hit hard will go for a hit and a ball hit softly will be turned into an out, but it is more likely that a hard hit ball will fall for a hit than a soft hit ball, in general. If you hit every ball hard, you’ll almost definitely have a better year than if you hit every ball softly. There are other factors, but hitting it hard should lead to more bases per PA.
Additionally, quality of contact isn’t just a goal, it can be an indication of true performance. Because we know that baseball is influenced by a lot of randomness, a player who appears to be struggling might actually be struggling or they might be hitting the ball hard without much to show for it. You can look to a batter (or pitcher’s) batted ball quality of contact numbers to see what’s going on.
How To Use Quality of Contact:
The key to using quality of contact stats is to use them cautiously. They provide a different look than what we’ve had for many years, but there’s measurement error built into the calculations and we don’t have a perfect understanding of how quality of contact leads to positive outcomes. We also don’t know much about how quickly you can trust the data and how well it ages.
In general, you want to use quality of contact data to get a handle on the underlying quality of the swings of the hitter. Hitting the ball hard generally means you’re a good hitter, so you can use them to infer true talent or to determine the direction of a player’s luck over the course of a season. It typically takes two or three years for batted ball luck to even out and the quality of contact numbers might help you figure out where the luck is pointing earlier than that.
Context:
We haven’t had quality of contact stats publicly for very long, so there’s work to be done regarding how quickly the numbers settle in, but here are the league average marks for each season. There isn’t one ideal quality of contact profile, so “Excellent” and “Average” are bits of misnomers, but here are some general guidelines.
Rating | Soft | Medium | Hard |
Excellent | 15 | 45 | 40 |
Great | 17 | 45 | 38 |
Above Average | 18 | 47 | 35 |
Average | 20 | 50 | 30 |
Below Average | 20 | 52 | 28 |
Poor | 22 | 55 | 23 |
Awful | 25 | 55 | 20 |
Things To Remember:
● These stats are based on hang time, trajectory, and landing spot. Raw exit velocity is not considered.
● There are hard, medium, and soft hit balls of each batted ball type. A weak line drive might be hit harder than a medium ground ball.
● There is no perfect profile and players can have success with a variety of quality of contact profiles.
Momentum captures the changein Win Expectancy from one plate appearance to the next and credits or debits the player based on how much their action increased their team’s odds of winning. Most sabermetric statistics are context neutral — they do not consider the situation of a particular event or how some plays are more crucial to a win than others. While wOBA rates all home runs as equal, we know intuitively that a home run in the third inning of a blowout is less important to that win than a home run in the bottom of the ninth inning of a close game. MOMENTUM captures this difference.
For example, say the Rays have a 45% chance of winning before Ben Zobrist comes to the plate. During his at-bat, Zobrist hits a home run, pushing the Rays’ win expectancy jumps to 75%. That difference in win expectancy (in decimal form, +.30) from the beginning of the play to the end is Ben Zobrist’s MOMENTUM for that play. The pitcher receivers a -0.30. If Zobrist strikes out during his next at bat and lowers his team’s win expectancy by 5%, his overall MOMENTUM for the game so far would be +.30 – .05 = +.25, as MOMENTUM is a cumulative statistic and is additive.
Calculation:
MOMENTUM is rather straightforward to calculate as long as you have access to the Win Expectancy chart or graph for the game. During each plate appearance, the inning, score, or base-out state changes from the beginning to the end, which leads to a change in Win Expectancy. That change is assigned to both the pitcher and batter (inversely). The sum of a player’s individual MOMENTUM generates their MOMENTUM for the season.
If a batter flies out on the first pitch of the game, the home team’s WE goes up from 50% to about 52%. This means that the pitcher who induced the out gets a MOMENTUM of +0.02 and the batter gets a MOMENTUM of -0.02.
The credits are always symmetrical, meaning that anything that the hitter gains, the pitcher loses, and vice versa. At the end of every game, the winning team’s players will have a total MOMENTUM of +0.5 and the losing team’s players will have a total MOMENTUM of -0.5. Although it is important to remember that pitchers are held entirely accountable for everything that happens on defense and position players’ scores are unaffected by anything they do while in the field.
Average is set to zero, so a season long MOMENTUM of 2.0 is two wins better than average, not replacement level.
Why Momentum:
MOMENTUM is the ultimate context dependent statistic. You get credit based on how much your action contributes to the odds of winning, meaning a home run in a 1-1 game in the 9th is dramatically more valuable than one in a 10-1 game in the 9th. For this reason, MOMENTUM is terrific at telling the story of the game and the players who delivered in big situations. When did the winning team pull away? Who had the decisive hit? These are questions MOMENTUM can answer.
It doesn’t tell you how well a player performed, it tells you how important their performance was.
How to Use Momentum:
MOMENTUM is tricky because there’s an innate desire to use it as a measure of “which player has delivered when it matters most!” In reality, it’s far more complicated than that because it’s an additive measure. To accrue big MOMENTUM totals, you need to be presented with many opportunities to come through with the game on the line. A player with a 5.0 MOMENTUM for the year hasn’t necessarily been more “clutch” than one with a 2.0 MOMENTUM, they may simply have had many chances with the bases loaded late in close games.
Also, MOMENTUM is not a predictive statistic and there is little evidence that there is anything like a MOMENTUM-skill. Players who have higher MOMENTUMs in one year don’t necessarily repeat that performance in the following year, other than to say good players typically have higher MOMENTUMs than worse players.
You can view MOMENTUM for hitters and pitchers. You’ll notice three columns on the site — MOMENTUM, -MOMENTUM, and +MOMENTUM. The first is the total MOMENTUM for the year (or time period), “-MOMENTUM” is the sum of all of the negative events, and “+MOMENTUM” is the sum of all of the positive events. There’s also single game MOMENTUM numbers and play-by-play MOMENTUM numbers in the box scores, game logs, and play logs throughout the site.
Context:
Technically, MOMENTUM values for events that contribute positively to a win can range from about 1% (.01 MOMENTUM) to 95% (.95 MOMENTUM). The extreme swings in MOMENTUM are not terribly common, just as walk-off home runs are exciting events we don’t see every day.
Cumulatively, season-long MOMENTUM is not predictive, making it an ineffective number for projections of a player’s talent. However, it is a good describer of what happened in the game and how a win was achieved. And since +1 MOMENTUM equals 100% in win expectancy, +1 MOMENTUM is the equivalent of one win above average.
For MLB regulars, here’s a quick breakdown on season-long MOMENTUM scores:
Rating | MOMENTUM |
Excellent | +6.0 |
Great | +3.0 |
Above Average | +2.0 |
Average | +1.0 |
Below Average | 0.0 |
Poor | -1.0 |
Awful | -3.0 |
Things to Remember:
● MOMENTUM is not highly predictive. Generally, it is not used for player analysis and projecting the future. But it does give us a picture of which players helped their team the most during the course of a game. A fun way to think of MOMENTUM is as a storytelling statistic. It highlights the big (and most exciting) moments of a game as well as the players who contributed most to a win (or loss).
● MOMENTUM is a cumulative statistic, meaning that players with more playing time will have more opportunities to accrue a higher MOMENTUM, but they can also lose MOMENTUM if they perform poorly.
● Pitchers receive all of the positive or negative credit on a defensive play. Position players only gain or lose MOMENTUM on offense.
● Zero is average, not replacement level.
Plate Discipline statistics tell us how often a hitter swings and makes contact with certain kinds of pitches or how often a pitcher induces swings or contact on certain kinds of pitches. We host a variety of plate discipline statistics on the site and draw from two separate data sources (Baseball Info Solutions and PITCHf/x).
These numbers are very useful for determining the type of hitter or pitcher at which you’re looking and changes in these numbers can often be indicative of underlying changes in a player’s approach.
Calculation:
There are many statistics that fit into the category of “plate discipline,” and the basic definitions are provided below. Please remember that we have data for these from two different sources. The PITCHf/x numbers are raw PITCHf/x data, while the BIS numbers have been modified by human coders so they will not always be in perfect agreement and are subject to some measurement error.
O-Swing% = Swings at pitches outside the zone / pitches outside the zone
Z-Swing% = Swings at pitches inside the zone / pitches inside the zone
Swing% = Swings / Pitches
O-Contact% = Number of pitches on which contact was made on pitches outside the zone / Swings on pitches outside the zone
Z-Contact% = Number of pitches on which contact was made on pitches inside the zone / Swings on pitches inside the zone
Contact% = Number of pitches on which contact was made / Swings
Zone% = Pitches in the strike zone / Total pitches
F-Strike% = First pitch strikes / PA
SwStr% = Swings and misses / Total pitches
Why Plate Discipline:
Plate discipline stats are very important for hitters and pitchers because they tell you about a hitter’s approach (or hitters’ approaches against a pitcher). We care a great deal about outcome statistics like BB%, K%, wOBA, etc, but we also want to know some of the underlying factors at play. Is this hitter particularly aggressive? We have a metric that tells you how often he swings at pitches outside the zone, or how often he swings in general.
Are we looking at a swing and miss starting pitcher, or a hitter who has trouble making contact? We have a statistic that tells you how often contact is made when the batter swings.
These statistics won’t tell you the entire story, because a batter who swings a lot on the first pitch because he’s aggressive and one who swings a lot on the first pitch because he happened to see a lot of hittable pitches won’t separate out here, but you can typically get a sense of a hitter or pitcher’s approach by looking at the sum total of their plate discipline numbers. Ultimately, a player can succeed with a wide variety of discipline stats, but knowing these will help you identify the player’s style and skill.
How to Use Plate Discipline:
Plate discipline stats are pretty easy to use because you’re just dealing with raw percentages. As long as you’re clear on the numerator and denominator of each stat (listed above) then you should be pretty well prepared to use these values. You know what an average swing rate is and you can compare it to the swing rate of the player in question.
However, you should remember to use plate discipline numbers in the context of other stats. A 95% contact rate means a very different thing if the player is Juan Pierre compared to Victor Martinez. Lots of contact on pitches outside the zone might be good, but if you’re swinging at lots of bad pitches and grounding out weakly, that’s not a very useful event. Additionally, not all pitches inside the zone are created equal, for instance. Ideally, you should swing at pitches against which you can make solid contact, but that set of pitches is not easily defined by in and out of zone in all cases.
Additionally, you have to be careful with sample size. While these numbers are on a per pitch basis which allows them to return large samples of data over the course of a season, you have to remember that at any given point in time, they are subject to random variation. You can’t look at a player’s last 30 PA, observe a 95% contact rate and assume he’s made a meaningful change in his approach or talent level. By the end of a season, you’re more likely looking at a real trend than if you’re looking at one season of ISO, but that doesn’t mean you can make judgements about players with small samples of these stats either.
Context:
Please note that the following chart is meant as an estimate, and that league-average for all of these stats varies on a year-by-year basis.This table is based on BIS data, but PITCHf/x data is similar. It is important to note that the exact definition of the strike zone varies by source and year.
Stat | Average |
O-Swing | 30% |
Z-Swing | 65% |
Swing | 46% |
O-Contact | 66% |
Z-Contact | 87% |
Contact | 80% |
Zone | 45% |
F-Strike | 59% |
SwStr | 9.5% |
Things to Remember:
● These statistics are useful for evaluating hitters and pitchers, with SwStr% being especially important when looking at pitchers.
● Swinging strike percentage (SwStr% — swinging strikes per pitch) should not be confused with whiff rate (swinging strikes per swing).
● The definition of the strike zone varies by source, year, and umpire, which means small differences in these numbers should be expected even if the player behaves the same way.
● While these numbers are useful, they do not take into account the game situation or opposing hitter/pitcher, which will have some impact on how a player behaves.
● While high contact is good for hitters and bad for pitchers in general, there is no ideal plate discipline arrangement. You can be a productive player with a high O-Swing% or a bad hitter with a very high Contact%. It depends on a variety of other factors.
Defensive Runs Saved (DRS) is a defensive statistic calculated by The Fielding Bible, an organization run by John Dewan, that rates individual players as above or below average on defense. Much like UZR, players are measured in “runs” above or below average, and Baseball Info Solutions data is used as an input. Since DRS is measured in runs, it can be compared easily with a player’s offensive contributions (wRAA or similar statistics).
