Identifying HR/FB Surgers Using Statcast

by
June 1, 2016

It seems that 2016 will be the year that Statcast begins to permeate Fantasy Baseball analysis. Recently there has been a wealth of articles exploring the possibilities of using these kinds of data. These pieces have provided relevant insights on how to improve our understanding of well-hit balls and launch angles. Also, they’ve facilitated access to information on exit velocity leaders and surgers, as well as provided thoughtful analyses to the possible workings behind some early-season breakouts.

However, there is still a lot we don’t know about Statcast data. For instance, we are uncertain of how consistent these skills are over time, both across seasons or within seasons. Also we don’t know what constitutes a relevant sample size or when rates are likely to stabilize. All in all, this makes using 2016 Statcast data to predict rest of season performance a potentially brash and faulty proposition. Having said that, we can’t help but to try; so here’s our attempt at using early-season 2016 Statcast data to partially predict future performance.

One of the early gospels of Statcast data analysis posits that the “sweet spot” for hitting homers comes from a combination of a launch angle in the range of 25 – 30 degrees and a 95+ MPH exit velocity. If this is indeed the ideal combination for hitting home runs, one could argue that players that have a higher share of fly balls that meet these criteria should perform better in other more traditional metrics such as HR/FB%.

Following this line of thought we dug up all the batted balls under the “sweet spot” criteria, and divided them by all balls hit at a launch angle of 25 degrees or higher (which MLB determines as fly balls) to come up with a Sweet Spot%. In an attempt to identify potential HR/FB% surgers, we compare Sweet Spot% and HR/FB% z-scores (to normalize each rate) for all qualified hitters with at least 25 fly balls and highlight the biggest gaps.  Here are the Top five gaps considering the games up to May 28th:

Name Team HR/FB  % HR/FB  %         Z-Score Sweet Spot % Sweet Spot % Z-Score Z-Score Diff
Kole Calhoun Angels 6% -1.15 26% 2.24 3.39
Stephen Piscotty Cardinals 11% -0.35 26% 2.33 2.68
Matt Carpenter Cardinals 16% 0.44 29% 2.73 2.29
Denard Span Giants 3% -1.66 15% 0.52 2.18
Yonder Alonso Athletics 3% -1.69 15% 0.43 2.12

Calhoun seems like a good candidate for a power uptick. He has the third-highest Sweet Spot% of 2016, and he has sustained similar Hard% and FB% to the previous two seasons. Yet somehow he has managed to cut his HR/FB% to less than half of what he put together in either 2014 or 2015.  More so, he has had some bad luck with balls hit in the “sweet spot”; his batting average in these kinds of balls is .500, whereas the league average is around .680. He is not killing fly balls in general, with an average exit velocity of 84.6 MPH, but if he keeps consistently hitting balls in the “sweet spot” range he should improve in the power department. Look out for a potential turnaround in the coming weeks and a return to 2015 HR/FB% levels.

Piscotty holds second place in the Sweet Spot% rankings. However, his FB% is very similar to what he did in 2015 whilst his Hard% is down from 38.5% to 32.5%. Lastly, he plays half of his games in Busch Stadium, which has a history of suppressing home runs. I would be cautious of expecting a major home-run surge, but in any case Piscotty is likely to at least sustain his performance in the power department, which would be welcome news to owners that got him at bargain prices.

Carpenter is another dweller of Busch Stadium, however his outlook might be a bit different. He is the absolute leader in Sweet Spot%. He is posting the highest Hard% and FB% marks of his career. Carpenter is also crushing his fly balls in general, with an average Exit Velocity of 93.7 MPH. Just as a point of reference Miguel Cabrera, Josh Donaldson and Giancarlo Stanton fail to reach an average of 93 MPH on their own fly balls. Lastly, he has had some tough luck with balls hit in the “sweet spot”, posting a batting average of just .420. Carpenter is already putting up the highest HR/FB% of his career, and he is a 30-year-old veteran of slap-hitting fame, but the power looks legit and perhaps there is more to come.

Denard Span and Yonder Alonso show up in this list not because of their Sweet Spot% prowess but rather due to their putrid HR/FB%. They barely crack the Top 50 in Sweet Spot%. They play half their games in two of the bottom three parks for HR Park Factor. Span is putting up his lowest FB% and Hard% rates since 2013, when he ended up with a HR/FB% of 3.4%. Meanwhile, Yonder’s rates most closely resemble those of 2012, when he had a HR/FB of 6.2%. Whilst their batting average of “sweet spot” batted balls is just .500, there is nothing to look here. In any case, their power situation looks to improve from bad to mediocre.

