RESEARCH: Quality of Batted Balls


It is well established that for batters, the greater the exit velocity, the better. We’ve also published work here showing that a greater mean launch angle is also good, with no apparent upper limit, and that smaller launch angle variability is also correlated with better batted ball results. Today we’ll set out to create a metric to capture the overall quality of a hitter’s batted balls.


This study will be backwards. We are going to use what we know about batted ball parameters to create a quality of batted ball score (QBaB), and then test that score for its usefulness in both capturing past outcomes and predicting future batted ball goodness.

The dataset will be all batted balls from the Statcast era, i.e. 2015-2019.  We’ll show the correlation between Exit Velocity (EV), Launch Angle (LA), and Launch Angle Variability (LAV) with outcomes, then assign grades to a batter’s average profile. [Note: LAV ≡ standard deviation (LA).]


To evaluate outcomes, we will use three metrics:

  • Hits per batted ball (H/BaB)
  • Total bases per batted ball (TB/BaB)
  • Homeruns per batted ball (HR/BaB)

Let’s reorient ourselves and recall the general dependence of outcomes on EV and LA:

For exit velocity, hits, HR, and XBH happen at 90 mph and up.

For Launch Angle, HR are between 10 and 50 degrees, while hits in general happen most between 0 and 50.

The aim is to assign a quality of batted ball (QBaB) grade for EV, LA, and LAV averaged over a batter’s full season. So, now let’s refresh our understanding of how the average batted ball outcomes vary as a function of these three parameters (using the mean from an entire batter-season, min 100 batted balls). These curves are smoothed to show the general trends, and normalized to 1.00 to show relative increases for all three on the same chart.

Note that for LAV, the batter means are normally distributed, with 95% of the batter-seasons falling between 22.5° and 31°. In that region, increased variance bad for hits, but mixed/good for home runs, and slightly bad for total bases (with a flat spot in the middle).

Here are the takeaways from the three charts above:

  • Increased Exit Velocity is good for Homeruns, Total Bases, and Hits. In terms of fantasy stats, this helps with HR & BA obviously, which in turn helps RBI, OBP, and Runs.
  • Increased mean Launch Angle is good for Homeruns and Total Bases (therefore good for RBI too) but neutral for hits (and therefore neutral for OBP and Runs)
  • Decreased Launch Angle Variability is good for Total Bases and Hits, but mixed for Home Runs, conveying some help for BA, OBP, R, RBI.

Player Grades

The next step is to assign grades for the mean results of player seasons.  We evaluated the distribution of player seasons and assign letter grades to percentile groups with the thresholds below.

Because LAV is mixed depending what outcome we are looking at, we’ll use a lower case letter to distinguish it. And with that, we are ready to assign QBaB grades to batter seasons. These range from AAa to FFf, and they correlate well with outcomes. On the chart below, we plot HR/BaB versus H/BaB, coloring the points by TB/BaB:

The Ds and Fs are at the bottom and left, and the As and Bs are at the top and right. Some of these grades only have a few members, because we have 125 grades weighted toward the middle, and only ~2000 player-seasons (with min 100 batted balls). For example, there have only been two AAa players in our sample: 2018 Mike Trout and 2015 Greg Bird.

On the other end, there have been nine FFf players:

  • 2015 Jason Bourgeois
  • 2016 Ezequiel Carrera
  • 2017 Ezequiel Carrera
  • 2016 Miguel Rojas
  • 2016 Billy Burns
  • 2015 JB Shuck
  • 2018 Delino Deshields
  • 2017 Mallex Smith
  • 2018 Magneuris Sierra

Considering these players reputations, or lack thereof, these names are generally not surprising. On the other hand, there are exceptions: Greg Bird didn’t ever do much again after 2015, and Mallex Smith and Miguel Rojas did follow up those bad years with good seasons. So, we should look at how the grades trend over time. Can we count on our high QBaB guys to stay high? Fortunately, the answer is yes. The violin plots below show “Next Year’s Grade” plotted against the current year, to illustrate the movement between tiers (or lack thereof). The larger the area, the more batter-seasons fall in that box.

