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.
D.K. Willardson enjoys research connecting data, mechanics, and technology and is the author of Quantitative Hitting: Surprising Discoveries of the Game’s Best Hitters. He is also the developer of the Quant Tee and SwingGraphs.
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.
My approach to this whole issue started from a bottom up mechanics and strategy/approach perspective. In other words, a view that there is likely valuable player specific information to be had in understanding the bat ball collision – that it is differentiated and persistent at the player level. I watch baseball different than most – with a remote in hand constantly clicking through frames. From this vantage point, I had little doubt that some players are using spin much more so than others. I was also expecting to find the most consistent hitters like Votto, Cabrera, and Mauer significantly under average in terms of unexplained distance because of what I see – very flat or square contact. This was clearly evident in the data. Above, the second group should have outperformed in distance by 17 ft due to EV and another 5 ft due to loft. The fact that they were on average 2 ft shorter than the first group is significant – that distance is coming from somewhere.
To answer your question, modeled distance was based on 2015 data starting with one MPH and LA groups and then refining to tenths for each creating 78,260 EV and LA combinations. This was a first run and there are more factors that could be added to the model such as park conditions, weather, etc to filter out as much “expected distance” that would leave you with an unexpected distance value. On marginal distance issue, I don’t disagree – it’s just a matter of how flat the square hitting group is hitting the ball. Looking at the extremely low IFFBs of some of the flat hitting group, it certainly seems possible they are at the very low end of the spin scale.
Admittedly, I am not an expert on the data side so I’m sure there are significant opportunities for improvement. Whether the above view is correct or not will ultimately be proved over the course of time as spin rates for hitters are probably not too far away.
You mentioned early in the article about the three ways to generate loft and the various tradeoffs. I discussed that a bit in my series of articles on Optimizing The Swing from last year (e.g., http://www.hardballtimes.com/optimizing-the-swing-part-deux-paying-homage-to-teddy-ballgame/). Is that the kind of thing you had in mind? I only found two ways to generate loft.
From what I can gather from your article, you use Statcast number for exit speed, launch angle, and distance. From these, you can then calculate average distance for specified values of the first two. Then you look at deviations from the average for specific players. Am I getting this close to right?
My Trajectory Calculator has been fine-tuned to model the average distance, even taking into account temperature and elevation. Might that be useful to your analysis?
On the Statcast numbers, yes that is exactly correct – thanks for the clarification. Thanks for the offer on the Trajectory Calculator. At this point, I’ve spent much more time on this detour than I intended. I will get into your first question in detail in my next piece if it seems like there’s interest in an in between data and mechanics type research piece.
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.
Thanks for the comment. I didn’t mean to imply players are digging into the data. On the player side, I think distance through spin is easily observed through experimenting with different types of contact. If you watch the HR derby from this year, both Frazier and Stanton were actively spinning the ball and Stanton even talked about it afterward. As with all mechanical changes, the players will always be out in front of the data.
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.
Thanks – very good question. The 96 EV bucket at 27 deg. was much the same in terms of the shape of the curve and deviation from the model as the 100 EV bucket. I also looked at it from the LA side since the second group is higher (so 25 deg. vs. 28 degree) and there don’t appear to be major differences. With that said, I need to run for more EV and LA combinations to confirm.