A Cann Diagram to compare Elo Ratings and FIFA Rankings: 2018 World Cup

I have compiled a Cann Diagram to compare Elo Ratings and FIFA Rankings prior to the 2018 FIFA World Cup.

Jenny Cann shared her first diagram in 1998. In it she sought to visualise the gaps between teams in the English Premier League in terms of points.

My 2018 version uses rating and ranking positions:

Photo Credit

World Cup Brazil (Diego Sideburns, CC BY-NC-ND 2.0)

Predicting the outcome of the 2018 FIFA World Cup using Elo ratings


The 2018 FIFA World Cup starts in a week’s time. The first game is between Russia and Saudi Arabia in the Luzhniki Stadium in Moscow.

In the most recent World Football Elo Ratings (8 June 2018) Russia is rated 44 and Saudi Arabia 63. (The  FIFA Rankings place Russia 70 and Saudi Arabia 67.)

The Elo rating system for international football is calculated from the results of international A matches. The rating takes into account the kind of match, home team advantage, and goal difference in the match result.

I used Elo ratings in my analysis of the 2014 FIFA World Cup tournament.

Gracenote‘s predictions for 2018 and a paper written by Lorenz Gilch and Sebastian Müller use Elo ratings to propose outcomes for the tournament.


Gracenote uses the Elo ratings “to estimate the percentage chance of each match at the 2018 World Cup being a win, draw or loss for each of the teams”. These percentages are fed into a simulation of the World Cup “to produce estimates of the chance that each team reaches a particular stage of the competition”.

Gracenote will be run their simulations after each day’s matches to update their forecasts during the tournament.

Before the tournament:

  • Brazil has 21% chance of winning the tournament.
  • Spain, Germany and Argentinawill be Brazil’s main challengers.
  • 47% chance that the World Cup winners will be from a country other than Argentina, Brazil, France, Germany or Spain.
  • European and Latin American teams should dominate the knockout phase.

Lorenz Gilch and Sebastian Müller

Lorenz and Sebastian’s paper appeared on arxiv.org this week (7 June). It is titled ‘On Elo based prediction models for the FIFA World Cup 2018‘.

Their paper favours Germany as the tournament winners.

The abstract of their paper is:

We propose an approach for the analysis and prediction of a football championship. It is based on Poisson regression models that include the Elo points of the teams as covariates and incorporates differences of team-specific effects. These models for the prediction of the FIFA World Cup 2018 are fitted on all football games on neutral ground of the participating teams since 2010. Based on the model estimates for single matches Monte-Carlo simulations are used to estimate probabilities for reaching the different stages in the FIFA World Cup 2018 for all teams. We propose two score functions for ordinal random variables that serve together with the rank probability score for the validation of our models with the results of the FIFA World Cups 2010 and 2014.
Lorenz and Sebastian use a single Sankey diagram (Figure 4 in their paper) to visualise the probabilities of their models.


It is fascinating to observe the approaches used to predict tournament outcomes. The two examples shared here illustrate the range of expertise brought to sport analytics.

Gracenote started its football rating system in 2002. I will be following Simon Gleave’s tweets during the tournament to see how a dynamic prediction system works:

Lorenz’s Twitter account is sharing links to datatreker.com. I will be following Lorenz’s tweets too.

Photo Credit

New construction in St Petersburg (Ninara, CC BY 2.0)


After writing this post I found Kiko Llaneras and Borja Andrino’s article in EL PAIS (5 June 2018) ‘¿Quién ganará el mundial?‘. Their analysis has “La base de nuestro ranking es un método Elo, inspirado en el que se emplea en ajedrez y otros deportes”. There are three weighted Elo components:

  • Ranking Elo clásico (peso 50%)
  • Ranking Elo esperado (peso 30%)
  • Ranking Elo de jugadores (peso 20%)

Grazing on the periphery

It has been a great week for grazing … much of it enabled by Mara Averick’s open sharing.

It started with news of Alison Hill’s speakerdeck presentation.

Alison discusses courage, enchantment, permission, persistence and trust as elements of creative learning. She concludes with this slide:

What fascinated me about Alison’s presentation was her synthesis of profound ideas about sharing and learning with each other in an aesthetic that grabbed and held my attention for 94 slides.

She is part of a remarkable R community that shares openly.

Three other members of this community enabled even more grazing this week. Each offered me possibilities to extend my knowledge of visualisation using R.

Matt Dancho has shared the Anomalize package that enables a “tidy” workflow for detecting anomalies in time series data. There is a vignette for the package to share the process of identifying these events. I think this will be very helpful in my performance research as I investigate seasonal and trend behaviours.

Ulrike Groemping shared the prepplot package in which “a figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added”.  There is a detailed vignette to support the package.

Guangchuang Yu shared the ggplotify package that converts “plot function call (using expression or formula) to ‘grob’ or ‘ggplot’ object that compatible to the ‘grid’ and ‘ggplot2’ ecosystem”.  Guangchang shares a detailed vignette that illustrates the potential of the package.

Mara, Alison, Matt, Ulrike and Guangchuand epitomise for me the delights in open sharing. A post in The Scholarly Kitchen, written by Alice Meadows, added to my grazing on the margins of openly sharing.

In the post Alice shares a wide range of resources. She makes a particular mention of the Metadata 2020 project that is “a collaboration that advocates richer, connected, and reusable, open metadata for all research outputs, which will advance scholarly pursuits for the benefit of society.”

The opportunities for such collaboration are increasing as we find new ways to share synchronously and asynchronously. These become easier as we make a bold decision to think out loud and share our thoughts with others.

Alison’s presentation includes this slide as a stimulus for that sharing:

This sharing permits grazing for me in the sense of the word used in Leonard Cohen’s Preface to the Chinese translation of his collection of Beautiful Losers poems includes this passage:

When I was young, my friends and I read and admired the old Chinese poets. Our ideas of love and friendship, of wine and distance, of poetry itself, were much affected by those ancient songs. … So you can understand, Dear Reader, how privileged I feel to be able to graze, even for a moment, and with such meager credentials, on the outskirts of your tradition.

Photo Credits

Slide grabs from Alison Hill’s speakerdeck.

Pictures from Twitter and Beuth Hochschule.

Collaboration image from Alice Meadow’s post.