The year-to-year movement is limited.

  • 64% of A&B were A or B the year before, and only 3% were D and none were F.
  • 66% of A&B remained A or B.
  • 71% of D&F were D or F, and only 3% were A or B.
  • 65% of D&F remained D or F.
  • Only 6% of players moved by more than one grade.

We’ll create a similar plot for Launch Angle Grade:

A similar story for Launch Angle.

  • 65% of A&B were A or B the year before, and only 1% were D and none were F.
  • 75% of A&B remained A or B.
  • 77% of D&F were D or F, and only 1% were A or B.
  • 67% of D&F remained D or F.
  • Only 6% of players moved by more than one grade.

Finally, Launch Angle Variability:

There is a little more movement in LAV, but still not a lot:

  • 66% of A&B were A or B the year before, and only 7% were D and none were F.
  • 60% of A&B remained A or B, and 7% became D or F
  • 56% of D&F were D or F, and only 7% were A or B.
  • 64% of D&F remained D or F, and 7% became A or B
  • Only 11% of players moved by more than one grade.

While LAV is still consistent year to year, it appears there is a little bit more ability for batters to make a big jump: 7% in all the examples above.

Let’s consider our FFf outliers we noted above. 

  • Mallex Smith was FFf in 2017. In 2018 he slashed .296/.367/.406 for a 118 wRC+, but his QBaB was FFd. In 2019, he reverted to .227/.300/.335, wRC+ of 74, and spent time in the minors.
  • Miguel Rojas was FFf in 2016. In 2017 he slashed .290/.361/.375, for a 98 wRC+, but his QBaB was FDd. In 2018 he reverted to .252/.297/.346, wRC+ of 78

Anecdotally at least, QBaB has identified the weakest hitters, and would have cautioned against believing their “breakouts”.

  • Finally, Greg Bird was AAa in 2015, and sadly spent most of the next 4 seasons injured, with over 70% of his days on the IL. Oh, what might have been…

Underperformers and Overperformers

The above raises an interesting question. What happens to players who outperform or underperform their QBaB? We can answer this.  First calculate an average performance within each grade. Some of these have very few members; we plot them anyway, but we’ve sized the circles by number of batter seasons with that grade.

Using those points as the expectation for that grade, what happens when a batter over- or underperforms?

The equation of the line of fit is:

With an R2 value of 0.23.  The previous year’s under- or overperformance accounts for 23% of the next season’s increase or decrease, respectively.  And on average, batters will give back 60% of their over- or under-performance.

Armed with our new grades and expected performance, let’s identify the players with the best QBaB scores, and the players with biggest deviation from expected performance based on QBaB.

Best and Worst 2019 QBaB

Now let’s look at the best and worst hitters from 2019. We’ll tabulate the names, along with the grades, and the estimated overperformance/underperformance relative to their grade. Using the Outcomes by Grade chart above, we see that the best of the best are those with AA, AB, AC, or BA grades.

QBaB  Player                QBaB  Player                QBaB  Player
====  ===================   ====  ===================   ====  ===================
AAb   Mookie Betts          ACa   Yordan Alvarez        ACd   Rafael Devers
AAb   Brandon Lowe          ACb   Kendrys Morales       ACf   Manny Machado
AAc   Matt Olson            ACb   Howie Kendrick        -------------------------
AAd   Joey Gallo            ACb   Jason Castro          BAa   Justin Turner
AAf   Gary Sanchez          ACb   Danny Santana         BAa   Stephen Vogt
-------------------------   ACb   Jose Abreu            BAa   Mike Trout
ABb   Trevor Story          ACb   Ji-Man Choi           BAb   Anthony Rendon
ABb   Keston Hiura          ACb   Yoan Moncada          BAb   Eugenio Suarez
ABc   Christian Walker      ACc   Nelson Cruz           BAb   Kyle Seager
ABc   Jorge Soler           ACc   Hunter Pence          BAb   Will Smith
ABc   Hunter Dozier         ACc   Marcell Ozuna         BAc   Jay Bruce
ABc   Mike Ford             ACc   C.J. Cron             BAc   Eric Thames
ABc   Kyle Schwarber        ACc   Bryce Harper          BAc   Alex Bregman
ABd   Brad Miller           ACc   JaCoby Jones          BAc   Tom Murphy
ABd   Joc Pederson          ACc   Christian Yelich      BAc   Cody Bellinger
ABd   Miguel Sano           ACc   Josh Bell             BAc   Rhys Hoskins
ABd   Mitch Garver          ACc   Eloy Jimenez          BAd   Edwin Encarnacion
ABf   Teoscar Hernandez     ACc   Nate Lowe             BAd   Gregory Polanco
ABf   Matt Chapman          ACc   Juan Soto             BAd   Nolan Arenado
-------------------------   ACd   Carlos Santana        BAd   Hunter Renfroe
ACa   David Freese          ACd   Ryan Zimmerman        BAd   Max Kepler
ACa   J.D. Martinez         ACd   Josh Donaldson        BAd   Renato Nunez
ACa   Aaron Judge           ACd   Franmil Reyes         BAd   Austin Riley
ACa   J.D. Davis            ACd   Jung Ho Kang          BAf   Byron Buxton


