All Fly Balls Are Not Created Equal

When Juan Lagares took over in center, the fly ball outs started coming in bunches (via slgckgc).

When Juan Lagares took over in center, the fly ball outs started coming in bunches (via slgckgc).

The longer a ball is in the air, the more time a fielder has to catch it. Sounds simple, doesn’t it? Sometimes, in this world of advanced fielding stats, we forget the simple things. While we’re busy measuring things like pitch velocity and deflection, or angle of the ball off the bat, or the exact zone a ball lands in, why not also talk about hang time?

The first research on this subject was published in the 2004 Hardball Times Annual when Robert Dudek manually tracked the fly ball hang times of eight pitchers and tabulated the results. Nowadays, thanks to Inside Edge, the data are available on every batted ball that is in the air for at least 1.5 seconds. (This includes line drives, flies and pop-ups—but we’re going to use the generic term “fly ball.”) There is a lot we can do with this data.

First, here is a comparison of Dudek’s original data with the Inside Edge 2013 data. The next table shows the percentage of the time a fly ball was caught for an out, broken out by how many seconds it was in the air. The data include all fair (not foul) fly balls (balls in the air at least 1.5 seconds) except for home runs.

2004 Dudek Data 2013 Inside Edge Data
Hang Time # Out% # Out%
1.5 to 3.0 449 12.7% 15,952 19.5%
3.0 to 4.0 354 48.9% 11,749 58.3%
4.0 to 5.0 368 73.6% 14,719 71.3%
5.0 to 6.0 324 90.7% 16,717 84.7%
6.0 plus 138 97.1% 6,281 93.3%

Despite some differences in the percentages, the pattern remains the same: the longer the air time, the more often the ball is caught.

There’s something else we can examine…how often does a fly ball land for an extra-base hit (in this case, just doubles and triples)? Does this depend on hang time?

Yes, it does. Balls that are in the air between 1.5 and 3 seconds land for an extra-base hit 15 percent of the time (think of line drives down the line or in the gap). If the ball is in the air three to four seconds, the percentage rises to 19 percent. After that, however, the odds of a fly ball landing for an extra-base hit decline the longer it is in the air. The data from this past season:

Frequency of Fly balls Landing for Extra-Base Hits, 2013
Hang Time # XBH%
1.5 to 3.0 15,952 15.0%
3.0 to 4.0 11,749 18.6%
4.0 to 5.0 14,719 11.0%
5.0 to 6.0 16,717 2.3%
6.0 plus 6,281 0.8%

The last figure amounts to essentially 50 extra-base hits on balls that stay in the air that long. We’re talking those high wall scrapers, or that random ball that gets lost in the lights/sun, or perhaps even those bloopers that land in that Bermuda Triangle between first-second-right or third-short-left. Those plays happened fewer than 10 times a month last season, so when you see one, know that it’s pretty random.

Of course, where the ball is hit matters, too. For instance, a ball that is in the air for three to four seconds is fielded for an out…

  • 44 percent of the time if the hit is shallow
  • 75 percent of the time is the ball is hit a routine depth
  • 55 percent of the time if the ball is hit slightly deep, and
  • 21 percent of the time if the ball is hit very deep.

These are somewhat arbitrary distinctions, of course, but I didn’t want you to think that I had forgotten how important location is. There are two essential components to every batted ball: how long it is in the air and where it lands. Most people pay a lot of attention to the latter, including Inside Edge — you can see its data in the fabulous new spray charts over at FanGraphs. Today though, we’re going to talk about the former — hang time. Let me slap some data on you:

