MLB DFS Glossary
NOTE: This is an old blog from 2017, though it still has plenty of useful information for a beginning player.
Daily fantasy sports has a language all its own. Whether its “stacking” or “chasing overlay” or “fading the chalk and getting exposure to some contrarian, low-floor, high-ceiling players in order to take down a large-field GPP,” for new players, cracking the code can be half the battle. Particularly in baseball, where each offseason seems to bring a new batch of difficult-to-process, not-quite-catchy sabermetric acronyms (cFIP, anyone?), the insider jargon can be intimidating.
In this blog, I’ll attempt to simplify some terms commonly used in daily fantasy baseball circles. For each term, you’ll find a brief definition, followed by a bit of supplementary information. I’ll assume that anyone who is reading a blog at RotoGrinders understands that OBP stands for on-base percentage, that a PA (plate appearance) is different from an AB (at-bat), and that there’s more to evaluating a hitter than looking at his slash line. In other words, “traditional” baseball stats will not be covered here (although I couldn’t resist including an ERA rant). Instead, I’ll focus on the stats and terms that newer players might not be familiar with.
It isn’t a comprehensive list, but I’ve tried to include terms and information that will be useful for both new and experienced players alike. Without further ado…
Batting Average on Balls in Play. Measures the rate at which a ball put in play results in a hit. Over time, pitchers tend to regress toward the league average rate, which is around .300. While league average for hitters remains around that same mark, hitters, more so than pitchers, are able to control their own BABIP – extremely fleet-of-foot players can outrun seemingly routine grounders, which allows them to sustain higher BABIP numbers (think Dee Gordon, who ended 2015 with a .383 BABIP despite hitting a league-leading 339 ground balls). Conversely, slow, injured, or old players (think Albert Pujols, who checks all those boxes) can sustain very low BABIP marks (Pujols had a league-low .217 mark in 2015) without it seeming especially fishy.
Walk Rate. For pitchers, BB% refers to walks allowed divided by batters faced. BB% is useful because, unlike many metrics, it stabilizes quickly, after just a few dozen batters, in fact. According to FanGraphs, an “excellent” walk rate is around 4.5% (Madison Bumgarner owned this exact rate last year), an “average” walk rate is around 7.7% (Scott Kazmir last year), and anything beyond 9.0% is considered “awful.” And then there’s Trevor Bauer, who at 10.6% last year, is in a category all to himself.
Walk Rate. For hitters, BB% refers to frequency with which a plate appearance results in a walk. According to FanGraphs, it only takes around 120 plate appearances for a hitter’s walk rate to “stabilize.” An “excellent” walk rate for a hitter is around 15.0% (seven players last year – Joey Votto, along with six others, exceeded this mark in 2015), while an “average” rate should hover around 8.0% (Addison Russell last year). An “awful” walk rate refers to anything below 4.0%. Last year, Jean Segura was the awfulest, sporting an MLB-worst 2.2% rate.
Do aliens exist? What happened to DB Cooper? Did Adnan do it? Do batter versus pitcher stats matter? These are life’s burning questions. According to The Book (basically the baseball nerd’s bible), BvP numbers mean far less than some might lead you to think: “…having twenty to thirty PA against an opponent is a drop in the bucket, and it tells you almost nothing about what to expect.” The Book continues, “Knowing a player will face a particular opponent, and given the choice between that player’s 1,500 PA over the past three years against the rest of the league, or twenty-five PA against that particular opponent, look at the 1,500 PA.” In other words, when you hear someone say that Josh Harrison is a great play against Adam Wainwright because he’s a career 6-for-13 against him (he is, and you will, as they face each other this Sunday), use the advice at your own peril.
Unfortunately, The Book does not offer any insight on aliens, DB Cooper, or Adnan Syed.
The greatest hitting environment in MLB, which has the ability to make players like Tom Murphy look like Mike Piazza, DJ LeMahieu look like Dustin Pedroia, and Daniel Descalso look like…actually, no ballpark can save Daniel Descalso. In 2015, for every hit in a league average park, Coors allowed 1.3 hits. For every run scored in a league average park, 1.44 runs were scored at Coors. “Play everyone at Coors” is one of those DFS truisms that seems overstated, but probably isn’t. Still, according to ESPN.com, despite Coors’ deserved reputation as a run-scoring utopia, it ranked as only the fifth-best home run park last season – Coors is far more generous in giving up triples (twice as many as in a league average park in 2015).
A home run. A word of caution: using the phrase “dong” in any social context not directly related to daily fantasy sports is highly discouraged. Shouting “Dong!” at any non-DFS event, or posting about dongs repeatedly on social media, may result in puzzled looks, unfriending, and/or general shunning. Using the phrase “double dong” or “triple dong” is (arguably) even worse.
In DFS vernacular, typically refers to a game or situation that is best avoided. This blog does not endorse setting any dumpsters (or anything else) on fire as a result of playing daily fantasy. If you want to see something go up in flames, start a pitcher in Coors and watch your money burn.