If you are interested in the perusing the Top 50 gaps between HR/FB% and Sweet Spot%, please find them below:

Name Team HR/FB  % HR/FB  %          Z-Score Sweet Spot % Sweet Spot % Z-Score Z-Score Diff
Kole Calhoun Angels 6% -1.15 26% 2.24 3.39
Stephen Piscotty Cardinals 11% -0.35 26% 2.33 2.68
Matt Carpenter Cardinals 16% 0.44 29% 2.73 2.29
Denard Span Giants 3% -1.66 15% 0.52 2.18
Yonder Alonso Athletics 3% -1.69 15% 0.43 2.12
Kendrys Morales Royals 10% -0.61 21% 1.38 1.99
Addison Russell Cubs 12% -0.27 22% 1.67 1.94
Yadier Molina Cardinals 2% -1.72 13% 0.11 1.83
Adam Jones Orioles 11% -0.46 20% 1.29 1.75
Alcides Escobar Royals 0% -2.10 10% -0.44 1.66
Jose Abreu White Sox 11% -0.35 19% 1.11 1.46
Joe Mauer Twins 17% 0.56 24% 1.96 1.40
Chris Owings Diamondbacks 3% -1.59 11% -0.26 1.32
Jacoby Ellsbury Yankees 5% -1.28 12% -0.09 1.19
Justin Turner Dodgers 6% -1.20 12% -0.01 1.19
Victor Martinez Tigers 12% -0.19 18% 0.95 1.14
Daniel Murphy Nationals 10% -0.60 16% 0.54 1.14
Justin Upton Tigers 4% -1.43 11% -0.29 1.14
Josh Harrison Pirates 5% -1.37 11% -0.25 1.12
Anthony Rendon Nationals 6% -1.23 12% -0.11 1.12
Corey Dickerson Rays 16% 0.42 21% 1.50 1.07
Brandon Crawford Giants 11% -0.41 16% 0.66 1.07
Ian Desmond Rangers 16% 0.35 21% 1.41 1.06
Derek Norris Padres 12% -0.30 17% 0.74 1.04
Ryan Zimmerman Nationals 19% 0.78 23% 1.81 1.03
Gregory Polanco Pirates 14% 0.11 19% 1.11 1.00
Austin Jackson White Sox 0% -2.10 6% -1.13 0.97
Nick Markakis Braves 2% -1.79 7% -0.86 0.93
Corey Seager Dodgers 18% 0.66 22% 1.56 0.91
Michael Saunders Blue Jays 20% 1.00 24% 1.88 0.89
Mike Napoli Indians 23% 1.38 26% 2.27 0.88
Brandon Belt Giants 7% -0.97 11% -0.15 0.81
Matt Kemp Padres 17% 0.59 20% 1.36 0.77
Nick Ahmed Diamondbacks 8% -0.81 12% -0.05 0.77
Matt Duffy Giants 4% -1.45 8% -0.73 0.71
David Ortiz Red Sox 19% 0.90 21% 1.53 0.63
Joe Panik Giants 9% -0.69 12% -0.06 0.63
Elvis Andrus Rangers 2% -1.72 6% -1.10 0.63
Brandon Phillips Reds 11% -0.41 14% 0.21 0.62
Adam Eaton White Sox 8% -0.81 11% -0.20 0.62
Gerardo Parra Rockies 8% -0.87 11% -0.26 0.61
C.J. Cron Angels 6% -1.18 9% -0.58 0.61
Dexter Fowler Cubs 13% -0.04 16% 0.56 0.60
Jose Altuve Astros 17% 0.53 19% 1.11 0.58
Prince Fielder Rangers 4% -1.42 7% -0.90 0.51
Jose Ramirez Indians 7% -1.09 9% -0.58 0.51
Joey Rickard Orioles 8% -0.91 10% -0.42 0.48
Asdrubal Cabrera Mets 7% -1.00 9% -0.53 0.46
Mark Teixeira Yankees 10% -0.50 12% -0.05 0.46
Ben Zobrist Cubs 13% -0.12 14% 0.34 0.45

Note: This analysis is also featured in our emerging blog www.theimperfectgame.com





 

Douglas is a Venezuelan baseball enthusiast. Background in economic development. You can read more of his work at: www.theimperfectgame.com

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