knitr::opts_chunk$set( collapse = TRUE, comment = "##", message = FALSE, warning = FALSE, echo = TRUE )

This vignette provides a brief introduction to the Poisson distribution for modeling count data along with a short case study about the goals scored at the 2018 FIFA World Cup. It is intended as an illustrative introduction that can be used as self-study material or as a class room exercise in a course covering GLMs.

The classic basic probability distribution employed for modeling count data is the Poisson distribution. Its probability mass function $f(y; \lambda)$ yields the probability for a random variable $Y$ to take a count $y \in {0, 1, 2, \dots}$ based on the distribution parameter $\lambda > 0$:

[ \text{Pr}(Y = y) = f(y; \lambda) = \frac{\exp\left(-\lambda\right) \cdot \lambda^y}{y!}. ]

The Poisson distribution has many distinctive features, e.g., both its expectation and variance are equal and given by the parameter $\lambda$. Thus, $\text{E}(Y) = \lambda$ and $\text{Var}(Y) = \lambda$. Moreover, the Poisson distribution is related to other basic probability distributions. Namely, it can be obtained as the limit of the binomial distribution when the number of attempts is high and the success probability low. Or the Poisson distribution can be approximated by a normal distribution when $\lambda$ is large. See @Wiki+Poisson for further properties and references.

In the `distributions3`

package Poisson distribution objects can be generated with
the `Poisson()`

function. Subsequently, the object can be handled like other distribution
objects in `distributions3`

: print; extract mean and variance; evaluate
density, cumulative distribution, or quantile function; or simulate random samples.

library("distributions3") Y <- Poisson(lambda = 1.5) print(Y) mean(Y) variance(Y) pdf(Y, 0:5) cdf(Y, 0:5) quantile(Y, c(0.1, 0.5, 0.9)) set.seed(0) random(Y, 5)

Using the `plot()`

method the distribution can also be visualized which we use here
to show how the probabilities for the counts $0, 1, \dots, 15$ change when
the parameter is $\lambda = 0.5, 2, 5, 10$.

par(mfrow = c(2, 2)) plot(Poisson(0.5), main = expression(lambda == 0.5), xlim = c(0, 15)) plot(Poisson(2), main = expression(lambda == 2), xlim = c(0, 15)) plot(Poisson(5), main = expression(lambda == 5), xlim = c(0, 15)) plot(Poisson(10), main = expression(lambda == 10), xlim = c(0, 15))

In this vignette we will illustrate how this infrastructure can be leveraged to obtain predicted probabilities for the number of goals in soccer matches from the 2018 FIFA World Cup.

To investigate the number of goals scored per match in the 2018 FIFA World Cup,
the `FIFA2018`

data set provides two rows, one for each team, for each of the 64 matches
during the tournament. In the following, we treat the goals scored by the two
teams in the same match as independent which is a realistic assumption for this
particular data set. We just remark briefly that there are also bivariate
generalizations of the Poisson distribution that would allow for correlated
observations but which are not considered here.

In addition to the goals, the data set provides some basic meta-information for the matches (an ID, team name abbreviations, type of match, group vs. knockout stage) as well as some further covariates that we will revisit later in this document. The data looks like this:

data("FIFA2018", package = "distributions3") head(FIFA2018)

For now, we will focus on the `goals`

variable only. A brief summary yields

summary(FIFA2018$goals)

showing that the teams scored between $0$ and $6$ goals per match with
an average of $\bar y = `r round(mean(FIFA2018$goals), digits = 3)`

$
from the observations $y_i$ ($i = 1, \dots, 128$). The corresponding
table of observed relative frequencies is:

observed <- prop.table(table(FIFA2018$goals)) observed

(Note that in recent versions of R using `proportions()`

rather than `prop.table()`

is recommended.)

This confirms that goals are relatively rare events in a soccer game with each team scoring zero to two goals per match in almost 90 percent of the matches. Below we show that this observed frequency distribution can be approximated very well by a Poisson distribution which can subsequently be used to obtain predicted probabilities for the goals scored in a match.

In a first step, we simply assume that goals are scored with a constant
mean over all teams and matches and hence just fit a single Poisson
distribution for the number of goals. To do so, we obtain a point
estimate of the Poisson parameter by using the empirical mean
$\hat \lambda = \bar y = `r round(mean(FIFA2018$goals), digits = 3)`

$
and set up the corresponding distribution object:

p_const <- Poisson(lambda = mean(FIFA2018$goals)) p_const

In the technical details below we show that this actually corresponds to the maximum likelihood estimation for this distribution.

