Lunar Motion

Is the shakeup only athletic?

lunarmotion — Fri, 03 Sep 2010 21:26:55 +0000

Rankings from the most recent U.S. News & World Report college rankings. National rankings are seperate from the Regional rankings. All colleges ranked Regionally on this chart are in the West Region.
© 2010 John Schneider. All rights reserved.

World Cup probabilities 5.2

lunarmotion — Fri, 09 Jul 2010 22:33:43 +0000

So, we are down to the 3rd place match and the final. My model likes Germany to win the 3rd place match and Spain the final. I personally want to see Germany and the Netherlands win. We’ll see.

Round of 16
61%	Uruguay	2	1	Korea	39%
55%	USA	1	2	Ghana	45%
64%	Netherlands	2	1	Slovakia	36%
65%	Brazil	3	0	Chile	35%
59%	Argentina	3	1	Mexico	41%
50%	Germany	4	1	England	50%
53%	Paraguay	0.5	0.3	Japan	47%
56%	Spain	1	0	Portugal	44%
Quarterfinals
54%	Uruguay	1.4	1.2	Ghana	46%
45%	Netherlands	2	1	Brazil	55%
52%	Argentina	0	4	Germany	48%
32%	Paraguay	0	1	Spain	68%
Semifinals
39%	Uruguay	2	3	Netherlands	61%
42%	Germany	0	1	Spain	58%
3rd Place
44%	Uruguay			Germany	56%
Final
47%	Netherlands			Spain	53%

The model continues to perform within expectations.

Matches played	62
Cumulative expected model accuracy	58.9%
Actual matches resulting in probable outcome	40
Actual model accuracy	64.5%

World Cup probabilities 5.1

lunarmotion — Wed, 30 Jun 2010 03:00:58 +0000

Matches for the quarterfinals have been determined. Here is how the knockout round is going so far. The matches for the semifinals, third place match, and final (all in italics) are theoretical and based on the probabilities up to the quarterfinals.

Round of 16
61%	Uruguay	2	1	Korea	39%
55%	USA	1	2	Ghana	45%
64%	Netherlands	2	1	Slovakia	36%
65%	Brazil	3	0	Chile	35%
59%	Argentina	3	1	Mexico	41%
50%	Germany	4	1	England	50%
53%	Paraguay	0.5	0.3	Japan	47%
56%	Spain	1	0	Portugal	44%
Quarterfinals
54%	Uruguay			Ghana	46%
45%	Netherlands			Brazil	55%
52%	Argentina			Germany	48%
32%	Paraguay			Spain	68%
Semifinals
38%	Uruguay			Brazil	62%
43%	Argentina			Spain	57%
3rd Place
45%	Uruguay			Argentina	55%
Final
50%	Brazil			Spain	50%

As to the model accuracy to this point — it is still doing well.

Matches played	56
Cumulative expected model accuracy	59.0%
Actual matches resulting in probable outcome	36
Actual model accuracy	64.3%

World Cup probabilities 5.0

lunarmotion — Mon, 28 Jun 2010 20:45:21 +0000

Let’s start with the advancements to the Round of 16. The probabilities in the model were calculated using FIFA rankings at the beginning of the tournament. I did not adjust the probabilities after each match like I do for the match predictions. It’s possible to do so, but more work than I wanted to do this year. Still, the model faired well, though performing slightly better than expected.

Teams advanced	16
Cumulative expected model accuracy	57.73%
Actual highest probability teams advancing	11
Actual model accuracy	68.75%

The model continues to do well with the match to match probabilities.

Matches played	54
Cumulative expected model accuracy	59.22%
Actual matches resulting in probable outcome	34
Actual model accuracy	62.96%

So, for the knockout round so far and the probabilities for upcoming matches which have been determined:

61%	Uruguay	2	1	Korea	39%
55%	USA	1	2	Ghana	45%
64%	Netherlands	2	1	Slovakia	36%
65%	Brazil	3	0	Chile	35%
59%	Argentina	3	1	Mexico	41%
50%	Germany	4	1	England	50%
53%	Paraguay			Japan	47%
56%	Spain			Portugal	44%
54%	Uruguay			Ghana	46%
45%	Netherlands			Brazil	55%
52%	Argentina			Germany	48%

World Cup probabilities 4.0

lunarmotion — Wed, 23 Jun 2010 21:33:24 +0000

Eight teams have advanced. How well did my model predict those teams? Within expectations. Here is the analysis.

Teams advanced	8
Cumulative expected model accuracy	54.72%
Actual highest probability teams advancing	5
Actual model accuracy	62.50%

As for the match to match probabilities:

Matches played	40
Cumulative expected model accuracy	58.82%
Actual matches resulting in probable outcome	25.5
Actual model accuracy	63.75%

And here is the results table with the probabilites for upcoming fixtures, including Round of 16 matchups that have already been determined.