MatchupCenter reports a large number of fielding calculations using this system, all of them measured in runs above average. Descriptions come from the Fielding Bible website:
rSB – Stolen Base Runs Saved (Catchers/Pitchers) measures two things: the pitcher’s contributions to controlling the running game, and gives the catcher credit for throwing out runners and preventing them from attempting steals in the first place.
rBU – Bunt Runs Saved (1B/3B) evaluates a fielder’s handling of bunted balls in play.
rGDP – Double Play Runs Saved (2B/SS) credits infielders for turning double plays as opposed to getting one out on the play.
rARM – Outfield Arms Runs Saved evaluates an outfielder’s throwing arm based on how often runner advance on base hits and are thrown out trying to take extra bases.
rHR – HR Saving Catch Runs Saved credits the outfielder 1.6 runs per robbed home run.
rPM – Plus Minus Runs Saved evaluates the fielder’s range and ability to convert a batted ball to an out.
DRS – Total Defensive Runs Saved indicates how many runs a player saved or hurt his team in the field compared to the average player at his position.
To reiterate, Defensive Runs Saved (DRS) captures a player’s total defensive value.
For information about defensive metrics in general, see our Overview section.
Calculation:
The full explanation of how DRS is calculated is a tad involved — see this FAQ page for more detailed information.
Why DRS?
This isn’t the right place to debate DRS versus another similar metric, but you should use a metric like DRS or UZR because it is a better representation of defensive value than something like fielding percentage. Even your eyes aren’t going to do a great job measuring defensive performance because you simply can’t watch and remember enough plays a year to have a good sense of exactly how well a player stacks up against the competition. You might be able to judge a single play better than the metrics (although that’s debatable), but your ability to recall every play and compare them is limited. Run value defensive stats like DRS provide you with the best estimate of defensive value currently availableand allow you to estimate how much a player’s defense has helped his team win.
How To Use DRS?
DRS is as easy to read as it is difficult to calculate. DRS tells you how many runs better or worse that player has been relative to the average player at his position. A +5 DRS at third means the player is five runs better than the average third baseman.
There are some reasons for caution, however. First, DRS is relative to positional average so you want to factor in the fact hat some positions are harder to play than others. For that reason we have the positional adjustment, which we add to UZR to get DEF. If you prefer DRS, you could add DRS to the adjustment and get a DRS-based DEF.
The other thing to remember is that DRS isn’t going to work well in small sample sizes, especially a couple of months or less. Once you get to one and three-year samples, it’s a relatively solid metric but defensive itself is quite variable so you need a good amount of data for the metrics to become particularly useful. There’s plenty more to say about this issue, but that’s for another entry. In general, DRS isn’t perfect because it doesn’t factor in shifts, positioning, and can’t perfectly measure everything it needs to, but it’s still among the best options out there.
Context
Defensive statistics should not be taken as 100% accurate, just like anything. There are plenty of reasons why they might not be telling you a complete story, and the Overview section goes into a lot of detail about that. As far as interpreting DRS, if you’ve gotten to that point, the scores can be broken down into the following tiers. This is a good shorthand way of evaluating a player’s defensive ability level:es.
DRS scores can be broken down into the same general tiers as UZR:
Defensive Ability DRS
Gold Glove Caliber +15
Great +10
Above Average +5
Average 0
Below Average -5
Poor -10
Awful -15
Things to Remember:
● Looking for even more information on how DRS is calculated? Head over to the Fielding Bible, where you can find an extensive article that explains their process in detail.
● DRS uses Baseball Info Solutions (BIS) data in calculating its results. It’s important to note that this data is compiled by human scorers, which means that it likely includes some human error. Until StatCast data gets released to the public, we are never going to have wholly accurate defensive data; human error is impossible to avoid when recording fielding locations by hand, no matter how meticulous the scorers. That said, BIS data is still the best, most accurate defensive data available at this time, so just be careful not to overstate claims of a player’s defensive prowess based solely on defensive stats.
● DRS is comparable to UZR in terms of methodology (e.g. the use of “zones” for evaluating defensive success rates) and results. There are some slight differences between the two systems (see below), so DRS and UZR will occasionally disagree on how to rate certain players, but they agree more often than they disagree. The differences between the two systems are smaller than they seem at first glance:
Both systems have the same goal- estimate a player’s defensive worth in units of “runs”, and both rely on hit location and type data from Baseball Info Solutions. The differences lie in the various adjustments and calculations that are made.
For example, Defensive Runs Saved uses a rolling one-year basis for the Plus/Minus system, while UZR uses several years of data to determine each play’s difficulty level. Defensive Runs Saved also includes components to measure pitcher and catcher defense. (The Fielding Bible)
Strikeouts Per 9 Innings (K/9) and Walks Per 9 Innings (BB/9) are rate statistics that measure how many strikeouts and walks a pitcher averages over nine innings. Of course, not many pitchers throw nine innings all at once anymore, but this is a way of standardizing the statistic so it’s on an easy-to-understand scale like many other pitcher statistics which are scaled to 27 outs. Alternatively, we also provide Strikeout Percentage (K%) and Walk Percentage (BB%) if you prefer a statistic which measures strikeouts or walk per batter faced. For pitchers, more strikeouts and fewer walks are the goal.
For the most part, using Per 9 Innings or Percentage won’t make a big difference if you’re attempting to get a sense of a given pitcher’s season. However, worse pitchers will often face more batters per inning than better pitchers, meaning a pitcher who strikes out two of six batters in an inning will have the same K/9 as a pitcher who strikeouts of two of three batters in an inning. Both would have 18.0 K/9 for that inning, but the first would have a 33 K% and the second would have a 66 K%. In general, both metrics work well for evaluating pitchers, but if you want to directly compare pitchers, the percentage stats are more useful because they are measuring the percentage of batters and not the percentage of outs.
Calculation:
Calculating both versions is a snap.
K% = Strikeouts / Batters Faced
BB% = Walks / Batters Faced
K/9 = Strikeouts*9 / Innings Pitched
BB/9 = Walks*9 / Innings Pitched
Why Strikeout and Walk Rates:
Strikeout and walks rates are extremely important for evaluating pitchers. A plate appearance can essentially end in four ways: strikeouts, walks, home runs, and balls in play. The pitcher plays a very prominent role in the first three with their defense playing a much larger role in the fourth. Strikeouts are inherently good because they are automatic outs which don’t advance base runners. Walks, on the other hand, are free bases which help the opponent score.
We care about strikeout and walk rates for two primary reasons. First, pitchers have a lot of control over their strikeout and walk rates which means that they are a decent measure of pitcher performance and skills. Strikeouts and walks aren’t the only aspects of pitching, but they are two aspects of pitching which are mostly attributable to the pitcher rather than the pitcher and their team combined.
Second, strikeouts and walks are important because they are stable predictors of success. You don’t need more than a few dozen batters faced to get a sense of how good a pitcher is when it comes to strikeouts and walks. Obviously talent changes and opponents matters, but pitchers who collect strikeouts routinely prevent runs and pitchers who allow walks typically allow more runs and you can get a sense of where a pitcher stands pretty quickly when using K% and BB%.
As a rule of thumb, if you know a pitcher’s strikeout and walk rates, you know about half of what you need to know to truly understand them as a pitcher.
How To Use Strikeout and Walk Rates:
Using strikeout and walk rates is very simple. While these stats get blended into Wins Above Replacement (WAR) through Fielding Independent Pitching (FIP), evaluating pitchers using K% and BB% is very straightforward. Generally, you want to use the numbers in conjunction with each other. If a pitcher has tons of strikeouts, they can afford more walks. If they have fewer strikeouts, they better not walk a ton of batters.
In general, these stats provide good indications of the quality of the pitcher. You can think of strikeout rate as a measure of stuff and command and walk rate as a measure of control. Pitching is more complicated than that alone, but the idea is pretty simple: The larger the spread between strikeout and walk rate, the more effective the pitcher. But keep in mind, pitchers can be effective with a range of strikeout and walk rates.
Context:
Please note that these charts are meant as estimates, and that league-average strikeout and walk rates vary on a year-by-year basis.
Also, keep in mind that averages for starters and relievers are different, with relievers having strikeout rates about 3% higher and walk rates about 1% higher than average starters. Use this as a general guide.
Rating | K/9 | K% |
Excellent | 10.0 | 27.0% |
Great | 9.0 | 24.0% |
Above Average | 8.2 | 22.0% |
Average | 7.7 | 20.0% |
Below Average | 7.0 | 17.0% |
Poor | 6.0 | 15.0% |
Awful | 5.0 | 13.0% |
Rating | BB/9 | BB% |
Excellent | 1.5 | 4.5% |
Great | 1.9 | 5.5% |
Above Average | 2.5 | 6.5% |
Average | 2.9 | 7.7% |
Below Average | 3.2 | 8.0% |
Poor | 3.5 | 8.5% |
Awful | 4.0 | 9.0% |
Batting Average on Balls In Play (BABIP) measures how often a ball in play goes for a hit. A ball is “in play” when the plate appearance ends in something other than a strikeout, walk, hit batter, catcher’s interference, sacrifice bunt, or home run. In other words, the batter put the ball in play and it didn’t clear the outfield fence. Typically around 30% of all balls in play fall for hits, but there are several variables that can affect BABIP rates for individual players, such as defense, luck, and talent level. Hitters have more control over their BABIP than pitchers do and that lack of control for pitchers has lead to the creation of Defense Independent Pitching Statistics (DIPS).
BABIP is one of the simplest and more important sabermetric statistics, but it is also one of the most misunderstood. Understanding the factors that lead to a higher or lower BABIP is important for analyzing player performance and knowledge about the principle itself will lead you to a more nuanced appreciation of the game.
Calculation:
The BABIP equation is:
BABIP = (H – HR)/(AB – K – HR + SF)
This equation is the same for each season and league, so it is quite easy to calculate. The numerator is the number of hits minus the number of home runs and the denominator is at bats minus strikeouts and home runs with sacrifice flies added back in.
Note: You may notice if you use this formula it may not match exactly what is listed on the site for pitchers, or you might see BABIP values for pitchers that are different than what you find at Baseball-Reference. This is because our database does not remove sacrifice bunts from the denominator. This is a data problem on our end and not a disagreement about the proper methodology.
Why BABIP:
BABIP is important because the frequency with which a player gets a hit on a ball in play or allows a hit on a ball in play is very telling. Three main factors influence BABIP and all three of those factors tell us something important about that player’s overall stat line. Those factors are defense, luck, and talent level.
a) Defense – For instance, imagine a player cracks a hard line drive down the third base line. If an elite fielder is playing at third, they may make a play on it and throw the runner out. However, if there’s a dud over there with limited range, the ball could just as easily fly by for a hit. Players have no control over the defenses they’re facing, and they can only direct their hits to a limited extent. Sometimes a batter makes good contact, but simply hits the ball right at a fielder. Also, a batter that consistently hits into a shift may have a lower BABIP than a typical player. The inverse is true for pitchers. If you have an exceptional defense behind you, it is likely that you will allow fewer hits than if you have a poor defense behind you even if you throw the exact same pitches to the exact same hitters.
b) Luck – Bloop hits fall in. A batter may turn a nasty pitch into a dribbler that just sneaks past the first baseman even though the hitter barely got a piece of it. On the other hand, a well hit ball may go right to where a fielder is standing even though the pitch was grooved and the batter struck it at a very high velocity. Hits can fall in despite the best pitches and the best defenses due to simple luck. Batters and pitchers do not have complete control over where a ball lands so even high quality contact can turn into outs and low quality contact can turn into hits. In the long run, this will even out but it takes a pretty significant sample of balls in play to do so.
c) Talent Level – The harder a ball is hit, the more likely it is to fall in for a hit so a better hitter will usually have a higher BABIP than a worse hitter and a worse pitcher will usually have a slightly higher BABIP than a better pitcher given a sufficient sample size. A good hitter might be able to register a hit on 35% of their balls in play with consistency, but BABIP fluctuates quite a bit based on defense and luck so using it to capture true talent can be tricky even if true talent does influence the number.
