A Humidor In Chase Field Is A Big Deal
A few days ago, Alan Nathan wrote an article for The Hardball Times about the humidor’s potential impact on exit velocity in Chase Field. He referenced a physics model that estimates a 3.8 mile per hour drop in exit velocity. He also showed that over the past two seasons the Diamondbacks Exit Velocity is 2 miles per hour higher in home games when compared to away games. I encourage you to read the whole article prior to reading what I have produced here today, as I will be building upon what he wrote.
Measurement Bias?
In the comment section of that Alan Nathan’s article I mentioned a potential batted ball calibration problem in Chase Field, and in fact all parks around the game, which may bias the readings up or down. At the time, I had the signs confused (addition versus subtraction), but the point stands.
Yesterday, I dug through my exit velocity data, and I found that batted balls in Chase Field, during the 2016 season, were roughly 2 mph *too slow* given their game results. In other words, if I raise the exit velocity for all balls in Chase Field by 2 mph, the expected batted ball success rates (xStats) had smaller error when compared to the real game stats. I tested 1.5 mph and 2.5 mph intervals as well, both had increased error. xStats doesn’t have the granularity for precision greater than a half mile per hour, so narrowing down the number even further is impossible using this model.
| 1B | 2B | 3B | HR | |
|---|---|---|---|---|
| Actual | 2060 | 695 | 117 | 390 |
| xStats Raw EV† | 2042.4 | 636.0 | 122.4 | 294.5 |
| Error | -0.9% | -8.5% | 4.6% | -24.5% |
| xStats EV +2‡ | 2042.8 | 689.2 | 129.6 | 398.1 |
| EV +2 Error | -0.8% | -0.8% | 10.8% | 2.1% |
† Raw Exit Velocity from Baseball Savant
‡ Chase Field Exit Velocity increased by 2mph
I calculated the Diamondbacks home and away game average exit velocity to double check Alan Nathan’s claims. The data appears to be in perfect agreement; there is a two mile per hour increase in Exit Velocity during their home games.
| Year | Home | Away | Difference |
|---|---|---|---|
| 2015 | 90.0 | 88.2 | 1.8 |
| 2016 | 90.9 | 88.7 | 2.2 |
| 2017 | 89.9 | 88.2 | 1.7 |
| Total | 90.4 | 88.4 | 2.0 |
If I take this measured 2 mph difference, and the inferred 2 mph measurement bias I calculated using xStats success rates, I find a roughly 4 mph swing in Exit Velocity between the Diamondbacks home and away games. Give or take a half mile per hour. Again, Nathan’s physics model predicted a 3.8 mph difference, so our two models are in agreement.
My Model
With a humidor, much or all of this difference in velocity (ΔV) may disappear overnight, which will obviously play a large role in future offensive production. Since xStats treats every batted ball on a case by case basis, using exit velocity and launch angle to predict success rates, I can use it to predict how the stats may change, and which players may be impacted the most.
To run this analysis, I merely had to subtract 4 miles per hour from all batted balls in Chase Field, and then run my xStats algorithms on that modified data set. Consequently, I have adjusted stats for every batter who had even one single at bat (or faced a single batter) in Chase Field since 2015. However, I will be focusing primarily on the 2016 season.
Results
| 1B | 2B | 3B | HR | |
|---|---|---|---|---|
| Observed | 995 | 331 | 62 | 221 |
| EV -4 Estimate | 1006.7 | 298.1 | 57.4 | 142.8 |
| 1.2% | -9.9% | -7.4% | -35.4% |
As you might expect, the home run rate dropped substantially, by 35%. Which is almost embarrassingly close to what Alan Nathan initially predicted back in 2011.
At this point I should state that I ran this analysis many times, testing out many variables, including EV -2 mph (25% fewer home runs) and EV -3 (I lost the result, sorry). I tested raw EV -4 (raw meaning the number from Baseball Savant), which would reduce home runs by 45%.
In each case, I had home run rate drop between 20 and 50%. However, I believe the version I am sharing in this piece, with 35% fewer home runs, may be the most accurate.
