Author Archives: Stubborn Mule

More on the Hottest 100

Following on from the last post on the Hottest 100, I received a few tweets from @mjdart demanding a deeper dive into the data. One of his questions was

Of artists charting in at least 5 yrs, are Oz artists higher represented?

I decided to broaden the questions to look at artists with at least five tracks in Hottest 100s (so artists with two tracks in one year and one track in three other years would be in). On this criterion, Australia still comes out on top.

Number of artists with at least 5 “Hot” tracks

In the last post, I complained that 2010 data is currently missing from Wikipedia. It seems that this is because Wikipedia is yet to get permission from the ABC. I have decided to risk the wrath of Auntie and have posted the full chart in the table at the bottom of the post. Having pieced it together, I have updated my original chart to include 2010.

As you can see, 2010 was a good year for Australian artists. It also turns out that the artist with the most Hottest 100 tracks is also Australian: Powderfinger. Here are all the artists with at least 10 Hottest 100 tracks.

Artist	Count	Country
Powderfinger	22	Australia
Foo Fighters	20	United States
Grinspoon	17	Australia
Silverchair	17	Australia
Muse	16	United Kingdom
The Living End	16	Australia
Regurgitator	15	Australia
Pearl Jam	14	United States
Placebo	14	United Kingdom
You Am I	14	Australia
Green Day	13	United States
Something for Kate	13	Australia
Eskimo Joe	12	Australia
Garbage	12	United States
Red Hot Chili Peppers	12	United States
Hilltop Hoods	11	Australia
Radiohead	11	United Kingdom
Spiderbait	11	Australia
The White Stripes	11	United States
The Whitlams	11	Australia
Wolfmother	11	Australia
Beck	10	United States
Ben Harper	10	United States
Jebediah	10	Australia
Metallica	10	United States
Rage Against the Machine	10	United States
The Strokes	10	United States

UPDATE:

@mjdart asked another question which I thought I should also answer:

@stubbornmule If you assign 100 pts for #1 thru 1 pt for #100 each year, is Oz proportion higher/lower? eg Oz filling out top or bottom 50?

To answer this, I assigned 100 points for the top spot, 99 points for the second and so on down to one point for last place. I summed the score for each country and then scaled it by dividing by 50.5. This odd choice arises because 100 + 99 + 98 + … + 2 + 1 = 5050 and so dividing by 50.5 would give a score of 100 for a country that managed to win every spot in the top 100. This makes the score directly comparable to a simple count of places in the top 100. So, how does this weighted score compare to a simple count? The answer, evident in the chart below is not much! So, each country’s artists must be fairly evenly spread through the top 100 over time.

Finally, here is the complete listing of the 2010 Hottest 100, including country of origin. If you are feeling brave, you may wish to update Wikipedia. Just keep in mind, the list may be deleted again if the ABC does not provide permission for the list to be published!