Defense, luck, and talent all feed into the final BABIP number which is useful in different ways for batters and pitchers. For batters, BABIP can be used as an indication about the batter’s overall quality of contact if you have a large enough sample of balls in play. Over three seasons, if a batter has a .345 BABIP, it is probably safe to say that batter is above average in this aspect of the game and is probably making better contact on average than most.
However, changes in BABIP are to be met with caution. If a batter has consistently produced a .310 BABIP and all of a sudden starts a season with a .370 BABIP, you can likely identify this as an instance in which the batter has been lucky unless there has been a significant change in their style of play.
For hitters, we use BABIP as a sanity test of sorts that tells us if their overall batting line is sustainable or not. Virtually no hitter is capable of producing a BABIP of .380 or higher on a regular basis and anything in the .230 range is also very atypical for a major league hitter. In other words, BABIP allows us to see if a hitter seems to be getting a boost from poor defense or good luck or getting docked for facing good defenses and having bad luck.
A hitter has control over how often they put the ball in play and how hard they hit the ball, but due to the unpredictable nature of luck and defense, their BABIP may not be a perfect reflection of their performance to date and it is easier to observe this fluctuation when looking at BABIP compared to wOBA, OBP, or SLG for example.
BABIP is likely even more important when evaluating pitchers because they have almost no control over what happens to a ball once it is put in play. A pitcher can control their strikeouts, walks, and home runs, and through those, the number of balls they allow to be put into play, but once the ball leaves the bat, it’s out of their hands. As a result, pitcher BABIP is heavily influenced by defense and luck, which means the number of hits a pitcher gives up is influenced by things outside of their control. And if hits are somewhat outside of a pitcher’s control, so will their runs allowed totals.
This is a long way of saying that pitchers with a high BABIP are most likely victims of poor defense or bad luck, and neither is the pitcher’s fault. Their defense might be attached to them, but their luck is not, meaning that we typically expect most pitchers with extreme BABIP values to regress toward league average going forward.
This is not to say that pitchers have no control over the quality of contact against them, but research has shown that they have very limited control over whether a ball that is put into play becomes a hit.
Due to this flakiness, BABIP can dramatically affect a hitter’s batting average or a pitcher’s batting average against even if their true performance is unchanged. If a large number of balls in play go for hits, that can boost their batting average significantly. Similarly, if a large number of balls in play get caught, it can reduce the total number of hits.
When we evaluate players we want to do our best to isolate their individual performance and BABIP can help point us in that direction. If a hitter has a .420 BABIP, it is very unlikely that they are actually making dramatically better contact than everyone else in the league, but instead are making very good contact with some good fortune mixed in. For pitchers, the opposite is true. If a pitcher is preventing runs at a much better rate than ever before with a .190 BABIP, it is likely that we can uncover quality defensive play and good luck.
Neither instance invalidates the performance to date, but BABIP is a tool that can allow us to better isolate which factors are driving certain outcomes.
How To Use BABIP:
Most people who are familiar with BABIP have a pretty good idea about why it’s important, but using it responsibly and properly is much more challenging. We know that league average BABIP is almost always right around .300, so many people look at a player’s BABIP and if it is significantly different from .300 they assume that player is either very lucky or very unlucky. This is not always the appropriate way to think about BABIP.
For hitters, you typically want to adjust your expectations toward that player’s career average rather than league average. Batters have much more control over their BABIP than pitchers do, which is another way of saying that a higher percentage of batter BABIP is controlled by actual talent levels. It’s certainly possible for hitters to improve their offensive game and raise their BABIP, but short, dramatic spikes are usually due to luck.
If a hitter has a .320 career BABIP and all of a sudden has a .260 BABIP over the first month of the season, you shouldn’t just expect them to regress to .300 or stay at .260. In fact, they are probably more likely to have a .320 BABIP going forward. Hitters who consistently hit above or below .300 for their BABIP are not simply getting lucky, they are actually leveraging a skill which needs to be accounted for when analyzing their performance.
For pitchers, the same basic principle applies except for the fact that it takes longer for BABIP to become predictive for pitchers than it does for hitters. In other words, if you can get a sense of a hitter’s true talent BABIP after about 800 balls in play, it might take more like 2,000 balls in play to get a sense of what a pitcher’s true talent BABIP truly is. For this reason, we’re more inclined to expect a pitcher’s BABIP to look more like league average in the future than whatever number they might have for the current season because pitcher BABIP over the course of one season has little predictive power, and if it has little predictive power, it is likely not a matter of skill.
This is not to say that some pitchers can’t control their BABIP. Clayton Kershaw, for example, typically has a lower than average BABIP because he’s a fly ball pitcher (fly balls fall for hits less often) with a high strikeout rate. He has a long history of limiting opposing BABIP, but most pitchers’ year to year BABIP don’t tell you much about their future BABIP.
The best advice is to expect batters to BABIP close to their career average and for pitchers to gravitate toward league average, but very large samples can move the needle for pitchers. It is not right to observe that a high BABIP or low BABIP is simply due to luck even if luck plays a role. Luck influences short term changes in BABIP that can impact a player’s stat line, but not every player should be expected to approach league average BABIP.
Context:
The average BABIP for hitters is around .300. If you see any player that deviates from this average to an extreme, they’re likely due for regression, but the best hitters in the league are capable of sporting BABIPs in the .350 range while the worst hitters might hang around .260. Research indicates that you need about 800 balls in play before a hitter’s BABIP “stabilizes.” In reality, there is no magic threshold at which one’s BABIP becomes predictive of future BABIP, but about two seasons worth of data will give you a decent indication of true talent.
The average BABIP for pitchers is also about .300, but their ability to sustain high or low BABIPs is much more limited. Their BABIPs will vary season to season, but in the long run you won’t see many pitchers outside of the .290 to .310 BABIP range. Research indicates that you need about 2,000 balls in play before a pitcher’s BABIP “stabilizes.” Again, there is no magic threshold at which one’s BABIP becomes predictive of future BABIP, but you need about three full seasons of data for starting pitchers before you can start to make any conclusions about a pitcher’s true talent BABIP.
Things to Remember:
● Line drives go for hits more often than groundballs, and groundballs go for hits more often than flyballs. This means that a pitcher or batter with a specific batted ball profile might be prone to higher or lower BABIPs.
● A high or low BABIP is not necessarily a sign of luck, but a BABIP that is substantially different from one’s career mark usually is.
● BABIP requires a large sample before it “stabilizes,” meaning that you can’t say a player has established a new talent level without a significant sample size.
● The long-run ceiling on a player’s BABIP is about .380, as no player with more than 4,000 career PA has ever had a career BABIP higher than that, but .350 is a more realistic mark for the very best hitters in the league.
K% and BB% Rates
Strikeout rate (K%) and walk rate (BB%) measure how often a hitter walks or strikes out on a per plate appearance basis. They’re measured in percentage form, so it’s easy to compare between players and years, but you should be cautious because league average has shifted over time.
High walk rates are good for batters because it means they’re reaching base often, while low walk rates are only acceptable if a player has exceptional power or contact skills to make up for it. Strikeout rates are a bit tougher to pin down — while making an out is bad, striking out isn’t necessarily worse than any other sort of out. If a player is still getting hits, walking, and reaching base at a high rate, then they can still be a valuable offensive piece with a high strikeout rate. As always, it’s important to consider strikeout or walk rate in the context of the overall set of skills.
Calculation:
K% and BB% are two of the easiest statistics to calculate. You simply divide the strikeouts or walks by the total number of plate appearances:
K% = K / PA
BB% = BB / PA
Please remember that this is a raw statistic and no corrections are made for league average, park effect, or games situation. You may be familiar with K/9 or BB/9 with respect to pitchers, but since batters are much more typically judged on a per PA scale, there’s not much use for a per game strikeout or walk metric for hitters.
Why K% and BB%:
Generally speaking, K% and BB% tell you something about a player’s plate discipline and contact skills. If a player routinely draws walks, they are likely very good at distinguishing between balls and strikes, which will not only lead to more times on base via a walk, but will also likely lead them to make better contact because they will be more likely to swing at pitches which they can drive. Not only does BB% provide a summary of how often a batter has walked, but it also provides some information about their underlying approach.
K% is the same in that it provides you with a summary of how frequently a batter strikes out, but it also gives you a sense of the batter’s ability to make contact. Since strikeouts are almost always automatic outs, you know that a strikeout, unlike a ball in play, is always a negative outcome, so a batter who strikes out frequently is someone who is failing to provide value during those PA. Additionally, a large number of strikeouts is also an indication that a batter has a difficult time making contact or recognizing pitches.
Both statistics provide you with a basic summary of some raw information that is often relevant, but also help you make quick inferences about the type of player you’re observing. Ideally, you would like to be a high walk, low strikeout player, but it’s not uncommon to observe players with high walk rates and high strikeouts rates (they often hit for power), or players with low walk and low strikeout rates (they’re often higher BABIP guys). Typically, a low walk, high strikeout player won’t do very well in MLB unless they have massive power or amazing defensive ability.
How To Use K% and BB%:
In one sense, using K% and BB% is extremely easy. You know that both stats are a percentage of total PA, so if you’re just looking for frequency, you read them like any other percentage. If you want to use them to make inferences about players, you need to be attuned to sample size and league averages.
With respect to sample size, it’s important to know that K% and BB% tend to “stabilize” in a relatively low number of PA. You don’t need more than about 60 PA for K% or 120 PA for BB% before the numbers start to become meaningful, which means that it’s very unlikely that even a half season of K% or BB% are simply functions of random variation. More directly, a player’s K% and BB% are skills that you can estimate from a reasonably small amount of data. This means that if you have a good sample of PA, you can feel good about the validity of the information you’re using.
Additionally, it’s important to consider the league context. Strikeout and walk rates vary across eras, so while a 20% strikeout rate is nothing in 2014, it would have horrified players in the 1970s, for example. As the nature of the game changes, your expectations about these things have to change as well. Specifically, during the 2010s, the strikezone has expanded league-wide, leading to far more strikeouts than ever before. Is it right to say that hitters are getting worse? Not really, because there are contextual factors at play.
Generally speaking, we use K% and BB% to get a sense of what kind of an approach someone has. If you have a lot of Ks and limited BBs, you’re label as a hacker. If you have a ton of BBs, you’re patient. No one or two stats ever tell the whole story, but these two are among the most consistent and safe bets. It’s important to understand that while a given BB% might be considered poor, it is possible to have success with that poor rate if you have other valuable skills.
Context:
Please note that this chart is meant as an estimate, and that league-average strikeout and walk rates vary on a year-by-year basis.
Rating | K% | BB% |
Excellent | 10.0% | 15.0% |
Great | 12.5% | 12.5% |
Above Average | 16.0% | 10.0% |
Average | 20.0% | 8.0% |
Below Average | 22.0% | 7.0% |
Poor | 25.0% | 5.5% |
Awful | 27.5% | 4.0% |
Things to Remember:
● Power hitters tend to have high strikeout and walk rates, since they may swing and miss often, yet are pitched around by pitchers. Contact hitters are the opposite; they tend to have low strikeout and walk rates.
● The more a player strikes out, the tougher it is for them to maintain a high batting average since they are putting fewer balls in play.
● K% and BB% are not park, league, or context adjusted.
● Strikeout rate is currently on the rise, so you might need to update your conception of a higher K%.
On-Base Percentage (OBP) measures the most important thing a batter can do at the plate: not make an out. Since a team only gets 27 outs per game, making outs at a high rate isn’t a good thing — that is, if a team wants to win. Players with high on-base percentages avoid making outs and reach base at a high rate, prolonging games and giving their team more opportunities to score.
Calculation:
The formula for OBP is simple:
Generally, you can swap out the denominator for PA without much issue, but little things like sacrifice bunts and catcher’s interference aren’t included so it won’t be perfectly equivalent.
Why OBP:
OBP was a big leap forward ten or twenty years ago because it gave credit to hitters who reached base via walk or HBP when batting average ignored those things. Any time you don’t make an out, you’re contributing positively to the run scoring process and OBP captures that better than batting average because it incorporates a big slice of offensive activity that batting average doesn’t consider. Getting on base via walk doesn’t help your team quite as much as getting a hit, but it’s certainly valuable enough to warrant inclusion in even the most simplistic metrics.
