Expected stats are all the rage in the world of baseball. For years, we’ve said things like ‘this guy should be doing better than he has been showing’ or ‘this guy is performing over his head.’ Now, with some of the expected stats that are available we have some data to support those lines of thought in a more objective manner. We will touch on these measures in what follows, but let me begin with a caveat. My co-hort, Jeff Mans, is fond of saying something along the lines of ‘just look at the expected stats and you will find the players that folks are pushing in fantasy.’ He’s not wrong. While the measurements can certainly be used to help us paint a better picture of what is going on with a player, you shouldn’t take it too far, to the point where the overarching read you get on a player is that ‘well, his expected stats say…’ Piece of the puzzle it is folks, but it’s not the whole puzzle, so be careful not to treat it as such.
WHAT ARE EXPECTED STATS?
I assume you’ve heard of Statcast and all the amazing data that Major League Baseball is starting to share with the public. We know that there are proprietary data that we simply won’t get our hands on, but that doesn’t mean that they aren’t willing to share some of their data with us.
Over at Baseball Savant they have a whole section of expected stats. Here is the intro explaining just what the numbers try to get across.
Expected Outcome stats help to remove defense and ballpark from the equation to express the skill shown at the moment of batted ball contact. By looking at the exit velocity and launch angle of each batted ball, a Hit Probability is assigned based on the outcomes of comparable historic balls in play. By accumulating the expected outcomes of each batted ball with actual strikeouts, walks and hit by pitches, Expected Batting Average (xBA), Expected Slugging (xSLG), and (most importantly) Expected Weighted On-Base Average (xwOBA) tell the story of a player’s season based on quality of and amount of contact, not outcomes.
Basically, we’re able to play the game of probability with much more accuracy than ever before. With all the batted ball data we’re able to capture now we can start to plot things on a graph and paint a fairly clear picture of what should happen in the majority of similar instances. An obvious example. Someone lines a ball at 107 mph to the pull side of the field, but the third baseman makes a diving grab. It’s an out in this one instance, but the majority if time that someone hits a similar ball, they produce a single.
SOME LINKS TO PRESEASON WRITE UPS
WHAT ARE THOSE MEASURES TELLING US AT THE MOMENT?
Using the data at our disposal, I’m going to go through each measurement and pick out some data points that stood out to me. Hopefully, you will glean something useful from them.
EXPECTED HITTING DATA
BATTING AVERAGE
Franmil Reyes is batting .220, but his xBA is actually .364. That’s a difference of .144 points, the largest gap in baseball. In plain English, given the way that Reyes has hit the baseball to this point, the results have been lower versus the expected outcome than any hitter in baseball. Simpler – Reyes has been the unluckiest player in baseball in terms of the loss in batting average he’s experienced.
Here are some other men who have at least a .100-point gap between their real-life batting average and what they expected measures say the mark should be: Joey Gallo (-.120), Yonder Alonso (-.115), Chris Davis (-.110), J.D. Davis (-.108) and Mikie Mahtook (-.104).
Let’s look at the other side. Here are the players who have the highest batting average relative to the expected performance. These are the guys who have performed well above what should be expected.
Tyler Flowers is batting .423 but his expected average is .279 (.144 over). Martin Prado (.128), Tyler O’Neil (.118), Tim Anderson (.112) and Robinson Chirinos (.111) round out the top-5. Others or note: Freddy Galvis (.097), Alex Gordon (.082), Fernando Tatis Jr. (.067), Brandon Lowe (.064), Byron Buxton (.062), Rhys Hoskins (.062) and Tim Beckham (.060).
SLUGGING
This the list of men who should have better SLG marks than they are currently saddled with:
Here is a list of the men who have greatly overperformed in SLG according to the expected stats: Dan Vogelbach (.298 over), Robinson Chirinos (.275), Rhys Hoskins (.232), Hernan Perez (.220), Freddy Galvis (.213) and David Peralta (.207). Others of note: Victor Robles (.190), Alex Verdugo (.189), Derek Dietrich (.188), Jason Heyward (.185), Kolten Wong (.181), Fernando Tatis (.167), Tim Beckham (.157) and Tim Anderson (.151).
Next, here is the list of men who have drastically overperformed their listed SLG mark meaning their actual SLG should be much higher.
Franmil Reyes (-.323), J.D. Martinez (-.258), Kendrys Morales (-.225), Jesus Aguilar (-.222), Ian Desmond (-.190), Niko Goodrum (-.176), Danny Jansen (-.169), Jose Ramirez (-.157), Austin Hedges (-.152), Miguel Cabrera (-.142), Hunter Dozier (-.130) and Harrison Bader (-.121).
QUALITY OF CONTACT
*Quality of contact + strikeouts + walks
Franmil Reyes has been the best worst player in baseball according to this measure. His (-167) mark here is the greatest gap between what is expected and what has actually happened.
Chris Davis hit a bit the last few days and has a (-.129).
Ian Desmond (-.112), Jesus Aguilar (-.110), Joey Gallo (-.107), Danny Jansen (-.100), Jose Ramirez (-.090) and Yadier Molina (-.074) also deserve a notation.
EXPECTED PITCHING DATA
BATTING AVERAGE ALLOWED
Here are some of the hurlers who have given up more hits than they should have: Rick Porcello (.142), Carlos Carrasco (.124), Reynaldo Lopez (.092), James Paxton (.080), Jose Leclerc (.080).
Here are the arms that have been very fortunate at limiting base hits: Ryan Brasier (-.124), Shane Greene (-.121), Domingo German (-.116), Shane Bieber (-.107), Kevin Gausman (-.095), David Hess (-.087), Max Fried (-.077) and Jake Arrieta (-.073).
SLUGGING ALLOWED
These are the arms that have given up more bases than they should have on those batted balls: Jeurys Familia (.251), Brad Boxberger (.208), Raisel Iglesias (.190), Carlos Carrasco (.173), Rick Porcello (.170), Corbin Burnes (.162), Dylan Bundy (.161)
Here are the hurlers who have been fortunate to have a SLG as low as it is: Shane Bieber (-.256), Ryan Brasier (-.256), Felix Pena (-.226), Jordan Lyles (-.203), Shane Greene (-.194), Aaron Sanchez (-.194), Jake Arrieta (-..190), Matt Shoemaker (-..164), Jeff Samardzija (-.156), Chris Archer (-.150).
QUALITY OF CONTACT
*Quality of contact + strikeouts + walks
Here are the arms that have pitched better than the actual results would suggest: Carlos Carrasco (.113), Rick Porcello (.110), Chris Devenski (.102), Reynaldo Lopez (.088), Jose Leclerc (.085), Corbin Burnes (.078) and Raisel Iglesias (.078).
Here are the hurlers that have seen their performance end up artificially worse than one would expect: Ryan Brasier (-.143), Shane Bieber (-.139), Shane Greene (-..125), Domingo German (-.106), Matt Shoemaker (-.106), Jordan Lyles (-.104), Jake Arrieta (-.094), Kevin Gausman (-.089), Aaron Sanchez (-.084), Max Fried (-.083), Vince Velasquez (-.080) and Jeff Samardzija (-.079).
Ray Flowers can be heard Monday-Friday, 8-10 PM EDT on SiriusXM Fantasy Sports Radio (Sirius 210, XM 87). Follow Ray’s work on Twitter (@baseballguys) and be sure to listen to his podcast work too.