Earned Run Average. An antiquated, arcane rate statistic that measures the earned runs given up by a pitcher per nine innings pitched. Once upon a time (pre- The Book, FanGraphs, hoverboards, etc.), ignorant. knuckle-dragging savages believed that ERA was an accurate measuring stick for a pitcher’s success. Now, we know this to be utter nonsense (partially because ERA would have us believe that Marco Estrada is a good pitcher). I’m exaggerating here, but seriously…don’t rely on ERA in 2016. There are better stats.
Fielding Independent Pitching. A metric designed to filter out the noise inherent in ERA. FIP dismisses the parts of hitting that involve defense (which is out of the pitcher’s control), only counting strikeouts, walks, hit by pitches, and home runs allowed. Essentially, it estimates what a pitcher’s ERA would look like if that pitcher had league average defense behind him. FIP is conveniently scaled to ERA, so if a pitcher’s FIP is drastically higher than his ERA, it’s reasonable to assume that pitcher’s ERA will spike as the pitcher faces more batters. Conversely, if a pitcher’s FIP is drastically lower than his ERA, it’s reasonable to assume that pitcher’s success is due in part to above average defense and/or luck.
Ground Ball Percentage, Fly Ball Percentage, Line Drive Percentage. In 2015, a league average ground ball rate was 45.3%, a league average fly ball rate was 33.8%, and a league average line drive rate was 20.9%. Elite ground ball pitchers tend to be safer while offering less strikeout upside, unless you’re talking about Tyson Ross and Dallas Keuchel, who last year were the only two pitchers with strikeout rates above 25.0% and ground ball rates above 60.0%. A word of caution: while ground ball and fly ball rates can stabilize within a month or two, line drive rates can take years to reveal anything meaningful about a hitter.
Hard-Contact Rate, aka Hard-Hit Rate. FanGraphs bases this metric on a hit ball’s hang time, trajectory, and landing spot. While a league average rate tends to be around 30%, it’s not clear how predictive this metric is. A quick glance at 2015’s hard-contact leaderboard tells us what we already know…that guys like David Ortiz, Paul Goldschmidt, and Chris Davis hit the ball really, really hard. Batted ball data can also take a long time to stabilize. For those reasons, it’s best to not use this data independently, but to use it in conjunction with other data. For example, last season Hector Santiago allowed the fourth-highest hard-contact rate in the majors (33.6%), yet his BABIP was at .252, the fourth-lowest in the majors. It’s reasonable to project that if batters continue to make hard contact against him at the same rate, he’ll allow a few more runs in 2016.
Home Run to Fly Ball Rate. The ratio of home runs a player hits (or allows, in the case of pitchers) divided by their total fly balls. Here’s the (over)simplified version of home run to fly ball rate: over time, roughly 10% of balls hit in the air will result in home runs. Therefore, if a pitcher, let’s say Kyle Kendrick (gas can that he is), is getting shelled but is maintaining a HR/FB% of 5.0%, we know that’s mostly just good luck.
Still, it’s best, when incorporating HR/FB% into your DFS research, to gauge a player’s current stats against his career stats rather than against other players. Common sense would tell us that if Dee Gordon and Giancarlo Stanton hit the same number of fly balls in a given time, Stanton’s will leave the yard at a higher rate. But if Stanton, who has a career HR/FB% of 25.9%, slumps out of the gate and has a 10.0% HR/FB% on June 1, it’s reasonable to assume a power surge is imminent.
Isolated Power, which is a measure of a hitter’s power. Frequently calculated by subtracting batting average from a player’s slugging percentage. According to FanGraphs, a league average ISO should be around .170, or roughly HALF of Giancarlo Stanton in 2015. Yeah, he’s got power.
Coined by noted DFS player and Canadian pepsi7, refers to a player hitting a home run (jack) and stealing a base (bag) in the same game. The leader in jack-and-a-bags (jacks-and-bags?) in 2015 was, weirdly, Houston’s Jake Marisnick, who had five. The all-time leader is Barry Bonds with 102 (presumably most of these came from the version Bonds on the left side of this picture).
Strikeout Rate, or sometimes, K-rate. Finding a pitcher with an elite K% is one of the most important ingredients to a winning lineup in MLB DFS. K% is particularly useful in DFS primarily because of its predictability; pitchers can (mostly) control their own strikeouts, but as soon as a ball hits a bat, a lot is left up to chance. It also doesn’t hurt that, according to FanGraphs, K% can stabilize after just a few dozen batters faced. According to FanGraphs, an “average” strikeout rate is around 20.0%, while they deem anything above 27.0% “excellent” and anything below 13.0% “awful.” Sorry Mark Buehrle (11.0% in 2015) and Mike Pelfrey (12.0%) – you’re awful.
