Collecting data from the Women’s Football World Cup 2019

I am delighted there are so many ways to collect data from this year’s Women’s World Cup football tournament (FIFA link). I have data going back to the 2011 Tournament (link). These are shared on my blog and on Google Sheets for ease of access. I have posted some early tweets on Twitter too (link) and I have a GitHub repository (link). I am monitoring the # tag #FIFAWWC (link).

Overnight, I started searching for other links to the data.

I noticed a feed from the FAW Analysis Office in Newport announcing work on their analysis of the Tournament (link). The same night, I heard of WomensFootyStat (link) and their use of Opta data to provide “shotmaps, xG timeline, and pass maps for now. Tweeting manually for now but hoping to automate soon and get them out straight after each game”. Some weeks earlier, I heard about the StatsBomb Open Data project and the availability of data on their GitHub account (link). Ron Smith (link) and Ben Mayhew (link) are tweeting regularly.

I am trying to learn more about R and RStudio during this World Cup and have started searching for other papers on this topic. I was delighted to find a post on RBloggers by Achim Zeileis (link) that referred in detail to a paper by Andreas Groll and coplleagues’  (2019) paper “Hybrid Machine Learning Forecasts for the FIFA Women’s World Cup 2019”, arXiv:1906.01131, arXiv.org E-Print Archive (link).

In their paper, their abstract notes that they combine:

two different ranking methods together with several other predictors in a joint random forest approach for the scores of soccer matches. The first ranking method is based on the bookmaker consensus, the second ranking method estimates adequate ability parameters that reflect the current strength of the teams best. The proposed combined approach is then applied to the data from the two previous FIFA Women’s World Cups 2011 and 2015. Finally, based on the resulting estimates, the FIFA Women’s World Cup 2019 is simulated repeatedly and winning probabilities are obtained for all teams. The model clearly favors the defending champion USA before the host France.

I need to look up this paper in more detail now and hope to find lots more connections and open sharing, including the Hope Floats blog (link) that is collecting as many voices as it can about the World Cup in sixty days. I will be keeping an eye on what Simon Gleave is doing too (link).

My Project

Back in June 2008, I started writing this WordPress blog (link). I had written on other blogs before and had first dipped my toes with Geocities in the late 1990s.

In 2008, I was emboldened by CCK08 (link) to explore thoughts openly about learning in a digital world. I had not considered that what I wrote would be of interest to any other reader. It was framed by the delight of thinking out loud.

This delight in thinking out loud led me to explore many ways to share openly through emerging cloud resources. Many of these accounts remain and include wikis, talks, slides, documents and data. I was even naive enough to start Facebook pages for some of my units.

Another preoccupation of mine has been the linking of ideas about learning, coaching and performing enriched by my formative experiences of social sciences, teacher education, human movement studies, performance analysis and analytics. This has led me to think deeply about how ideas are formed in social contexts. Many of my posts are about how performance analysts and their collaborators emerged at particular times and particular places and constructed knowledge.

My blog at Clyde Street continues to be my platform for this sharing. I hope to add many more posts to the 1800 produced already. My new guide is the R community that is providing exciting ways to share openly and my old guide, the ever inspiring, Stephen Downes (link).

It has been fascinating how this project has emerged and changed.

Photo Credit

Blue sky thinking (Keith Lyons, CC BY 4.0)

Microcontent: triads, facilitators and a micro-campus

Distant view

Stephen Downes (link) has been exploring microlearning in some of his recent newsletter postings.

Today he has linked to a Paul Greatrix post about the concept of a micro-campus connected to tertiary education’s outreach plans (link).

Stephen points out that in an earlier iteration of this discussion, Stephen himself has considered a triad model to explore this move to a facilitation of learning by a course broker (link).

In my particular area of interest, sport, I see enormous opportunities for co-operation and collaboration in this space. There are so many shared interests in designing and facilitating microlearning to a burgeoning sport ‘industry’ with the learner deciding ‘why?’, ‘when?’, ‘what?’ and ‘how?’

Photo Credit

Giovanni Corti on Unsplash