Olympics: 200m Men Final
Continuing on with fun, if silly, analyses: a little voice in my head suggests a time series model and, rather than putting any thought into, I'll use some R-goodness. Setting up the data as Rui provided, we need to add some NA's to account for WWII: library(zoo) golddata.ts <- as.ts(zoo(golddata[,6], order.by = golddata[,1])) # Here we let Gabor and Achim think about how get the NAs in there smoothly library(forecast) golddata.model <- auto.arima(golddata.ts) # Prof Hyndman has forgotten more about time series than I will ever know summary(golddata.model) # ARIMA(2,1,0)+drift seems a bit heavy handed, but that's what it gives forecast(golddata.model, 1) Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 2012 19.66507 19.23618 20.09396 19.00915 20.321 # But looking at a graph, this seems to have an odd jump up plot(forecast(golddata.model)) # Maybe we overfit -- let's kill the drift golddata.model2 <- auto.arima(golddata.ts, allowdrift = FALSE) summary(golddata.model2) # ARIMA(1,1,0) seems better plot(forecast(golddata.model2)) # I like the graph more too forecast(golddata.model, 2) Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 2012 19.56139 19.04134 20.08145 18.76604 20.35674 Not so very good at all, but a little bit of R fun nevertheless ;-) And in the category of "how good is your prediction when you already know the answer and don't care at all about statistical rigor", it seems that "regress on year" might still be winning. Anyone want to take some splines out for a spin? Cheers, Michael
On Thu, Aug 9, 2012 at 11:31 PM, Mark Leeds <markleeds2 at gmail.com> wrote:
Hi Rui: I hate to sound like a pessimist/cynic and also I should state that I didn't look at any of the analysis by you or the other person. But, my question, ( for anyone who wants to chime in ) is: given that all these olympic 100-200 meter runners post times that are generally within 0.1-0.3 seconds of each other or even less, doesn't it stand to reason that a model, given the historical times, is going to predict well. I don't know what the statistical term is for this but intuitively, if there's extremely little variation in the responses, then there's going to be extremely little variation in the predictions and the result is that you won't be too far off ever as long as your predictors are not too strange. !!!!! ( weight, past performances, height, whatever ) Anyone can feel free to chime in and tell me I'm wrong but , if you're going to do that, I'd appreciate statistical reasoning, even though I don't have any. thanks. mark On Thu, Aug 9, 2012 at 4:23 PM, Rui Barradas <ruipbarradas at sapo.pt> wrote:
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-100m.html<http://lamages.blogspot.pt/2012/07/london-olympics-and-prediction-for-100m.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<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-100m.html<http://lamages.blogspot.pt/2012/07/london-olympics-and-prediction-for-100m.html> library(XML) library(drc) url <- "http://www.databasesports.**com/olympics/sport/sportevent.** htm?sp=ATH&enum=120<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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______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.