Scoring First and Losing: European football leagues 2018-2019

Early exposure to Charles Reep’s work has led me to a four decade fascination with the observation and analysis of goal-scoring in association football.

I have a particular interest in those games in which a team scores first and loses.

So far, in five of Europe’s major leagues (Bundesliga yet to start) there have been seven games of this type:

Time in minutes to equalise (TE), team that scores first (TSF), team that wins (TW).

I have explored these games with some R packages in R Studio. The packages are: tidyverse, ggplot2 and ggrepel.

My basic visualisations:

The Games

The scores in these games

The teams that overcame conceding the first goals:

The basic code I used to create these visualisations:

library(tidyverse)
library(ggplot2)
library(ggrepel)
df <- read.csv(“SFL.csv”)
df %>%
 arrange(TE)
ggplot(df, aes(x=League, y=TE, label = Game)) +
  geom_point(colour = “firebrick1”) +
  geom_label_repel(size = 3, colour = “black”, fontface =  “bold”) +
  ggtitle(“Games in which team scores first and loses”) +
  labs(subtitle = “European Leagues 2018-2019”) +
  xlab (“League”) + ylab (“Time Taken to Equalise (Minutes)”) +
  theme_minimal()

Photo Credit

AFC Bournemouth (Twitter)

Scoring patterns in #WorldCup Knockout Phases 2010-2018

The Quarter Finals are about to take place at the 2018 FIFA World Cup.

I have been using a naive Bayes approach to anticipate when goals might be scored in the knockout phase of the 2018 tournament.

I chose some priors from the outcomes of the 2010 and 2014 tournament knockout phases. The posteriors for these were:

2010

2014

My priors for 2018 were:

The posteriors for the Round of 16 were:

A comparison of Priors and Posteriors after Round of 16:

It will be interesting to see if this relationship changes in the forthcoming games … particularly in regard of extra time.

Temperature, humidity and ball in play time at 2018 FIFA #WorldCup after 20 games

There is a rich variety of data available on the 2018 FIFA World Cup website.

The FIFA live blog for each game records temperature and humidity.

After 20 games played in the tournament, I thought I would explore these data with regard to ball in play time in each game.

The data and the RCode I used are available on GitHub. This post is another learning out loud approach to my use of R and RStudio.

Temperature and Humidity for each of the 20 games:

Humidity and Ball in Play Time:

Temperature and Ball in Play Time:

These ggplots are created with secondary data. As with all my World Cup posts, I am mindful that I have not investigated the validity and reliability of these data. I do make some basic face validity assumptions about these data.

Photo Credit

_IGP5474 (Victor,  CC BY-SA 2.0)