How Diamondback Players May Be Impacted
Exit velocity may decrease overall, but that doesn’t mean every player is impacted equally. If your average home run is has a 108 mph EV, then the lost velocity may not make a huge difference. On the flip side, if your average home run has a 92 mph exit velocity, it could doom you.
| name | HR | xHR | ΔHR | Percent HR Lost | BIP | wOBAcon | xOBAcon | ΔOBAcon |
|---|---|---|---|---|---|---|---|---|
| Jake Lamb | 19 | 9.0 | -10.0 | -52.4% | 183 | .505 | .418 | -.062 |
| Brandon Drury | 12 | 5.9 | -6.1 | -50.9% | 180 | .489 | .385 | -.104 |
| Paul Goldschmidt | 15 | 9.0 | -6.0 | -40.0% | 211 | .480 | .574 | -.062 |
| Yasmany Tomas | 16 | 10.1 | -5.9 | -37.2% | 203 | .427 | .413 | -.013 |
| Jean Segura | 12 | 6.7 | -5.3 | -44.1% | 275 | .438 | .361 | -.076 |
| Welington Castillo | 8 | 4.8 | -3.2 | -39.7% | 147 | .423 | .383 | -.040 |
| Rickie Weeks | 6 | 2.9 | -3.1 | -52.5% | 52 | .444 | .409 | -.036 |
| Chris Owings | 5 | 2.3 | -2.7 | -53.3% | 157 | .383 | .367 | -.016 |
| Yangervis Solarte | 4 | 1.6 | -2.4 | -58.9% | 31 | .535 | .419 | -.117 |
| Ryan Schimpf | 4 | 1.7 | -2.3 | -58.2% | 14 | .814 | .586 | -.228 |
| David Peralta | 3 | 0.8 | -2.2 | -72.0% | 80 | .429 | .374 | -.055 |
| Derek Norris | 2 | 0.3 | -1.7 | -85.1% | 11 | .673 | .360 | -.313 |
| Chris Herrmann | 3 | 1.5 | -1.5 | -49.8% | 49 | .478 | .392 | -.086 |
| Scott Schebler | 3 | 1.5 | -1.5 | -49.3% | 13 | .669 | .528 | -.142 |
| Ryan Braun | 2 | 0.5 | -1.5 | -72.5% | 12 | .408 | .376 | -.032 |
Unfortunately, according to this model, a few of these Diamondbacks hitters appear to fall into the ‘doomed’ category. Namely, Jake Lamb, who lost a jaw dropping 52% of his home runs in Chase Field. Considering he has hit nearly 60% of his home runs at home, that could be a 30% drop in his home run totals each season.
Brandon Drury is in a similar boat, although he hasn’t necessarily defined his value with the long ball. Goldschmidt and Tomas should each lose roughly 30-40% of their home runs, which isn’t especially surprising considering the model predicts a 35% reduction in total home runs.
| Team | HR | xHR | ΔHR | Percent Lost |
|---|---|---|---|---|
| ARI | 190 | 113.9 | -76.1 | -40.1% |
| Opponents | 200 | 137.7 | -62.3 | -31.2% |
| ATL | 8 | 5.2 | -2.8 | -34.6% |
| CHC | 7 | 4.7 | -2.3 | -32.7% |
| CIN | 10 | 6.9 | -3.1 | -31.3% |
| CLE | 1 | 2.5 | 1.5 | 146.1% |
| COL | 25 | 18.3 | -6.7 | -26.9% |
| HOU | 9 | 6.2 | -2.8 | -31.0% |
| LAA | 2 | 0.5 | -1.5 | -75.9% |
| LAD | 26 | 18.2 | -7.8 | -30.0% |
| MIA | 5 | 2.8 | -2.2 | -44.3% |
| MIL | 6 | 4.3 | -1.7 | -29.2% |
| NYM | 12 | 4.7 | -7.3 | -60.7% |
| NYY | 2 | 2.2 | 0.2 | 7.6% |
| OAK | 3 | 2.8 | -0.2 | -5.8% |
| PHI | 5 | 4.0 | -1.0 | -19.0% |
| PIT | 4 | 4.1 | 0.1 | 2.1% |
| SD | 21 | 13.2 | -7.8 | -37.0% |
| SF | 23 | 16.0 | -7.0 | -30.6% |
| STL | 10 | 5.6 | -4.4 | -44.1% |
| TB | 5 | 4.2 | -0.8 | -16.1% |
| TEX | 2 | 1.5 | -0.5 | -25.3% |
| TOR | 3 | 2.5 | -0.5 | -15.2% |
| WSH | 11 | 7.4 | -3.6 | -33.0% |
This table represents *all* of the seasons I have on record, 2015-2017. The Diamondbacks have 40% fewer home runs, while their opponents are down only 31%. This may look like a worst case scenario for the DBacks; Not only are you limiting offense, but you’re disproportionately hurting the home team.
