Military spending in the US by regional mean rank

Ever since a quarter of a century ago when I read Trotskyist magazines arguing that if all military spending were scrapped and the rich properly taxed there would be far more than enough money to solve all the social problems facing the world today, I have taken an interest in US military spending, in part because I found a lot of information from a late-1970s textbook at Latrobe University.

A Six-Way Division of the US

Since reading the late James Löwen’s Sundown Towns, I have come to propose a six-way division of US states according to the map below:

A six-region map of the United States. An alternative divide of the two western regions is also possible, as well as splitting the into three or four; however, the four more easterly regions are relatively clear even for seven- and eight-way divisions

Comparing Regions’ Military Spending

In the following brief analysis, I hope to look at how these six regions compare in terms of military spending. My first experience with variation in US military spending by region occurred from reading R.W. DeGrasse’s Military Expansion, Economic Decline: Impact of Military Spending on United States Economic Performance as a Melbourne University student exploring the Latrobe library over two decades ago. DeGrasse developed a system of dividing US states slightly different from the conventional one, but one which I have come to see as the most logical available. However, the Southern and Western regions of DeGrasse are sufficiently varied that I have split them into two, as shown in the map above. Löwen’s work from a quarter-century after DeGrasse further supports such a division.

In the table below, I have assessed and compared the ranks of each state in defence spending from, firstly, the latest available state-by-state report (from 2024), and secondly, from DeGrasse’s 1981 work. Comparing DeGrasse’s work with the latest available can, of course, show continuities and changes in how states and regions compare.

US Military Spending By State and Region — 2024 and 1981

2024 Rank	State	1981 Rank
1	Virginia	4
2	Hawaii	3
3	Connecticut	5
4	District of Columbia	1
5	Alaska	2
6	Maryland	13
7	Kentucky	32
8	Alabama	22
9	Maine	23
10	Mississippi	7
11	New Mexico	9
12	Arizona	14
13	Oklahoma	17
14	Colorado	21
15	Texas	16
16	Utah	6
17	Rhode Island	29
18	Missouri	8
19	Massachusetts	15
20	Florida	24
21	South Carolina	19
22	Washington	12
23	Pennsylvania	44
24	Indiana	35
25	North Carolina	27
26	Georgia	18
27	Kansas	25
28	South Dakota	33
29	California	10
30	New Hampshire	20
31	Nevada	31
32	New York	37
33	Vermont	30
34	Wyoming	36
35	New Jersey	40
36	North Dakota	26
37	Michigan	48
38	Louisiana	11
39	Ohio	41
40	Nebraska	39
41	Montana	45
42	Illinois	50
43	Iowa	47
44	Wisconsin	46
45	Delaware	28
46	Idaho	42
47	Arkansas	34
48	Tennessee	38
49	West Virginia	49
50	Minnesota	43
51	Oregon	51
17.00	Southwest Average	13.40
18.63	Lowland (Plantation) South Average	16.50
21.33	Northeast Average	23.75
28.14	Upland (Nonplantation) South average	27.71
30.00	Northwest Average	28.16
39.86	Midwest Average	44.57

Results and Conclusion:

What the table shows is that:

1. with a few exceptions, the states of high and low relative military spending in 1981 and 2024 are the same:
1. seven of the ten states with lowest defence dependence are the same in both years
2. six of the ten states with highest defence dependence are the same in both years
2. military spending in the US is heavily concentrated in the lowland South and the Southwest, and to a lesser degree in the Northeast
3. almost no state in the Midwest (only Indiana in 2024) is outside the bottom third in defence spending
4. eight of twelve states in the Northwest are also consistently below average in military dependence
1. this is more significant than it looks before the four “defence dependent” states in the Northwest, alongside Kansas — the next most dependent in the region — form a periphery either:
1. substantially part of the Southwest at a local level (Colorado, Utah) or
2. peripheral to the Northwest region
2. we can perhaps define a “core Northwest” consisting of Idaho, Montana, Nebraska, North Dakota, Oregon, South Dakota and Wyoming
1. this “core Northwest”
5. three of the seven states in the Upland South are also consistently amongst the least defence-dependent

In Sundown Towns, Löwen demonstrated that racial segregation has tended to be less in military towns than almost anywhere else in the United States. Löwen also showed that virtually the entire “core Northwest” has historically absolutely excluded blacks, with the exception of a small number of the largest cities. The Midwest was almost as rigid. Löwen’s observation stands in complete agreement with the observation that military spending is concentrated in the lowland South and Southwest, where blacks (and, often, other nonwhites) were valued as sources of labour rather than feared as competitors for land. In the Northeast, whilst blacks were not valued, they were feared less than in the Midwest, Northwest or upland South because land was less abundant.

