1. About Landing

The Story Behind “Landing”

Sometime in the mid 1990s, I got an idea in my head:  for no good reason, I thought it would be cool to be able to randomly select a specific latitude/longitude location in the United States every day; keep track of the state and the watershed for each location, and see what town or city or interesting geographical site might be nearby.  I am a mathematical kind of guy and knew that I could use a programmable calculator along with a computer-based map program to make it happen.

So I used my programmable calculator to be able to calculate a random latitude/longitude (lat/long) somewhere in the lower 48, at the push of a button.  For simplicity, I ignored Alaska and Hawaii.  Also for simplicity, I just programmed in that the random lat/long would be somewhere in the large rectangle that would include all of the land and water ranging from northeast Maine to the Florida Keys to southwest California to northwest Washington State.  I knew that often, a random lat/long location would be in the Atlantic and Pacific Oceans, the Gulf of Mexico, Mexico and Canada, but figured I would just ignore those.  (Check out a map of North America, and you’ll see why the large rectangle that would include all of the U.S. would also include large areas of land and water outside the U.S.)

For the mapping portion of the exercise, I used the program StreetAtlas.  StreetAtlas allowed me to enter in lat/long info, and it would jump right to the location specified.  I enjoyed being “zoomed in” as much as possible when I went to the specified location.  I might see just a little bit of a street and maybe a stream; or maybe see no features at all.  Then, I’d zoom out one click at a time, and various map features would begin to appear on the map:  the roads, towns and streams in the vicinity.

StreetAtlas had a lot of detail on streams, so I could see what the nearest creek was, and follow it downstream until I hit another creek or a river, etc., etc., eventually to an ocean.

In my head, I called the process “landing.”  So, every day, I could “land” somewhere.  Knowing that I have a somewhat-addictive personality, I made a rule:  no more than one landing a day.

For a while, I kept track of the states and watersheds where I landed on a piece of paper, and plotted my approximate landing locations by hand on a blank U.S. map.  But then I realized that I should be keeping track of my landings on a computer spreadsheet.  So, I began using Excel to keep track of each day’s landing.  In addition, I transferred the landing process from the programmable calculator to Excel.

I had several versions of my landing spreadsheet.  But at a minimum, I was keeping track of what state and what river watershed I was landing in.  I began to notice that I seemed to be landing in some states more than I should, and in other states less than I should.  What I mean is that, intuitively, I should be landing more often in Texas than, say, in Ohio.  But after 100 or so landings, I’d find that I had more landings in some smaller states than in bigger states.  This is just a statistical quirk, due to the random nature of the whole process.

I came up with a spreadsheet function that allowed me to quantify the statistical quirk just mentioned. Here’s what I did:

I figured out how to determine exactly how many landings each state “should” have, based on its area.  For example, Texas contains about 9% of the area of the lower 48, so it “should” have about 9% of the landings.  I had Excel do a similar calculation for every state.  I could then keep track of whether a state had more landings than it should (in my head, I called that being “Over-Subscribed”), or if a state had fewer landings than it should (I called that being “Under-Subscribed.”)

I’m no statistician, but I kind of knew that if I did a million landings, I’d end up with pretty close to every state having very close to their “correct” share of landings.  I came up with a way to measure how out of whack I was relative to the “correct” number of landings for the various states.  For any given state, I determined the difference between the following two values:

[The percentage of total landings that a given state should have based on its area]

and

[The percentage of total landings that the given state actually has]

I then add up the differences for all 50 states.  (For math-oriented readers, I add up the absolute differences.)  Anyway, generally speaking, this sum inevitably starts out larger and gets progressively smaller as I land in additional states (headed toward a theoretically-nearly zero value after many, many landings, say a million just for the heck of it).  For no good reason, I multiply this number times 100.  I call this number my Score.  (More about this later).

It turns out that the Score gets lower when I land in an Under-Subscribed (“US”) state, and the Score gets higher when I landed in an Over-Subscribed (“OS”) state.  Every day that I land, I find myself rooting for USers and rooting against OSers.  I want the Score to get lower, and am excited when I hit an all-time low.

While I’m mentioning abbreviations, I use the two-letter U.S. Post Office abbreviations for the states all the time.  (Quick!  What’s Nebraska?  Not NB; it’s NE.  How about Arizona?  Not AR, that’s Arkansas.  Maine?  ME.)

