The Home Run Conundrum: Is It a Matter of How You Spin It?

I was looking into a separate but overlapping issue when I ran into the puzzling home run question. As has already been pointed out in prior research, exit velocities (EV) are up about a half a mile per hour over the last year; however, for most, this is not really a satisfying conclusion given the relatively small expected distance change from that amount of an EV increase. There has to be more to the story.

My other overlapping project was initially looking into loft. There seems to be an organizational push for more loft and players have made comments along these lines. Although the benefits of loft in terms of incremental runs are well-known, there has been very little discussion of the cost side of the equation – what is a player sacrificing in terms of optimal bat path / ball path matching? Of the three ways to generate loft, what is the cost for each and how do they rank? More to follow on all that in another article.

Organizations and players have touted backspin even longer than the more recent focus on loft. In terms of additional distance from backspin, it is significant. Research by Alan Nathan indicates spin could add 30-50 ft starting from a low spin rate. What if backspin was a key piece in the missing home run puzzle?

Since spin rates on hits are not yet available, I created a Distance Model based on EV and LA data from Baseball Savant where combinations of both EV and LA could be held constant (to a tenth) in order to separate out Unexpected Distance where spin is likely the largest component. I excluded all balls hit at Coors Field and focused on balls hit 90 MPH or more between the launch angles of 15 and 45 degrees. The Unexpected Difference was calculated for each hit in the range above for 2015 and 2016. Since the data showed a clear bias depending on the location of the hit, I made the following adjustments to take out directional bias based on the 2015 data:

Hit Location          Directional Bias (Ft)

Pull-Side Gap                   +17

Oppo-Side Gap                 + 7

Center                                + 7

Pull                                    –  6

Oppo                                  -12

 

Clearly, balls hit predominantly with backspin have more lift than those hit flat or with side-spin. Considering that Coors Filed alone was a +17.5 average difference, the average ball hit to the pull-side gap is about the same magnitude as hitting at 5,200 feet. Just for fun, I ran the Unexpected Distance for a pull-side gap hit at Coors Field — a whopping 39.8 feet!

Analysis of Launch Angle Buckets

On the whole, exit velocity, launch angle and distance on well-hit balls (>=90 MPH and >=15 degree LA) are all little changed from last year. However, the launch-angle buckets indicate that backspin is likely a factor, particularly in the 30-35 and 35-40 degree segments which account for a combined 58% of the increase in HRs over 2015 while only representing a combined 32% of the categories. Additionally, the majority of the 6ft and 7ft increase in these categories, respectively, are coming from the Mean Unexpected Distance (MUD) — or most likely spin.

15-20 20-25 25-30 30-35 35-40 >40
Chng EV (MPH) 0.4 0.4 0.6 0.5 0.3 0.1
Chng Avg. Dist (Ft) (1.1) 1.4 2.5 6.0 7.1 2.8
Chng MUD (Ft) (3.6) (0.9) 0.3 3.9 5.6 2.5
Chng HRs (23) 90 111 190 54 (7)

Note: Home runs in both years only include those with EV and LA data.

Looking at the distribution of balls in the launch-angle groups over the past two years, there has been very little movement between the groups other than a slight move from the lowest to the highest group (below).

Distribution of Balls Hit >=90 MPH and >=15 Degrees

15-20 20-25 25-30 30-35 35-40 >40
2015 23.3% 20.6% 17.8% 13.6% 9.7% 15.0%
2016 22.6% 20.6% 17.8% 13.6% 9.6% 15.8%

 

As reflected in the data, it is not that there are significantly more lofted balls being hit but the ones in the 30-40 degree range are being hit with significantly more backspin relative to last year.

In diving into the home runs in the 30-40 degree category for both years, I was expecting to see players with either high or increasing MUD values. While there were some of those players…

HRs in the 30-40 Degree Group (Backspin Gainers)

2015 HRs 2016 HRs Chng 2015 MUD 2016 MUD MUD Chng
Brad Miller 2 7 5 (3.7) 8.3 12.0
Ryan Braun 4 9 5 (1.9) 8.1 10.0
Mookie Betts 4 8 4 0.6 8.9 8.3

 

There were also some in the “flat” hitting group that were simply just hitting the ball “less flat than last year” that are showing up in the positive MUD change group…

HRs in the 30-40 Degree Group (Flat Hitters – Hitting Less Flat)

2015 HRs 2016 HRs Chng 2015 MUD 2016 MUD MUD Chng
Kris Bryant 13 25 12 (17.0) (10.2) 6.8
Evan Longoria 3 13 10 (4.0) 0.0 4.1
Miguel Cabrera 3 9 6 (8.4) (5.6) 2.8
Victor Martinez 4 11 7 (5.5) (2.0) 3.5

 

At this point, I was about to conclude that spin is definitely a factor but it could just be noise rather than an organizational push for more loft and/or backspin…and then I read Jeff Sullivan’s post the other day and now it all fits! Look at the table below of the players with the highest and lowest MUD values for 2016 and see if you can find it.