How I got “pet room Archie’s”!

This evening looking at my unfortunate — if at times hilarious as when “Dick Tyldesley” was corrected to a body part — history of tampering with AutoCorrect, I asked AI about why on earth “petromonarchies” could and would be corrected to the ludicrous “pet room Archie’s”!

AI: That is a perfect example of a "mondegreen" error—where the software tries to turn a complex technical term into several simpler, common words [Mondegreen and phonetic errors in software]. 

It’s easy to see how the algorithm got there:

1. Petro- becomes Pet room 
2. -monarchies (which sounds like “mon-ark-ees”) gets mangled into the name Archie’s [Common AutoCorrect linguistic error].

While hilarious, it highlights why AutoCorrect struggles with academic or geopolitical terms; it’s programmed to prioritise everyday language over niche vocabulary [1]. If you use that term often, you can usually “teach” your phone to stop by adding petromonarchies to your Personal Dictionary or Text Replacement settings [4].

AI: Do you have any other ridiculous corrections saved in your phone's history?

I did not answer on that one, but I do not have any other ridiculous AutoCorrect corrections since I stopped trying to tamper with AutoCorrect out of outrage at toilet wall graffiti [and in fact toilet wall graffiti as I have seen it today is not nearly so horrible as I recall in the Richard Berry Building or in Monash’s Hargrave–Andrew Library].

A parody and a true cricket tale

Over recent months, I have repeatedly sang this song (or variations) to my brother:

“Modern batsmen have a pitch
Ninety percent rigged
And on that pitch they score many runs
But deserve very few
With a cross-bat here
And a cross-bat there
Here a cross
There a cross
Everywhere a cross-bat
Modern batsmen have a pitch
Ninety percent rigged”

I have often thought of writing further lines about how covered pitches plainly constitute kangaroo courts for the spin bowler, but have never sung them.

Me and my brother have fiercely debated whether covered or uncovered pitches are fairer. The facts, at least judging from English first-class bowling data, firmly suggest covered pitches on average are more difficult for spin bowlers than even abnormally bad “sticky” wickets were for batsmen. Plainly put:

1. many batsmen managed to consistently cope with the most difficult uncovered pitches and achieve averages comparable to what the best batsmen under covering achieve
2. on covered pitches, zero spin bowlers achieve consistently anything like the same averages that old spin bowlers expected on uncovered pitches
• even overseas spinners — over most of history superior to English ones under unfavourable conditions — have never consistently come close to either the averages of the old spinners nor those of the best fast bowlers

My brother consistently says that with proper coaching spin bowlers can master covered pitches properly. He also said that the effort required to bowl spin effectively outside England was accepted there and ought to have been accepted in England. However, so early as the 1956 Wisden — when concern at the declining appeal of first-class cricket was becoming serious — it was noted that the emphatic finger spin needed outside England was the most difficult method of bowling to master, and fewer and fewer were developing it over easily mastered seam and pace. My brother also ignores how in many countries where “rollers” [a term used by Ashley Mallett in opposition to genuine “tweakers” in one 1996 Wisden article] were unable to turn the ball genuine finger spinners disappeared so completely as in England. This was especially true in the West Indies and South Africa, and suggests two things:

1. by 1960, the “tweaker” was already viewed as an extravagant luxury both in runs conceded and in cost of training.
1. John Woodcock would note in 1963 how the “tweaker” was already viewed too expensive in runs to compete against constantly improving fast and even medium-pace bowlers
2. it was undoubtedly clear, though never discussed, that improving the “tweaker” to be less costly in runs was constantly becoming less and less financially viable as pace bowling became better and better
2. development of spin bowling even partly able to cope with fundamentally rigged conditions is possible only under very restricted social conditions:
1. in pre-war Australia, leisure was sufficiently abundant that young boys had the time to develop the abnormal effort to develop “real finger spin” that might turn on fundamentally unfair pitches
2. in the Indian subcontinent, extreme abundance of labour means that young boys could be trained to develop genuine finger spin at relatively low cost vis-à-vis the rest of the cricket world