Those are the batters, though, how about the pitchers?
| name | HR | xHR | D | Percent Lost |
|---|---|---|---|---|
| Archie Bradley | 12 | 5.7 | -6.3 | -52.7% |
| Patrick Corbin | 14 | 10.2 | -3.8 | -27.2% |
| Jonathon Niese | 4 | 0.5 | -3.5 | -87.9% |
| Robbie Ray | 11 | 8.0 | -3.0 | -27.6% |
| Dominic Leone | 5 | 2.1 | -2.9 | -57.1% |
| Randall Delgado | 6 | 3.6 | -2.4 | -39.3% |
| Braden Shipley | 5 | 2.7 | -2.3 | -46.3% |
| Jorge De La Rosa | 3 | 0.7 | -2.3 | -76.7% |
| Zack Godley | 10 | 7.7 | -2.3 | -22.7% |
| Shelby Miller | 10 | 8.2 | -1.8 | -17.9% |
| Edwin Jackson | 2 | 0.3 | -1.7 | -85.8% |
| Chris Archer | 2 | 0.3 | -1.7 | -84.3% |
| Tyler Lyons | 2 | 0.4 | -1.6 | -81.1% |
| Josh Collmenter | 3 | 1.4 | -1.6 | -53.9% |
| Casey Fien | 2 | 0.5 | -1.5 | -76.0% |
| Matt Moore | 2 | 0.5 | -1.5 | -74.5% |
| Adam Wainwright | 2 | 0.6 | -1.4 | -69.2% |
| John Lackey | 2 | 0.7 | -1.3 | -66.9% |
| Francisco Liriano | 2 | 0.7 | -1.3 | -64.1% |
| Julio Teheran | 2 | 0.8 | -1.2 | -60.5% |
| Carlos Estevez | 2 | 0.8 | -1.2 | -58.3% |
| Jake McGee | 3 | 1.9 | -1.1 | -37.3% |
| Chad Bettis | 3 | 1.9 | -1.1 | -36.2% |
| Jeff Hoffman | 2 | 0.9 | -1.1 | -53.9% |
| Mike Bolsinger | 2 | 0.9 | -1.1 | -52.9% |
Man, Archie Bradley may love the humidor, 52% fewer home runs! A lot of these DBack pitchers are listed with 2 and 3 fewer home runs. That is definitely nice, Bradley’s FIP, for example, would drop from 4.10 to 3.66. Having said that, we’re talking about pitchers saving 2-3 home runs while the offensive players are losing 4-6, each.
Finally, the team pitching numbers for the past three seasons.
| Team | HR | xHR | ΔHR | Percent Lost |
|---|---|---|---|---|
| ARI | 200 | 137.7 | -62.3 | -31.2% |
| Opponents | 190 | 113.9 | -76.1 | -40.1% |
| ATL | 7 | 4.3 | -2.7 | -38.7% |
| CHC | 8 | 4.0 | -4.0 | -49.9% |
| CIN | 3 | 1.9 | -1.1 | -37.2% |
| CLE | 2 | 1.1 | -0.9 | -47.3% |
| COL | 22 | 13.0 | -9.0 | -41.1% |
| HOU | 6 | 3.5 | -2.5 | -42.0% |
| LAA | 2 | 1.8 | -0.2 | -8.9% |
| LAD | 25 | 14.5 | -10.5 | -42.2% |
| MIA | 3 | 2.2 | -0.8 | -25.4% |
| MIL | 7 | 4.2 | -2.8 | -40.4% |
| NYM | 9 | 4.8 | -4.2 | -47.1% |
| NYY | 4 | 2.6 | -1.4 | -36.0% |
| OAK | 2 | 1.6 | -0.4 | -18.7% |
| PHI | 9 | 6.1 | -2.9 | -31.9% |
| PIT | 9 | 4.4 | -4.6 | -51.2% |
| SD | 22 | 10.7 | -11.3 | -51.1% |
| SF | 26 | 20.0 | -6.0 | -23.0% |
| STL | 9 | 5.9 | -3.1 | -34.7% |
| TB | 5 | 1.2 | -3.8 | -76.4% |
| TEX | 1 | 0.3 | -0.7 | -73.8% |
| TOR | 1 | 1.0 | 0.0 | 4.0% |
| WSH | 8 | 4.9 | -3.1 | -38.3% |
Conclusion
If the Humidor is in fact installed in Chase Field, you should expect to see the Diamondback power hitters lose between 4 and 6 home runs, prorated to the remainder of the season. Which, for the purposes of this season, may be a reduction of around 3-4 homers, each.