As already illustrated above, the expected probabilities of observing counts of
$0, 1, \dots, 6$ goals for this Poisson distribution can be extracted using
the `pdf()`

method. A comparison with the observed empirical frequencies yields

expected <- pdf(p_const, 0:6) cbind(observed, expected)

By and large, all observed and expected frequencies are rather close. However, it is not reasonable that all teams score goals with the same probabilities, which would imply that winning or losing could just be attributed to "luck" or "random variation" alone. Therefore, while a certain level of randomness will certainly remain, we should also consider that there are stronger and weaker teams in the tournament.

To account for different expected performances from the teams in the 2018 FIFA World Cup,
the `FIFA2018`

data provides an estimated `logability`

for each team. These
have been estimated by @Zeileis+Leitner+Hornik:2018 prior to the start of the
tournament (2018-05-20) based on quoted odds from 26 online bookmakers using
the bookmaker consensus model of @Leitner+Zeileis+Hornik:2010. The `difference`

in
`logability`

between a team and its opponent is a useful predictor for the
number of `goals`

scored.

Consequently, we fit a generalized linear model (GLM) to the data that links
the expected number of goals per team/match $\lambda_i$ to the linear predictor
$x_i^\top \beta$ with regressor vector $x_i^\top = (1, \mathtt{difference}_i)$
and corresponding coefficient vector $\beta$ using a log-link:
$\log(\lambda_i) = x_i^\top \beta$. The maximum likelihood estimator $\hat \beta$
with corresponding inference, predictions, residuals, etc. can be obtained
using the `glm()`

function from base R with `family = poisson`

:

m <- glm(goals ~ difference, data = FIFA2018, family = poisson) summary(m)

Both parameters can be interpreted. First, the intercept
corresponds to the expected log-goals per team in a match of two equally strong teams,
i.e., with zero difference in log-abilities. The corresponding prediction for the
number of goals can either be obtained manually from the extracted `coef()`

by applying `exp()`

(as the inverse of the log-link).

lambda_zero <- exp(coef(m)[1]) lambda_zero

Or equivalently the `predict()`

function can be used with `type = "response"`

in order to get the expected $\hat \lambda_i$ (rather than just the linear
predictor $x_i^\top \hat \beta$ that is predicted by default).

predict(m, newdata = data.frame(difference = 0), type = "response")

As above, we can also set up a `Poisson()`

distribution object and obtain the
associated expected probability distribution for zero to six goals in
a mathc of two equally strong teams:

p_zero <- Poisson(lambda = lambda_zero) pdf(p_zero, 0:6)

Second, the slope of $`r round(coef(m)[2], digits = 3)`

$ can be interpreted
as an ability elasticity of the number of goals scored. This is because the
difference of the log-abilities can also be understood as the log of the ability
ratio. Thus, when the ability ratio increases by $1$ percent, the expected
number of goals increases approximately by $`r round(coef(m)[2], digits = 3)`

$
percent.

This yields a different predicted Poisson distribution for each team/match
in the tournament. We can set up the vector of all $128$ `Poisson()`

distribution objects by extracting the vector of all fitted point estimates
$(\hat \lambda_1, \dots, \hat \lambda_{128})^\top$:

p_reg <- Poisson(lambda = fitted(m)) length(p_reg) head(p_reg)

Note that specific elements from the vector `p_reg`

of Poisson distributions
can be extracted as usual, e.g., with an index like `p_reg[i]`

or using the
`head()`

and `tail()`

functions etc.

As an illustration, the following goal distributions could be expected for the FIFA World Cup final (in the last two rows of the data) that France won 4-2 against Croatia:

tail(FIFA2018, 2) p_final <- tail(p_reg, 2) p_final pdf(p_final, 0:6)

This shows that France was expected to score more goals than Croatia but both teams scored more goals than expected, albeit not unlikely many.

Assuming independence of the number of goals scored, we can obtain
the table of possible match results (after normal time) by multiplying
the marginal probabilities (again only up to six goals). In R this be done
using the `outer()`

function which by default performs a multiplication
of its arguments.

res <- outer(pdf(p_final[1], 0:6), pdf(p_final[2], 0:6)) round(100 * res, digits = 2)

For example, we can see from this table that the expected probability for
France winning against Croatia 1-0 is $`r round(100 * res[2, 1], 2)`

$ percent
while the probability that France loses 0-1 is only $`r round(100 * res[1, 2], 2)`

$
percent.