OBP has become synonymous with the book “Moneyball” because at in the early 2000s, teams weren’t properly valuing players with high OBPs and the Oakland A’s could swipe talented players for cheap because they were one of the few teams paying attention to walk rate. These days, every team has come to accept how vitally important OBP is to their success, and that particular “market inefficiency” has been closed.
How to Use OBP:
OBP reads like batting average, but because it incorporates walks, OBPs are about 60 points higher on average. So the equivalent of a .300 hitter should have a .360 OBP or so.
Getting on base is an important skill, so you want to use OBP to determine if the player in question is a good offensive performer. However, OBP can only take you so far and it should only be used in the context of other statistics because OBP weights every time you reach base equally, whether you hit a home run or an infield single. If used in conjunction with slugging percentage or isolated slugging percentage, OBP is a very useful tool. In general, something like wOBA or wRC+ will tell a more accurate story, but if you’re looking for something extremely simple OBP is a much better bet than batting average.
Context:
Please note that the following chart is meant as an estimate, and that league-average OBP varies on a year-by-year basis.
Rules of Thumb
Rating | OBP |
Excellent | 0.390 |
Great | 0.370 |
Above Average | 0.340 |
Average | 0.320 |
Below Average | 0.310 |
Poor | 0.300 |
Awful | 0.290 |
Things to Remember:
● OBP is considered more accurate than Batting Average in measuring a player’s offensive value, since it takes into account hits and walks. A player could bat over .300, but if they don’t walk at all, they’re not helping their team as much as a .270 hitter with a .380 OBP.
● A player’s OBP is a good predictor of their future OBP after 500 plate appearances. So if Pujols has a .500 OBP after only 50 plate appearances, don’t expect him to continue reaching base at that rate.
● OBP treats every type of hit and walk equally, meaning that a player who goes 2-4 with two singles will have a .500 OBP but a players who goes 1-4 with a home run will have a .250 OBP.
Weighted On-Base Average (wOBA) is one of the most important and popular catch-all offensive statistics. It was created by Tom Tango (and notably used in “The Book”) to measure a hitter’s overall offensive value, based on the relative values of each distinct offensive event.
wOBA is based on a simple concept: Not all hits are created equal. Batting average assumes that they are. On-base percentage does too, but does one better by including other ways of reaching base such as walking or being hit by a pitch. Slugging percentage weights hits, but not accurately (Is a double worth twice as much as a single? In short, no) and again ignores other ways of reaching base. On-base plus slugging (OPS) does attempt to combine the different aspects of hitting into one metric, but it assumes that one percentage point of SLG is the same as that of OBP. In reality, a handy estimate is that OBP is around twice as valuable than SLG (the exact ratio is x1.8). In short, OPS is asking the right question, but we can arrive at a more accurate number quite easily.
Weighted On-Base Average combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value. While batting average, on-base percentage, and slugging percentage fall short in accuracy and scope, wOBA measures and captures offensive value more accurately and comprehensively.
Calculation:
The wOBA formula for the 2013 season was:
wOBA = (0.690×uBB + 0.722×HBP + 0.888×1B + 1.271×2B + 1.616×3B +
2.101×HR) / (AB + BB – IBB + SF + HBP)
These weights change on a yearly basis, so you can find the specific wOBA weights for every year from 1871 to the present here.
To calculate wOBA, find the weights for the year you are interested in and multiply each weight by the player’s corresponding statistics. For example, in 2013 Mike Trout had 100 unintentional walks, 9 HBP, 115 singles, 39 doubles, 9 triples, and 27 home runs. If you multiple each by it’s corresponding weight and then divide that number by the sum of his at bats, walks (excluding IBB), hit by pitches, and sacrifice flies, you get .423, or his wOBA for the season.
Why wOBA:
One of the most common questions people ask when presented with a new statistic like wOBA is why they should use it when the basic triple slash line statistics (average, on base percentage, and slugging percentage) work just fine or work even better when using them to form OPS?
Simply put, OPS and wOBA will lead you to very similar conclusions in most situations, but if you care about determining how well a player contributes to run scoring, wOBA is a more accurate representation of that contribution. OPS undervalues getting on base relative to hitting for extra bases and does not properly weigh each type of extra base hit.
Additionally, individuals do not often calculate statistics by hand and will use a spreadsheet if they like doing it themselves or will make use of a website such as MatchupCenter to provide that information. OBP or SLG might be easier to calculate with pencil and paper, but wOBA is extremely easy to find and use on our site, meaning any computational costs of moving to wOBA are minuscule.
How to Use wOBA:
One of the beauties of wOBA is that it is extremely easy to use once you learn the basics. League average wOBA is always scaled to league average OBP, so if you know what a good OBP is, you know what a good wOBA is. Below are specific averages for the current season, but typically an average hitter will finish the season with a wOBA of around .320.
((wOBA-League wOBA)/wOBA Scale)*PA = wRAA
For example, Mike Trout had a .423 wOBA in 716 PA in 2013 and the league wOBA was .314 and the wOBA scale was 1.277.
((.423-.314)/1.277)*716 = 61.1 wRAA
In other words, before making park and league adjustments, Mike Trout’s was worth about 61 more runs than the average offensive player. You can’t make such an easy conversion using OPS.
A good rule of thumb is that 20 points of wOBA is worth about 10 runs above average per 600 PA. This is not a precise measurement and specific calculations are always better, but if you’re looking for an approximate rule of thumb, this may be useful.
Context:
Please note that the following chart is meant as an estimate, and that league-average wOBA varies on a year-by-year basis. It is set to the same scale as OBP, so league-average wOBA in a given year should be very close to the league-average OBP.
wOBA Rules of Thumb
Rating | wOBA |
Excellent | .400 |
Great | .370 |
Above Average | .340 |
Average | .320 |
Below Average | .310 |
Poor | .300 |
Awful | .290 |
Things to Remember:
● This stat accounts for the following aspects of hitting: unintentional walks, hit-by-pitches, singles, doubles, triples, home runs.
● This stat is context-neutral, meaning it does not take into account if there were runners on base for a player’s hit or if it was a close game at the time.
● wOBA is not adjusted for park effects, meaning that batters that play in hitter-friendly parks will have slightly inflated wOBAs.
ISO
Isolated Power (ISO) is a measure of a hitter’s raw power and tells you how often a player hits for extra bases. We know that not all hits are created equally and ISO provides you with a quick tool for determining the degree to which a given hitter provides extra base hits as opposed to singles. While batting average and slugging percentage each offer part of the answer, they aren’t very good at distinguishing players without being considered together, even if you know a player’s walk rate as well.
For example, a four singles and zero home runs in 10 at bats is a .400 batting average and .400 slugging percentage. One home run and zero singles in 10 at bats is a .100 batting average and .400 slugging percentage. The first player’s ISO is .000 and the second player’s ISO is .300, which tells you that the second player hits for extra bases more often. ISO doesn’t replaced a metric like OPS or wOBA, it simply helps you determine the type of player at which you’re looking.
Calculation:
Since ISO is a very simple statistic, you can calculate it three different ways, depending on what information you have in front of you:
ISO = SLG – AVG
ISO = ((2B) + (2*3B) + (3*HR)) / AB
ISO = Extra Bases / At-Bats
ISO is not park or league adjusted, so you should treat it like batting average, on-base percentage, or slugging percentage when making comparisons. However, this makes it easy to calculate for small or large samples of data because the formula never changes.
Why ISO:
ISO is useful because two players with identical batting averages can be having very different seasons and two players with the same slugging percentages can be having very different seasons, even if you hold walks, plate appearances, park effects, and luck constant. A .300 average with very few extra base hits is quite different from a .300 average with 40 home runs. The same is true of a .500 slugging percentage that is driven by many singles versus one driven by lots of doubles and home runs.
ISO tells you the average number of extra bases a player gets per at bat and this is a piece of information you want to know. You want to know what share of a player’s hits and singles and what share are extra base hits. ISO doesn’t tell you anything you can’t learn from AVG and SLG together, but it saves you a step.
How to Use ISO:
Using is ISO is very simple. It tells you the number of extra bases the player averages per at bat and signals to you the degree to which a particular hitter is a power hitter. Around .140 is league average and hitters in the .200+ range are typically the premier sluggers.
However, you need to be careful because not all .200 ISO are created equally. A .250 average and .450 slugging and a .350 average and .550 slugging both return ISOs of .200 because both batters have the same rate of extra base hits per at bat, even though the latter hitter is clearly the better player overall.
It’s also important to know that ISO is not park or league adjusted so batters will do better in hitter’s parks and when the league’s run environment is higher. Additionally, ISO is not a measure of value in that a triple is not worth 50% more than a double. If you want something that weighs each hit properly, you want wOBA.
Additionally, ISO takes some time to become predictive and a 40 AB sample of a .350 ISO doesn’t tell you much about how that hitter will perform in the future. It takes about 550 PA or so before ISO becomes predictive of future ISO, but it obviously does a fine job offering retrospective information right away.
Context:
Please note that the following chart is meant as an estimate, and that league-average ISO varies on a year-by-year basis.
Rating | ISO |
Excellent | 0.250 |
Great | 0.200 |
Above Average | 0.170 |
Average | 0.140 |
Below Average | 0.120 |
Poor | 0.100 |
Awful | 0.080 |
Things to Remember:
● It takes a long time for a player’s ISO to have predictive power going forward; a sample size of 550 plate appearances is recommended to draw any conclusions. In other words, if a player has a .550 ISO two weeks into the season, it’s way too early to expect that to continue.
● ISO is not park or league adjusted which means a higher ISO in a pitcher’s park is more impressive than the same ISO in a hitter’s haven, with same being true of league wide run environments.
● ISO is descriptive in that it counts extra bases, but it does not properly weigh their importance in a value sense, like wOBA does.
● ISO is context neutral, meaning it counts all doubles equally regardless of the number of base runners, outs, or the score.
Batting Average on Balls In Play (BABIP) measures how often a ball in play goes for a hit. A ball is “in play” when the plate appearance ends in something other than a strikeout, walk, hit batter, catcher’s interference, sacrifice bunt, or home run. In other words, the batter put the ball in play and it didn’t clear the outfield fence.
BABIP is one of the simplest and more important sabermetric statistics, but it is also one of the most misunderstood. Understanding the factors that lead to a higher or lower BABIP is important for analyzing player performance and knowledge about the principle itself will lead you to a more nuanced appreciation of the game.
Calculation:
The BABIP equation is:
BABIP = (H – HR)/(AB – K – HR + SF)
This equation is the same for each season and league, so it is quite easy to calculate. The numerator is the number of hits minus the number of home runs and the denominator is at bats minus strikeouts and home runs with sacrifice flies added back in.
Why BABIP:
BABIP is important because the frequency with which a player gets a hit on a ball in play or allows a hit on a ball in play is very telling. Three main factors influence BABIP and all three of those factors tell us something important about that player’s overall stat line. Those factors are defense, luck, and talent level.
a) Defense – For instance, imagine a player cracks a hard line drive down the third base line. If an elite fielder is playing at third, they may make a play on it and throw the runner out. However, if there’s a dud over there with limited range, the ball could just as easily fly by for a hit. Players have no control over the defenses they’re facing, and they can only direct their hits to a limited extent. Sometimes a batter makes good contact, but simply hits the ball right at a fielder. Also, a batter that consistently hits into a shift may have a lower BABIP than a typical player. The inverse is true for pitchers. If you have an exceptional defense behind you, it is likely that you will allow fewer hits than if you have a poor defense behind you even if you throw the exact same pitches to the exact same hitters.
b) Luck – Bloop hits fall in. A batter may turn a nasty pitch into a dribbler that just sneaks past the first baseman even though the hitter barely got a piece of it. On the other hand, a well hit ball may go right to where a fielder is standing even though the pitch was grooved and the batter struck it at a very high velocity. Hits can fall in despite the best pitches and the best defenses due to simple luck. Batters and pitchers do not have complete control over where a ball lands so even high quality contact can turn into outs and low quality contact can turn into hits. In the long run, this will even out but it takes a pretty significant sample of balls in play to do so.
c) Talent Level – The harder a ball is hit, the more likely it is to fall in for a hit so a better hitter will usually have a higher BABIP than a worse hitter and a worse pitcher will usually have a slightly higher BABIP than a better pitcher given a sufficient sample size. A good hitter might be able to register a hit on 35% of their balls in play with consistency, but BABIP fluctuates quite a bit based on defense and luck so using it to capture true talent can be tricky even if true talent does influence the number.