On the flip side, you might expect their starting pitchers so give up roughly a third fewer home runs. Greinke may seem like an obvious beneficiary, but my analysis suggests he may not be helped very much. Instead, look to guys like Archie Bradley and Robbie Ray.
If the Diamondbacks install a Humidor, I imagine it would be paired with plans for moving in the fences. It doesn’t make sense to install a Humidor without adjusting the ballpark. You may ask why install the thing at all if you’re just going to bring the fences in afterward. Think about it this way: it is very difficult to make a stadium larger. You would need to move home plate or tear out seating and demolish walls. Installing a Humidor is like pressing the restart button. You get to make the field play as through it were larger, so you can then make fine adjustments to the dimensions to achieve the run environment. Presumably, an offensively neutral ballpark.
Edit: I put more stats in a spreadsheet! I know home runs do not tell the whole story, but it is hard to fit all of the stats into one article like this. I encourage you to browse the spreadsheet.
Andrew Perpetua is the creator of CitiFieldHR.com and xStats.org, and plays around with Statcast data for fun. Follow him on Twitter @AndrewPerpetua.
I was looking forward to this article as soon as I saw your comment on Alan Nathan’s article. You did not disappoint. Great research and analysis!
Is there any affect on doubles? Maybe slight decreases in BABIP?
Yes, indeed there were. When writing this, I wasn’t entirely sure which stats people would be most interested in seeing. I just added wOBAcon, so that should offer you a more well rounded snapshot. I have more stats listed in a linked spreadsheet. I just added that at the bottom of the article, if you’d like to look at it.
But, yes, the doubles, triples, and home runs all down. BABIP down as well.
That’s quite interesting thanks…
Love the data, but I wouldn’t necessarily try to predict individual pitcher impacts based on one season of batted ball distributions.
All in all, what’s your estimate of the post-humidor park factor?
That is probably true. But in the other hand, exit velocity and flyball rate seem to stabilize fairly quickly.
Agree regarding EV and FB rates. I’m assuming that “just-enough” HR events are one of the major drivers of your player-specific Lost Home Runs analysis, and that they are not very predictable from one season to the next.
Wouldn’t a fairer test be to compare a player’s xHR from 2016 to his estimated 2016 xHR with the humidor adjustment? I believe you are using actual 2016 HRs, which would seem to introduce normal HR luck-correction to the humidor effect analysis.
Nice work. Just want to be sure I understand. My physics-based estimate is a 3.8 mph increase in exit speed. My analysis of DBacks home-vs-away gives only 2 mph. However, with a 2 mph bias on the low side at Chase, that takes it back up to 4 mph. Am I understanding this correctly? If so, then that sound you hear is a big sigh of relief by me. Plus a big thanks for resolving the discrepancy.
Yes sir, you’re understanding it correctly. I believe Chase Field measures batted balls between 1.5 and 2 mph too slow. So, where they say a ball is 91, it is probably closer to 93.
Does it strike you as strange that MLB would be this far off (and that they wouldn’t be adjusting the numbers themselves)? I wonder if you can reach out privately to them and get their take on the measurement error.
Well, Pitchfx was this far off, too. I can’t remember the exact timeline, but when Pitchfx first came out, it faced many of the same problems we see today with Statcast. It had missing data, measurement errors, and all sorts of headaches.