Hence, Clive Lloyd’s discovery, in Vic Marks’ word, that

“spin bowling was a luxury if four genuinely fast bowlers were available”

was entirely consistent with earlier captains’ experience over the past quarter-century as the extreme difficulty of bowling spin on well-prepared covered pitches became less and less affordable for young boys. The problem is that once spin bowling becomes viewed as an extravagant luxury first-class cricket is financially doomed in the long term, and its problems have been noted by Huw Turberville in his recent The Final Test. Virtually all historical instances where first-class cricket was economically self-supporting correlate practically perfectly with the greatest weaknesses in fast and medium-pace bowling, and exclusive reliance upon spin of whatever quality. Nonetheless, it is tempting to say that the greatest spin attacks in history — circa 1934 Australia and circa 1970 India — are fundamentally so costly to develop that first-class cricket becomes unprofitable once those costs are factored in, although attendance figures imply first-class cricket would always be profitable if the quantity and quality of spin developed in those two cases could be maintained. History shows that those standards of spin were never maintained, and the logical explanation is that maintaining the extreme effort involved in developing the bowlers was always too expensive and difficult. This further supports the contention that covered pitches are, fundamentally, rigged against the spinner incomparably more than uncovered pitches are against batsmen.

Victor Conrad’s missing data

Ever since reading Robert Dewar and James Wallis’ ‘Geographical Patterning of Interannual Rainfall Variability in the Tropics and Near Tropics’ about two decades ago, it has occured to me that when the authors say:

“Conrad mapped variability in precipitation for the entire world, using records from 384 stations. For the area between 30˚N and 30˚S, he had 149 stations.”

Yet, Dewar and Wallis fail to note that for many of the high-variability regions they discuss, Conrad had no stations whatsoever. This is true particularly for:

1. Queensland
2. eastern Melanesia (the “Fiji–New Caledonia” region of Ropelewski and Halpert)
• of the regions discussed by Ropelewski and Halpert as having coherent El Niño Southern Oscillation precipitation responses, “Fiji–New Caledonia” is the only one where all stations in Dewar and Wallis’ database are “highly variable”
3. northwestern coastal Australia
4. coastal Angola
5. eastern Kenya and Somalia (“Greater Somalia”)
• if you read ‘Geographical Patterning of Interannual Rainfall Variability in the Tropics and Near Tropics’ you will see that Dewar and Wallis do discuss the Horn of Africa as noted by Conrad
• in reality, Conrad had no stations between Zanzibar and Aden, nor in present-day Ethiopia or Eritrea
6. Baja California
• of all the high-variability regions in the 1999 dataset, Baja is undoubtedly the most “excusable”, at least in the sense that no data for the region existed as of 1928 when Conrad’s data was collected

Asking the question on Google, I have discovered that the reason most of Australia and eastern Melanesia were unsampled in Victor Conrad‘s 1941 study. Apart from a few stations published in Quarterly Journal of the Royal Meteorological Society — which were those listed by Conrad — Australia’s large assembly of meteorological records was some of the world’s most extensive but also most fragmented:

1. before the formation of the national Bureau of Meteorology (BoM) in 1908, rainfall data was collected independently by individual Australian colonies (states like New South Wales, Queensland, etc.)
2. each colony or state published its own monthly meteorological summaries
3. these were mostly distributed locally or held in national and state archives and libraries rather than in global meteorological circulars
4. records were held locally or in state archives like the Australian Bureau of Meteorology’s state volumes
1. the great majority of stations in Australia (and Melanesia) thus had their records published only in state-specific histories and never in global summaries
2. the vast majority of high-quality long-term records remained “buried” in colonial-era journals, ship logs, or government documents
3. global sources like Smithsonian’s World Weather Records rarely carried data from these regions

Most Australian and Melanesian meteorological records when Conrad was writing were thus localised and inaccessible to international researchers, being not yet integrated into widely-circulated international compendiums. The most important of these were the UK’s Meteorological Office and the Smithsonian Institution’s World Weather Records.

Even so, one might argue that articles like Steven Sargent Visher’s ‘Variability Versus Uniformity in the Tropics’ published nineteen full years before Conrad’s work should have provided enough data for Onslow and perhaps other parts of tropical Australia. It is possible that the raw data used by Visher were not kept in his archives, but the basic information should have been available to Conrad yet he clearly did not use it.