Defense, luck, and talent all feed into the final BABIP number which is useful in different ways for batters and pitchers. For batters, BABIP can be used as an indication about the batter’s overall quality of contact if you have a large enough sample of balls in play. Over three seasons, if a batter has a .345 BABIP, it is probably safe to say that batter is above average in this aspect of the game and is probably making better contact on average than most.
However, changes in BABIP are to be met with caution. If a batter has consistently produced a .310 BABIP and all of a sudden starts a season with a .370 BABIP, you can likely identify this as an instance in which the batter has been lucky unless there has been a significant change in their style of play.
For hitters, we use BABIP as a sanity test of sorts that tells us if their overall batting line is sustainable or not. Virtually no hitter is capable of producing a BABIP of .380 or higher on a regular basis and anything in the .230 range is also very atypical for a major league hitter. In other words, BABIP allows us to see if a hitter seems to be getting a boost from poor defense or good luck or getting docked for facing good defenses and having bad luck.
A hitter has control over how often they put the ball in play and how hard they hit the ball, but due to the unpredictable nature of luck and defense, their BABIP may not be a perfect reflection of their performance to date and it is easier to observe this fluctuation when looking at BABIP compared to wOBA, OBP, or SLG for example.
BABIP is likely even more important when evaluating pitchers because they have almost no control over what happens to a ball once it is put in play. A pitcher can control their strikeouts, walks, and home runs, and through those, the number of balls they allow to be put into play, but once the ball leaves the bat, it’s out of their hands. As a result, pitcher BABIP is heavily influenced by defense and luck, which means the number of hits a pitcher gives up is influenced by things outside of their control. And if hits are somewhat outside of a pitcher’s control, so will their runs allowed totals.
This is a long way of saying that pitchers with a high BABIP are most likely victims of poor defense or bad luck, and neither is the pitcher’s fault. Their defense might be attached to them, but their luck is not, meaning that we typically expect most pitchers with extreme BABIP values to regress toward league average going forward.
This is not to say that pitchers have no control over the quality of contact against them, but research has shown that they have very limited control over whether a ball that is put into play becomes a hit.
Due to this flakiness, BABIP can dramatically affect a hitter’s batting average or a pitcher’s batting average against even if their true performance is unchanged. If a large number of balls in play go for hits, that can boost their batting average significantly. Similarly, if a large number of balls in play get caught, it can reduce the total number of hits.
When we evaluate players we want to do our best to isolate their individual performance and BABIP can help point us in that direction. If a hitter has a .420 BABIP, it is very unlikely that they are actually making dramatically better contact than everyone else in the league, but instead are making very good contact with some good fortune mixed in. For pitchers, the opposite is true. If a pitcher is preventing runs at a much better rate than ever before with a .190 BABIP, it is likely that we can uncover quality defensive play and good luck.
Neither instance invalidates the performance to date, but BABIP is a tool that can allow us to better isolate which factors are driving certain outcomes.
How To Use BABIP:
Most people who are familiar with BABIP have a pretty good idea about why it’s important, but using it responsibly and properly is much more challenging. We know that league average BABIP is almost always right around .300, so many people look at a player’s BABIP and if it is significantly different from .300 they assume that player is either very lucky or very unlucky. This is not always the appropriate way to think about BABIP.
For hitters, you typically want to adjust your expectations toward that player’s career average rather than league average. Batters have much more control over their BABIP than pitchers do, which is another way of saying that a higher percentage of batter BABIP is controlled by actual talent levels. It’s certainly possible for hitters to improve their offensive game and raise their BABIP, but short, dramatic spikes are usually due to luck.
If a hitter has a .320 career BABIP and all of a sudden has a .260 BABIP over the first month of the season, you shouldn’t just expect them to regress to .300 or stay at .260. In fact, they are probably more likely to have a .320 BABIP going forward. Hitters who consistently hit above or below .300 for their BABIP are not simply getting lucky, they are actually leveraging a skill which needs to be accounted for when analyzing their performance.
For pitchers, the same basic principle applies except for the fact that it takes longer for BABIP to become predictive for pitchers than it does for hitters. In other words, if you can get a sense of a hitter’s true talent BABIP after about 800 balls in play, it might take more like 2,000 balls in play to get a sense of what a pitcher’s true talent BABIP truly is. For this reason, we’re more inclined to expect a pitcher’s BABIP to look more like league average in the future than whatever number they might have for the current season because pitcher BABIP over the course of one season has little predictive power, and if it has little predictive power, it is likely not a matter of skill.
This is not to say that some pitchers can’t control their BABIP. Clayton Kershaw, for example, typically has a lower than average BABIP because he’s a fly ball pitcher (fly balls fall for hits less often) with a high strikeout rate. He has a long history of limiting opposing BABIP, but most pitchers’ year to year BABIP don’t tell you much about their future BABIP.
The best advice is to expect batters to BABIP close to their career average and for pitchers to gravitate toward league average, but very large samples can move the needle for pitchers. It is not right to observe that a high BABIP or low BABIP is simply due to luck even if luck plays a role. Luck influences short term changes in BABIP that can impact a player’s stat line, but not every player should be expected to approach league average BABIP.
Context:
The average BABIP for hitters is around .300. If you see any player that deviates from this average to an extreme, they’re likely due for regression, but the best hitters in the league are capable of sporting BABIPs in the .350 range while the worst hitters might hang around .260. Research indicates that you need about 800 balls in play before a hitter’s BABIP “stabilizes.” In reality, there is no magic threshold at which one’s BABIP becomes predictive of future BABIP, but about two seasons worth of data will give you a decent indication of true talent.
The average BABIP for pitchers is also about .300, but their ability to sustain high or low BABIPs is much more limited. Their BABIPs will vary season to season, but in the long run you won’t see many pitchers outside of the .290 to .310 BABIP range. Research indicates that you need about 2,000 balls in play before a pitcher’s BABIP “stabilizes.” Again, there is no magic threshold at which one’s BABIP becomes predictive of future BABIP, but you need about three full seasons of data for starting pitchers before you can start to make any conclusions about a pitcher’s true talent BABIP.
Things to Remember:
● Line drives go for hits more often than groundballs, and groundballs go for hits more often than flyballs. This means that a pitcher or batter with a specific batted ball profile might be prone to higher or lower BABIPs.
● A high or low BABIP is not necessarily a sign of luck, but a BABIP that is substantially different from one’s career mark usually is.
● BABIP requires a large sample before it “stabilizes,” meaning that you can’t say a player has established a new talent level without a significant sample size.
● The long-run ceiling on a player’s BABIP is about .380, as no player with more than 4,000 career PA has ever had a career BABIP higher than that, but .350 is a more realistic mark for the very best hitters in the league.
Batted Ball Statistics are fairly straightforward: they express the share of a batter’s balls in play are line drives, ground balls, or fly balls. This includes balls that leave the park (home runs), so the sum of a batter’s batted ball statistics should be 100%. Major leaguers have a variety of swings, resulting in different batted ball profiles. Some batters hit lots of fly balls (typically power hitters), others put lots of balls on the ground (contact hitters), and many others fall somewhere in between.
Calculation:
The statistics published on MatchupCenter are drawn from data from Baseball Info Solutions (BIS) and reflect the share of a batter’s total balls in play that are of a certain type, classified as line drives, fly balls, and ground balls. Fly balls are also divided up between infield fly balls and total fly balls. To wit, the following are the formulas to calculate the percentages you can find on the site:
Line Drive Percentage (LD%) = Line Drives / Balls in Play
Fly Ball Percentage (FB%) = Fly Balls / Balls in Play
Ground Ball Percentage (GB%) = Ground Balls / Balls in Play
Infield Fly Ball Percentage (IFFB%) = Infield Fly Balls / Fly Balls
Our batted ball data goes back to 2002, but it’s important to remember that there is no perfect way to define each type of batted ball so some balls that you might consider a fly balls might get classified as line drives and vice versa. In reality, batted balls exist on a continuous distribution from rolling perfectly on the ground to being launched straight up in the air. The cut points between the three classifications are somewhat arbitrary and imprecise, so do not treat the data as infallible.
Why Batted Ball Stats:
Batted ball stats are extremely useful for determining the type of hitter at which you’re looking. There is no ideal batted ball distribution, but batters who hit a lot of line drives typically perform better than hitters who hit lots of fly balls or ground balls. Generally speaking, line drives go for hits most often, ground balls go for hits more often than fly balls, and fly balls are more productive than ground balls when they do go for hits (i.e. extra base hits). Additionally, infield fly balls are essentially strikeouts and almost never result in hits or runner advancement. Here are the numbers from 2014:
Type | AVG | ISO | wOBA |
GB | .239 | .020 | .220 |
LD | .685 | .190 | .684 |
FB | .207 | .378 | .335 |
We use these stats to tell us two things. First, we want to get a sense of a batter’s swing or style of play. A big slugger hits lots of fly balls. A weak hitter probably hits a lot of ground balls. But more than descriptive information like that, batted ball data can be an indication of a hitter’s underlying performance.
We often look at a hitter’s Batting Average on Balls in Play (BABIP) to make determinations about the sustainability of their performance and batted ball data informs that analysis in an important way. If a batter hits a lot of line drives, a high BABIP is more likely a function of his true talent than a hitter who hits a lot of fly balls, who has probably just been lucky in running that higher BABIP.
Batted ball stats are a proxy for the nature of the batter’s swing. We know outcome metrics like wOBA are a good measure of value and performance, but they don’t tell us much about process. Batted ball data tells us something about process because the data isn’t a function of the defense. The data is admittedly binned into only three categories without any sort of velocity information, but it’s useful information if used properly.
How to Use Batted Ball Stats:
Batted ball statistics, like most statistics, should be used with caution for three key reasons. First, sample size is very important for the batted ball stat you likely care most about for hitters — line drive rate. While you can get a good sense of fly ball and ground ball rate with a month or two of data, it takes more like a year and a half for line drive rate to “stabilize.” All this means is that six weeks of batted ball data shouldn’t change your opinion of a player’s talent level.
Second, batted ball classification is tricky. What’s the difference between a fly ball and a line drive? At what angle does one become the other? While BIS has a great team scouting each major league game, video data only offers only a certain level of detail. Even the most diligent stringer can’t get it right 100% of the time because they just don’t always have the proper angle to distinguish between a fly ball and line drive. When StatCast becomes fully operational, this problem should disappear because we will be able to use a simple numeric cut point.
Finally, and most importantly, not all line drives/fly balls/ground balls are created equally. A pulled fly ball traveling at 105 mph to deep left field and one that lands harmlessly in the glove of the right fielder are extremely different. A screaming line drive up the game and one that’s easily caught by the shortstop are different. This is essentially another example of the data being a continuous (in launch angle, direction, and velocity) but presented as discrete data. A ball isn’t a fly ball or a line drive, it is hit at X launch angle, Y degrees from center, at Z velocity.
Our categorization is helpful, but it is far from perfect. For example, in 2014, Brandon Crawford and Anthony Rizzo had very similar batted ball statistics, but Rizzo was clearly the better hitter overall as the quality of his contact within those categories was much better than Crawford’s.
Essentially, use batted ball stats as a guide, not an anchor.
Context:
Please note that the following chart is meant as an estimate, and that league-average batted ball rates varies slightly on a year-by-year basis.
Type | League Average |
LD | 21% |
GB | 44% |
FB | 35% |
IFFB | 11% |
Power hitters will generally have higher fly ball rates (~44%), while contact hitters normally have high ground ball rates (50+%). And all hitters will hit their share of infield flies and they generally do not correlate that strongly from year to year.
