---
title: "formula1: top 9 drivers"
output: html_document
---

```{r setup, include=FALSE}
set.seed(0)
knitr::opts_chunk$set(echo = TRUE)
library("hyper2")
library("magrittr")
options("digits" = 5)
options(width = 120)
```

## Formula 1: top 9

(this document is largely the same as `formula1.Rmd` but considers
only the top 9 drivers).

Formula 1 racing is an important and prestigious motor sport.  Here I
analyse a series of racing results using the `hyper` package which
implements the Plackett-Luce likelihood function.

Consider the following dataset, taken from Wikipedia:

```{r cars}
f2017 <- read.table("formula1_2017.txt",header=TRUE)
n <- 9  # top 9
f2017 <- f2017[seq_len(n),]
f2017
```

Now process it to give the ranks:

```{r}
f <- function(x){
	noscore <- c("Ret", "WD", "DNS", "DSQ", "DNP", "NC")
	x <- as.character(x)
	x[x %in% noscore] <-  "0"
	x <- as.numeric(x)
	wanted <- x != 0
	xw <- x[wanted]
	xw[order(xw,decreasing=FALSE)] <- seq_along(xw)
	x[wanted] <- xw
	x
}
o  <- apply(f2017[,seq_len(20)],2,f)
rownames(o) <- rownames(f2017)
(f2017 <- o)
```

Each row is a driver and each column (after the first) a venue.  We
see that Hamilton, the first row, came second in Australia, first in
China, second in Bahrain, fourth in Russia, and so on.  In the first
column, we see the result from Australia (AUS) in which Hamilton came
second, Vettel first, Bottas third, and so on.  Notation used also
includes six different classes of no-score such as "Ret" for retired,
"WD" for withdrawn, and so on.

We can represent this data in a different form using
`wikitable_to_ranktable()`:

```{r}
wikitable_to_ranktable(f2017)
```

Looking at the first column ("c1" is read "came first") we see who
came first at each venue: Vettel in Australia (AUS), Hamilton in China
(CHN), Vettel in Bahrain (BHR), and so on.  Now create a likelihood
function `H`:

```{r,calcH,cache=TRUE}
H <- hyper2(pnames=rownames(o))
 for(i in seq_len(ncol(o))){
	H <- H + ordervec2supp(o[,i])
}
head(H)
```
Above, we see a small part of the Placket-Luce likelihood function for
the 2017 results, just the top 9 drivers.  We can find the maximum
likelihood estimator by using the `maxp()` function:


```{r,maxp2017,cache=TRUE}
mL2017 <- maxp(H)
mL2017
dotchart(mL2017,pch=16,main='2017 Formula 1')
pie(mL2017,main='2017 Formula 1')
```

We see Hamilton being the strongest, followed by Vettel and Bottas who
are approximately equal.

# Analysis of nulls:

## Hypothesis: all players have equal strengths

Here we write $H_0\colon p_1=p_2=\cdots =p_n=\frac{1}{n}$.

```{r,equalstrength,cache=TRUE}
equalp.test(H)
```

highly signficant!

## Null of Hamilton being average

We can consider Hamilton and ask various questions about his playing
strength.  One natural null hypothesis would be that Hamilton's
strength is equal to the mean strength, or $\frac{1}{9}$.  (we attempt
to reject the null $H_0$ against the alternative of unconstrained
$p$).  Noting that the evaluate for Hamilton's strength
$\widehat{p_\mathrm{Hamilton}}$ is about 0.28, we ask how much support
is lost, relative to the support at the unconstrained evaluate.

```{r,specifichamiltonstrength,cache=TRUE}
specificp.test(H,"Hamilton")  # default value is 1/9=11%
```

(note that the constrained maximum is on the boundary of the
admissible region with $p_\mathrm{Hamilton}=\frac{1}{9}$).


# Conditions

```{r,loadtable}
(details <- read.table("formula1_conditions.txt",header=TRUE))
drivers <- pnames(formula1)
```

At this point, we introduce a new player: `Ham_wet`, for "Hamilton in
the wet", and we can test the hypothesis that the `Hamilton ==
Ham_wet`.  We will cycle through all the venues and, if it is wet
(according to dataframe `conditions`) we will credit Hamilton's
placing to `Ham_wet`.

```{r,hamiltonwetanddry,cache=TRUE}
details <- read.table("formula1_conditions.txt",header=TRUE)
alldrivers <- c("Ham_dry","Ham_wet",rownames(f2017)[-1])
(L2017 <- hyper2(pnames=alldrivers)) # uninformative
for(i in seq_len(ncol(f2017))){
	venue <- colnames(f2017)[i]
	conditions <- details[which(details$venue == venue),4]
	o <- f2017[,i]
	names(o) <- rownames(f2017)
	if(conditions == "wet"){
	   names(o)[names(o) == "Hamilton"] <- "Ham_wet"
	} else if(conditions == "dry"){
	   names(o)[names(o) == "Hamilton"] <- "Ham_dry"
	} else {
	   stop("neither wet nor dry?")
	}
	print(o)
	L2017 %<>% order_obs(o)
}  # for(i) loop closes
L2017
```

We can now test the hypothesis that Hamilton's performance in the wet
is the same as his performance in the dry:

```{r,hamwetdrytest,cache=TRUE}
samep.test(L2017,c("Ham_wet","Ham_dry"))
```
## Try the same thing but with function pwa()


```{r,trypwa,cache=TRUE}

details <- read.table("formula1_conditions.txt",header=TRUE)

H <- hyper2(pnames=c(rownames(f2017),"S"))
noscore <- c("Ret", "WD", "DNS", "DSQ", "DNP", "NC")

for(i in seq_len(ncol(f2017))){
	y <- f2017[,i]
	cond <- details$conditions[i]
        jj <- ordervec2supp(y)
	if(cond == "dry"){
            pnames(jj) <- pnames(H)  # do nothing!
	} else if (cond == "wet"){
            jj <- pwa(jj,"Hamilton")
	} else {
            stop("neither wet nor dry?")
	}  # if wet closes
        H <- H + jj
} # i loop closes
head(H)
maxp(H)
```
Now we can test the hypothesis that Hamilton's wet strength `S` is zero:

```{r,testwetstrengthzero}
pnames(H)
specificp.gt.test(H,"S",0)
```

see this