Match
35%	South Africa	1	1	Mexico	65%
46%	Uruguay	0	0	France	54%
54%	Argentina	1	0	Nigeria	46%
40%	Korea	2	0	Greece	60%
53%	England	1	1	USA	47%
49%	Algeria	0	1	Slovenia	51%
55%	Germany	4	0	Australia	45%
53%	Serbia	0	1	Ghana	47%
62%	Netherlands	2	0	Denmark	38%
42%	Japan	1	0	Cameroon	58%
59%	Italy	1	1	Paraguay	41%
35%	New Zealand	1	1	Slovakia	65%
41%	Ivory Coast	0	0	Portugal	59%
85%	Brazil	2	1	N Korea	15%
45%	Honduras	0	1	Chile	55%
65%	Spain	0	1	Switzerland	35%
36%	South Africa	0	3	Uruguay	64%
54%	France	0	2	Mexico	46%
50%	Greece	2	1	Nigeria	50%
63%	Argentina	4	1	Korea	37%
48%	Slovenia	2	2	USA	52%
58%	England	0	0	Algeria	42%
55%	Germany	0	1	Serbia	45%
51%	Ghana	1	1	Australia	49%
64%	Netherlands	1	0	Japan	36%
55%	Cameroon	1	2	Denmark	45%
48%	Slovakia	0	2	Paraguay	52%
74%	Italy	1	1	New Zealand	26%
65%	Brazil	3	1	Ivory Coast	35%
82%	Portugal	7	0	N Korea	18%
50%	Chile	1	0	Switzerland	50%
68%	Spain	2	0	Honduras	32%
50%	Mexico	0	1	Uruguay	50%
67%	France	1	2	South Africa	33%
57%	Nigeria	2	2	Korea	43%
45%	Greece	0	2	Argentina	55%
46%	Slovenia	0	1	England	54%
55%	USA	1	0	Algeria	45%
46%	Ghana	0	1	Germany	54%
47%	Australia	2	1	Serbia	53%
53%	Denmark			Japan	47%
39%	Cameroon			Netherlands	61%
39%	Slovakia			Italy	61%
67%	Paraguay			New Zealand	33%
43%	Portugal			Brazil	57%
24%	N Korea			Ivory Coast	76%
39%	Chile			Spain	61%
56%	Switzerland			Honduras	44%
61%	Uruguay			Korea	39%
55%	USA			Ghana	45%
59%	Argentina			Mexico	41%
50%	Germany			England	50%

World Cup probabilities 3.1

lunarmotion — Tue, 22 Jun 2010 07:20:50 +0000

Now that all teams have played two matches, how is the revised model holding up? Pretty well.

Matches played	32
Cummulative expected model accuracy	59.60%
Actual matches resulting in probable outcome	20
Actual model accuracy	62.50%

Here is the full table of group play fixtures, with probabilities and scores.

	Match
35%	South Africa	1	1	Mexico	65%
46%	Uruguay	0	0	France	54%
54%	Argentina	1	0	Nigeria	46%
40%	Korea	2	0	Greece	60%
53%	England	1	1	USA	47%
49%	Algeria	0	1	Slovenia	51%
55%	Germany	4	0	Australia	45%
53%	Serbia	0	1	Ghana	47%
62%	Netherlands	2	0	Denmark	38%
42%	Japan	1	0	Cameroon	58%
59%	Italy	1	1	Paraguay	41%
35%	New Zealand	1	1	Slovakia	65%
41%	Ivory Coast	0	0	Portugal	59%
85%	Brazil	2	1	N Korea	15%
45%	Honduras	0	1	Chile	55%
65%	Spain	0	1	Switzerland	35%
36%	South Africa	0	3	Uruguay	64%
54%	France	0	2	Mexico	46%
50%	Greece	2	1	Nigeria	50%
63%	Argentina	4	1	Korea	37%
48%	Slovenia	2	2	USA	52%
58%	England	0	0	Algeria	42%
55%	Germany	0	1	Serbia	45%
51%	Ghana	1	1	Australia	49%
64%	Netherlands	1	0	Japan	36%
55%	Cameroon	1	2	Denmark	45%
48%	Slovakia	0	2	Paraguay	52%
74%	Italy	1	1	New Zealand	26%
65%	Brazil	3	1	Ivory Coast	35%
82%	Portugal	7	0	N Korea	18%
50%	Chile	1	0	Switzerland	50%
68%	Spain	2	0	Honduras	32%
50%	Mexico			Uruguay	50%
67%	France			South Africa	33%
57%	Nigeria			Korea	43%
45%	Greece			Argentina	55%
46%	Slovenia			England	54%
55%	USA			Algeria	45%
46%	Ghana			Germany	54%
47%	Australia			Serbia	53%
53%	Denmark			Japan	47%
39%	Cameroon			Netherlands	61%
39%	Slovakia			Italy	61%
67%	Paraguay			New Zealand	33%
43%	Portugal			Brazil	57%
24%	N Korea			Ivory Coast	76%
39%	Chile			Spain	61%
56%	Switzerland			Honduras	44%