Anyway, I finalized a spreadsheet that kept track of all the things I wanted to keep track of, and started from scratch on April 1, 1999.  I still use the same spreadsheet.

Here are three more-or-less randomly-selected entries (without the watershed info):

1/19/2007	194.8	0	2	16	-105.8883	38.6783	CO; Cen; 11.1 mi SE of Nathrop
1/22/2007	193.6	1	3	17	-103.3271	34.7680	NM; E-Cen; 3.7 mi S of Grady
1/23/2007	194.3	0	3	18	-112.1080	45.6632	MT; SW; 5.8 mi SE of Waterloo

On 1/19/07, I landed in central Colorado; the town closest to my landing was Nathrop.  My landing was located at a longitude of W105.8883, and a latitude of N38.6783.  The “0” in the third column means that Colorado is OS (conversely, a “1” means that the state is US).  The “2” in the fourth column simply means that 2 of the last 10 landings have been USers, and the red “16” indicates that I’ve had 16 landings in a row where I’ve had less than 4/10 OSers.

Generally speaking, 4/10 or more tends to lower the Score (yea!) and 3/10 or less tends to raise the Score (boo!).  When I have a streak of 4/10 or greater, the numbers showing the length of the streak are black (as opposed to red).

My Score for this landing is at 194.8.  Given that I have a streak of 18 landings in a row where I have been at 3/10 or worse, this is a bad stretch of landings where my Score has been generally rising.  As I mentioned previously, I’m very excited when I get an all-time record low Score (in fact, I enter it in my spreadsheet as Red & Bold.

As you can see, I then landed in New Mexico (a US state that lowered the Score), and then in Montana (an OS state that raised the score).

As I discussed earlier, I do a calculation that determines my Score, where I add up values for each state that indicate how OS or how US each state is.  Below is a cut-and-paste from my spreadsheet.  A negative number indicates that a state is US; a positive number indicates that a state is OS.  The magnitude of the number tells me just how far out of whack each state is.  Obviously, I’ve landed in Texas way less than I should, and I’ve landed in Montana way more than I should.  Why is that?  I have no idea, except that peculiar things can happen when random events occur (just ask any gambler).

Texas	-20
California	-7
Virginia	-7
Florida	-7
Idaho	-5
Alabama	-4
Kentucky	-4
Georgia	-4
Indiana	-4
New Mexico	-4
Arkansas	-3
Maine	-2
South Carolina	-2
Massachusetts	-2
Pennsylvania	-1
Wisconsin	-1
Mississippi	-1
Louisiana	-1
New Jersey	-1
Delaware	-1
Illinois	-1
Missouri	-1
Ohio	0
North Carolina	0
Tennessee	0
Washington	0
West Virginia	0
Vermont	1
Nevada	1
Rhode Island	1
Colorado	1
Maryland	1
Connecticut	1
South Dakota	1
Iowa	2
New York	3
New Hampshire	3
Arizona	3
Kansas	4
Nebraska	4
North Dakota	4
Wyoming	5
Michigan	5
Oklahoma	6
Oregon	7
Utah	7
Minnesota	10
Montana	12
Total Score:	165.9

The states with zeros are “Perfectly-Subscribed” (not surprisingly abbreviated PS); that is, the number of landings for these states is just what it should be.  But remember, there’s no particular significance to the magnitude of the positive and negative numbers.  However, generally speaking, the individual scores for each state will tend to decrease, as will the overall Score.

At the time of my cut-and-paste, my Score was 165.9 (which is the sum of all of the individual state scores after you ignore the minus signs in front of the US states).  For reference, note that my Score was about 194 back in January of  2007 (from my example landings cited earlier).  As I said, it is statistically inevitable that my Score will slowly get lower.

Moving right along to streams, rivers and watersheds:

Here are my watershed entries for the same three January 2007 landings that I listed above (the first in CO, the second in NM, and the third in MT):

ut; Herring Ck; Badger Ck; Arkansas R; Mississippi R

ut; Frio Draw; Tierra Blanca Ck; Prairie Dog Town Fk Red R; Red R; Atchafalaya R

Beall Ck; Parot Ditch; Jefferson R; Missouri R; Mississippi R

For the Colorado landing, I landed in the watershed of an “unnamed tributary”, or “ut.”  This unnamed tributary flows into Herring Creek, which flows into Badger Creek, which flows to the Arkansas River, which flows to the Mississippi River.