Fly Ball Count, 2013
Hang Time Infield Shallow Routine Slightly Deep Deep
1.5 to 3.0 2,349 20,732 6,926 1,438 128
3.0 to 4.0 676 6,073 7,859 7,264 1,536
4.0 to 5.0 2,104 5,444 6,472 8,580 6,982
5.0 to 6.0 5,660 7,250 6,502 7,254 6,912
6.0 plus 3,036 3,772 2,411 2,162 1,291
Fly Ball Out%, 2013
Hang Time Infield Shallow Routine Slightly Deep Deep
1.5 to 3.0 80.5% 8.2% 31.6% 26.0% 3.1%
3.0 to 4.0 95.8% 44.3% 74.8% 54.8% 21.5%
4.0 to 5.0 99.6% 83.1% 95.7% 75.6% 25.4%
5.0 to 6.0 99.7% 96.6% 99.2% 90.6% 37.9%
6.0 plus 99.3% 98.3% 99.2% 94.6% 49.0%
Fly Ball XBH%, 2013
Hang Time Infield Shallow Routine Slightly Deep Deep
1.5 to 3.0 2.8% 11.1% 22.4% 51.9% 69.5%
3.0 to 4.0 1.1% 7.1% 12.5% 33.8% 36.6%
4.0 to 5.0 0.0% 3.2% 2.4% 15.7% 21.2%
5.0 to 6.0 0.0% 1.1% 0.4% 3.3% 6.0%
6.0 plus 0.1% 0.6% 0.4% 1.3% 2.1%

To make some categorical observations about these data, I concentrated on two categories of batted balls:

  • All batted balls (in the air for at least 1.5 seconds)
  • Batted balls in the air between 2.5 and 4.0 seconds.

If a ball is in the air 2.5 to 4.0 seconds, it is fielded for an out nearly 50 percent of the time. Also, if the ball is not caught, it is more likely than other fly balls to end up as an extra-base hit. Plays in this time range are the most critical for determining which teams make the most important run-preventing plays and which ones don’t.

Hang Time Comparison, 2013
Hang Time Out% XBH%
2.5 to 4 49.8% 17.9%
All 62.0% 10.0%

Going forward, I’m going to call balls that hung in the air between 2.5 and 4.0 seconds “sharp fly balls.” It’s just easier that way.

A Case Study

During the first couple of months of 2013, the Mets primarily played Lucas Duda in left field, a series of players in center—including Rick Ankiel for a while—and Marlon Byrd in right. The Mets needed Duda’s bat in the lineup, but he was a liability in left. Ankiel was a decent center fielder but didn’t hit a lick and only Byrd managed to put together a solid year.

In mid-June, Duda was placed on the disabled list, Ankiel was let go, and the Mets picked up Eric Young from the Rockies and handed the center field job to youngster Juan Lagares. Voila, new outfield defense. Young and Lagares did an excellent job covering ground with their speed and instincts and Byrd continued his solid play before being traded to the Pirates in August.

Homestretch: The 1967 AL Pennant Race, Part 3
A tight race shows no signs of letting up.

In April and May, the Mets turned just 41 percent and 42 percent of sharp fly balls into outs, a number far below the major league 50 percent average. In June, as their personnel changed over, their out rate climbed to 50 percent. It was 47 percent in July, but then shot up to a remarkable 70 percent in August! Young and Lagares fielded everything batted their way, and I’m pretty sure the Mets’ pitchers took notice.

Their percentage dipped back down to 55 percent in September, which was still the second-highest month for them. The outfield play should stay at a high level in 2014. Lagares and Eric Young will be joined by new additions Curtis Granderson and Chris Young, and even though Granderson’s best defensive days may be behind him, the additions should permanently move Duda to first base or the bench, and that can only be a good thing.

Mets Fly Ball Percentages By Month, 2013
Type April May June July August Sept.
All out% 60% 59% 61% 64% 65% 63%
All XBH% 11% 10% 12% 9% 8% 10%
Sharp fly ball Out% 42% 41% 50% 47% 70% 55%
Sharp fly ball XBH% 15% 15% 23% 16% 11% 15%

Ballparkin’ It

Certain ballparks will affect the fielding values differently. Here is how each team did at home and on the road, and how the road team did at the home team’s park in 2013. The first table is looking at the out percentage and the second table is looking at the extra-base hit percentage for each team. (The major league average out percentage was 62 percent for all fly balls and 49.8 percent for sharp fly balls. The league average of extra-base hits is 10% for all fly balls and 17.9% for sharp fly balls.)