What I will do below is:

1. select a representative list of stations in areas of Australia not covered by Conrad
1. only data from 1928 and before — when his first data were compiled — are included
2. data will be done in a calendar year format as they were done by Conrad
3. data will be tabulated as Conrad did for his actual stations, although I have omitted latitude and longitude
4. I have deviated from Conrad in ordering stations by region in a clockwise order, rather than purely by longitude as Conrad did
2. compile:
1. mean annual rainfall by calendar year to 1928
2. average departure from the mean, and then comparing it in two ways with Conrad’s expected value:
1. by simple difference, as Conrad did
2. by ratio to expected average deviation minus 100 percent

Method 2) was added because studying Conrad’s article does suggest that areas of high rainfall but abnormal variability are not easily identified by mere difference from expected value. Of the few high-variability regions identified by Dewar and Wallis for which Conrad did have data, two — the northern South China Sea region and lowland eastern Indonesia — were not identified by Conrad as regions of abnormal variability, not discussed as such. This despite the fact that Nha Trang and Ambon Island were shown as having a variability that was clearly exceptional, whilst nearby stations had sufficiently high variability that a pattern could quite likely have been recognised. Conrad’s failure to recognise the northern South China Sea and lowland eastern Indonesia as areas of unusual rainfall variability related to his use of arithmetic rather than geometric (as used by Dewar and Wallis and by Pierre Camberlin’s 2010 ‘More variable tropical climates have a slower demographic growth’) departure.

Explanatory Shading:

1. Departures above 20 percent or more than twice Conrad’s expected value have been shaded in dark red
2. Departures above 10 percent but below 20 percent or more than 1.5 times but less than than twice Conrad’s expected value have been shaded in red
3. Departures above 5 percent but below 10 percent or more than 1.25 times but less than than 1.5 Conrad’s expected value have been shaded in pink
4. stations who departure is less than 5 percent and/or less than 1.25 times Conrad’s expected value are unshaded

Representative Stations from Areas of Australia Unrepresented in Conrad’s Study (Courtesy Australian Bureau of Meteorology):

Region	Station	Elevation		Mean		Average deviation		Percent	% deviation from expected	departure as % of expected
		feet	metres	inches	millimetres	inches	millimetres
Tropical Queensland	Boulia	532	162	10.6	269	5.53	141	52%	+28%	+117.43%
	Burketown	20	6	27.9	709	10.04	255	36%	+18%	+99.70%
	Coen	653	199	46.7	1,187	11.47	291	25%	+9%	+58.37%
	Cooktown	20	6	69.9	1,775	17.23	438	25%	+10%	+64.36%
	Innisfail	33	10	142.8	3,627	26.13	664	18%	+4%	+30.71%
	Townsville	13	4	47.3	1,203	13.59	345	29%	+13%	+79.46%
	Rockhampton	36	11	39.2	995	10.70	272	27%	+11%	+70.65%
	Barcaldine	876	267	19.8	502	6.86	174	35%	+16%	+87.67%
Murray–Darling Basin	Roma	981	299	23.6	599	6.87	174	29%	+11%	+61.80%
	Tamworth	1,326	404	26.8	682	5.07	129	19%	+1%	+4.95%
	Dubbo	853	260	22.2	563	5.22	133	24%	+6%	+30.82%
	Albury	515	157	27.9	709	4.72	120	17%	-1%	-6.02%
	Wentworth	121	37	11.8	301	3.13	79	26%	+4%	+19.96%
Western Interior	Daly Waters	696	212	26.4	670	6.71	170	25%	+7%	+41.29%
	Tennant Creek	1,237	377	14.4	366	5.10	129	35%	+15%	+77.03%
	Moonaree	787	240	7.4	187	2.44	62	33%	+6%	+22.76%
	Wiluna	1,709	521	9.6	244	3.18	81	33%	+8%	+32.48%
	Mount Magnet	1,398	426	9.4	239	3.53	90	38%	+13%	+50.14%
	Halls Creek	1,181	360	21.0	532	6.04	154	29%	+11%	+60.25%
West Coastal	Geraldton	10	3	18.5	469	3.87	98	21%	+2%	+13.36%
	Carnarvon	16	5	9.5	241	3.61	92	38%	+13%	+52.57%
	Onslow	13	4	9.0	228	5.46	139	61%	+36%	+143.67%
	Roebourne	39	12	11.7	297	4.78	121	41%	+19%	+86.16%
	Derby	26	8	25.8	655	8.01	204	31%	+13%	+72.53%
Southern Coastal	Bega	164	50	33.5	852	9.25	235	28%	+11%	+62.23%
	Melbourne	102	31	25.5	648	3.78	96	15%	-3%	-17.66%
	Hobart	171	52	23.9	608	4.26	108	18%	0%	-1.18%
	Eucla	305	93	10.0	253	2.13	54	21%	-3%	-10.88%
	Albany	10	3	37.3	947	4.98	127	13%	-3%	-18.98%