My New Mexico landing ended up in the Atchafalaya River (which flows into the Gulf of Mexico), and my Montana landing ended up in the Missouri, on to the Mississippi.

I like to think of my watershed entries this way:  if a drop (or a bucket, or a garden-hose stream) of water is released at my landing spot, and it flowed to the nearest stream, what streams and rivers would the water encounter on the way to the ocean?

To keep track of my watersheds (on another part of the spreadsheet) I’ve organized all of the rivers (not any creeks, that would be overwhelming) as follows:  First, I put the rivers into one of the following six major categories, depending on where the water in those rivers ends up:

1. The Mississippi River, (excluding the Missouri River, because it’s so big that it has its own category);
2. The Missouri River;
3. The Atlantic Ocean;
4. The Gulf of Mexico (other than the Mississippi River);
5. The Pacific Ocean; and
6. “Internally-drained,” that is, the water ends up in places like the Great Salt Lake where it just evaporates and/or sinks into the ground.

It turns out that the number of landings in each of the major categories is very roughly equal (except the internally-drained, which is about half the size of the other categories, but still surprisingly large).

I break rivers down further.  For example, let’s look at the Atchafalaya (from the New Mexico example, the second watershed entry above).  The Atchafalaya is part of the “Other Gulf Coast” major category (#4 above).  Here’s a cut-and-paste from my spreadsheet that includes the Atchafalaya:

Other Gulf Coast	217
Apalachicola	5
… Chattahoochee	… 2
… Flint	… 3
Aransas (TX)	3
Atchafalaya	42
… Bayou Des Glaises	… 1
… … Bayou Du Lac	… … 1
… Big Saline Bayou	… 1
… … Little R	… … 1
… Courtableau Bayou	… 1
… … Cocodried Bayou	… … 1
…Red	… 36
…… Black (LA, AR)	… … 7
… … … Ouachita R	… … … 7
… … … … Bartholomew Bayou	… … … … 1
… … … … … Deep Bayou	… … … … … 1
… … … … Bayou D’Arbonne	… … … … 1
… … … … Boeuf	… … … … 2
… … … … … Big Bayou	… … … … … 1
… … … … Two Bayou	… … … … 1
… … … … … South Bayou	… … … … … 1
… … Blue (OK)	… … 1
… … Cross Bayou	… … 2
… … … 12 Mile Bayou	… … … 2
… … … … Big Cypress Bayou	… … … … 2
… … … … … Little Cypress Bayou	… … … … … 1
… … Flat (LA)	… … 1
… … Kiamichi (OK)	… … 1
… … Little	… … 1
… … … Saline	… … … 1
… … Loggy Bayou (LA)	… … 1
… … … Bayou Dorcheat	… … … 1
… … N Fork Red	… … 1
… … … Elm Fk	… … … 1
… … Pease	… … 2
… … … Middle Pease	… … … 1
… … Prairie Dog Town Fk	… … 3
… … Saline Bayou	… … 1
… … Washita (OK)	……4
… … Witchita (TX)	… … 1

These two columns in my spreadsheet continue for a long time, including all the rivers (besides the Mississippi) that flow into the Gulf of Mexico.

The above shows that I’ve landed in Gulf Coast river watersheds (other than the Mississippi) 217 times.  I’ve landed in the Atchafalaya River basin 42 times.  Of those 42, 36 landings were in the Red River basin.  My New Mexico example had me landing in the Prairie Dog Town Fork of the Red River, where I’ve landed 3 times.

As another example from the spreadsheet above, I’ve landed in the Little Cypress Bayou once.  The Little Cypress Bayou flows into the big Cypress Bayou, on to the 12-Mile Bayou, to the Cross Bayou, on to the Red River, on to the Atchafalaya.  You can see that I’ve come up with standard font styles (and numbers of dots preceding the names) to represent each level of tributary.  You can also see that I treat bayous as rivers (although sometimes the bayous are creek-sized, in which case I treatment them like creeks.)

I keep track of other things that I’ll get into later, but now I’ll discuss a distinctly different aspect of the landing process . . .