This is a list of pretty good hitters. If there are players on this list that are not on your radar, they probably should be (with obvious exceptions of guys without jobs: looking at you, Jung-Ho Kang).

It is also worth remembering that a barrier between QBaB and good outcomes is contact rate. These batted balls can only do what they do if the batter doesn't strike out.

Our next table is a list of the biggest overperformers from 2019. The players on this list were in the top 5% in overperformance at least one of our three categories (TB/BaB, H/BaB, HR/BaB).  We should expect some negative regression:

                          Overperformance vs. QBaB     

Player             QBaB   TB/BaB    H/BaB   HR/BaB
================   ====   ======   ======   ======
Joey Gallo          AAd    0.381    0.104    0.079
Miguel Sano         ABd    0.258    0.072    0.056
Fernando Tatis      BDd    0.248    0.117    0.036
Nelson Cruz         ACc    0.228    0.079    0.048
Christian Yelich    ACc    0.215    0.077    0.040
Carlos Correa       CCd    0.213    0.054    0.051
Willson Contreras   CDd    0.211    0.053    0.053
Yordan Alvarez      ACa    0.206    0.039    0.046
Pete Alonso         BBc    0.186    0.023    0.057
Ian Happ            BBd    0.179    0.043    0.042
Eugenio Suarez      BAb    0.178    0.048    0.049
Michael Chavis      CCc    0.168    0.074    0.039
Cameron Maybin      CCf    0.166    0.083    0.019
Tim Beckham         CCc    0.161    0.030    0.032
Aristides Aquino    CAf    0.155    0.040    0.048
David Dahl          CCa    0.155    0.066    0.011
Chris Taylor        DCa    0.155    0.038    0.021
Mike Freeman        FDc    0.150    0.092    0.020
George Springer     BCc    0.149    0.030    0.043
Bo Bichette         BCc    0.146    0.067    0.012
Mike Trout          BAa    0.146    0.022    0.041
Ronald Acuna        BCb    0.141    0.045    0.040
Will Smith          BAb    0.140    0.006    0.048
Mitch Garver        ABd    0.138    0.024    0.040
Wil Myers           CCf    0.138    0.064    0.020
Brandon Lowe        AAb    0.138    0.076    0.015
Roberto Perez       CFc    0.135    0.007    0.053
Tim Anderson        CDa    0.131    0.075    0.018
Corey Dickerson     CBd    0.128    0.055   -0.001
Gleyber Torres      CBb    0.119    0.020    0.041
Tom Murphy          BAc    0.114    0.061    0.021
Lourdes Gurriel     BBf    0.109    0.056    0.012
Yoan Moncada        ACb    0.107    0.072   -0.001
Adam Engel          DCd    0.088    0.056    0.004
Matt Wieters        CCc    0.061   -0.034    0.047
Adalberto Mondesi   CCd    0.057    0.056   -0.019
Donovan Solano      CBb    0.012    0.083   -0.025