Results:

The results clearly show that if Conrad could have obtained existing data for tropical Queensland — extending into the extreme northern Murray–Darling Basin represented by Roma — and the north of Western Australia, he would have recognised them as regions of abnormal rainfall variability analogous to Northeastern Brazil and the northwestern Indian subcontinent. This is true even if we use Conrad’s arithmetic departure method. By geometric departure, as I presumed, tropical Queensland appears somewhat more variable relative to Conrad’s calculated expected value, and the southern arid zone less so. This difference, however, is less than I anticipated.

It is quite possible that had Conrad some of the data tabulated above, he would have seen the two as one region of unusual rainfall variability: the figures for the central Northern Territory [Tennant Creek and Daly Waters] make this quite plausible. However, mechanistically, Queensland is more closely related to lowland eastern Indonesia and eastern Melanesia than to northwestern Australia. The high variability of the latter region is purely due to dependence for rainfall upon random tropical cyclonic disturbances that frequently produce a year’s rain in two or three days. Contrariwise, lowland eastern Indonesia, eastern Melanesia, and almost all of Queensland owe their high variability to lying in the core of the “ENSO horseshoe” where convection is most sensitive to El Niño and La Niña events. The geometric departures in Boulia and Burketown, it might be noted, are only slightly higher than Conrad tabulated for Ambon Island [+87 percent].

The south coast of New South Wales, represented by Bega, is similar to northwest Australia in owing its high variability to distinctly random Tasman Sea cyclones producing exceptionally heavy rainfall.

The remaining unsampled regions of Australia — tabulated here for both fairness and completeness — do not show much surprise in light of later studies like those of Dewar, Eddie van Etten and Camberlin.

VFL/AFL Grand Final Day temperatures, 1898-2025

Ever since I studied the 1987 Grand Final in what was then Melbourne’s earliest 30˚C day on record, the relationship between the weather and football has always been on interest to me. The occasional very wet or very hot days are the usual scene of attention, given that the average weather in Melbourne in late September or early October is very pleasant — 17˚C to 19˚C with over 6 hours sunshine each day, or warm enough for a light cotton jumper and jeans, although frequent strong winds make it feel cooler and require warmer clothing.

For this table I have tabulated the maximum temperature in Melbourne on every VFL/AFL Grand Final day since the first was played in 1898, except for 1924 when no grand final was played, and 2020 and 2021 when COVID caused the Grand Final to be played outside Melbourne. The 1948, 1977 and 2010 replays have been included, and weighted equally with the draws when calculating 5-year means. Temperatures have been colour-coded into bands thus:

Temperature band	Range
*“Frigid”	below -9.4˚C	below 15˚F
*“Freezing”	-9.4˚C to 0˚C	15˚F to 32˚F
*“Chilly”	0˚C to 7.2˚C	32˚F to 45˚F
“Cold”	7.2˚C to 12.8˚C	45˚F to 55˚F
“Cool”	12.8˚C to 18.3˚C	55˚F to 65˚F
“Comfortable”	18.3˚C to 23.9˚C	65˚F to 75˚F
“Warm”	23.9˚C to 29.4˚C	75˚F to 85˚F
“Hot”	29.4˚C to 35˚C	85˚F to 95˚F
*“Sweltering”	above 35˚C	above 95˚F
* = not found in Grand Final Day sample

VFL/AFL Grand Final Day Maximum Temperatures (Second Games are Replays)

For this table, because the Bureau of Meteorology is reluctant to trust temperature data before 1910 — unfortunate given that the 1900s were globally likely the coolest decade since the last glacial period — I have italicised years before 1910. (Data on a first glance suggest that during the 1900s standard shelters were in use in Melbourne much earlier than in more newly established temperature stations).

Although maximum temperatures usually occur during the hours when the Grand Final is played, it must be noted that they do not necessarily occur at this time due to abrupt wind changes. This happened, for instance in 1960 when a vigorous frontal system produced heavy rainfall before the game and drove temperatures far below the tabulated maximum.