Rank	Title	Artist	Country
1	Big Jet plane	Angus & Julia Stone	Australia
2	Rock It	Little Red	Australia
3	Dance The Way I Feel	Ou Est Le Swimming Pool	England
4	Plans	Birds Of Tokyo	Australia
5	Fall At Your Feet	Boy & Bear	Australia
6	Teenage Crime	Adrian Lux	Sweden
7	Fuck You!	Cee Lo Green	United States
8	Tokyo (Vampires & Wolves)	The Wombats	England
9	Magic Fountain	Art Vs. Science	Australia
10	Somebody To Love Me {Ft. Boy George & Andrew Wyatt}	Mark Ronson & The Business Intl.	England
11	ABC News Theme {Remix}	Pendulum	Australia
12	Rapunzel	Drapht	Australia
13	Clap Your Hands	Sia	Australia
14	Runaway {Ft. Pusha T}	Kanye West	United States
15	Barbara Streisand	Duck Sauce	United States
16	Mace Spray	The Jezabels	Australia
17	Bang Bang Bang {Ft. MNDR & Q-Tip}	Mark Ronson & The Business Intl.	England
18	There’s Nothing In The Water We Can’t Fight	Cloud Control	Australia
19	Crave You {Ft. Giselle}	Flight Facilities	Australia
20	Sunday Best	Washington	Australia
21	Undercover MartynU	Two Door Cinema Club	Northern Ireland
22	Jellylegs	Children Collide	Australia
23	Addicted	Bliss N Eso	Australia
24	Talking Like I’m Falling Down Stairs	Sparkadia	Australia
25	Eyes Wide Open	Gotye	Australia
26	Not In Love {Ft. Robert Smith}	Crystal Castles	Canada
27	You’ve Got The Dirtee Love {Live}	Florence & The Machine/Dizzee Rascal	England
28	Radar Detector	Darwin Deez	United States
29	It Can Wait {Ft. Owl Eyes}	Illy	Australia
30	O.N.E	Yeasayer	United States
31	Bloodbuzz Ohio	The National	United States
32	Pumped Up Kicks	Foster The People	United States
33	Solitude Is Bliss	Tame Impala	Australia
34	Punching In A Dream	The Naked And Famous	New Zealand
35	The Bike Song {Ft. Kyle Falconer & Spank Rock}	Mark Ronson & The Business Intl.	England
36	Opposite Of Adults	Chiddy Bang	United States
37	Doncamatic {Ft. Daley}	Gorillaz	England
38	Young Blood	The Naked And Famous	New Zealand
39	Revolution	John Butler Trio	Australia
40	Baby, I’m Getting Better	Gyroscope	Australia
41	Down By The River	Bliss N Eso	Australia
42	On Melancholy Hill	Gorillaz	England
43	We No Speak Americano	Yolanda Be Cool	Australia
44	Baptism	Crystal Castles	Canada
45	Rabbit Song	Boy & Bear	Australia
46	Way Back Home	Bag Raiders	Australia
47	Wild At Heart	Birds Of Tokyo	Australia
48	Witchcraft	Pendulum	Australia
49	Easy To Love	The Jezabels	Australia
50	One Life Stand	Hot Chip	England
51	Ambling	Yeasayer	United States
52	Overpass	The John Steel Singers	Australia
53	Reflections	Bliss N Eso	Australia
54	Holidays {Ft. Alan Palomo}	Miami Horror	Australia
55	Giving Up The Gun	Vampire Weekend	United States
56	Bring Night	Sia	Australia
57	Kickstarts	Example	England
58	The Suburbs	Arcade Fire	Canada
59	Rich Kids	Washington	Australia
60	My Eagle	Children Collide	Australia
61	Jackson’s Last Stand	Ou Est Le Swimming Pool	England
62	Hold On	Angus and Julia Stone	Australia
63	Ready To Start	Arcade Fire	Canada
64	Jona Vark	Gypsy & The Cat	Australia
65	One Step	Dead Letter Circus	Australia
66	Audience =	Cold War Kids	United States
67	Holiday	Vampire Weekend	United States
68	Dog {Ft. Lisa Mitchell}	Andy Bull	Australia
69	Watercolour	Pendulum	Australia
70	Paper Romance	Groove Armada	England
71	Piper’s Song	Gypsy & The Cat	Australia
72	I Can Talk	Two Door Cinema Club	Northern Ireland
73	Time To Wander	Gypsy & The Cat	Australia
74	Lucidity	Tame Impala	Australia
75	Coming Around	Hungry Kids Of Hungary	Australia
76	Radioactived	Kings Of Leon	United States
77	Shutterbugg {Ft. Cutty}	Big Boi	United States
78	Stylo {Ft. Bobby Womack and Mos Def}	Gorillaz	England
79	Slow Motion Slow Motion	Little Red	Australia
80	Howlin’ For You Howlin’ For You	The Black Keys	United States
81	Echoes Echoes	Klaxons	England
82	Tighten Up Tighten Up	The Black Keys	United States
83	Modern Man Modern Man	Arcade Fire	Canada
84	The Hardest Part The Hardest Part	Washington	Australia
85	I Feel Better I Feel Better	Hot Chip	England
86	Queensland Queensland	Evil Eddie	Australia
87	The Saddest Thing I Know The Saddest Thing I Know	Birds Of Tokyo	Australia
88	Monster {Ft. JAY-Z, Rick Ross, Nicki Minaj & Bon Iver}	Kanye West	United States
89	Barricade Barricade	Interpol	United States
90	Finally See Our Way Finally See Our Way	Art Vs. Science	Australia
91	Northcote (So Hungover)	The Bedroom Philosopher	Australia
92	I Can ChangeI Can Change	LCD Soundsystem	United States
93	Anyone’s Ghost Anyone’s Ghost	The National	United States
94	Time To Smile Time To Smile	Xavier Rudd	Australia
95	The High Road The High Road	Broken Bells	United States
96	Go Do Go Do	Jonsi	Iceland
97	Sleepwalker	Parkway Drive	Australia
98	Spanish Sahara	Foals	England
99	BigBig	Dead Letter Circus	Australia
100	Neutron Star Collision (Love Is Forever)	Muse	England