Out%, by Team, Ballpark and Fly Ball Type, 2013
At home On the road Away team
Team All Sharp Fly All Sharp Fly All Sharp Fly
Angels 63.8% 48.6% 60.8% 48.4% 62.1% 48.9%
Astros 60.3% 46.8% 61.6% 49.2% 59.7% 45.9%
Athletics 66.9% 53.3% 65.6% 49.4% 65.2% 46.0%
Blue Jays 61.0% 48.9% 64.3% 49.4% 61.3% 49.1%
Braves 63.0% 52.5% 59.9% 43.7% 62.0% 52.3%
Brewers 59.7% 48.9% 61.8% 46.4% 62.8% 51.2%
Cardinals 62.0% 51.0% 60.5% 53.1% 60.4% 49.0%
Cubs 63.1% 48.0% 60.9% 49.5% 62.9% 52.3%
Diamondbacks 60.3% 48.0% 60.7% 47.0% 61.7% 49.8%
Dodgers 61.5% 50.0% 64.2% 52.6% 62.2% 50.2%
Giants 64.2% 52.7% 63.8% 51.6% 61.8% 47.1%
Indians 61.2% 52.7% 61.8% 49.2% 60.0% 44.5%
Mariners 63.4% 49.1% 66.5% 50.4% 60.3% 45.3%
Marlins 63.3% 53.4% 63.7% 49.7% 62.6% 52.2%
Mets 63.8% 52.5% 64.4% 53.6% 61.0% 47.9%
Nationals 62.1% 49.0% 62.9% 52.4% 63.3% 51.2%
Orioles 61.2% 51.8% 61.0% 45.9% 62.6% 46.2%
Padres 62.4% 50.6% 61.9% 51.1% 61.4% 50.4%
Phillies 60.7% 47.5% 61.7% 50.7% 61.2% 48.2%
Pirates 63.4% 54.4% 63.4% 50.7% 60.3% 48.4%
Rangers 62.2% 51.4% 61.4% 48.0% 64.0% 52.1%
Rays 64.2% 54.6% 64.7% 46.5% 62.3% 49.0%
Red Sox 59.4% 47.7% 57.1% 46.1% 61.9% 47.6%
Reds 62.6% 52.4% 61.8% 49.0% 64.2% 52.6%
Rockies 52.8% 44.9% 54.5% 44.3% 60.7% 48.9%
Royals 63.4% 46.9% 62.5% 49.8% 64.0% 50.5%
Tigers 62.7% 50.1% 61.2% 48.4% 61.6% 48.2%
Twins 61.4% 50.8% 61.8% 54.3% 61.5% 48.1%
White Sox 63.1% 49.5% 62.6% 51.0% 64.1% 52.8%
Yankees 62.0% 51.9% 62.3% 45.4% 61.5% 51.3%
XBH%, by Team, Ballpark and Fly Ball Type, 2013
At home On the road Away team
Team All Sharp Fly All Sharp Fly All Sharp Fly
Angels 9.3% 17.3% 10.7% 18.6% 10.9% 21.1%
Astros 11.7% 19.6% 11.0% 19.4% 12.6% 21.9%
Athletics 8.8% 16.9% 10.5% 20.6% 8.6% 16.2%
Blue Jays 13.3% 23.7% 11.5% 24.5% 11.6% 18.7%
Braves 10.6% 18.0% 11.6% 24.2% 10.9% 18.1%
Brewers 9.6% 17.6% 11.6% 22.8% 10.0% 17.0%
Cardinals 11.1% 18.7% 12.0% 19.7% 13.1% 23.3%
Cubs 11.5% 22.1% 12.2% 19.2% 11.2% 20.6%
Diamondbacks 12.2% 19.9% 13.8% 24.6% 11.9% 20.3%
Dodgers 10.5% 18.5% 9.3% 16.4% 11.3% 20.6%
Giants 11.1% 19.8% 12.0% 19.6% 11.1% 20.2%
Indians 11.8% 19.8% 11.5% 21.3% 12.1% 22.2%
Mariners 10.7% 21.1% 8.7% 17.5% 12.0% 23.0%
Marlins 12.0% 19.7% 10.7% 18.6% 10.8% 18.2%
Mets 9.3% 15.3% 10.5% 17.6% 11.6% 21.0%
Nationals 12.0% 20.5% 10.7% 20.5% 10.5% 17.8%
Orioles 10.1% 16.9% 10.5% 20.3% 11.2% 23.0%
Padres 10.6% 17.6% 11.5% 17.9% 12.6% 23.4%
Phillies 12.3% 21.5% 10.9% 20.0% 12.0% 23.0%
Pirates 10.1% 15.7% 12.2% 21.4% 11.1% 20.4%
Rangers 11.0% 18.2% 10.4% 21.0% 10.5% 19.0%
Rays 10.4% 18.1% 9.6% 18.3% 10.6% 18.5%
Red Sox 12.7% 19.5% 16.4% 24.4% 10.9% 19.6%
Reds 10.5% 21.5% 11.3% 22.3% 11.1% 17.4%
Rockies 13.7% 20.8% 14.2% 21.4% 12.8% 20.7%
Royals 10.1% 19.7% 11.6% 18.7% 9.6% 17.4%
Tigers 11.1% 20.0% 10.6% 17.9% 11.2% 19.3%
Twins 12.0% 19.9% 11.4% 18.7% 10.7% 19.3%
White Sox 8.6% 17.3% 8.2% 15.8% 10.2% 18.8%
Yankees 11.8% 19.1% 9.2% 17.8% 12.0% 21.6%