This different aspect of the landing process has to do with my next door neighbor’s son, Dan.  Dan is a college student who developed an interest in my landings.  He just happened to be around for numerous landings, and he thought the whole thing was pretty cool.  So, I began emailing Dan on a daily basis, to keep him abreast of all the aspects of my landings.  A typical email to Dan starts out something like this:

Dan –  I landed today in a solid OS WBer:  ND; 43/36; 5/10; 10; 165.2.  Two new rivers!   The South Branch of the Goose, which predictably flows into the Goose, on to the Red River of the North (30^th hit, now in a tie for 16^th with the Canadian); on to the Nelson (46^th hit, now in a tie for 10^th with the Platte).

I landed in an area with numerous little north High Plains towns; but I was closest to Clifford.

The above requires some explanations.  As the reader should know by now, “OS” = Over-Subscribed (a state where I’ve landed more than is expected based on area.)  “WB” refers to “Western Block,” which is a large group of OS states out west.  It has just so happens that most of the western states are OS!  I can’t explain it (it’s just one of those things), but the only exceptions are California, New Mexico, Texas and Idaho.  These four states are US (Under-Subscribed); in fact, Texas is the most US of all of the states.

Moving right along:

• “ND” is North Dakota.  I always use the official Post Office two-letter state abbreviations in my emails to Dan.
• “43/36”:  The 43 is how many times I’ve landed in ND; the 36 is how many times I should have landed in ND, based on its area.  As you can see, since I’ve landed in ND more than I should have, ND is OS.
• “5/10” :  This simply states that 5 of my last 10 landings have been in US (Under-Subscribed) states.
• “10”:  This says that I’m on a string of 10 in a row where I’ve had at least 4/10 USers or better.  (In terms of lowering my overall Score, this is a good streak).
• “165.2” is next.  This is my Score.

I then go on to describe the watershed that I landed in (the South Branch of the Goose River watershed); and that the South Branch of the Goose flows into the various rivers mentioned.  As you can see, I also keep track of the number of hits (i.e., landings) that various watersheds have had, and I rank them in the order of number of hits.  Here’s the top 13 as of this writing:

River Hits
Mississippi	626		1
Missouri	297		2
Colorado	131		3
Columbia	112		4
Ohio	97		5
Arkansas	84		6
St Lawr	73		7
Snake	55		8
Kansas	48		9
Platte	46		10
Nelson	46		11
Atchafalaya	42		12
Yellowstone	42		13

(The list above goes on and on, and includes all rivers that have had at least 5 hits.  There are more than one hundred rivers on that list, and the number of rivers on the list continues to grow).

I then mention to Dan what town I landed close to; in this case, the town of Clifford ND.  I then proceed to do an internet search to see what gems I can find out about the town (or maybe about some other local spot of interest that I see on the StreetAtlas map near my landing).  I try to find something about history, insert some nice pictures, and maybe find an interesting angle or two.  On that day, I sent Dan a couple of pages of pictures & other at-least-somewhat-interesting (I hope) stuff about Clifford and environs.

To try and keep things clear, I use a blue lettering for everything that I write and black for anything that I cut and paste from the internet.

Here’s a list of abbreviations that I use in my emails to Dan:

OS = Over-Subscribed

US = Under-Subscribed  (if I’m referring to the United States, I’ll say U.S.)

PS = Perfectly-Subscribed

WB = Western Block (of OS states)

SB = Southern Block (of US states)

LG = Landing God (the tongue-in-cheek deity that “controls” where I land)

GD = Google-Deprived (when a town is so small that Google has nothing or next-to-    nothing of interest about the town)

KS = Keep Smiling (my sign-off)

MM = the Mighty Mississippi

M&M = the evil OS twins, Michigan and Minnesota

M&M&M = the evil OS triplets, Michigan, Minnesota and Montana

ut = unnamed tributary (for no good reason, not in caps)

R = River

Ck = Creek

Fk = Fork

Br = Branch

N,S,E,W = Cardinal directions

lat/long = Latitude & Longitude

Other, more obscure things on my spreadsheet (that I may occasionally mention in my emails to Dan) include:

• I quantify the number of “Try Again” landings that fall outside of the lower 48.  Here’s what it looks like:

Try Agains	1142
Atlantic O	369
Pacific O	210
Mexico	159
Canada	149
Gulf of Mex	145
Gulf of Cal	24
Lk Superior	19
Baha	22
Lk Huron	14
Lk Michigan	16
Lk Erie	8
Lk Ontario	3
Delaware Bay	1
Chesapeake Bay	2
Gulf of St. Lawrence	1

Even more obscure note:  For those who are quantitatively minded and worry about such things, I have subtracted the areas of the Great Lakes and the Delaware and Chesapeake Bays from the various states around these water bodies for all of my spreadsheet calculations.