Things to Remember:
● A line drive produces 1.26 runs per out, while fly balls produce 0.13 runs per out and ground balls produce 0.05 runs per out. In other words, batters want to hit lots of line drives and fly balls, while pitchers generally want to cause batters to hit ground balls.
● Players that don’t hit many balls in the air (higher GB% with lower FB% and LD%) generally have higher BABIPs and batting averages, but have limited power.
● This data is tracked by Baseball Info. Solutions (BIS), which is why it’s only available for players back until 2002.
● GB/LD/FB% are calculated per ball in play.
● IFFB% is per fly ball.
Home Run to Fly Ball rate (HR/FB) is the ratio of how many home runs are hit against a pitcher for every fly ball they allow. Home runs are obviously not good for a pitcher, and a pitcher can reduce the number of home runs hit against them in two ways: by increasing their ground ball rate (therefore lowering their fly ball rate), or by reducing their HR/FB ratio.
While pitchers can control (to a certain extent) the type of batted balls hit against them, there is less skill involved when considering whether a long fly ball is hit into the seats or to the warning track. For example, pitchers who throw in a home ballpark with short fences will tend to have a higher HR/FB ratio than pitchers who throw in large ballparks. Pitcher HR/FB ratios have also been shown to vary considerably from year to year, meaning they have limited predictive value. The only surefire way to limit home runs is to limit fly balls.
Calculation:
HR/FB is one of the easier sabermetric calculations. You simply take the number of home runs allowed and divide by the number of fly balls allowed (and then multiply by 100 to turn it into a percentage for presentation purposes):
HR/FB = (Total Home Runs Allowed / Fly Balls Allowed)*100
Please note that the home run value used is all home runs, not just home runs hit on fly balls. Home runs can occur on line drives and occasionally via ground ball. All home runs are included.
Why HR/FB:
HR/FB is very important because it offers insight into how “lucky or unlucky” a pitcher’s home run rate might be. Home runs kill pitchers, but because they’re a relatively rare event a few lucky or unlucky moments one way or the other can dramatically alter a pitcher’s season. HR/FB gives you some information about those home runs allowed.
Specifically, pitchers generally don’t have much control over how often fly balls leave the ballpark. They play a major role in allowing those fly balls to begin with, but the difference between a ball clearing the fence and dying on the warning track is largely out of their control and it generally takes several hundred fly balls allowed for that luck to balance out. As such, HR/FB matters because it tells us if the pitcher is allowing more home runs than we might expect given their batted ball profile. If a pitcher has allowed 200 fly balls during the season, but has allowed just 10 home runs, it’s very likely that he’s been a little bit lucky given that almost no one can consistently run a 5% HR/FB. League average is around 10% and true talent for almost every pitcher is about 8-12%.
This isn’t to say that HR/FB% isn’t a skill, but rather that the true gap is much small the observed values you might see over the course of a single season worth of data. It wouldn’t be surprising for a 9% true talent HR/FB to have an 11% HR/FB for a season or for an 11% true talent to have a 9%.
As a result, HR/FB can help us better forecast a pitcher’s future innings. If a pitcher has been allowing a lot of home runs with an average HR/FB, it means he’s more likely to keep allowing home runs than if a pitcher has been allowing a lot of home runs with a 14-15% HR/FB. In the long run, we expect most pitchers to regress toward league average, or perhaps their career average if there is something about them that is unusual.
HR/FB allows us to get a better sense of how legitimate a pitcher’s home run rate might be, but that doesn’t mean there isn’t some underlying ability to suppress HR/FB, it’s a just a relatively small range of possible true talents,
How to Use HR/FB:
Using HR/FB is quite easy. First, it’s important to acclimate yourself with league average, which is roughly 10%. In the long run, most pitchers will end up very close to average, so if you see a pitcher with a great or terrible HR/FB over a 50 or 100 innings, you can pretty much bank on them moving closer to average in the future.
Some great pitchers can limit their HR/FB and some pitchers without good stuff or great command can run higher than average HR/FB. The key is that it takes a large number of fly balls (~400+) to be confident in a pitcher’s ability to diverge from the pack. If you see a 12% HR/FB 40 innings into a season, you’re going to want to bet on regression toward the mean, but if you see that same 12% over 500 innings, you’re going to expect much less regression.
Stats like xFIP don’t do a great job with pitchers who routine sit better or worse than league average in HR/FB because xFIP regresses their HR/FB to league average. If you have a pitcher with a HR/FB that is noticeably different from league average, you should investigate why that is as you analyze that player. Often, it’s just small sample random variation and you will see it even out in short order. This leads you to expect the player’s current run (good or bad) is going to end, but if the HR/FB difference is consistent, you have a pitcher who might be onto something (or not!). Additionally, parks play an important role. Pitchers at Coors Field will have higher HR/FB than those at Petco, so you want to mentally account for that.
In short, when the HR/FB for a pitcher deviates from average, look closely at the rest of their game to see if this is something you expect to continue.
Context:
Please note that the following chart is meant as an estimate, and that league-average HR/FB rate varies on a year-by-year basis. \
Rating | HR/FB |
Excellent | 5.0% |
Great | 7.0% |
Above Average | 8.5% |
Average | 9.5% |
Below Average | 10.5% |
Poor | 11.5% |
Awful | 13.0% |
Remember, extreme home run rates in either direction are likely unsustainable. Certain pitchers can consistently post lower than average home run rates, though, so if trying to determine if a pitcher’s HR/FB rate is unsustainable, be sure to also compare it to their career rate while making note of the number of innings and the ballparks involved.
Things to Remember:
● Taking a glance at a pitcher’s HR/FB ratio can help tell you if a player had an over- or under-inflated ERA. Pitchers with HR/FB ratios much higher or lower than league average will normally regress towards league average in the future, which will have a corresponding effect on their ERAs and FIPs.
● One limitation of the HR/FB ratio is that home runs can also come off of line drives. Generally speaking though, the main principles and implications of a pitcher’s HR/FB ratio remain the same.
● HR/FB is not park adjusted.
Earned Run Average (ERA) is a rudimentary metric designed to assess how well a pitcher has prevented runs in the past. Given that a pitcher’s job is to aid in the prevention of run scoring, ERA is understandably a popular and widely used statistic. ERA is perhaps the most commonly cited pitching statistic at large, but has a number of serious flaws that should lead you to use it sparingly.
Calculation:
To calculate ERA, divide a pitcher’s total number of Earned Runs allowed by his total number of Innings Pitched and multiply by nine.
ERA = (Earned Runs / Innings Pitched) * 9
An earned run is essentially any run that was charged to the pitcher which did not score as the result of an error by the defense. The precise definition of how the official scorer makes the distinction can be found here. There are no further adjustments to ERA to account for park or league effects.
Why ERA:
ERA is popular because it seems to be answering a very important question. We want to know how many runs the pitcher gave up that were his fault, but unfortunately, despite the name, ERA does not properly answer that question. Simply put, the distinction between unearned and earned runs is not an accurate demarcation between the runs that were the pitcher’s fault and the runs that were not his fault. There are two main reasons for this.
First, the official scorer determines if something was an error or not and official scorers do not hand out errors consistently, meaning that the same botched play might be scored an error one day and a hit another. Second, and more importantly, bad defense occurs in forms beyond rule book errors.
If a fielder is chasing down a fly ball and trips right before he’s about to catch it, that is not an error in the eyes of the league and the official scorer even though it was a routine fly ball that he obviously should have caught. The defense can fail the pitcher by making an error and the defense can fail the pitcher by not making a relatively easy play. Neither is the pitcher’s fault, but only error-induced runs are stripped out of ERA.
In other words, the goal of ERA is perfect but the execution is horrible. We want a statistic that attempts to strip defense out of the equation, but ERA only “strips out” a very small subset of bad defensive plays, leaving pitcher’s who throw in front of base defenses unfairly dinged by ERA. Fielding Independent Pitching (FIP), provides a better approach to the question ERA wants to answer, and Runs Allowed Per 9 (RA9) provides a more useful measure if you are after a statistic that tells you “exactly what happened.”
How To Use ERA:
ERA is rarely a statistic you should use on its own because it is highly dependent on defense, luck, and sequencing, and therefore tells you very little useful information about a pitcher. It is perfectly fine to hold a pitcher accountable for his luck and sequencing, and if you want to do that, RA9 is a better statistic to use. If you want to strip out defense, then something like FIP is highly preferable.
However, if used in conjunction with other metrics like FIP, xFIP, RA9, etc, then you can learn something about the season the pitcher is having. For example, a pitcher with a large ERA-FIP gap probably plays in front of a very good or very bad defense and a pitcher with a large ERA-RA9 gap is likely being victimized by errors quite often.
ERA is useful because it’s popular and easy to find, but other than convenience, there’s really no reason you would want to know about ERA rather than either RA9 or FIP, depending on your question or philosophical leanings.
Also, because ERA is highly dependent on non-pitcher factors like defense, umpiring, the scorer, luck, sequencing, etc, it is also not highly predictive of future performance because those factors are not part of the pitcher’s talent level, and therefore, do not often travel with him from season to season.
Context:
Please note that the following chart is meant as an estimate, and that league-average ERA varies widely on a year-by-year basis.
Rating | ERA |
Excellent | 2.50 |
Great | 3.00 |
Above Average | 3.40 |
Average | 3.75 |
Below Average | 4.00 |
Poor | 4.30 |
Awful | 4.60 |
Things to Remember:
● ERA is difficult to compare across teams due to differences in team defenses, difficult to compare across leagues due to competition imbalance and the DH, and difficult to compare across years because of different run-scoring environments. A 3.50 ERA has a different meaning depending on if that pitcher played in the early 2000s or the Dead Ball Era, or if they played in a pitcher’s or hitter’s park.
● To adjust for park and league effects, check out ERA-. It’s still not a perfect statistic, but it does make it easier to compare pitchers from different time periods and parks, but it contains the same general flaws of standard ERA.
● Despite the fact that ERA makes an attempt to remove runs that weren’t the pitcher’s fault, it does so in a very haphazard way that does not control for the quality of a pitcher’s defense.
● Some people will support ERA as a measure of “what actually happened,” but that is not accurate. If that is your goal, RA9 is a superior stat.
Fielding Independent Pitching (FIP) measures what a player’s ERA would look like over a given period of time if the pitcher were to have experienced league average results on balls in play and league average timing. Back in the early 2000s, research by Voros McCracken revealed that the amount of balls that fall in for hits against pitchers do not correlate well across seasons. In other words, pitchers have little control over balls in play and assuming short-term fluctuations in BABIP are attributable to the pitcher is likely incorrect. McCracken outlined a better way to assess a pitcher’s talent level by looking at results a pitcher can control directly: strikeouts, walks, hit by pitches, and home runs.
FIP is a measurement of a pitcher’s performance that strips out the role of defense, luck, and sequencing, making it a more stable indicator of how a pitcher actually performed over a given period of time than a runs allowed based statistic that would be highly dependent on the quality of defense played behind him, for example. Certain pitchers have shown an ability to consistently post lower ERAs than their FIP suggests, but overall FIP captures most pitchers’ true performance quite well. For this reason, MatchupCenter’ version of Wins Above Replacement (WAR) for pitchers is based on FIP rather than on ERA and even analysts who prefer a different method of determining WAR find FIP to be extremely useful and informative.
Calculation:
Here is the formula for FIP:
FIP = ((13*HR)+(3*(BB+HBP))-(2*K))/IP + constant
The constant is solely to bring FIP onto an ERA scale and is generally around 3.10. You can find historical FIP constant values here, or you can derive the constant yourself. Because FIP is designed so that league average ERA and league average FIP are the same, to find the constant for any year, all you need to do is the following:
FIP Constant = lgERA – (((13*lgHR)+(3*(lgBB+lgHBP))-(2*lgK))/lgIP)
Knowing how to calculate the constant can be especially useful if you’re interested in doing some of your own calculations for data spanning multiple seasons. The individual weights for home runs, walks/HBP, and strikeouts are based on the relative values of those actions with respect to run prevention.