Hottest 100 for 2011

3 Replies

Another year, another Australia Day. Another Australia Day, another Triple J Hottest 100. And that, of course, means an excellent excuse to set R to work on the chart data.

For those outside Australia, the Hottest 100 is a chart of the most popular songs of the previous year, as voted by the listeners of the radio station Triple J. The tradition began in 1991, but initially people voted for their favourite song of all time. From 1993 onwards, the poll took its current form* and was restricted to tracks released in the year in question.

Since the Hottest 100 Wikipedia pages include country of origin**, I thought I would see whether there is any pattern in whose music Australians like best. Since it is Australia Day, it is only appropriate that we are partial to Australian artists and they typically make up close to half of the 100 entries. Interestingly, in the early 90s, Australian artists did not do so well. The United Kingdom has put in a good showing over the last two years, pulling ahead of the United States. Beyond the big three, Australia, UK and US, the pickings get slim very quickly, so I have only included Canada and New Zealand in the chart below.

Number of Hottest 100 tracks by Country

If you have excellent eyesight, you may notice that 2010 is missing from the chart. For some reason, this is the only year which does not include the full chart listing on the Wikipedia page. There is a link to a list on the ABC website, but unfortunately it does not include the country of origin. Maybe a keen Wikipedian reading this post will help by updating the page.

I make no great claims for the sophistication or the insight of this analysis: it was really an excuse to learn about using the XML package for R to pull data from tables in web pages.

require(XML)
require(ggplot2)
require(reshape2)

results <- data.frame()
col.names <- c("year", "rank", "title", "artist", "country")

# Skip 2010: full list is missing from Wikipedia page
years <- c(1993:2009, 2011)

for (year in years) {
    base.url <- "http://en.wikipedia.org/wiki/Triple_J_Hottest_100,"
    year.url <- paste(base.url, year, sep="_")
    tables <- readHTMLTable(year.url, stringsAsFactor=FALSE)
    table.len <- sapply(tables, length)
    hot <- cbind(year=year, tables[[which(table.len==4)]])
    names(hot) <- col.names
    results <- rbind(results, hot)
}

# Remap a few countries
results$country[results$country=="Australia [1]"] <- "Australia"
results$country[results$country=="England"] <- "United Kingdom"
results$country[results$country=="Scotland"] <- "United Kingdom"
results$country[results$country=="Wales"] <- "United Kingdom"
results$country[results$country=="England, Wales"] <-"United Kingdom"

# Countries to plot
top5 <- c("Australia", "United States", "United Kingdom",
  "Canada", "New Zealand")

# Create a colourful ggplot chart
plt <- ggplot(subset(results, country %in% top5),
    aes(factor(year), fill=factor(country)))
plt <- plt + geom_bar() + facet_grid(country ~ .)
plt <- plt + labs(x="", y="") + opts(legend.position = "none")

Created by Pretty R at inside-R.org

UPDATE: there is a little bit more analysis in this follow-up post.