Strikeout Rate. FanGraphs considers a hitter with a sub-10.0% K-rate “excellent,” a hitter with a K-rate over 27.5% “awful,” and a hitter with a 20.0% K-rate to be “average.” In 2015, Daniel Murphy 7.1% whiff rate led the majors, while Chris Davis was the most strikeout-prone player in baseball, fanning at a rate of 31.0%. For what it’s worth (which isn’t much), Davis actually improved in K% last year, dropping two whole percentage points from his league-worst (again) mark in 2014.
Left-On-Base Rate, also known colloquially as “strand rate,” refers to the percentage of hitters left on base over the course of a season. League average hovers around 70-72%, and pitchers tend to regress upward or downward toward this number as their innings accrue. Some pitchers (namely high-strikeout pitchers) are able to outperform league average LOB%. For example, Clayton Kershaw has been at 78.3% or higher each of the past three seasons, and he ain’t lucky – he’s Kershaw.
I do not understand the science behind the marine layer. All I know is that, because of the Pacific Ocean, the air near West Coast stadiums changes in density, making the air much more “soup-like” according to this MLB.com article, which in turn makes it more difficult to hit home runs. This, of course, works in the pitcher’s favor. Stadiums impacted by the marine layer include Angel Stadium of Anaheim, SafeCo Field (Mariners), O.Co Coliseum (Athletics), AT&T Park (Giants), PetCo Park (Padres), and Dodger Stadium. For a more detailed description of how the marine layer works, consult your local scientist.
When a player who normally enjoys hitting in a hitter-friendly venue travels to a more pitcher-friendly environment (negative park shift), or vice versa (positive park shift). In DFS, it’s crucial to know which hitting environments favor hitters (and which handedness), and which favor pitchers. I recommend RotoGrinders’ Ballpark Factors tool under the “Daily Research” tab.
RotoGrinders’ resident meteorologist. If you like money, you’ll heed his timely, indispensable, color-coded advice regarding rainouts, wind, etc.
Skill Interactive Earned Run Average, also referred to (by me) as hipster ERA. Tragically, not named after Ruben Sierra (note the spelling). SIERA attempts to estimate what a pitcher’s ERA should be if not for all that pesky randomness. Like FIP and xFIP, SIERA is similar in scale to ERA, so if a player has an extremely low SIERA but a high ERA, you can expect some positive regression for the pitcher down the road.
Swinging Strike Rate. Refers to number of swings and misses divided by total pitches. For a hitter, SwStr% can tell you a lot about the his approach at the plate. Is he aggressive early in the count? Is he terrible at hitting sliders? Over a large enough sample size, SwStr% can begin to clue you in to these answers. For pitchers, swinging strike rate can illustrate how good a pitcher’s “stuff” is, and can also serve as an predictor of an uptick in K%. If a player has an elite SwStr% and a low K%, it’s reasonable to expect some positive regression. League average SwStr% for both hitters and pitchers last year was 9.9%.
Weighted On-Base Average. wOBA is like on-base percentage for smart people. According to FanGraphs, wOBA “[c]ombines 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.” In other words, the recipe for wOBA is complicated. But it’s best if you don’t ask how the sausage is made; just know that it tastes better than one made of batting average, on-base percentage, and/or slugging percentage.
Weighted Runs Created Plus. The soylent of MLB stats, wRC+ is the closest thing we have to an all-in-one stat, containing all the elements of a full statistical meal without the excess statistical sugars, saturated fats, and cholesterol. Unlike wOBA, wRC+ is park-adjusted, meaning it isn’t skewed by Coors or other curve-breaking hitter’s parks. And no mathematical chops are needed to interpret it, either – league average is around 100, so if a player is over 100, he’s doing well, and if he’s under 100, he’s not. Easy peasy. Last year, Bryce Harper had a 197 wRC+, meaning he’s worth roughly two league average hitters, or Martin Prado AND Marlon Byrd (who were both at 100 in 2015).
Expected Fielding Independent Pitching. Sort of like the Pepsi to SIERA’S Coke (or maybe it’s the other way around). Like FIP, xFIP only takes into account outcomes that pitchers can directly control: strikeouts, walks, and hit by pitches. Also like FIP, xFIP plugs in a league average BABIP in order to filter out bad defense from a pitcher’s ERA. Unlike FIP, xFIP normalizes a pitcher’s home run to fly ball rate to league average (around 10.0%), as well. For this reason, xFIP is great for comparing pitchers with vastly different home parks. A Reds pitcher who makes 16 starts in Great American Ballpark (which allows tons of homers) and a Giants pitcher who makes 16 starts in AT&T Park (which allows fewer homers) are both put on a level playing field with xFIP.
If you made it this far, thanks for reading, and congratulations, because that a long, long blog post. If you enjoyed it, click the green thumbs up in the upper right corner, and feel free to leave a comment in the thread below!
I’ve made it my goal to blog about MLB DFS every day in April. Check back for more of my MLB thoughts, including new “10 Definitely Interesting, Possibly Helpful Notes” blogs once the season starts.
Good luck this MLB season!