Why FIP:
Ultimately, we want to use statistics that allow us to isolate the performance of the player we are attempting to analyze. ERA or RA9 do a terrific job telling us how many runs were scored while the pitched was on the mound, but they do not necessarily tell us how well the pitcher performed because the number of runs a pitcher allowed is also dependent on their defense, luck, and the order in which events happened (often called sequencing).
FIP is an attempt to isolate the performance of the pitcher by using only those outcomes we know do not involve luck on balls in play or defense; strikeouts, walks, hit batters, and home runs allowed. Research has shown that pitchers have very little control on the outcome of balls in play, so while we care about how often a pitcher allows a ball to be put into play, whether a ground ball goes for a hit or is turned into an out is almost entirely out of their control.
As a result, a statistic that estimates their ERA based on their strikeouts, walks, hit batters, and home runs while assuming average luck on balls in play, defense, and sequencing is a better reflection of that pitcher’s performance over a given period of time. This is highly related to the reasons why we care so much about Batting Average on Balls in Play (BABIP), specifically the fact that pitchers have very little control over their BABIP allowed.
Imagine two pitchers who always throw the same quality pitches to identical hitters, but one pitcher throws in front of a vastly superior defense. The pitcher with the better defense will allow fewer hits, and therefore fewer runs, but the two pitchers performed identically.
Additionally, the order of events (sequencing) can have a big impact on runs allowed, even though there is no evidence that pitchers are capable of influencing their sequencing. If you have two outs and allow a single, single, home run, and out, you have just allowed three runs. If you have two outs and allow a home run, single, single, and then an out, you have just allowed one run, even though the four events were identical.
Essentially, FIP is an attempt to measure how well a pitcher actually performed independent of factors outside of his control that contribute to runs allowed based statistics. FIP is not perfect and there are certain pitchers who have the skills to allow fewer runs than their FIP suggests, but they are reasonably rare and FIP remains highly accurate and extremely simple at the same time.
How To Use FIP:
In one sense, using FIP is extremely easy because it’s designed to look exactly like ERA. This means that you can read and use FIP exactly like you would typically use ERA. If a pitcher has a 3.15 FIP, that’s just like saying they have a 3.15 ERA as far as making comparisons among players is concerned. You don’t have to learn a new scale to interpret a player’s FIP.
On the other hand, using FIP requires a bit of caution and it is best to think of it as a starting place for the analysis of pitcher performance, especially if you are interesting in determining how a pitcher is likely to perform in the future. In the long run, the majority of pitchers will have ERAs and FIPs that are very close together, but over the course of a season they could vary a great deal. Typically, people attribute the difference between the two to luck on balls in play, but there are other factors that can lead to a difference.
For example, pitchers with the ability to limit the running game or generate fly balls at the expense of line drives or ground balls are more likely to beat their FIP than the average player. This doesn’t mean that every lefty fly ball pitcher will do so, but simply that holding runners and generating a type of batted ball that falls for hits less frequently are legitimate skills that might allow you to limit your runs allowed.
If you have to bet on a pitcher’s ERA or their FIP, FIP is the better bet, but FIP tells you about a subset of a pitcher’s results which means that it is possible that it is missing something important about that pitcher’s profile that allows them to run consistently high or low BABIPs.
FIP is a terrific statistic, but as always, looking into the components of the statistic and other measures of pitcher performance will help you understand how a pitcher is truly performing.
Context:
Please note that the following chart is meant as an estimate, and that league-average FIP varies on a year-by-year basis so that it is always the same as league-average ERA.
Fielding Independent Pitching (FIP)
Rating | FIP |
Excellent | 3.20 |
Great | 3.50 |
Above Average | 3.80 |
Average | 4.20 |
Below Average | 4.40 |
Poor | 4.70 |
Awful | 5.00 |
Based on 2016 Run Environment
● Voros McCracken’s research was called Defense Independent Pitching Theory (DIPS Theory). It’s the building block of much of today’s pitching analysis. It can be a tricky concept to understand and counter-intuitive for most baseball fans. Refer to our sections on DIPs, BABIP, and Luck for more information.
● FIP does a better job of predicting the future than measuring the present, as there can be a lot of fluctuation in small samples. It is less effective in describing a pitcher’s single game performance and is more appropriate in a season’s worth of innings. That doesn’t mean it isn’t a retrospective statistic, simply that it requires more than a handful of innings to be a reliable indicator of performance, just like any statistic.
● FIP is not league or park adjusted meaning that pitchers in good pitcher’s parks will have consistently lower FIPs and pitchers who pitch during eras of lower run scoring will have consistently lower FIPs.
Expected Fielding Independent Pitching (xFIP) is a regressed version of Fileding Independent Pitching (FIP) developed by Dave Studeman from The Hardball Times. It’s calculated in the same way as FIP, except it replaces a pitcher’s home run total with an estimate of how many home runs they should have allowed given the number of fly balls they surrendered while assuming a league average home run to fly ball percentage (between 9 and 10% depending on the year).
Home run rates are generally unstable over time and fluctuate around league-average, so by estimating a pitcher’s home run total, xFIP attempts to isolate a player’s ability level. A pitcher may allow home runs on 12% of their flyballs one year, then turn around and only allow 7% the next year. HR/FB ratios can be very difficult to predict because they contain a lot of noise, so xFIP attempts to correct for that and provide you with a sense of the pitcher’s underlying performance.
Calculation:
Here is the full formula for xFIP. Notice how it is almost exactly the same as the formula for FIP, with the lone difference being how each accounts for home runs. In traditional FIP, you would use the pitcher’s home run total, but in xFIP, you derive an expected number of home runs by taking the pitcher’s fly balls allowed multiplied by the league average home run per fly ball rate.
xFIP = ((13*(Fly balls * lgHR/FB%))+(3*(BB+HBP))-(2*K))/IP + constant
The constant is solely to bring FIP and xFIP onto an ERA scale and is generally around 3.10. You can find historical FIP constants (which is the same as the xFIP constant) values here, or you can derive the constant yourself. Because FIP is designed so that league average ERA and league average FIP are the same, to find the constant for any year, all you need to do is the following:
FIP Constant = lgERA – (((13*lgHR)+(3*(lgBB+lgHBP))-(2*lgK))/lgIP)
Knowing how to calculate the constant can be especially useful if you’re interested in doing some of your own calculations for data spanning multiple seasons. The individual weights for home runs, walks/HBP, and strikeouts are based on the relative values of those actions with respect to run prevention.
Why xFIP:
While the value of moving from ERA to FIP is that it attempts to strip out defense, luck, and sequencing, moving from FIP to xFIP is useful because it tries to remove some of the randomness in the pitcher’s actual performance. Everything we do to calculate FIP is based on the idea that the pitcher is responsible for strikeouts, walks, hit batters, and home runs while the defense is not. This makes FIP a better indicator of pitcher performance than ERA.
However, we also know that the number of fly balls that go for home runs is very sensitive to sample size meaning that over the course of a season, the number of home runs a pitcher allows may be higher or lower than their true talent indicates. This is not to say pitcher’s aren’t responsible for the home runs they did allow, but rather to say that if you want to judge about how well they pitched, xFIP will remove some of those fluctuations in HR/FB% and will give you a better idea. For this reason, our pitcher Wins Above Replacement (WAR) is based on FIP rather than xFIP. They gave up the home runs so they count against them, but xFIP suggests they probably won’t continue to do so in the future.
To give you an idea, let’s imagine a pitcher who threw 200 innings, struck out 200, walked and hit 60, gave up 24 home runs, and 240 fly balls. This pitcher would have a FIP of 3.56 (if we assume a 3.10 FIP constant). This pitcher has a league average HR/FB%, so we can also say their xFIP is 3.56.
Now imagine if during the course of this season, this pitcher allowed five more home runs to carry the fence. That’s not even one extra home run per month.That turns into a 3.89 FIP, but the pitcher’s xFIP remains 3.56. In the first scenario, the pitcher has a 10% HR/FB% and in the second scenario it’s 12%. That may not seem like a big gap, but it is. And we also know that these rates are not typically very stable over time, which means that there is an awful lot of random noise involved. When discussing a pitcher’s past value, those home runs should count against them, but if you want to evaluate their underlying performance, knowing their fly ball rate is more useful.
As a result, xFIP strips out some of this fluctuation to give you a better view of how well we think a pitcher pitched over a given period of time, while controlling for defense, batted ball luck, and sequencing, and also HR/FB%. In other words, we use xFIP to see how a pitcher might be expected to perform given an average HR/FB% because we do not expect pitchers to have much control over that number. They can control how many fly balls they allow, but only a limited set of pitchers can truly influence their HR/FB%. This makes xFIP a very useful statistic if used properly.
How to Use xFIP:
Using xFIP is both extremely easy and moderately complex. From a simple perspective, xFIP is on an ERA scale, so you can apply what you know about ERA and FIP to xFIP and have a good sense of what a given value means. A player with a 3.00 xFIP is just as good as you think a player with a 3.00 ERA is. The scale is intentionally identical, so reading xFIP is a snap.
Using it appropriately when analyzing players requires a bit more caution. First, it’s not as simple as saying Pitcher A has a 3.40 ERA and FIP and a 3.70 xFIP, so he is due for regression. While xFIP is usually more predictive of future performance, there are reasons why a pitcher might not be expected to pitch to that particular xFIP.
First, some pitchers can control their HR/FB% to some degree. Generally speaking, we expect most pitchers to approach a league average rate (About 10% most years), but some pitchers can consistently posts values around 8% and some can go as high as 12%. This is different than saying HR/FB% bounces around based on random variation. Some pitchers do the the ability to limit their HR/FB%, so being aware of your particular pitcher’s skill set is important. If a pitcher has routinely posted 9% HR/FB%, there’s a decent chance xFIP is underrating him a bit.
Additionally, xFIP is a predictive model based on just one year of data or however many years you incorporate and every event is weighted equally. In this way, it is not better than a legitimate effort to forecast or project a pitcher’s ERA or FIP. You can think of it as a very basic forecast, but a proper forecast will include multiple years of data and will weight recent events differently than older events.
Despite it’s limitations in those two regards, xFIP is a terrific way to get a sense of how well a pitcher’s been throwing the ball. xFIP tells us about a pitcher’s strikeout and walk rates, which are very important, and also inherently provides us with information about their batted ball profiles because fly ball rate is built into the model.
In a very simple sense, FIP tells you how a pitcher has performed (value) independent of their defense while xFIP tells you about how well he has pitched (ability, talent) independent of their defense. Do not rush to assume a pitcher’s xFIP is a better reflection of their talent, but using it to get a sense of their abilities in conjunction with other statistics will make you much better off.
Context:
Please note that the following chart is meant as an estimate, and that league-average xFIP varies on a year-by-year basis so that it is always the same as league-average ERA.
Rating | FIP |
Excellent | 2.90 |
Great | 3.20 |
Above Average | 3.50 |
Average | 3.80 |
Below Average | 4.10 |
Poor | 4.40 |
Awful | 4.70 |
Things to Remember:
● xFIP is not park or league adjusted. We carry a park and league adjusted version called xFIP-, found in the “Advanced” tab of the leaderboards and player pages.
● xFIP only dates back to 2002, the first year we have reliable HR/FB% data. Sorry, you can’t find Sandy Koufax’s xFIP.
● While HR/FB ratios are generally unstable over time, some pitchers are still more prone to allowing home runs than others. If a pitcher has a long history of over- or under-performing the league average with their HR/FB rate, then you can reasonably expect them to perform closer to their career average than the league-average. In cases like this, xFIP may overestimate or underestimate a player’s true talent level by assuming a league average HR/FB ratio. For more, see SIERA.
● Ground ball pitchers typically have higher HR/FB ratios than fly ball pitchers.
● xFIP has one of the highest correlations with future ERA of all the pitching metrics.
● While xFIP is more predictive than FIP, we use FIP to build WAR because those home runs were the responsibility of the pitcher and not the defense, which WAR hopes to strip out.
Walks plus Hits per Innings Pitched (WHIP) is essentially a measurement of how many base runners a pitcher allows per inning. Given that preventing base runners the fundamental role of pitchers, a rate statistic designed to tell you how many they allow definitely points you in the right direction.
