Fantasy 101: Explaining Advanced Stats
Don’t let the name fool you—”advanced” stats are a basic part of our game. We’ve come a long way since guys like Bill James started publishing works like The Bill James Baseball Abstract. Fantasy owners, from rookies to the oldest vets, rely heavily upon statistical analysis to make projections, decipher player performance, and much, much more. Nowadays, thanks to that series of tubes we call the internet, we have a plethora of data available to us at all times to tell us anything we wanted to know, from how hard a player hits a ball, to how much that ball spun on its way to the bat, to whether that ball should have landed in a glove, and everything in between. To succeed in today’s stat-filled fantasy landscape, players need to have an understanding of how these metrics can be used to their advantage. This article will attempt to talk about the most common and useful stats, while pointing out their strengths, shortcomings, and alternatives. I urge all readers to open up our glossary in a separate tab for reference—stat discussions can quickly turn into an alphabet soup that can be tough to keep track of if you aren’t used to the acronyms.
One more quick disclaimer: There is no such thing as a truly predictive stat! Even the fanciest of stats can only tell us what already happened, or in the case of expected stats, what probably should have happened. Because statistics are, by their nature, rooted in the past, they cannot tell us whether a player will develop a new skill or pitch, or whether they’re going to be injured, or whether opposing teams will find a flaw in their game. We also cannot predict luck—though we can generally identify luck if someone has it for long enough. This is important because believing that a single stat is giving you the whole story on a player’s performance is one of, if not the biggest mistake you can make!
While exploring the wide world of advanced hitting stats, I’ve generally come across three distinct flavors of metrics: raw, expected, and adjusted. This will be the framework of our dive into stats, so buckle in and enjoy!
The first and most common type of advanced stat we find are what I call raw stats. I think of them as raw stats because they are, at their essence, a measurement of something that actually happened. This category includes measurements (like exit velocity and launch angle), rate stats (like strikeout rate and ground-ball rate), and averages (like BABIP and IPS). They always show us something that actually happened, and they aren’t tweaked or adjusted based on outside factors like ballpark dimensions. They pure, straight-forward numbers. On one hand, the simplicity of this type of stat is beautiful—there’s no complex formula or spreadsheet that you need to plug these stats into to understand how they were calculated. Exit velocity, for example, is simply how fast the ball traveled off the bat of a hitter. Big numbers for hitters are better than small numbers. Easy, right? Well, the downside to these stats stems from that very simplicity—on their own, they can’t tell us enough information to make good decisions.
In 2018, the balls off the bat of the burly Yandy Diaz (3B, Tampa Bay Rays) traveled at a scorching 92.1 mph on average. That would have been good for the 19th-best mark among qualified hitters, had he qualified, right behind a guy named Mookie Betts (OF, Boston Red Sox). After Diaz’s offseason trade to the Rays, he needs only to beat out the soft-hitting Matt Duffy (3B, Tampa Bay Rays) to win a full time job at the hot corner, and these exit velocity numbers would have us believe that home runs would surely follow. That is, of course, until we dug deeper and found that Yandy has a grudge against worms that he still can’t let go—his average launch angle (another raw stat that should always be taken into account when looking at exit velocity) was just 4.4 degrees off the bat. No matter how big his biceps get, he won’t be able to hit home runs by bouncing the ball off the infield dirt (even Hercules himself could only get a double that way because rules are rules). It’s not necessarily a deal-breaker, though. Other hitters with similar profiles in terms of raw stats do exist, and some of them are pretty good. Christian Yelich (OF, Milwaukee Brewers) famously won his 2018 NL MVP with a 92.3 mph average exit velocity and 4.7-degree launch angle. Those numbers are really close to Yandy’s, meaning that a bit of growth and skill can go a long way (particularly when it comes to his approach, as Yelich’s home runs tend to come from finding his favorite pitches to hit, while Yandy has been much less selective in his desire to make contact).
Long story short, raw stats are really useful when you can put several of them together. Exit velocity and launch angle are a classic combination, as are batted-ball stats like ground-ball, fly-ball, and line-drive rates. I’d also steer clear of looking at strikeout rates without walk rates, or pitch velocity without movement numbers and the zone and contact rates (no matter how much a sinker moves or how fast it travels, it simply won’t generate strikeouts due to how it’s usually kept in the zone and is designed to find a bat). While we don’t have time here to dig around and talk about every possible combination, hopefully the fact that you want combinations will put you one step ahead of your leaguemates in the search for knowledge.
My Favorite Raw Stats: Hard drive rate (xStats), exit velocity and launch angle, swinging-strike rate, strikeout rate, walk rate, and ground-ball, fly-ball, and line-drive rates.