The Rockies, who play at the expansive Coors Field (the second most spacious field in the game) are easily the worst fielding team. At Coors Field, the home and away teams field 53 percent and 55 percent of fly balls, respectively, which is well under the league average of 62 percent. It’s not just Coors Field, though. Over the past two seasons, the Rockies have been below average on the road. There are some who believe that by the time the Rockies adjusts when they are on the road, the road trip is over, but that probably doesn’t account for the entire discrepancy.

And at home, their fielding rate is actually a few percentage points less than the average away team at Coors. With this information, some defensive metrics, like UZR, have the Rockies’ outfield defense near the bottom of the league. Considering their fly ball fielding percentage, the Rockies may be getting short changed in some defensive metrics, but they’re still not very good.

The second notable item is Boston’s low fielding rate.

Red Sox Fielding Rates, 2013
Location and Team Sharp Fly ball Out% Sharp Fly ball XBH%
At home 47.70% 19.50%
Away team at Fenway 46.10% 24.40%
On the road 47.60% 19.60%

This seems to indicate that Red Sox fielders have an ability to limit the amount of hits, especially extra-base hits at Fenway. Boston allows an extra-base percentage of 19.5 at home; the away team stands at 24.4 percent. The difference in extra-base hits hits allowed is almost twice the value of the next largest difference (Pirates at PNC Park). The difference can probably be attributed to fielding balls ricocheted off the Green Monster. That is something that we have heard for years, but now we can see that there is something to it.

Besides just looking at the data at the team level, here are the hang times for some individual pitchers. Each pitcher has what seems to be his own unique mix of batted balls. By grouping pitchers by their groundball and fly ball tendencies, clear baselines of pitcher batted ball types can be determined. Here are the top 10 2013 groundball and fly ball starters:

Top Groundball Pitchers, 2013
Out% Fly ball%
Name BABIP GB% FB% 1.5-2.5 2.5-4.0 4.0+ 1.5-2.5 2.5-4.0 4.0+
Justin Masterson 0.285 58% 24% 14% 62% 75% 22% 37% 51%
A.J. Burnett 0.305 57% 24% 18% 46% 84% 20% 42% 47%
Rick Porcello 0.315 55% 24% 20% 56% 74% 22% 40% 50%
Doug Fister 0.332 54% 24% 13% 52% 80% 21% 37% 54%
Jeff Locke 0.278 53% 26% 17% 59% 85% 15% 45% 49%
Andrew Cashner 0.269 53% 29% 14% 46% 84% 16% 34% 58%
Wade Miley 0.296 52% 27% 7% 61% 76% 16% 37% 57%
Stephen Strasburg 0.263 52% 31% 15% 54% 83% 13% 31% 61%
Felix Hernandez 0.314 51% 27% 12% 43% 83% 21% 38% 51%
Edwin Jackson 0.322 51% 28% 6% 50% 70% 18% 40% 53%
Average 0.298 54% 26% 14% 53% 79% 18% 38% 53%
Top Fly ball Pitchers, 2013
Out% Fly ball%
Name BABIP GB% FB% 1.5-2.5 2.5-4.0 4.0+ 1.5-2.5 2.5-4.0 4.0+
A.J. Griffin 0.242 32% 50% 5% 53% 82% 10% 30% 66%
Max Scherzer 0.259 36% 45% 9% 55% 87% 10% 28% 67%
Travis Wood 0.248 33% 45% 24% 57% 88% 13% 33% 61%
Mike Minor 0.272 35% 43% 12% 58% 83% 14% 33% 60%
Dan Haren 0.302 36% 42% 16% 48% 81% 11% 35% 60%
Shelby Miller 0.280 38% 41% 0% 48% 83% 11% 30% 65%
Julio Teheran 0.288 38% 41% 8% 40% 83% 12% 31% 64%
R.A. Dickey 0.265 40% 41% 12% 48% 79% 14% 25% 67%
Jeremy Hellickson 0.307 40% 40% 6% 64% 80% 16% 29% 62%
Miguel Gonzalez 0.260 39% 40% 12% 55% 80% 14% 28% 65%
Average 0.272 37% 43% 10% 52% 83% 12% 30% 64%

