How Diverse Is Kew Becoming?

For those who are not as nerdly as the Wheatley household, the Australian Bureau of Statistics Census Data was released this week. I asked Ms Wheatley today what she would be interested in me finding out with the data, and she said finding out if Kew, Victoria (the suburb we are currently situated in) is becoming more diverse.

 I had a look at two data series: the percentage of persons in Kew who were born outside Australia, and the percentage of persons in Kew who spoke a language at home other than English. By both measures, Kew has become more diverse over the past decade, as I suspect most Australian suburbs have. Driving this trend is the doubling in Kew's population that were born in China. I haven't yet noticed this trend becoming apparent at the Hotel Kew though.

	2001	2006	2011
% born outside Australia	26.5	26.8	28.8
% language spoken at home other than English	19.8	21.4	23.9

AFL Power Rankings: Round 12 2012

Very little movement this week; Hawks extend their margin at the top and Swans move up to 2nd after the Eagles struggle to beat the Blues at home.

1 (1) Hawthorn 29.2 (28.6)
2 (3) Sydney 20.7 (20.5)
3 (2) West Coast 20.1 (21.0)
4 (4) Geelong 20.0 (20.2)
5 (5) Collingwood 18.5 (18.4)
6 (6) St. Kilda 15.0 (16.1)
7 (7) Adelaide 11.0 (9.7)
8 (8) Carlton 9.5 (8.7)
9 (10) Essendon 6.6 (7.0)
10 (9) Richmond 6.2 (8.6)
11 (11) North Melbourne -7.4 (-4.5)
12 (12) Western Bulldogs -10.2 (-10.1)
13 (13) Fremantle -10.8 (-10.6)
14 (14) Brisbane -13.4 (-12.6)
15 (15) Port Adelaide -19.9 (-19.7)
16 (16) Melbourne -34.1 (-33.8)
17 (17) Greater Western Sydney -51.5 (-53.3)
18 (18) Gold Coast -53.9 (-57.2)

The Home Town Free Kick Bias In The AFL

A suggestion that came into my feed on Twitter was that there is a greater "home ground" bias in relation to free kicks at Subiaco than at other grounds. I thought this would be easy enough to investigate - turns out though that there is no handy compilation of free kicks data to be found on the net (at least not that I'm aware of). Nevertheless I was able to compile some free kicks data for last season to see how the "home ground" bias varied across teams. The method I have used is to compare, for each team, the ratio of free kicks for to free kicks against when playing at home against interstate sides with the ratio of free kicks for to free kicks against when playing away against interstate sides. I used this difference to measure the "home ground" bias because some teams are good at picking up free kicks both home and away (see West Coast Eagles).

The results are in the table below. The first thing to note is that teams playing at home against interstate sides were far more successful at getting free kicks than teams playing away against interstate sides in 2011. But in terms of which teams were more successful at getting free kicks at home compared to on the road there doesn't seem to be much of a pattern. Keep in mind these are small samples, particularly for the Victorian teams. But for 2011 at least, it seems that the Subiaco co-tenants were good at picking up free kicks wherever they played.

	Ratio of free kicks for to free kicks against when playing at home against interstate sides (2011)	Ratio of free kicks for to free kicks against when playing away against interstate sides (2011)	Difference
Carl	1.65	0.87	0.77
Haw	1.31	0.72	0.59
NM	1.17	0.65	0.52
Rich	1.18	0.70	0.48
GC	1.18	0.79	0.40
PA	1.35	0.96	0.39
Geel	1.17	0.87	0.30
Adel	1.10	0.84	0.27
Syd	1.09	0.84	0.25
Melb	0.94	0.70	0.24
Fre	1.24	0.99	0.24
Bris	1.08	0.85	0.23
WB	1.21	0.99	0.22
WC	1.29	1.09	0.19
StK	0.91	0.73	0.18
Ess	0.87	0.87	0.00
Coll	1.08	1.26	-0.18
Total	1.16	0.86	0.30

Mid-Year AFL All-Australian Team 2012

B: Beau Waters, Darren Glass, Heath Shaw
HB: Brendon Goddard, Ben Rutten, Brett Deledio
C: Brent Stanton, Scott Pendlebury, Jobe Watson
HF: Steele Sidebottom, Lance Franklin, Steve Johnson
F: Taylor Walker, Jack Riewoldt, Lewis Jetta
R: Dean Cox, Scott Thompson, Gary Ablett
I: Ivan Maric, Joel Selwood, Patrick Dangerfield, Josh Kennedy

The Finger Points Outwards - No. 42: Does Distracting Free Throw Shooters Work?

Watching the NBA Finals today, and seeing the Thunder fans hold up "Welcome to Brick Town" signs as the Miami Heat players aimed to make free throws, I wondered if players do actually shoot worse from the free throw line when they play away compared to when they play at home.

My suspicion was that they do not, or at least not by much. This article (though it's a few years old now) supports my suspicion.

(If that really is the case, it would be interesting to see in which areas teams actually are worse in when they are away - obviously they must do worse in at least some aspects of the game, because teams win less on the road.)