World Cup probabilities 3.0

lunarmotion — Fri, 18 Jun 2010 21:18:05 +0000

After the first twelve matches completed, I revised my methodology for probabilites. The old method was:

Start with the FIFA ranking points
Add my modifier based on injuries, regional adder (+50 points for African teams), and my personal assessment of momentum
Calculate a probability factor as 10^(points/400)

The tendancy of this method was to overrate the good teams. I modified step 3 to be closer to what I used in the 2006 World Cup:

Start with the FIFA ranking points
Add my modifier based on injuries, regional adder (+50 points for African teams), and my personal assessment of momentum
Use the points as the initial probability factor

Since this World Cup I am also recalculating the probabilities after each match, I needed to modify how to get the probability factor for each subsequent match. I use Elo ranking to do this. (See Wikipedia for a good article on Elo ranking.) Following the notation in the Wiki article, I use the FIFA points as the team’s initial Q. I back-calculate R. From there I recalculate R after each match then forward-calculate the new Q. Simple, right?

Well, here is how the new method looks diagnostically:

Matches played	23
Cummulative expected model accuracy	58.58%
Actual matches resulting in probable outcome	13
Actual model accuracy	56.52%

And now the prediction table, with current results added.

Match
South Africa	35%	1	1	65%	Mexico
Uruguay	46%	0	0	54%	France
Argentina	54%	1	0	46%	Nigeria
Korea	40%	2	0	60%	Greece
England	53%	1	1	47%	USA
Algeria	49%	0	1	51%	Slovenia
Germany	55%	4	0	45%	Australia
Serbia	53%	0	1	47%	Ghana
Netherlands	62%	2	0	38%	Denmark
Japan	42%	1	0	58%	Cameroon
Italy	59%	1	1	41%	Paraguay
New Zealand	35%	1	1	65%	Slovakia
Ivory Coast	41%	0	0	59%	Portugal
Brazil	85%	2	1	15%	N Korea
Honduras	45%	0	1	55%	Chile
Spain	65%	0	1	35%	Switzerland
South Africa	36%	0	3	64%	Uruguay
France	54%	0	2	46%	Mexico
Greece	50%	2	1	50%	Nigeria
Argentina	63%	4	1	37%	Korea
Slovenia	48%	2	2	52%	USA
England	58%	0	0	42%	Algeria
Germany	55%	0	1	45%	Serbia
Ghana	51%			49%	Australia
Netherlands	64%			36%	Japan
Cameroon	55%			45%	Denmark
Slovakia	48%			52%	Paraguay
Italy	74%			26%	New Zealand
Brazil	65%			35%	Ivory Coast
Portugal	82%			18%	N Korea
Chile	50%			50%	Switzerland
Spain	68%			32%	Honduras
Mexico	50%			50%	Uruguay
France	67%			33%	South Africa
Nigeria	57%			43%	Korea
Greece	45%			55%	Argentina
Slovenia	46%			54%	England
USA	55%			45%	Algeria
Ghana	45%			55%	Germany
Australia	49%			51%	Serbia
Denmark	51%			49%	Japan
Cameroon	41%			59%	Netherlands
Slovakia	39%			61%	Italy
Paraguay	67%			33%	New Zealand
Portugal	43%			57%	Brazil
N Korea	24%			76%	Ivory Coast
Chile	38%			62%	Spain
Switzerland	57%			43%	Honduras

Anything is possible at the World Cup

lunarmotion — Wed, 16 Jun 2010 16:59:06 +0000

With each team having completed their first match, it is a good time to check in on the accuracy of my modelled predictions. As you can see below, the model underperformed. This was driven largely by upsets against the biggest favorites.

Matches played	16
Cummulative expected model accuracy	80.50%
Actual matches resulting in probable outcome	9
Actual model accuracy	56.25%

This chart shows in blue the distribution of possible outcomes assuming the model is correct. The red bar represents the actual model performance. Notice that it is just barely in distribution. Basically, at predicting a winner, so far the model is just a little better than flipping a coin.