* Since the shift to single year charts, there have been two all-time Hottest 100s: 1998 and 2009.

** There are some country combinations, such as “Australia/England”, but the numbers are so small I have simply excluded them from the analysis.

Where is the cheapest petrol?

3 Replies

For some time now, I have been meaning to have a look at Beautiful Soup, a python library designed to make it easy to scrape data from web-sites. Now that I have finally tried it out, I wish I had got to it sooner. It really is very handy and easy to use.

As my first Soup project, I turned to an old Mule favourite: petrol data. The Australian Institute of Petroleum (AIP) publishes retail petrol price data, which it sources in turn from MotorMouth. The price data is spread across individual pages for each state, like this one for Victoria and there are separate pages for unleaded and diesel. It would be nice to pull together all of the data and, since the pages are all laid out in exactly the same way and there is a straightforward naming convention for the urls of each page, this is very easy to do using Beautiful Soup. You can see the results showing average weekly petrol prices for the week ending 18 December in the table below.

With all the data in hand, the obvious questions to ask are: where is the cheapest petrol and where is the most expensive petrol? As you can see in the chart below, Adelaide came in as the cheapest place to fill your tank in late December at 134.9 cent/L, while Broome was the most expensive at 165.9, an impressive 23% mark up over Adelaide.

Top 10 and bottom 10 unleaded petrol prices
(average for the week ending 18 December 2011)

Of course this only gives a snapshot at a point in time: Adelaide may not always offer such good value for money and Broome residents may not always pay such a premium. Unfortunately, I have not been able to find any historical data by town on the AIP website. So I have set my data-scraping routine up to collect the data each week. Some time late this year I will revisit this data to see if any patterns emerge over time.