Season	Grand Final Day Tmax
1898	69.3 ˚F	20.7 ˚C
1899	57.7 ˚F	14.3 ˚C
1900	61.0 ˚F	16.1 ˚C
1901	70.7 ˚F	21.5 ˚C
1902	53.8 ˚F	12.1 ˚C
1903	76.8 ˚F	24.9 ˚C
1904	68.0 ˚F	20.0 ˚C
1905	53.8 ˚F	12.1 ˚C
1906	57.4 ˚F	14.1 ˚C
1907	72.9 ˚F	22.7 ˚C
1908	64.0 ˚F	17.8 ˚C
1909	61.0 ˚F	16.1 ˚C
1910	70.5 ˚F	21.4 ˚C
1911	60.6 ˚F	15.9 ˚C
1912	70.7 ˚F	21.5 ˚C
1913	64.8 ˚F	18.2 ˚C
1914	57.0 ˚F	13.9 ˚C
1915	61.2 ˚F	16.2 ˚C
1916	68.7 ˚F	20.4 ˚C
1917	63.9 ˚F	17.7 ˚C
1918	59.2 ˚F	15.1 ˚C
1919	79.5 ˚F	26.4 ˚C
1920	63.9 ˚F	17.7 ˚C
1921	63.9 ˚F	17.7 ˚C
1922	74.5 ˚F	23.6 ˚C
1923	69.3 ˚F	20.7 ˚C
1925	64.9 ˚F	18.3 ˚C
1926	73.9 ˚F	23.3 ˚C
1927	55.2 ˚F	12.9 ˚C
1928	61.0 ˚F	16.1 ˚C
1929	68.5 ˚F	20.3 ˚C
1930	69.6 ˚F	20.9 ˚C
1931	61.3 ˚F	16.3 ˚C
1932	61.7 ˚F	16.5 ˚C
1933	61.5 ˚F	16.4 ˚C
1934	64.0 ˚F	17.8 ˚C
1935	63.5 ˚F	17.5 ˚C
1936	69.6 ˚F	20.9 ˚C
1937	68.9 ˚F	20.5 ˚C
1938	73.9 ˚F	23.3 ˚C
1939	65.8 ˚F	18.8 ˚C
1940	54.1 ˚F	12.3 ˚C
1941	76.1 ˚F	24.5 ˚C
1942	66.7 ˚F	19.3 ˚C
1943	59.2 ˚F	15.1 ˚C
1944	85.5 ˚F	29.7 ˚C
1945	69.8 ˚F	21.0 ˚C
1946	57.2 ˚F	14.0 ˚C
1947	76.1 ˚F	24.5 ˚C
1948	59.5 ˚F	15.3 ˚C
	55.2 ˚F	12.9 ˚C
1949	57.0 ˚F	13.9 ˚C
1950	70.0 ˚F	21.1 ˚C
1951	68.0 ˚F	20.0 ˚C
1952	66.9 ˚F	19.4 ˚C
1953	62.1 ˚F	16.7 ˚C
1954	57.9 ˚F	14.4 ˚C
1955	57.9 ˚F	14.4 ˚C
1956	64.9 ˚F	18.3 ˚C
1957	65.5 ˚F	18.6 ˚C
1958	52.3 ˚F	11.3 ˚C
1959	63.1 ˚F	17.3 ˚C
1960	71.1 ˚F	21.7 ˚C
1961	66.6 ˚F	19.2 ˚C
1962	57.2 ˚F	14.0 ˚C
1963	76.1 ˚F	24.5 ˚C
1964	69.4 ˚F	20.8 ˚C
1965	76.3 ˚F	24.6 ˚C
1966	56.1 ˚F	13.4 ˚C
1967	61.0 ˚F	16.1 ˚C
1968	70.5 ˚F	21.4 ˚C
1969	73.9 ˚F	23.3 ˚C
1970	57.7 ˚F	14.3 ˚C
1971	66.0 ˚F	18.9 ˚C
1972	19.3 ˚C	66.7 ˚F
1973	23.8 ˚C	74.8 ˚F
1974	17.5 ˚C	63.5 ˚F
1975	19.6 ˚C	67.3 ˚F
1976	15.8 ˚C	60.4 ˚F
1977	15.2 ˚C	59.4 ˚F
	17.1 ˚C	62.8 ˚F
1978	20.7 ˚C	69.3 ˚F
1979	15.8 ˚C	60.4 ˚F
1980	18.3 ˚C	64.9 ˚F
1981	17.7 ˚C	63.9 ˚F
1982	16.3 ˚C	61.3 ˚F
1983	13.5 ˚C	56.3 ˚F
1984	12.4 ˚C	54.3 ˚F
1985	13.3 ˚C	55.9 ˚F
1986	14.7 ˚C	58.5 ˚F
1987	30.7 ˚C	87.3 ˚F
1988	18.4 ˚C	65.1 ˚F
1989	21.7 ˚C	71.1 ˚F
1990	14.0 ˚C	57.2 ˚F
1991	16.6 ˚C	61.9 ˚F
1992	15.1 ˚C	59.2 ˚F
1993	17.4 ˚C	63.3 ˚F
1994	17.8 ˚C	64.0 ˚F
1995	21.5 ˚C	70.7 ˚F
1996	18.5 ˚C	65.3 ˚F
1997	19.6 ˚C	67.3 ˚F
1998	20.7 ˚C	69.3 ˚F
1999	17.5 ˚C	63.5 ˚F
2000	17.7 ˚C	63.9 ˚F
2001	25.9 ˚C	78.6 ˚F
2002	11.9 ˚C	53.4 ˚F
2003	13.7 ˚C	56.7 ˚F
2004	18.2 ˚C	64.8 ˚F
2005	15.9 ˚C	60.6 ˚F
2006	17.8 ˚C	64.0 ˚F
2007	17.5 ˚C	63.5 ˚F
2008	24.0 ˚C	75.2 ˚F
2009	14.2 ˚C	57.6 ˚F
2010	19.9 ˚C	67.8 ˚F
	21.0 ˚C	69.8 ˚F
2011	14.0 ˚C	57.2 ˚F
2012	13.5 ˚C	56.3 ˚F
2013	16.4 ˚C	61.5 ˚F
2014	23.4 ˚C	74.1 ˚F
2015	31.3 ˚C	88.3 ˚F
2016	18.6 ˚C	65.5 ˚F
2017	15.4 ˚C	59.7 ˚F
2018	14.0 ˚C	57.2 ˚F
2019	14.9 ˚C	58.8 ˚F
2022	14.7 ˚C	58.5 ˚F
2023	29.7 ˚C	85.5 ˚F
2024	22.0 ˚C	71.6 ˚F
2025	19.5 ˚C	67.1 ˚F

