Hitter Analysis, Statcast

Statcast Analysis: Identifying HR Underachievers

Statcast is pretty new to the larger sabermetric scene but it’s uses are already becoming relevant, in particular to the fantasy baseball world. The vast amount of data can be intimidating to parse and draw conclusions on. Luckily, there are others much smarter than myself who have already done the grunt work. Drawing from their work, can we identify potential HR sleepers for the 2017 season?

From Alan Nathan’s excellent analysis, we know that the ideal launch angle is between 25 and 30 degrees. Angles lower typically don’t have the loft to get out of the park and angles higher are typically flyouts. Alan also found that 95 mph seems to be the starting point for when batted balls in this launch angle range start turning into home runs. Using the fantastic search tool at BaseballSavant, we can now return some data within these ranges and run some numbers.

Unfortunately, Statcast is so new that we really only have reliable data from the 2015 and 2016 seasons so our sample size is small right off the bat. In order to eliminate some noise, I set the minimum pitch count for 200 pitches seen. This returned a data set of 2,435 batted balls in 2015 and 2,668 batted balls in 2016 that were put in play with a launch angle of between 25-30 degrees and an exit velocity of at least 95 mph. Based on Alan’s work we would expect to see a high number of these batted balls end in a home run.

  2015 2016 Total
HRs 1272 1455 2727
Batted Balls 2435 2668 5103
HR/BB% 52.23819 54.53523 53.43915

Considering that only about 10% of flyballs end in a home run, I think it’s safe to say that this launch angle range and exit velocity is the sweet spot with 53.4% of batted balls ending in home runs. Now that we have a general idea of the league environment, we can use it against individual players to draw conclusions.

Let’s start with what some 2015 underachievers did in 2016 to show that this analysis can be worthwhile. Exactly 100 players had at least 200 pitches recorded by Statcast in both 2015 and 2016 that fit into our range. Of these 100 players, the average gain in % HR/BB for balls in our range was 15.2. This means that these players on average turned 15% more of balls in our range into home runs compared to the previous year, bringing them closer to the mean aka positive regression. We know that not every home run falls into this ideal range so can this tell us anything about their overall home run totals. In order to eliminate the effects of injury-shortened seasoned, I analyzed players on HR/PA instead of raw home run totals. Of our 100 players, 74 of them saw an increase in HR/PA and only 26 saw a decrease. If we go back to raw home run totals (and ignore injury/playing time changes), 68 of the 100 players saw an increase with an average increase of 7.9 home runs. 7 players saw no change and 25 saw a decrease with the average decrease being 4.8 home runs. Last year the league played at a higher home run rate than in 2015. To add some context, for all players who saw their raw home run total go up in 2016, the average increase was 6.4 home runs.

  HR/PA 16 vs 15 HR 16 vs 15
# Increased 74 68
# Decreased 26 25
No Change 0 7

All of the small sample size caveats apply here, but what the data shows is that players who were unlucky the previous season in our ideal batted ball range, were more likely than not to increase both their HR/PA and their raw HR total in the following year. Knowing this information, we can see which players would be most likely to see this bump in 2017.

Below is a chart with all players from 2016 who had the necessary Statcast data who had at least 10 ideal batted balls and converted 40% or less of them into home runs. Steamer is projecting an increase in home runs for seven of these players, a decrease for 12 and eight to stay the same.

Player Ideal HR Ideal BB Ideal HR/BB% 16 HRs Steamer Proj
Yadier Molina 3 16 18.75 8 8
Kole Calhoun 4 20 20.00 18 21
Buster Posey 2 10 20.00 14 17
Joe Mauer 4 16 25.00 11 11
Brandon Crawford 4 15 26.67 12 13
Albert Pujols 4 15 26.67 31 30
J.T. Realmuto 3 11 27.27 11 9
Josh Harrison 3 11 27.27 4 8
Jose Abreu 4 14 28.57 25 29
Brandon Belt 5 17 29.41 17 17
Anthony Rendon 5 17 29.41 20 20
Zack Cozart 3 10 30.00 16 13
Brandon Phillips 3 10 30.00 11 12
Eduardo Nunez 4 13 30.77 16 12
Tim Anderson 4 12 33.33 9 12
Kyle Seager 6 18 33.33 30 25
Matt Carpenter 8 23 34.78 21 19
Stephen Piscotty 7 20 35.00 22 16
Joe Panik 4 11 36.36 10 10
Jose Ramirez 4 11 36.36 11 11
Victor Martinez 8 21 38.10 27 22
Addison Russell 5 13 38.46 21 18
Xander Bogaerts 5 13 38.46 21 17
Eric Hosmer 6 15 40.00 25 22
DJ LeMahieu 6 15 40.00 11 9
Salvador Perez 4 10 40.00 22 22
Adonis Garcia 4 10 40.00 14 14


Based on the above chart, Kole Calhoun will be someone I target in drafts. His 9.4% HR/FB was a career low and down four points from his prior career low. A return to mid-20s home runs is not out of the question. I won’t end up with Buster Posey on any teams but another guy who just posted a career low in HR/FB. I’ve never been a big fan of J.T. Realmuto but there might be some home run upside there. Jose Abreu disappointed in 2016 and is another guy who posted a career low in HR/FB by almost five points. Steamer likes him to get into the upper-20s again and I do as well. Brandon Belt has perennially been a good average, low power 1B never eclipsing 18 home runs. Last year he posted 17 with a 9.3% HR/FB. That’s not a career low, but much lower than the 18.2% and 13.6% he posted in 2014 and 2015 respectively. I’ve always liked Belt and this might finally be the year he drops 20+ dingers.

Anthony Rendon returned from a miserable 2015 with a solid 2016. His 20 HR were one off his career high and there might be a few extra in his bat. He’s a risky option but one I’d be willing to gamble on this year. Tim Anderson is going to be a tough guy to draft in OBP leagues this year given his anemic walk rate (3% in 2016) but he could be a mid-teens home run guy with 20+ steals. He’s a solid later round SS option if you miss out on the top guys. I had him ranked all the way at 10 for SS. Kyle Seager is a machine and continues to put up very good numbers. Last year he dropped career highs in both HR (30) and HR/FB (14.6%). It’s hard to imagine Seager adding more HRs but I buy the power increase from 2015.

We need more data to conclusively say that this metric is a good predictor of the future but it is worthwhile to take it into consideration. With how important power is in fantasy it’s always good to examine additional resources.


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