As this model fit shows, the model underperformed primarily at the high end, meaning the biggest favorites ended up being the biggest upsets. Greece, Camaroon, and Spain all lost and Mexico, Italy, Slovakia, and Portugal all tied. It’s like my brother, Daniel, says: “Anything is possible at the World Cup.”

Here are updated predictions for the remainder of group play, with the results so far.

Match
South Africa	1	1	Mexico
Uruguay	0	0	France
Argentina	1	0	Nigeria
Korea	2	0	Greece
England	1	1	USA
Algeria	0	1	Slovenia
Germany	4	0	Australia
Serbia	0	1	Ghana
Netherlands	2	0	Denmark
Japan	1	0	Cameroon
Italy	1	1	Paraguay
New Zealand	1	1	Slovakia
Ivory Coast	0	0	Portugal
Brazil	2	1	N Korea
Honduras	0	1	Chile
Spain	0	1	Switzerland
South Africa	9%	91%	Uruguay
France	72%	28%	Mexico
Greece	49%	51%	Nigeria
Argentina	91%	9%	Korea
Slovenia	38%	62%	USA
England	83%	17%	Algeria
Germany	74%	26%	Serbia
Ghana	53%	47%	Australia
Netherlands	95%	5%	Japan
Cameroon	67%	33%	Denmark
Slovakia	37%	63%	Paraguay
Italy	98%	2%	New Zealand
Brazil	98%	2%	Ivory Coast
Portugal	100%	0%	N Korea
Chile	47%	53%	Switzerland
Spain	99%	1%	Honduras
Mexico	44%	56%	Uruguay
France	95%	5%	South Africa
Nigeria	79%	21%	Korea
Greece	25%	75%	Argentina
Slovenia	26%	74%	England
USA	74%	26%	Algeria
Ghana	25%	75%	Germany
Australia	45%	55%	Serbia
Denmark	54%	46%	Japan
Cameroon	12%	88%	Netherlands
Slovakia	9%	91%	Italy
Paraguay	91%	9%	New Zealand
Portugal	10%	90%	Brazil
N Korea	3%	97%	Ivory Coast
Chile	3%	97%	Spain
Switzerland	77%	23%	Honduras

World Cup probabilities 1.0

lunarmotion — Thu, 10 Jun 2010 19:03:37 +0000

Here are my World Cup predictions for the first round. Thanks to Avi for updates on injuries. Updates forthcoming as the tournament progresses.

Group	Match
A	South Africa	9%	91%	Mexico
A	Uruguay	30%	70%	France
B	Argentina	69%	31%	Nigeria
B	Korea	13%	87%	Greece
C	England	65%	35%	USA
C	Algeria	44%	56%	Slovenia
D	Germany	74%	26%	Australia
D	Serbia	64%	36%	Ghana
E	Netherlands	94%	6%	Denmark
E	Japan	19%	81%	Cameroon
F	Italy	89%	11%	Paraguay
F	New Zealand	11%	89%	Slovakia
G	Ivory Coast	10%	90%	Portugal
G	Brazil	100%	0%	N Korea
H	Honduras	29%	71%	Chile
H	Spain	98%	2%	Switzerland
A	South Africa	9%	91%	Uruguay
A	France	70%	30%	Mexico
B	Greece	54%	46%	Nigeria
B	Argentina	93%	7%	Korea
C	Slovenia	35%	65%	USA
C	England	81%	19%	Algeria
D	Germany	67%	33%	Serbia
D	Ghana	45%	55%	Australia
E	Netherlands	96%	4%	Japan
E	Cameroon	73%	27%	Denmark
F	Slovakia	44%	56%	Paraguay
F	Italy	99%	1%	New Zealand
G	Brazil	99%	1%	Ivory Coast
G	Portugal	100%	0%	N Korea
H	Chile	53%	47%	Switzerland
H	Spain	99%	1%	Honduras
A	Mexico	49%	51%	Uruguay
A	France	96%	4%	South Africa
B	Nigeria	85%	15%	Korea
B	Greece	34%	66%	Argentina
C	Slovenia	22%	78%	England
C	USA	70%	30%	Algeria
D	Ghana	22%	78%	Germany
D	Australia	41%	59%	Serbia
E	Denmark	62%	38%	Japan
E	Cameroon	16%	84%	Netherlands
F	Slovakia	9%	91%	Italy
F	Paraguay	91%	9%	New Zealand
G	Portugal	12%	88%	Brazil
G	N Korea	3%	97%	Ivory Coast
H	Chile	2%	98%	Spain
H	Switzerland	68%	32%	Honduras

Dream On

lunarmotion — Fri, 04 Jun 2010 02:12:07 +0000