# Chance News 33

## Contents

## Quotation

It is the mark of a truly intelligent person to be moved by statistics.

George Bernard Shaw

## Forsooth

The following Forsooths are from the January 2008 issue of RSS NEWS.

In terms of platform use trends among the respondents, 53% cited Windows as their primary technical computing platform, with Linux following closely at 51%.

NAGNews email (NAG User Survey 2006 on technical

computing trends)

August 2006

Clearly, any product with a large user base is going to throw up some problems. Dell, for example, is shipping almost 40m PCs a year, so even if 95% of it users are happy, there could still be 6m or so with significant gripes.

The Guardian

25 January 2007

## High altitude effects on athletic performance

Effect of altitude on physiological performance: a statistical analysis using results of international football games. Patrick E McSharry. BMJ 2007; 335: 1278-1281 (22 December).

There is a strong belief that athletes who live and train at high altitudes have an unfair advantage over those athletes visiting from lower altitudes. In response,

football’s governing body, the Federation of International Football Associations (FIFA), banned international matches from being played at more than 2500 m above sea level.

There is a plausible mechanistic explanation for this concern.

At high altitude hypoxia, cold, and dehydration can lead to breathlessness, headaches, nausea, dizziness, and fatigue, and possibly altitude illness including syndromes such as acute mountain sickness, high altitude pulmonary oedema, and cerebral oedema. Activities such as football can exacerbate symptoms, preventing players from performing at full capacity.

What would the data say. An ideal database exists to explore whether high altitude has a detrimental effect on athletes visiting from lower altitudes. In South America, which has three large cities at high altitude (Bogota, Columbia, Quito, Ecuador, and La Paz, Bolivia), there are records of 1460 football matches played over a 100 year period at a wide range of altitudes. This data set included four variables:

(i) the probability of a win, (ii) the number of goals scored, (iii) the number of goals conceded, and (iv) the altitude difference between the home venue of a specific team and that of the opposition.

as well as indicators for individual countries. This study used a logistic regression model to predict the probability of a win by the home team, and two Poisson regression models: one to predict number of goals scored by the home team and a second to predict the number of goals conceded by the home team.

http://www.bmj.com/content/vol335/issue7633/images/medium/mcsp457549.f2.gif

The graph of the predicted equations appears above. These graphs show clearly that a thousand meter difference in altitude between the home team and the opposition produces a large change in the estimated probability of a win for the home team, the expected number of goals scored by the home team, and the expected number of goals allowed by the home team.

### Questions

1. Although the graphs are non-linear, a linear approximation is quite reasonable for the predicted values. Estimate how much change in probability of home team winning, goals scored by the home team, and goals allowed by the home team changes for each 1,000 meter change in altitude.

2. There are many variables that were not considered in this analysis. List some of the more important variables that were not included. Consider whether these variables are easy to measure or hard to measure.

3. Is there an alternate explanation other than change in altitude that could account for the differential in home team win probability, goals scored by the home team, and goals allowed by the home team?

4. Should international football matches be allowed in high altitude locations?

Submitted by Steve Simon

## What happened to the margin of error in New Hamshire?

We are going to write about this when the polls have had time to analyze their data. In the meantime you might enjoy reading Getting it wrong (*New York Times*, op-ed, Jan. 10, 2008) by
Andrew Kohut. President of the Pew Research Center and looking at the Pollster.com Blog. You will find here the following graph which shows the how far the various polls were from the classical 5% margin of error (indicated by the inner circle).

Submitted by Laurie Snell