Fly ball pitchers will have a lower BABIP because hitters put more air under the ball. The higher the ball goes, the more likely it will be an out. The issue with being a fly ball pitcher is that some of those balls will leave the yard as home runs.

One interesting case is Jeremy Hellickson. Hellickson had been known for dancing around his poor marks in ERA-predictive stats such as xFIP, FIP and SIERA. In fact, he became somewhat of a punching bag in sabermetric circles as a player you should not trust. From 2010 to 2012 he posted a FIP of 4.46 with an ERA of 3.06. In 2013, many analysts claimed victory, when Hellickson’s ERA (5.17) finally rose as expected while his FIP (4.22) stayed about the same.

Hellickson maintained a similar fly ball mix from 2012 to 2013. The main issue was that more batted balls which were in the air for a short time went for hits, especially extra-base hits.

Jeremy Hellickson Fly Ball Rates, 2012-2013
Season 2012 2013
%Total – 1.5 to 2.5 15% 16%
%Total – 2.5 to 4 33% 29%
%Total – 4 or more 59% 62%
Out% – 1.5 to 2.5 14% 6%
Out% – 2.5 to 4 67% 64%
Out% – 4 or more 78% 80%
XBH% – 1.5 to 2.5 11% 13%
XBH% – 2.5 to 4 8% 18%
XBH% – 4 or more 7% 7%

In the shortest two air time categories, he his out percentage declined and his extra-base percentage increased. Now, part of that could be that the Rays defense did not perform as well as it had in past seasons. The Rays struggled to make outs in 2013 (49 percent) compared to 2012 (55 percent) in the sharp fly ball category. It will be interesting to see how Hellickson fares in 2014 with Wil Myers spending more time in the outfield instead of Matt Joyce.

Wrapping Up and Next Steps

A fly ball’s hang time helps determine a fielder’s ability to catch it for an out. In 2004, Robert Dudek looked at a small sample of data to get the results of a fly ball depending on its air time. By using Inside Edge’s data, Dudek’s data is varied using an entire season’s data. The results were very similar. If a batted ball has too little hang time, it will be a hit. If it has too much hang time, it will likely be an out — if it stays in the field of play.

With Inside Edge, much more data can be examined, such as how each team performs at home and away. Different teams and players will be able to field these batted balls for an out at different rates. The key for some teams is to make sure their outfield defense matches the type of pitchers they plan on using.

The next step is to look at the data in a more detailed manner. For example, how many runs do the Red Sox save because of their familiarity with the Green Monster? I feel I created more questions by writing this article than I actually answered. Time to get back to work.


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Jeff writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won three FSWA Awards including on for his MASH series. In his first season in Tout Wars, he won the H2H league. Follow him on Twitter @jeffwzimmerman.
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Brad Johnson
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Brad Johnson

I wonder if we can easily use this to inform xBABIP.

tz
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tz

Thanks Jeff for ruining any chance I had of being productive today at work.

Excellent stuff!

Carl
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Carl

Great article Jeff. Thank you very much.

In September the Mets, having traded Byrd to the Pirates just before the Aug 31 trade deadline, moved Lagares to RF from CF and promoted VanDecker, which likely explains the drop from 70% to 55%.

Dave Studeman
Editor
Member

For half the games, at most. I think the 55% in September is mostly random variation. Byrd wasn’t a big contributor to the Mets’ outfield performance.

David Gassko
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David Gassko

Jeff – great stuff!