State	Town	Weekly Average
NSW	Albury	135.5
NSW	Armidale	143.6
NSW	Ballina	146.9
NSW	Batemans Bay	144.3
NSW	Bathurst	145.9
NSW	Bega	150.1
NSW	Broken Hill	145.9
NSW	Bulahdelah	138.1
NSW	Buronga	146
NSW	Canberra	146.4
NSW	Casino	144.8
NSW	Central Coast	143.2
NSW	Coffs Harbour	147
NSW	Cooma	150.3
NSW	Cootamundra	149.2
NSW	Deniliquin	148.9
NSW	Dubbo	143.1
NSW	Forbes	147.5
NSW	Forster	146.9
NSW	Glen Innes	141.9
NSW	Goulburn	141.9
NSW	Grafton	146.3
NSW	Griffith	144.4
NSW	Gundagai	141.7
NSW	Gunnedah	144
NSW	Hay	145.8
NSW	Inverell	146.9
NSW	Kempsey	146.2
NSW	Leeton	145.5
NSW	Lismore	144.5
NSW	Lithgow	139.9
NSW	Mittagong	149.9
NSW	Moama	145.9
NSW	Moree	148.8
NSW	Moruya	147.6
NSW	Moss Vale	142.1
NSW	Mudgee	150.4
NSW	Murwillumbah	143.1
NSW	Muswellbrook	146.6
NSW	Newcastle	144.3
NSW	Nowra	144.8
NSW	Orange	147.4
NSW	Parkes	146.7
NSW	Port Macquarie	146.5
NSW	Queanbeyan	146.6
NSW	Singleton	143.8
NSW	Sydney	138.2
NSW	Tamworth	145.7
NSW	Taree	144.9
NSW	Temora	148
NSW	Tumut	145.9
NSW	Tweed Heads South	141
NSW	Ulladulla	143.9
NSW	Wagga Wagga	144.9
NSW	Wauchope	143.6
NSW	West Wyalong	149.9
NSW	Wollongong	143
NSW	Woolgoolga	145.7
NSW	Yass	148.4
NT	Alice Springs	163.4
NT	Darwin	151.8
NT	Katherine	146.3
NT	Tennant Creek	164
QLD	Atherton	145.3
QLD	Ayr	145.6
QLD	Biloela	148.6
QLD	Blackall	157.8
QLD	Blackwater	148.2
QLD	Bowen	146.2
QLD	Brisbane	141.3
QLD	Bundaberg	144
QLD	Cairns	146
QLD	Charters Towers	149.4
QLD	Childers	142.9
QLD	Dalby	142.4
QLD	Emerald	146.9
QLD	Gladstone	143.5
QLD	Gold Coast	141.3
QLD	Goondiwindi	148
QLD	Gympie	143.3
QLD	Hervey Bay	143.9
QLD	Ingham	142.9
QLD	Innisfail	145.3
QLD	Kingaroy	143.7
QLD	Longreach	153.9
QLD	Mackay	141.2
QLD	Mareeba	145.9
QLD	Maryborough	143.2
QLD	Miles	151.3
QLD	Moranbah	146.5
QLD	Mt Isa	150.4
QLD	Rockhampton	148.8
QLD	Roma	148.9
QLD	Sunshine Coast	141
QLD	Toowoomba	139.6
QLD	Townsville	141.9
QLD	Tully	148.9
QLD	Warwick	143.7
QLD	Whitsunday	138.5
QLD	Yeppoon	148.6
SA	Adelaide	134.9
SA	Bordertown	147.9
SA	Ceduna	150.6
SA	Clare	138.2
SA	Coober Pedy	160.4
SA	Kadina	139.4
SA	Keith	145.9
SA	Loxton	147.8
SA	Mt Gambier	146.2
SA	Murray Bridge	140.6
SA	Naracoorte	143.9
SA	Port Augusta	138.4
SA	Port Lincoln	145.2
SA	Port Pirie	139.1
SA	Renmark	142
SA	Tailem Bend	146.4
SA	Victor Harbour	142.9
SA	Whyalla	143.5
TAS	Burnie	150.3
TAS	Devonport	149.8
TAS	Hobart	150.2
TAS	Huonville	149.9
TAS	Launceston	149.9
TAS	New Norfolk	149.9
TAS	Smithton	149.5
TAS	Sorell	144.9
TAS	Ulverstone	149.9
TAS	Wynard	152.5
VIC	Ararat	143.1
VIC	Bairnsdale	139.6
VIC	Ballarat	144.4
VIC	Benalla	145.9
VIC	Bendigo	142.4
VIC	Cobram	142.5
VIC	Colac	145.9
VIC	Corryong	148.2
VIC	Echuca	145.9
VIC	Euroa	140.2
VIC	Geelong	136.3
VIC	Hamilton	146
VIC	Horsham	145
VIC	Koo Wee Rup	139.5
VIC	Kyabram	143.9
VIC	Leongatha	142.9
VIC	Melbourne	137.9
VIC	Mildura	147.5
VIC	Moe	139.9
VIC	Morwell	142.9
VIC	Portland	146.9
VIC	Sale	140.9
VIC	Seymour	137.9
VIC	Shepparton	143.6
VIC	Swan Hill	146.7
VIC	Traralgon	142.2
VIC	Wallan	138.1
VIC	Wangaratta	142.3
VIC	Warrnambool	143.8
VIC	Wodonga	139.1
VIC	Yarrawonga	149.9
WA	Albany	146.8
WA	Boulder	151.7
WA	Bridgetown	144.9
WA	Broome	165.9
WA	Bunbury	137.1
WA	Busselton	141.3
WA	Carnarvon	154.3
WA	Dongara	153.9
WA	Esperance	145.9
WA	Geraldton	149
WA	Kalgoorlie	149.6
WA	Karratha	159.9
WA	Majimup	143.9
WA	Mount Barker	149.2
WA	Perth	138.3
WA	Port Hedland	160.2
WA	Waroona	144.4

Leni Riefenstahl

3 Replies

As a change from the usual fare of economics and finance, I recently read Jürgen Trimborn’s biography Leni Riefenstahl: A Life about Hitler’s favourite film-maker, Leni Riefenstahl. Riefenstahl was a highly controversial figure. Her films Triumph des Willen, chronicling the 1935 Nazi party rally in Nürnberg and Olympia, documenting the 1936 Olympic Games in Berlin were critically acclaimed around the world, but also served as propaganda for the Third Reich.

