[R] Olympics: 200m Men Final

[Mohan Radhakrishnan]

Hi,
      Can't we use these predictions  based on statistical principles 
for predicting payment gateway usages, transaction flows etc. ?
Sometimes our gateways fails and there could be a mean-time between
failures.

Is there a certain roadmap( book ? ) for learning this type of
prediction ? We are novice capacity planners.

Thanks,
Mohan

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of Rui Barradas
Sent: Friday, August 10, 2012 1:54 AM
To: r-help
Subject: [R] Olympics: 200m Men Final

Hello,

Have you seen the log-linear prediction of the 100m winning time in R 
mailed to the list yesterday by David Smith, subject  Revolutions Blog: 
July roundup?

"A log-linear regression in R predicted the gold-winning Olympic 100m 
sprint time to be 9.68 seconds (it was actually 9.63 seconds): 
http://bit.ly/QfChUh"

The original by Markus Gesmann can be found at
http://lamages.blogspot.pt/2012/07/london-olympics-and-prediction-for-10
0m.html

I've made the same, just changing the address to the 200m historical 
data, and the predicted time was 19.27. Usain Bolt has just made 19.32. 
If you want to check it, the address and the 'which' argument are:

url <- 
"http://www.databasesports.com/olympics/sport/sportevent.htm?sp=ATH&enum
=120"

Plus a change in the graphic functions' y axis arguments to allow for 
times around the double to be ploted and seen.

#
# Original by Markus Gesmann:
# 
http://lamages.blogspot.pt/2012/07/london-olympics-and-prediction-for-10
0m.html
library(XML)
library(drc)
url <- 
"http://www.databasesports.com/olympics/sport/sportevent.htm?sp=ATH&enum
=120"
data <- readHTMLTable(readLines(url), which=3, header=TRUE)
golddata <- subset(data, Medal %in% "GOLD")
golddata$Year <- as.numeric(as.character(golddata$Year))
golddata$Result <- as.numeric(as.character(golddata$Result))
tail(golddata,10)
logistic <- drm(Result~Year, data=subset(golddata, Year>=1900), fct =
L.4())
log.linear <- lm(log(Result)~Year, data=subset(golddata, Year>=1900))
years <- seq(1896,2012, 4)
predictions <- exp(predict(log.linear, newdata=data.frame(Year=years)))
plot(logistic,  xlim=c(1896,2012),
      ylim=range(golddata$Result) + c(-0.5, 0.5),
      xlab="Year", main="Olympic 100 metre",
      ylab="Winning time for the 100m men's final (s)")
points(golddata$Year, golddata$Result)
lines(years, predictions, col="red")
points(2012, predictions[length(years)], pch=19, col="red")
text(2012 - 0.5, predictions[length(years)] - 0.5, 
round(predictions[length(years)],2))

Rui Barradas

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