Expected stats, like xSLG and FIP, tell us what probably should have happened based on measurable outcomes like how high and hard a player hit the ball. They are always based on real events but change reality by removing parts of the equation. FIP, for example, only cares about strikeouts, walks, and home runs. The ideal way to utilize an expected stat is to compare it with its “real world” partner as a way to identify potential victims of bad luck and unusual fortune. Stats like SIERA and FIP are expected stats that you can use along with ERA to identify possible overachievers and breakout candidates. Shane Bieber (SP, Cleveland Indians) of the Indians is a common example of this in many draft-day guides—his ability to suppress walks and home runs kept his 2018 FIP more than full run below his ERA, which could indicate that his 4.55 ERA should have been much lower. Conversely, Mike Fiers‘ (SP, Oakland Athletics) 3.56 ERA is seen as highly suspect when compared to his 4.75 FIP.
While expected stats can help us get rid of some of the “noise” that lives in traditional stats like ERA, there’s more than luck that separates the real and the expected stats. Bieber can again help us illustrate this. FIP, perhaps the most well-known of the expected pitching stats, will always give pitchers a boost if they attack the strike zone. Bieber does this well—in fact, almost too well. His unwillingness to throw pitches outside the zone makes him utterly hittable at times, as does his limited arsenal of pitches. He’ll always manage to strike out his fair share of batters and limit walks thanks to his ability to keep the ball in the strike zone, but good hitters will always find ways to make contact with his stuff, which will inevitably lead to plenty of hits, and hits aren’t accounted for in FIP. Expected stats, while very useful, should always be taken in context. They’re best used as a starting point—if you see a large split between an expected stat and the actual one, it means it’s time to do some digging to find out why!
As a final note, some of the newer and more exciting stats that fall into this category utilize information like exit velocity and launch angle to provide an even more precise picture of what ought to have happened on a particular play. These expected stats, like xAVG, xSLG, and scFIP, use the average results of balls hit at a certain angle and velocity to determine the most likely result of that play, as opposed to what actually happened. These are really fun stats to use, but keep in mind that they suffer from the same weakness as the older stats like FIP—there is more than just luck that takes place in baseball. For example, the players with the biggest difference between their xSLG and SLG often have one thing in common—they can’t run! Because of their round physique, they have a harder time turning hard hit balls into doubles or triples, which in turn keeps their actual slugging percentage down. This context is important to remember when analyzing these stats. Also, as a final aside, keep in mind that just because we know a player ought to have better results doesn’t mean we know when those better results are coming. Even if an expected stat shows that a player should be performing better than he is, and a deeper dive shows that it truly is bad luck, we still have to understand that the universe is a cold, dark, unfeeling place that doesn’t care about what we deserve.
My Favorite Expected Stats: xAVG, xSLG, xBABIP, xwOBA, SIERA, and scFIP.
Finally, adjusted stats, like wRC+ and ERA-, put raw stats into perspective by comparing them to their peers and taking into account factors like leaguewide trends and ballpark dimensions. Baseball is a fickle game, and every team has its own unique home park that has advantages and disadvantages to hitters. Adjusted stats help us put those things in perspective and compare players across different environments by using a single number. For example, wRC+ puts offensive production on a simple scale where 100 is average, and points can be understood as a percentage. You know that Mike Trout (OF, Los Angeles Angels) is awfully good, but how much better is he than the average player offensively? Well, his 191 wRC+ means that he was 91% better than the average ballplayer, so it’s barely an exaggeration to say that he’s twice as good. That’s…incredible. Not only is it incredible, it’s easy! While comparing batting average and counting stats can be fun, adjusted stats help us boil a complex analysis down into a single number. While they can’t identify regression candidates like expected stats can or tell us the kinds of details that raw stats can, an adjusted stat can give us a big picture that those stats could never hope to portray. Use these stats to quickly compare players and enjoy the fruits of the intensive labor that smarter folks put into creating these metrics.
My Favorite Adjusted Stats: wRC+ and ERA-.
I hope this guide was useful as you take your initial plunge into the wide world of stats. There are stats for everything, and just when you think you’ve discovered your favorite stat, you’ll likely find that a new one has emerged that makes your favorite stat seem obsolete. The more you use, struggle with, and embrace stats, the better you’ll feel about your baseball opinions, fantasy-related or not. The beauty of our modern world is that you don’t need to know a lick of mathematics to use or appreciate these stats—those number-loving eggheads have done all the work for you!
And finally, know that we—the entire Pitcher List staff—believe in you. You can do this.
(Graphic by Nathan Mills.)