After the war, Riefenstahl was acquitted in de-Nazification trials, but for some years struggled to shake the taint of her association with Hitler and his regime. Over time she found much of the world shifting its attitudes towards her. As Trimborn observes:

The older she became, the more pronounced was the phenomenon of Riefenstahl’s “promotion to the status of a cultural monument,” as Susan Sontag described it in 1974. The critical disputes surrounding her receded further and further into the background, replaced by an enthusiastic, or at least respectful, tribute to Riefenstahl’s ceaseless vitality.

This vitality really was extraordinary. At the age of 71, she lied about her age, claiming to be twenty years younger in order to take a scuba-diving course and from there spent decades developing a new career as an underwater photographer. She died in 2003 at the age of 101.

But Trimborn argues that this fascination with Riefenstahl’s vitality was a distraction from the complexities of Riefenstahl’s character. His biography portrays a brilliant, driven woman who was also a narcissist and a liar, who spent most of her life denying her complicity with the Nazis. For instance, she spent the later years of the war working on a fiction feature, Tiefland. The exigencies of war stymied her plan to film in Spain, so instead she made use of gypsies from Nazi labour camps as extras. She later claimed to have seen her cast fit and well after the war, but later eyewitness testimony not only revealed that many had ended up in concentration camps like Dachau, but that Riefenstahl knew only too well what was happening.

Reading about Leni Riefenstahl is a good recipe for cognitive dissonance: the contradictions are hard to reconcile. Her documentaries were stylistically revolutionary, redefining the genre, and yet the content and the context are appalling. She was a gifted artists, but in many ways highly objectionable. But, who said the real world was simple?

More spreads

9 Replies

To provide a bit more context for the French government bond spreads discussed in the last post, the chart below shows the 5-year spreads to German bonds for a few more European countries.

With spreads over 4300 basis points (43%), the chart is dominated by Greece, so here is the chart again with Greece removed.

As you can see in both charts, while French spreads are certainly heading north, they have a long way to go.

For those who have spotted the break in the line for Ireland, my data source seems to be missing 2010 data. I am looking into that and will update the casts if I plug the gaps.

Data source: Bloomberg.

French spreads

4 Replies

Changes of leadership in both Greece and Italy were initially well-received by markets, but investors are getting nervous again. Attention is shifting to France, and French government bonds seem to be on the nose. The chart below shows the “spread” between French and German 5-year government bonds. Measured in basis points (1/100th of 1%), the spread is the difference between the yields on the respective bonds and it has now reached 183 basis points.

Given that yields on 5-year government bonds are only 0.95%, that is a big difference. Investors are demanding almost double the rate of interest on a French bond offered by a German bond if they are to take on the risk that France is not able to repay its debt in 5 years’ time.

Unlike France, the United Kingdom is lucky enough to have its own currency and the spread between UK and German government bonds is only 10 basis points. More on that in a future post.

Data source: Bloomberg.

Sculptures by the Sea

More colour wheels

2 Replies

In response to my post about colour wheels, I received a suggested enhancement from Drew. The idea is to first match colours based on the text provided and then add nearby colours. This can be done by ordering colours in terms of hue, saturation, and value. The result is a significant improvement and it will capture all of those colours with more obscure names.

Here is my variant of Drew’s function:

col.wheel <- function(str, nearby=3, cex=0.75) {
	cols <- colors()
	hsvs <- rgb2hsv(col2rgb(cols))
	srt <- order(hsvs[1,], hsvs[2,], hsvs[3,])
	cols <- cols[srt]
	ind <- grep(str, cols)
	if (length(ind) <1) stop("no colour matches found",
		call.=FALSE)
	s.ind <- ind
	if (nearby>1) for (i in 1:nearby) {
		s.ind <- c(s.ind, ind+i, ind-i)
	}
	ind <- sort(unique(s.ind))
	ind <- ind[ind <= length(cols)]
	cols <- cols[ind]
	pie(rep(1, length(cols)), labels=cols, col=cols, cex=cex)
	cols
}

I have included an additional parameter, nearby, which specifies the range of additional colours to include. A setting of 1 will include colours matching the specified string and also one colour on either side of each of these. By default, nearby is set to 3.