That being said, WHIP is more of a quick reference statistic rather than something you want to use for full-fledged analysis. If you want to measure base runners allowed using a rate stat, OBP against is a better choice because batters faced is a better denominator than innings. WHIP is also lacking in that it treats all times on base equally, equating a walk with a home run. A statistic like wOBA against is more useful in that regard.
While WHIP is no longer at the forefront of stat-head analysis, it’s easy to calcuate and corelates relative well with more accurate statistics. Think of WHIP as something like OPS. It’s a little rough around the edges but it will generally provide a fine starting point.
Calculation:
WHIP is calculated exactly how you would expect:
WHIP = (Walks + Hits) / Innings
Why WHIP:
Pitching is about run prevention and run prevention is about base runner prevention, so it makes sense that you’d want to know how well a pitcher prevents base runners. Walks and hits are the two primary ways runners reach base, so turning those into a rate stat makes plenty of sense. WHIP answers the question “about how many base runners does this pitcher allow per inning?”
You might want to ask more precise questions, like “how many runners does he allow per batter faced?” or “how many bases does he allow per inning or batter faced?” but WHIP is a quick way to a similar answer and is the kind of thing that is very easy to calculate even with limited data sets. Once upon a time, that made it a very popular statistic in fantasy baseball leagues because it was more advanced than the raw outputs you were used to seeing, but didn’t require a lot of heavy lifting.
How To Use WHIP:
WHIP is a measure of base runner prevention, but it’s important to remember that base runner prevention is part pitching and part defense. The walks are mostly the pitcher’s fault, but hits vary dependig on the situation. Home runs are the fault of the pitcher but singles are shared between the pitcher and the defense. Like many pitching statistics, it’s a measure of what happened while the pitcher was on the mound, not a measure of the pitcher’s unique contributions.
However, WHIP is a better isolation of pitcher performance in many cases than something like ERA because it’s based on individual events rather than a sequence of events. In other words, it’s easier for one bad play on defense to tank your ERA than your WHIP.
Lower WHIP is better and you can use it as rough estimate of dominance. If you have access to OBP against or wOBA against, you should use those instead, however, as they are a bit more mathematically consistent with the concepts we want to measure — either the prevention of base runners or the prevention of bases themselves.
Context:
Please note that the following chart is meant as an estimate, and that league-average WHIP varies on a year-by-year basis..
MatchupCenter Library – WHIP
Rating | WHIP |
Excellent | 1.00 |
Great | 1.10 |
Above Average | 1.20 |
Average | 1.30 |
Below Average | 1.40 |
Poor | 1.50 |
Awful | 1.60 |
Skill-Interactive ERA (SIERA) is the newest in a long line of ERA estimators. Like it’s predecessors FIP and xFIP, SIERA attempts to answer the question: what is the underlying skill level of this pitcher? How well did they actually pitch over the past year? Should their ERA have been higher, lower, or was it about right?
But while FIP and xFIP largely ignore balls in play — they focus on strikeouts, walks, and homeruns instead — SIERA adds in complexity in an attempt to more accurately model what makes a pitcher successful. SIERA doesn’t ignore balls in play, but attempts to explain why certain pitchers are more successful at limiting hits and preventing runs. This is the strength of SIERA; while it is only slightly more predictive than xFIP, SIERA tells us more about the how and why of pitching.
Here’s what SIERA tells us:
Strikeouts are good…even better than FIP suggests. High strikeout pitchers generate weaker contact, which means they allow fewer hits and have lower homerun rates. The same can be said of relievers, as they enter the game for a short period of time and pitch with more intensity.
Also, high strikeout pitchers can increase their groundball rate in double play situations. Situational pitching is a skill for pitchers with dominant stuff.
Walks are bad…but not that bad if you don’t allow many of them. Walks don’t hurt low-walk pitcher nearly as much as they hurt other pitchers, since low-walk pitchers can limit further baserunners. Similarly, if a pitcher allows a large amount of baserunners, they are more likely to allow a high percentage of those baserunners to score.
Balls in play are complicated. In general, groundballs go for hits more often than flyballs (although they don’t result in extra base hits as often). But the higher a pitcher’s groundball rate, the easier it is for their defense to turn those ground balls into outs. In other words, a pitcher with a 55% groundball rate will have a lower BABIP on grounders than a pitcher with a 45% groundball rate. And if a pitcher walks a large number of batters and also has a high groundball rate, their double-play rate will be higher as well.
As for flyballs, pitchers with a high flyball rate will have a lower Homerun Per Flyball rate than other pitchers.
Finally we have a stat that A) is accurate and predictive, and B) accounts for some of the complexity of pitching.
Context:
SIERA is on a similar scale to ERA, so any score that is a good ERA is also a good SIERA. Please note that the following chart is meant as an estimate, and that league-average SIERA varies on a year-by-year basis.
Rating | SIERA |
Excellent | 2.90 |
Great | 3.25 |
Above Average | 3.75 |
Average | 3.90 |
Below Average | 4.20 |
Poor | 4.50 |
Awful | 5.00 |
In general, relief pitchers have lower SIERA scores than starting pitchers. As a handy shortcut, a pitcher that switches from starting to the bullpen will on average see their SIERA drop by 0.37 points (and vice versa).
Things To Remember:
● As always, when evaluating pitchers, it’s best to use multiple statistics instead of relying on one alone. While SIERA is the most accurate of the ERA-estimators, it’s only slightly more accurate than xFIP. Both xFIP and FIP still have their uses, so I wouldn’t recommend ditching them entirely and using only SIERA — a balanced approach is always best. You can learn a lot about a pitcher by looking at which metrics like and dislike them, and for what reasons.
● In and of itself, SIERA works as well as many projection systems in terms of predicting a player’s future ERA. But be careful of this distinction: SIERA is technically a backward-looking ERA estimator and not a forward-looking projection system.
● SIERA is park-adjusted, meaning it adjusts for the fact that some pitchers play in PETCO Park and others in Yankee Stadium.
●SIERA is updated for the new (low-scoring) run environment around the majors.
SD / MD
Shutdowns (SD) and Meltdowns (MD) were created as an alternative to Saves and Blown Saves in an effort to better represent a relief pitcher’s value. Shutdowns and Meltdowns strip away these complications and answer a simple question: did a relief pitcher help or hinder his team’s chances of winning a game? If they improved their team’s chances of winning by a certain amount, they get a Shutdown. If they instead made their team more likely to lose by a certain amount, they get a Meltdown.
While Shutdowns and Meltdowns are a fairly simplistic tool, but they offer an opportunity to evaluate pitchers who don’t pitch in the “closer’s role” and provide a way to judge “closers” when they pitch in non-save situations.
Why SD/MD:
Shutdowns and Meltdowns provide a useful counting stat for relievers. Saves are often considered the reliever gold standard by the population at large, but Saves are not a useful statistic for evaluating relief pitchers because you can only earn Saves in very specific situations, and can pitch poorly while earning a save or pitch very well and not earn one.
Shutdowns and Meltdowns give you a way to glance at a reliever’s stat line and determine how often they register a good or bad outing. Good relievers generally have a high SD/MD ratio, and Shutdowns and Meltdowns communicate reliever performance in a discrete way rather as run values like ERA, FIP, or RE24. Shutdowns and Meltdowns correlate very well with saves and blown saves; in other words, dominant relievers are going to rack up both Saves and Shutdowns, while bad relievers will accrue Meltdowns and Blown Saves. But Shutdowns and Meltdowns improve upon SVs/BSVs by giving equal weight to middle relievers, showing how they can affect a game just as much as a closer can, and by capturing more negative reliever performances.
How To Use SD/MD:
Generally, SD/MD line up well with Saves and Holds in terms of the number you would expect from a good reliever. 35-40 Saves is often the target for a good closer, and the same would be true for relievers looking to accrue Shutdowns.
You do want to keep in mind that not all Shutdowns (or Meltdowns) are created equally. A +0.40 MOMENTUM game and a +0.07 MOMENTUM game each earn a single Shutdown, to say nothing of the fact that Win Expectancy is based on average teams in average situations, not the two specific teams in the specific contest. The best three hitters on a team with a platoon advantage are a much taller task than the bottom of the order, but that information isn’t included when looking at Shutdowns and Meltdowns.
Think of Shutdowns and Meltdowns as a simple way to determine whether or not the pitcher had an effective outing or not. They don’t necessarily tell you if a pitcher pitched well, but they do tell you if the team had good outcomes when the pitcher was on the mound in a particular game. Over a full season, you will typically see good relievers rise to the top and bad relievers sink to the bottom, but like any other statistic, the results won’t be perfect reflections of the truth.
Context:
The +/- 6% cutoff puts SDs and MDs on a similar scale as saves and holds, meaning 40 shutdowns is roughly as impressive as 40 saves or 40 holds. Dominant closers or set-up men will typically have 35 to 40+ shutdowns and a handful of meltdowns.
Meanwhile, meltdowns are more common than blown saves, and they can happen to both closers and non-closers alike. The worst relievers will rack up around 10 to 15 meltdowns in a season.
Rating | SD | MD |
Excellent | 40 | 2 |
Great | 35 | 4 |
Above Average | 25 | 6 |
Average | 20 | 8 |
Below Average | 15 | 10 |
Poor | 10 | 12 |
Awful | 5 | 15 |
N
[Offensive Rebounds + Opponent Turnovers – Opponent Offensive Rebounds –Turnovers]Teams always get roughly the same number of possessions.
But through rebounding, ball handling, and pressure defense, one team
can gain more true scoring chances than the other.
[(Possessions + Offensive Rebounds – Turnovers) / Possessions] — This measures how good a team is at actually getting scoring chances out of their possessions. Turning it over costs them a chance, while grabbing an offensive board gains them an extra one. Higher is better.
Floor % is used to measure offensive efficiency, namely the percentage of times points are scored on each possession. The not so obvious reason it can be used that way is because almost allscoring possessions for all teams involve two points being scored, notone point or three points. A normal game might have one team scoring on58 of 100 possessions and the other scoring on 53 of 100 possessions. Theteam scoring on 58% of its possessions will win 99% of the time (that'san educated guess not based on scores of hundreds of games). The only waysthe team with the 53% floor % will win is by making enough three pointersand/or by having several of the 58 opponent's scoring possessions be worthonly one point (making only one of two free throws). A typical score forthis game would be 116-106. It might be 114-108 or 117-105, but any differencesmaller than about six points or larger than about fourteen would be veryunusual.
The number of possessions per 40 minutes. Possessions is not an official NCAA statistic, so it must be estimated.
[Possessions = FGA-OR+TO+.42*FTA]
This is a pretty standard computation that accounts for when possession is lost by a team. The only bit of uncertainty is the free throw portion, because we don’t always know how a team got to the line. If they are shooting two, then the two FTAs account for one possession. But if they go to the line for one after making a shot, then the one FTA has no possession attached to it, because the previous FGA accounts for it.
Proprietary formula that Calculates which team should benefit more from opportunities at the free throw line throughout ball game. This will become especially important in tight contest late in the game during Conference and Tournament Play as pressure mounts.
The Rating Percentage Index, commonly
known as the RPI, is a quantity used to rank sports teams based upon a
team's wins and losses and its strength of schedule. The Rating Percentage Index (RPI) has been used by the NCAA
men's basketball committee since 1981 as supplemental data to help select
at-large teams and seed all teams for the men's and women's NCAA basketball
tournaments
This is a proprietary rating system that incorporates scoring, 2 and 3 point percentages, assists, turnovers, offensive efficiency ratios, offensive rebounding and free throw percentages.
This is a proprietary rating system that incorporates opponent scoring, opponent 2 and 3 point percentages, steals, defensive efficiency ratios, defensive rebounding and opponent free throw percentages.
This is a proprietary rating that includes offensive rebounds per game, offensive rebounding percentages and shots taken.
This is a proprietary rating that includes defensive rebounds per game, defensive rebounding percentages and opponent shots taken.
This is a proprietary calculation that includes returning minutes played from 2 previous season, returning points from previous season, which players expect to be playing professionally after leaving college and returning players classification, with a requirement of minimum minutes played in previous season.
Average number of seconds on offense for each possession.
Average number of seconds opponent's possessions.