Max Weinstein
Guest

Jeff, great work!

I would love to see some WOWY work done with FB_out% (like you did with Lagares) … Could be a good addition to any fielding metric.

Alan Nathan
Guest
Last summer I gave a talk at the Saberseminar (http://saberseminar.com/) entitled “Modern Techniques for Evaluating Hitting”. One of the things that interests me is how well the initial velocity vector (speed and direction) determines the landing point and hang time. The Trackman tracking system is a great tool for this kind of study, since all the quantities are measured. I found that, while the initial velocity vector is an approximate predictor of landing point and hang time, it is not perfect. One of my ongoing research projects is to understand in detail why it is not perfect. I also investigated… Read more »
Jon Roegele
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Nice work Jeff.

Yeah there is so much that can be examined with this data, you must be having fun! One that I would be interested in seeing at some point is looking at how pitch characteristics (i.e. velocity, movement, location, sequence) affect hang time on batted balls.

olethros
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olethros

I want some sweet giffage of the ~3 infield flies with +6 second hang times that went for extra bases.

Larry Tang
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Larry Tang

Interesting read.

The Red Sox section stood out to me. I think part of the difference is certainly the level of familiarity the fielders have with the park, but isn’t it also reasonably likely that the Red Sox hitters are more adept at knowing when they can take extra bases? Also did it take into account how good the Red Sox offense is, relative to their average visitor? How significant do you think the actual effect is?

Vil Blekaitis
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Vil Blekaitis

I was thinking the same thing. Over the years, even OFs from the other AL East teams have trouble with the peculiar dimensions and characteristics of Fenway Park.
In addition to the Green Monster, you have that bullpen jutting out into the OF in right center, that chasm between Pesky’s Pole and the bullpen and the cavernous area in CF.
It’s like something devised by a maker of pinball machines.

Regardless, it was a great piece.

channelclemente
Guest
channelclemente

Just a great read. Thanks. Maybe you can wax eloquent on ‘backspin’ as a hitting goal one day, it certainly has a bearing on the loiter time of a ball in the air. I suspect, the outs vs hits (HR) tradeoff is where the issue will be decided.

Nathan Aderhold
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Nathan Aderhold

This is fantastic work, Jeff. Thanks for sharing.

I’m surprised to not see Jered Weaver among your list of Top Fly Ball Pitchers. Was there an innings threshold for the guys you included on the list?

Peter Jensen
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Peter Jensen

A fly ball’s hang time helps determine a fielder’s ability to catch it for an out.

While the truth of this statement is pretty obvious, that the inverse is true for line drives and some hard hit fly balls is less obvious, but important to note. A hard hit line drive that is caught well be given less hang time than an identical line drive that is not caught and continues to add hang time until it hits the ground.

Alan Nathan
Guest
Commenting on Peter Jensen’s comment: The truth of your statement is also obvious. However, it does depend on how “hang time” is defined. I define the same way Trackman defines it, namely as the time it would take the ball to hit the ground regardless of whether it is caught or hits some obstruction prior to that. To me, that definition makes the most sense from an analysis point of view in that it gets around the problem that you raise. Commenting on cc: For sure, backspin/topspin matters for hang time. I have always thought that a line drive hit… Read more »
Jfree
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Jfree
I do not think you can possibly judge the Rockies OF – and especially the CF – until you explicitly adjust for the unique hang time there – and the consequences of that. That is the effect of altitude. The ball comes off the bat faster – and as long as it is in the air, it isn’t slowed down either because of the air pressure. That doesn’t affect fly balls as much (except re distance) – but it seriously affects line drives and fliners. OF have to position themselves way back in order to ensure that they can field… Read more »
Alan Nathan
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Jfree: You make some good points, although I don’t agree that the ball necessarily comes off the bat faster at Coors. Moreover, I think that the major effect of altitude on hang time is largely due to the Magnus force, not the air resistance. And whether the hang time increases or decreases with altitude depends on whether the ball is hit with backspin or topspin. A line drive hit with a low-ish launch angle could as well have topspin as backspin, depending on the details of how it is hit. For a ball hit with backspin, the hang time is… Read more »
Alan Nathan
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Just to clarify my last comment, my remarks about the major effect being due to the Magnus force applies to line drives. For fly balls, both the Magnus force and the air resistance play a role. Assuming a fly ball is hit with backspin (as it usually is), the effects go in opposite directions and, very roughly speaking, cancel out. That is, there is not much of a difference in hang time between Coors and sea level for a fly ball. That is basically what Jfree said in his comment, which I agree with.