Top 10 MUD (Backspin Hitters) 2016 Avg EV Avg LA Avg Dist MUD
Max Kepler 97.3 24.6 362.2 16.7
Melky Cabrera 97.0 24.1 349.3 12.5
Martin Prado 95.8 23.9 346.9 11.7
Ketel Marte 94.9 23.7 340.1 11.2
Aledmys Diaz 97.8 26.4 357.7 11.1
Cheslor Cuthbert 97.4 24.9 346.7 11.1
Aaron Hill 95.9 25.0 345.0 11.0
Yangervis Solarte 97.5 27.1 355.4 9.8
Alexei Ramirez 94.4 29.3 348.1 9.2
Adeiny Hechavarria 95.8 24.6 342.8 9.2
Average 96.4 25.4 349.4 11.3

 

Bottom 10 MUD (Flat Hitters) 2016 Avg EV Avg LA  Avg Dist MUD
Freddie Freeman 100.0 27.8 343.2 (14.6)
J.D. Martinez 102.1 27.7 355.7 (13.1)
Addison Russell 99.0 27.1 343.1 (12.4)
Chris Davis 101.5 28.6 358.7 (11.2)
Joe Mauer 97.7 25.2 330.2 (10.7)
Trevor Story 99.2 28.0 350.1 (10.6)
Kris Bryant 100.1 29.8 353.1 (10.2)
Joey Votto 98.8 28.2 344.2 (9.5)
Mark Teixeira 99.5 26.8 348.1 (9.4)
Nick Castellanos 99.5 28.3 350.0 (8.8)
Average 99.8 27.8 347.6 (11.0)

 

Yes, of course! The answer is that it is not just because chicks dig the long ball, it’s that the market that values the players digs the long ball. Notice the significant difference in the exit velocities of the two groups. The players who are relying on spin are doing so because they have to get more distance and HRs out of their existing tool kit and are willing to pay (in terms of consistency) in order to get it. The players with higher exit velocities and hence more “natural power” can continue in their square hitting ways since they have no need to pay a high price for something they already possess. I didn’t average the height and weight of the two groups but I think it is clear that the backspin group is significantly smaller in stature than the flat-hitting group. Note the 2 ft average distance advantage of the backspin group with a whopping 3.4 lower average MPH difference!

Another interesting tidbit from the above data is the average launch angle is significantly lower for the higher backspin group. While this may seem counter-intuitive, it actually makes complete sense – in order to get backspin, you have to have less loft in the swing and rely on the ball contact point for loft. Since this is no easy feat, balls will tend to come off the bat with more variability with many hits matching the amount of loft in the swing and hence a lower trajectory.

What is happening with the home run issue is not randomness that is going to revert to the mean. It is a secular trend that is the result of the incentives in the system. Hitting for average with no power is out of style and players, particularly those with lower EVs, are likely responding by getting the ball out of the park any way they can – whether it is swinging harder, utilizing more backspin, or hitting to the shorter (pull) side of the field. (Could the latter be the next big trend?) While there will likely be additional findings regarding the home run question, the way I see it, at least part of it is as clear as MUD.



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Alan Nathan
Member

I am having major problems understanding your methodology. For example, how do you determine the distance? Moreover, in the article that you linked, I showed that the *marginal* effect of increased backspin on distance is not very large, since the increase in lift is also accompanied by an increase in drag. While the difference in distance between 0 and 2000 rpm of backspin may be large, the difference between 2000 and 3000 is small. How does that factor into your analysis? Feel free to contact me privately to start a dialogue.

eyesoverthecity
Member

This is a very interesting premise, but no player in the MLB is digging this deep into any of this data. They understand that “presumed power” leads to more money, so they do what is necessary to exhibit more of it. The results are the numbers you are seeing, but they are not trying to increase the numbers specifically… it’s a byproduct only.

It’s a fun physics lesson, but I can’t see anyone actively using this in the game.

GuyMolyneux
Member
Member
GuyMolyneux

An important question is: are the actual distances distributed symmetrically around your projected distances? That is, do the pool of 96 EV balls at X angle form a bell curve around your projection for that ball? And 100 EV? Etc. If so, then your results may be telling us something important about the type of contact achieved by these two group of hitters. But it’s also possible that low-EV balls tend to exceed their projection more often than high-EV balls do, at least for certain angles. If so, what you are really seeing is just a limitation in your projection model.