• As I mentioned way back in the beginning, I start way “zoomed in” when I enter my landing lat/long in StreetAtlas.  Usually, some sort of map feature comes up.  But sometimes, I need to zoom out one or two clicks before I see any features.  For no good reason, I keep track of how many zoom-out clicks I need before I see a map feature.  Fairly commonly, I need one click.  Occasionally, 2.  Rarely, do I need 3 (like once every couple of hundred landings).  Once, I needed 4 (I landed in the middle of a large lake).

• I keep track of the number of named streams that a drop of water from my landing spot would flow in as it makes it way to the ocean.  Between 3 & 6 is most common;  10 is my record.  Here’s my entry for 10 named streams (a landing in WV):  Roaring Ck; Culverson Ck; Indian Ck; Sinking Ck; Muddy Ck; Greenbrier R; New R; Kanawha R; Ohio R; Mississippi R.

• I have a page in my spreadsheet where I have simply graphed the location of each landing.  Here’s what it looks like:

Cool, eh?

• I have graphed my Score through time.  Here’s what that looks like:

If you look closely, you can see that I have Excel fit a mathematical curve to the line.  The mathematical formula on the graph is the formula that represents the best-fit curve.  I have kept track of how the numbers in equation have changed through time (and, believe it or not, I have graphs showing how those change, which I’ll not bother with here.)

Here’s a closer in view of the latter part of the graph, zoomed in so you can see how my score changes every day.  Yes, each dot represents a landing . . .(and see how it generally keeps decreasing).

• I also keep track of common stream names (including both creeks & rivers).  I’ve compiled quite a list.  My arbitrary rule is that I must have at least 8 different streams with the same name to qualify for the list.  Here ‘tis:

1	78	Little
2	77	North
3	71	South
4	55	Big
5	44	West
6	40	Black
7	40	East
8	34	Spring
9	29	Middle
10	28	Beaver
11	28	Rock & Rocky
12	27	X Mile
13	21	Sand & Sandy
14	20	“Blank” water
15	20	Willow
16	19	Muddy & Mud
17	18	Salt & Saline
18	18	White
19	16	Just Black
20	16	Clear
21	16	Dry
22	16	Indian
23	15	Cottonwood
24	14	Just Big
25	14	Horse
26	14	Just Little
27	14	Red
28	13	Fish
29	13	Pine
30	11	Elm
31	10	Bear
32	10	Elk
33	9	Crooked
34	9	Deep
35	9	Green
36	9	Mill
37	9	Stone & Stony
38	8	Cedar
39	8	Deer
40	8	Falls & Falling
41	8	Long
42	8	Otter
43	8	Swamps
44	8	Town
45	8	Just White
46	8	Wolf
47	8	Wood & Woody

For clarification:  an example of “X-mile” is “8-mile Creek.”  An example of “Blank-water” is “Clearwater River.”  As you may surmise, the word can be part of the stream name, which is why I have so many Norths, Souths, Bigs, Littles, etc.

For each of the common names, I have a listing of each stream with that name.  I’ll use one of the small lists as an example (“Deer”):

CA	N Fk Mokelumne	Deer Ck
NE	Elkhorn	Deer Ck
TN	Obion	Forked Deer R
WY	Platte	Deer Ck
OK	N Canadian	Deer Ck
LA	Boeuf	Deer Ck
TN	Mississippi	Lower Forked Deer R
KY	Ohio	Deer Ck

Obviously, the first column is the State; the second column is the river that the named stream flows into; the third column is the actual name of the stream.

• I keep track of interesting stream names.  Here’s a partial listing:

Cat Claw Creek

Lac Qui Parle River

Left Hand Ditch

Fifty-Two Creek

Graveyard Bayou

Good Fortune Creek

Deception Brook

Marais Des Cygnes River

Spectacle Creek

Secret Pass Wash

Bacon Rind Creek

Kim-Me-Ni-Oli Wash

Pozo Hondo Wash

White Woman Creek

Beanblossom Creek

Wild Rice River

Painted Robe Creek

Troublesome Creek

Singsing Creek

Tse-Clani-To Wash

Tittabawassee River

World End Brook

Cabbage Patch Drain

Beauty Creek

Murder Creek

Negro Creek

Whiskey Creek

Shades of Death Creek

Poison Creek

Dead Horse Creek

Jackass Wash

Prickly Pear Creek

Dead Colt Creek

Gauno Slough

Halfbreed Creek

Soap Creek

Poison Spider Creek

Kickapoo Creek

Calfkiller River

Bug Run

Dead Stream

Oaklimeter Ck

No Business Ck

Sunstroke Ck

• I keep track of interesting, nearby place names.  Here’s a partial listing:

Choke to Death Butte

Squaw Tit

Maggie’s Nipple

St. Mary’s Nipple

Crazy Hill and Hooligan Hill

Rising City, Bee and Surprise

Dead Dutchman Hollow

Paradise

Bunkie

Buckaroo Flat

Jumpoff Joe

Troublesome Canyon

Koogle Jumpoff

Negrohead Butte

Through the years of landing, I have, for no good reason, divided the country into East and West.  Here’s a US map for reference.

Do you see the pretty-much north-south line of state boundaries that starts at the Canadian border with the boundary between & MN?  It runs south all the way to the TX-LA border.  That’s my dividing line.  So, MN, IA, MO, AR and LA belong to the East and ND, SD, NE, KS, OK and TX belong to the west.  FYI, this has nothing to do with the Mississippi River, traditionally thought of as the dividing line between east and west.  I like my division better.

Conveniently, the 95^th longitude line runs pretty close along that very same line.  So, when I first see my daily random lat/long, I know if I’m in the east or the west.

Although less of a big deal (in my mind), I use the 40^th parallel as my North-South divider (it runs from just south of New York City to a ways north of San Francisco).  Did you know that the 40^th parallel defines the NE/KS border?

Here’s a map showing my lat/long dividing lines:

Moving right along . . .

Some number of years ago, I found a list of U.S. rivers that are greater than 350 miles in length.  As one would expect, I had landed in nearly every one of these rivers’ watersheds (with only one exception, discussed later).  Being a steadfast tracker of statistical anomalies, I noted that these watersheds were clearly US.

Because I have my list of rivers with 5 or greater hits, I came up with a list of these large rivers in whose watersheds I had landed less than 5 times.  Tying in today’s landing, the Green River of KY was one of those rivers (with 4 hits coming into today’s landing).  So, with much fanfare (OK, so the fanfare is totally inside my cranium), I removed the Green from this list.

As mentioned above, when I first looked at the list of rivers of greater than 350 miles in length, there was one river’s watershed which was untouched by landing:  the Licking (also, interestingly enough, in KY).  Well, on May 31^st, 2007, I landed in the Licking R watershed, but haven’t landed there since.  So the Licking remains severely US.

I’m sure you’re curious to see the other rivers on this list.  Well, here they are, with the number of hits each (as of Jan 2009), and the state that makes up the primary (if not sole) portion of the watershed:

Altamaha (4)               GA

Licking (1)                  KY

Pearl (3)                      MS

Roanoke (3)                VA

St. Francis (2)              MO/AR

Washita (4)                  OK

Looking at the states, it makes perfect sense that the Altamaha, the Licking, the Pearl and the Roanoke are US, given how US these states are.  MO is PS, but AR is US, so taken together, the St. Francis in MO/AR also makes sense.  The crazy one is the Washita, as OK is way OS (45/37).  So, OK has the distinction of a unique anomaly.

In February 2009, this little write-up was in one of my posts:

“I must digress.  For those ‘About Landing’ aficionados, you know that I’ve always felt that generally, 4/10+ USers kept my Score going down.  I feel that truism just isn’t true anymore.

A case in point.  Since December 3^rd (landing #1589 when my Score was 166.7), I have had 48 landings in the “4/10+” category and only 13 landings in the “3/10‑” category.  My Score has actually risen for this group of landings, from 166.7 to 167.4!!  While I have an inkling of an explanation, I’m not quite ready for pontification.  However, it is time for a change.

Here’s what I’m going to try.  I’m going to move ‘4’ into a neutral category.  If I’m at 5, and drop to 4, I’ll keep it in the ‘4/10+'” category.  But likewise, if I’m at 3 and a slide up to a 4, I’ll keep that in the same category, which will now be known as ‘4/10-‘. ”

There are some more, even more obscure things that I keep track of.  However, a combination of embarrassment, complexity and sympathy for my reader cause me to exclude these (or at least save these explanations for another day).