AFL Power Rankings: Round 11 2012

The Swans move up to third this week - other than that there is not a lot of movement as six teams had the bye. However, even teams that had the bye change their ranking points this week as the value of the teams they recently played changes. For example, St. Kilda's mammoth win against the Gold Coast increases their ranking points and also gives a boost to the points of the teams they recently played, including the Eagles who had the bye (and the Swans).

1 (1) Hawthorn 28.6 (28.3)
2 (2) West Coast 21.0 (20.0)
3 (5) Sydney 20.5 (19.3)
4 (3) Geelong 20.2 (20.0)
5 (4) Collingwood 18.4 (19.9)
6 (6) St. Kilda 16.1 (12.4)
7 (8) Adelaide 9.7 (9.5)
8 (9) Carlton 8.7 (7.1)
9 (7) Richmond 8.6 (10.7)
10 (10) Essendon 7.0 (6.2)
11 (11) North Melbourne -4.5 (-5.2)
12 (12) Western Bulldogs -10.1 (-9.2)
13 (14) Fremantle -10.6 (-14.4)
14 (13) Brisbane -12.6 (-12.6)
15 (15) Port Adelaide -19.7 (-20.4)
16 (16) Melbourne -33.8 (-36.1)
17 (18) Greater Western Sydney -53.3 (-53.4)
18 (17) Gold Coast -57.2 (-54.8)

AFL Power Rankings: Round 10 2012

After dropping to third last week Hawthorn are back at the top following a Buddy-led demolition of North Melbourne. The Hawks have opened up a bit of a gap from a batch of teams that are bunched together at second to fifth, including last week’s leader, West Coast, who were one of several teams to lose this week to much lower-ranked opponents.

There is some movement in the middle of the rankings as well, with Richmond and Adelaide vaulting into the “top eight”, while Carlton fall from fifth to ninth, and Essendon from eighth to tenth. Some might be wondering how the Bombers, at 8-2 for the season, can be so low. Given that the ranking system is based on “relative margins”, their weak performances against bad opposition cost them in terms of ranking points – if their matches this season against Gold Coast, Port, and Melbourne were removed they would be up in sixth spot. So if you think the Dons’ struggles against the bottom teams don’t really matter, just imagine them as the sixth-ranked team instead.

1 (3) Hawthorn 28.3 (19.6)
2 (1) West Coast 20.0 (23.1)
3 (2) Geelong 20.0 (22.2)
4 (4) Collingwood 19.9 (18.9)
5 (7) Sydney 19.3 (13.2)
6 (6) St. Kilda 12.4 (14.9)
7 (9) Richmond 10.7 (8.0)
8 (10) Adelaide 9.5 (6.7)
9 (5) Carlton 7.1 (15.6)
10 (8) Essendon 6.2 (10.8)
11 (11) North Melbourne -5.2 (2.4)
12 (12) Western Bulldogs -9.2 (-4.8)
13 (14) Brisbane -12.6 (-14.7)
14 (13) Fremantle -14.4 (-12.0)
15 (15) Port Adelaide -20.4 (-28.4)
16 (16) Melbourne -36.1 (-40.2)
17 (18) Greater Western Sydney -53.4 (-56.2)
18 (17) Gold Coast -54.8 (-54.4)

How Best-of-Five-Set Matches Benefit Dominant Male Tennis Players

This post was inspired by an article on The Economist, which went through some possible explanations for why, in recent years, the best male tennis players have dominated Grand Slams more than the best female tennis players. An addendum at the end of the article noted that part of the reason for this is that women’s matches in Grand Slams are three sets, while men’s matches are five sets, therefore making upsets more likely in the women’s game.

Not being able to calculate probabilities in my head, I was curious to see how the different match lengths affects the chances of the “better” player winning the match. Assume that a given male player and a given female player have the same probability of winning a particular set. The tables below show, for selected probabilities of winning a set that are greater than 0.5, the probability of those players winning a particular match, if their probabilities of winning a particular set remains constant. They also show the probability of those players winning a Grand slam tournament (although more caution should be taken with these probabilities, given that a player’s probability of winning a particular set is likely reduced as they progress through the tournament).

	Probability of winning match
Probability of winning set	Female	Male
0.5	0.500	0.500
0.6	0.648	0.683
0.7	0.784	0.837
0.8	0.896	0.942
0.9	0.972	0.991

	Probability of winning Grand Slam
Probability of winning set	Female	Male
0.5	0.004	0.004
0.6	0.031	0.047
0.7	0.143	0.241
0.8	0.415	0.620
0.9	0.797	0.933

So the fact that men play five sets does make a bit of difference to a dominant male player’s chances of winning a Grand Slam match (and the tournament) compared to a dominant female player’s. But it doesn’t look like it’s enough to primarily explain the greater recent dominance of Federer, Nadal and Djokovic compared to the top female players. Also, as noted in the article, there have been long periods when the top female players have been more dominant (Graf, Seles, Navratilova, Evert, etc), so the set structure just favours the top male players being more dominant, it by no means guarantees it.