The advantage of France can also be brought out more clearly by aggregating the probabilities for winning (lower triangular matrix), a draw (diagonal), or losing (upper triangular matrix). In R these can be computed as:

sum(res[lower.tri(res)]) ## France wins sum(diag(res)) ## draw sum(res[upper.tri(res)]) ## France loses

Note that these probabilities do not sum up to $1$ because we only considered up to six goals per team but more goals can actually occur with a small probability.

Next, we update the expected frequencies table by averaging across the expectations per team/match from the regression model.

expected <- pdf(p_reg, 0:6) head(expected) expected <- colMeans(expected) cbind(observed, expected)

As before, observed and expected frequencies are reasonably close, emphasizing that
the model has a good marginal fit for this data. To bring out the discrepancies graphically
we show the frequencies on a square root scale using a so-called *hanging rootogram*
[@Kleiber+Zeileis:2016]. The gray bars represent the square-root of the observed
frequencies "hanging" from the square-root of the expected frequencies in the red line.
The offset around the x-axis thus shows the difference between the two frequencies
which is reasonably close to zero.

par(mar = c(4, 4, 1, 1)) bp <- barplot(sqrt(observed), offset = sqrt(expected) - sqrt(observed), xlab = "Goals", ylab = "sqrt(Frequency)") lines(bp, sqrt(expected), type = "o", pch = 19, lwd = 2, col = 2) abline(h = 0, lty = 2)

Finally, we want to point out that while the log-abilities (and thus their differences)
had been obtained based on bookmakers odds prior to the tournament, the calibration of the
intercept and slope coefficients was done "in-sample". This means that we have used the
data from the tournament itself for estimating the GLM and the evaluation above can only be
made *ex post*. Alternatively, one could have used previous FIFA World Cups for calibrating
the coefficients so that probabilistic forecasts for the outcome of all matches (and thus
the entire tournament) could have been obtained *ex ante*. This is the approach used by
@Groll+Ley+Schauberger:2019 and @Groll+Hvattum+Ley:2021 who additionally added further
explanatory variables and used flexible machine learning regression techniques rather than
a simple Poisson GLM.

Fitting a single Poisson distribution with constant $\lambda$ to $n$ independent observations $y_1, \dots, y_n$ using maximum likelihood estimation can be done analytically using basic algebra. First, we set up the log-likelihood function $\ell$ as the sum of the log-densities per observation:

[
\begin{align*}
\ell(\lambda; y_1, \dots, y_n) & = \sum_{i = 1}^n \log f(y_i; \lambda) \
\end{align*}
]

For solving the first-order condition analytically below we need the score function, i.e., the derivative of the log-likelihood with respect to the parameter $\lambda$. The derivative of the sum is simply the sum of the derivatives:

[
\begin{align*}
\ell^\prime(\lambda; y_1, \dots, y_n)
& = \sum_{i = 1}^n \left{ \log f(y_i; \lambda) \right}^\prime \
& = \sum_{i = 1}^n \left{ -\lambda + y_i \cdot \log(\lambda) - \log(y_i!) \right}^\prime \
& = \sum_{i = 1}^n \left{ -1 + y_i \cdot \frac{1}{\lambda} \right} \
& = -n + \frac{1}{\lambda} \sum_{i = 1}^n y_i
\end{align*}
]

The first-order condition for maximizing the log-likelihood sets its derivative to zero. This can be solved as follows:

[
\begin{align*}
\ell^\prime(\lambda; y_1, \dots, y_n) & = 0 \
-n + \frac{1}{\lambda} \sum_{i = 1}^n y_i & = 0 \
n \cdot \lambda & = \sum_{i = 1}^n y_i \
\lambda & = \frac{1}{n} \sum_{i = 1}^n y_i = \bar y
\end{align*}
]

Thus, the maximum likelihood estimator is simply the empirical mean $\hat \lambda = \bar y.$

Unfortunately, when the parameter $\lambda$ is not constant but depends on a linear predictor
through a log link $\log(\lambda_i) = x_i^\top \beta$, the corresponding log-likelihood of the
regression coefficients $\beta$ can not be maximized as easily. There is no closed-form
solution for the maximum likelihood estimator $\hat \beta$ which is why the `glm()`

function
employs an iterative numerical algorithm (so-called iteratively weighted least squares)
for fitting the model.

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