It also had this calibration problem, and there were stadiums that varied wildly with their pitch speed measurements. The difference is, PitchFX was a matured technology. Everyone knew about calibration problems, solutions were found, and people implemented them. It was part of doing business. If you wanted to use pitchfx, you applied the calibration corrections.
With statcast, those corrections do not yet exist, at least not that I know of, not in a public sense. They will, though. Give it time.
As to your question about MLB knowing about the problem. Trust me, they know. I’m not sure it is their responsibility to offer a solution, though. Just like they weren’t the ones to offer a solution for PitchFX.
Do they know about the problem in a general sense, or do they know that their readings at Chase are consistently 2 mph too low.
If the latter, I’m still a bit baffled.
They know the readings are off everywhere. I believe if you go back and read that piece you’re referencing on BP you may notice a bunch of the people in the comment section work for MLBAM.
I know this is only a small part of your research but I’m fascinated by the differences in calibration. I believe Russell Carleton of baseball prospectus did a detailed study and concluded there was a need for adjusted exit velocity since different stadiums calibrate the track man system differently. It seems like the same stadiums are continuing the trend this year despite the new machines. On the other side of the spectrum, Great American Ball Park seems to underestimate exit velocity and since stat cast was introduced they’ve been an offender every season.
I personally haven’t delved deeply into this. This is the first time I have looked at the park related effects on my own, before this I used the conclusions published by others, including Russell Carleton.
I believe Carleton concluded Chase Field had a 1.27 mph bias. My findings suggest this number is higher than 1.5. That could mean 1.6 or 1.8 or even 2.2, I have no way to narrow it down.
However, from what I understand, Carleton wasn’t only looking at the velocity itself, but rather the relationship between the batter and pitcher as well. Which, ultimately, may be a better methodology and could and maybe should become the standard in the future. In that case, perhaps he found that the pitching in Chase Field was somehow contributing to the increase in velocity. Perhaps a higher average velocity? I somehow doubt that would be the case, but that is the sort of consideration I believe his analysis forces you to think about.
Either way, his results were from only a portion of the season, half a season maybe. Maybe a little less. I think he wrote that last June. In that sense, maybe there are seasonal variations as well. Maybe spring and autumn have lower averages and summer has higher, in which case his dataset may have been disproportionately representing spring.
Although I believe he corrected for temperature, so perhaps that is a nonissue.
Anywho, I don’t have firm answers. I can only say the model I used seems to suggest a difference greater than 1.5mph. My model might be wrong. This is an area that needs more research.
Wonderful piece except that you now gave greedy owners a new idea on how to make more money. (Install a humidor and move in the fences.) Expect to soon see little league size fields and waterlogged baseballs at a stadium near you.
For what its worth, Segura has lost almost 3 MPH in his very small sample so far this year and Haniger has dropped 6 MPH and now has a below league average EV. Small samples and hard to pinpoint exactly how much is the park but they are definitely showing a large drop like you would expect.
Jean Segura:
2015 home: 87.6
2015 away: 86.1
2016 home: 91.5
2016 away: 89.5
2017 home: 57.5
2017 away: 89.7
There might be more going on with him.
A few points:
1) While the ARI hitters would have, in retrospect, been bigger HR “losers” than their opponenets, I personally don’t see that as being much more than chance, or very predictive.
2) This could be part of an organizational strategy, combined with their new pitching strategy http://www.fangraphs.com/blogs/the-d-backs-could-have-a-new-pitching-approach/
3) This team does seem obsessed with making good on Greinke contract, and helping him lower ERA to make it look good
4) buy ARI SP now 😛
Using your estimates, it looks to me like Chase Field will play a lot like AT&T (without the big differences by handedness). I’m getting the new park factors as being roughly:
Basic/ 1B/ 2B/ 3B/ HR/
95/ 100/ 100/ 115/ 84
As compared to AT&T
Basic/ 1B/ 2B/ 3B/ HR
95/ 99/ 97/ 115/ 84
Do we know what the effects of the humidor would be? It seems all of this assumes it completely cancels out the current ball dynamics but it’s just a temporary treatment to the ball. The air is still the same. It’s not like Denver was negated by the humidor.