Jfree
Guest
Jfree
re ball off the bat faster – much of that is what the humidor fixed. But, ceteris paribus, a pitch will still travel a bit faster at Coors – and the batters also tend to get better contact because of the other effects on pitches. re line drives – the real phenomenon I’m thinking of is – where will a line drive be after x seconds at Coors v the same line drive after x seconds everywhere else? I don’t know the answer but 5-10 feet further per second of hang time wouldn’t surprise me. That’s what would seem to… Read more »
Alan Nathan
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Re batted ball speed: I have quite a bit of data showing the average batted ball speed in Denver is the same as everywhere else. While you are correct about pitches being a bit faster at Coors, it is only about 1 mph, which has a pretty small effect on batted ball speed (about 0.2 mph). Re line drives at Coors: My own take on why there are so many extra-base hits at Coors is that the fences are so far away, as a way of compensating for the better carry. Moving the fences back reduces home runs but increases… Read more »
Jfree
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Jfree
Alan – so if speed-off-the-bat is not much different – but HRs/etc travel 10% further; then are they taking 10% longer to get there? I’ve never timed HRs at Coors but, anecdotally, that doesn’t feel right. They do seem to have a more ‘efficient’ trajectory (you sense they’re gone really early – and not just from the sound) and it also seems that they don’t slow down or drop out at the end. But could that offset the same initial ball speed? That latter, translated to non-popup non-HR FB’s, might also affect fielding – less time to make final adjustments… Read more »
Scott
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Scott

The Oakland A’s ballpark leading in highest out% and least xbh% is interesting. Their high fly ball% pitching staff, and high fly ball % hitters have already been examined by Andrew Koo but it seems there may be more to look at on how the A’s are using the Coliseum as an advantage.

Rawson Baggs
Guest
Rawson Baggs

” … data are …”

Thank you.

jonathan
Guest
jonathan

It would be cool to see some graphs.

Alan Nathan
Guest

Jfree: Here are some interesting facts on HR’s, courtesy of hittracker, for balls hit in a narrow range of initial speed and launch angle. In 2009, the mean HR distance and hang time was as follows:
all except Coors: 400′, 4.88 sec
Coors: 425′, 4.90 sec

So, you can see that the hang time for identical initial conditions is virtually the same, despite the fact that the distance is significantly longer. The similar hang time is due to the cancellation effect I referred to earlier. The very different distances is due to the reduce air drag.

Jfree
Guest
Jfree
That s interesting. Help me out with some physics here re non-HR. I’m still trying to get the effect on fielding. Initial ball speed is the same. Same baseball. Gravity is the same. Assume same launch angle. Lower air pressure/resistance would tend to produce a lower trajectory (80% sea level, 20% outer space/vacuum) – less vertical up/down/waste, more horizontal – overall a trajectory that is 5-8% ‘different’?? – as seen from a camera off to the side (or a fan in the stands). And at terminus, the ball speed would be higher (lower drag) and would come in at a… Read more »
Alan Nathan
Guest
You have asked some interesting questions. I don’t have any ready answers for you. However, let me suggest that you try to investigate these issues yourself by playing around with my “trajectory calculator”: http://baseball.physics.illinois.edu/trajectory-calculator.html. You can calculate trajectories under different conditions (e.g., different elevations, launch angles, initial speed, spin, etc.) to see how these things affect where the ball ends up and how long it takes to get there. However, I return to the point I raised earlier. One way to compensate for the better carry at Coors is to move the fences back, which is what was done. This… Read more »
Juan Pablo Zubillaga
Guest
Juan Pablo Zubillaga

Jeff, it seems to me that in the two tables showing percentages by team, the “On the Road” and “Away Team” columns are switched. You mentioned fielders at Coors having 53 and 55 percent of outs for home and away teams, respectively. But in the table, the 55% is shown as “On the road”, while the “Away Team” percentage was 60. It ceartainly makes more sense what you wrote than what the table shows, so I assume they were mistakenly switched.

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