Graph of Grand Final Day Temperatures and 5-Year Mean:

Maximum Temperatures in ˚C on Each VFL/AFL Grand Final Day in Melbourne (all data courtesy of Australian Bureau of Meteorology)

If we look at this graph, it is difficult to detect the global warming produced by the huge fossil fuel production for the profit of Australian coal barons and Persian Gulf oil sheikhs. This, of course, is substantially a reflection of small sample size. There are, indeed, many cases where a change of merely one day would produce a radically different temperature. For instance, in 1928 and 2008, the preceding Friday exceeded 29˚C.

However, very hot Grand Final days seem to have become more frequent, as seen by two such days in 2015 and 2023 equalling the total before 2015 [from 1944 and 1987].

One interesting fact is that both the hottest and the coolest Grand Final days seem to occur mostly in years of widespread droughts. The very hot Grand Finals of 1944 and 2015, and the very cool Grand Finals of 1902, 1940, and 2002, all occurred in years of extreme drought in various parts of Victoria and adjacent states. So did several slightly less hot or cool Grand Finals like 1946 (cool) and 1965 (hot). A plausible explanation for this is that exceptionally hot and exceptionally cool temperatures are both dependent on dominant anticyclones driving air from Central Australia (very hot weather) or Antarctica (very cool).

Another notable fact is that changes in the date of the Grand Final (not shown) do not seem to have had much effect upon temperatures. The earlier Grand Final (1916, September 2) was very nearly so hot as the latest one (1923, October 20) whilst the two hottest pre-Kyōtō Protocol Grand Finals were both played in September not October. Of other Grand Finals played with temperatures above 23.9˚C — well and truly warm enough to wear shorts and a T-shirt — only 1919 and 1963 were played in October, whilst 1903, 1941, 1947 and 1965 were played in September. [Regarding the reliability of temperature data from 1903, newspaper reports do suggest strongly the weather was very warm].