And on the other end, here are the biggest underperformers from 2019

                          Underperformance vs. QBaB         
Player             QBaB   TB/BaB    H/BaB   HR/BaB
================   ====   ======   ======   ======
Kendrys Morales     ACb   -0.375   -0.148   -0.057
Josh Harrison       DAc   -0.196   -0.096   -0.036
Martin Prado        CDc   -0.164   -0.056   -0.023
Jeff Mathis         DBd   -0.157   -0.047   -0.033
Daniel Descalso     CCc   -0.157   -0.067   -0.025
Lewis Brinson       DDd   -0.155   -0.068   -0.026
Mike Zunino         CAf   -0.153   -0.059   -0.028
Andrelton Simmons   CCf   -0.139   -0.033   -0.027
Travis Shaw         CAd   -0.139   -0.048   -0.020
Ryan Zimmerman      ACd   -0.135   -0.024   -0.033
Manny Machado       ACf   -0.134   -0.029   -0.032
Willians Astudillo  DBc   -0.133   -0.043   -0.019
Jose Rondon         CDc   -0.131   -0.065   -0.004
Lorenzo Cain        BDc   -0.123   -0.028   -0.025
Matt Duffy          CFc   -0.123   -0.032   -0.028
Jose Peraza         DBb   -0.120   -0.056   -0.015
Danny Jansen        CBd   -0.118   -0.059   -0.010
Joey Wendle         CCc   -0.115   -0.043   -0.027
Daniel Robertson    CFc   -0.114   -0.046   -0.023
Charlie Tilson      CFb   -0.113   -0.038   -0.016
Curtis Granderson   CAc   -0.108   -0.064   -0.015
Justin Smoak        BBc   -0.105   -0.071    0.001
Jake Lamb           BCb   -0.105   -0.085   -0.009
Chris Owings        CDc   -0.100   -0.086   -0.004
Luis Arraez         CCa   -0.097    0.009   -0.031
Jurickson Profar    CBd   -0.096   -0.068   -0.007
Christin Stewart    CAc   -0.094   -0.011   -0.033
Ryan O'Hearn        BCc   -0.092   -0.071   -0.002
Isiah Kiner-Falefa  CDc   -0.090   -0.020   -0.026
Austin Barnes       CBf   -0.084   -0.023   -0.026
Yonder Alonso       CCc   -0.081   -0.067    0.002
Jung Ho Kang        ACd   -0.063   -0.096    0.011
Stephen Vogt        BAa   -0.062   -0.017   -0.034
Travis Demeritte    BBd   -0.053    0.036   -0.038
Brock Holt          CCb   -0.050    0.026   -0.026
Tony Kemp           DCd   -0.035   -0.056    0.003
Jorge Polanco       CAc   -0.033    0.030   -0.028
Justin Bour         BCf   -0.026   -0.058    0.017
Alex Dickerson      BBc   -0.010    0.012   -0.025
Donovan Solano      CBb    0.012    0.083   -0.025
Jay Bruce           BAc    0.017   -0.057    0.030

These lists can be used a number of ways.

  • Overperformers:
    • Overperformers are likely to see negative regression.
    • Be especially wary if their price is driven by a 2019 breakout.
    • Those with good QBaB scores are still worth owning if their price is fair
    • Those with CCx and below are particularly likely to disappoint
  • Underperformers:
    • Underperformers are likely to see positive regression.
    • Those with good QBaB scores may make especially good bargains (provided they can post decent contact rates).
    • Those with poor scores are probably still not going to hit much, no matter how much regression is coming.
    • Morales, Zimmerman, Machado, Smoak, Vogt, A. Dickerson, and Bruce are particularly interesting names on this list (all save Smoak are 75% ct or higher).


We created a new score to capture quality of batted balls for batters, using Exit Velocity, Launch Angle, and Launch Angle Variability. The scores correlated well with batter output, and over and underperformers showed a strong tendency to regress to the mean (on average).

We’ve identified some QBaB outliers from 2019, and highlighted in broad strokes what to expect from them.  As the season progresses, we’ll also look to identify early season QBaB outliers as possible buy-low and sell-high candidates.

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  For more information about the terms used in this article, see our Glossary Primer.