The wheel below shows the results for col.wheel(“thistle”, nearby=5). As well as the various shades of “thistle”, this also uncovers “plum” and “orchid”.

This is far more powerful than the original function: thanks Drew.

Colour wheels in R

11 Replies

Regular readers will know I use the R package to produce most of the charts that appear here on the blog. Being more quantitative than artistic, I find choosing colours for the charts to be one of the trickiest tasks when designing a chart, particularly as R has so many colours to choose from.

In R, colours are specified by name, with names ranging from the obvious “red”, “green” and “blue” to the obscure “mintycream”, “moccasin” and “goldenrod”. The full list of 657 named colours can be found using the colors() function, but that is a long list to wade through if you just want to get exactly the right shade of green, so I have come up with a shortcut which I thought I would share here*.

Below is a simple function called col.wheel which will display a colour wheel of all the colours matching a specified keyword.

col.wheel <- function(str, cex=0.75) {
	cols <- colors()[grep(str, colors())]
	pie(rep(1, length(cols)), labels=cols, col=cols, cex=cex)
	cols
	}

To use the function, simply pass it a string:

col.wheel("rod")

As well as displaying the colour wheel below, this will return a list of all of the colour names which include the specified string.

 [1] "darkgoldenrod"        "darkgoldenrod1"       "darkgoldenrod2"
 [4] "darkgoldenrod3"       "darkgoldenrod4"       "goldenrod"
 [7] "goldenrod1"           "goldenrod2"           "goldenrod3"
[10] "goldenrod4"           "lightgoldenrod"       "lightgoldenrod1"
[13] "lightgoldenrod2"      "lightgoldenrod3"      "lightgoldenrod4"
[16] "lightgoldenrodyellow" "palegoldenrod"

In fact, col.wheel will accept a regular expression, so you could get fancy and ask for colours matching “r.d” which will give you all the reds and the goldenrods.

This trick does have its limitations: you are not likely to find “thistle”, “orchid” or “cornsilk” this way, but I have found it quite handy and others may too.

*My tip was inspired by this page about R colours.

Melbourne Cup

2 Replies

I have been resting on my laurels for too long. Two years ago I had Shocking success tipping a winner for the Melbourne Cup. Needless to say the analysis was entirely bogus, but it was fun. Since then I have been reluctant to tarnish my spotless prediction record, but fortune favours the brave, so I really should try again.

Inspired by my last analysis, The Hoopla is tipping Lost in the Moment, which is a 5 year-old bay horse starting at barrier three and is carrying 53 kg. Unfortunately, my statistics tell me that over the last 150 races going back to 1861, there have been five Cups in which a horse with three words in its name has won, but never one with four words. That does not bode well for Lost in the Moment.

I will not give up entirely on a good formula, so based on the analysis from two years ago, I would still like to tip a bay horse with a handicap in the 50-55 kg range starting in the barrier range 1 to 5. This time, however, I will take into account the fact that over half of previous Cup winners only had a one-word name.

Three of the horses in barriers 1 through 5 have a one-word name, and they are all carrying around 53 kg:

Illo
Precedence
Modun

According to this form guide (which is the only one not blocked at my place of work, which limits access to “gambling sites” and yet is doubtless hosting many Melbourne Cup festivities), Precedence and Modun are geldings, which rules them out (they come second to horses in the chart below).

So: the Stubborn Mule tip for the 2011 Melbourne Cup is Illo.

For anyone foolish enough to take heed of this tip, I appreciate it, but you are foolish.

UPDATE: after a good run for much of the race (always a bad sign) Illo failed to win or even place. So it seems that the Mule has lost his crown.