Profiling the 2017 #AFLGF Teams

Introduction

I have kept a record of each quarter played in this year’s AFL competition. My data are compiled manually from the official AFL site.

On the eve of the Grand Final, I have explored the median profiles of both teams in the Final, Adelaide and Richmond. The odds offered by one bookmaker on Friday, 29 September were: Adelaide $1.73, Richmond $2.15.

Darren O’Shaughnessy puts the likelihood of Adelaide winning the AFL Grand Final at 59.1%. Tony Corke provides an overview of predictions for the Final and concludes “Adelaide are forecast to score between 86 and 90 points, and Richmond between 82 and 88 points”.

The Teams

My data are presented here as box plots generated with the BoxPlotR web-tool:

Adelaide

Richmond

Adelaide Richmond Direct Comparison

Conclusion

Although I have kept a record of every quarter played this season in the AFL, I have not watched one minute of play. I do not read any of the newspaper coverage of the game nor do I listen to any of the football programs during the season.

I hope to find patterns in AFL scoring data and use only one indicator, the score at the end of each quarter of the game. For Adelaide and Richmond, these data give me these median profiles for each quarter of the games played. (Note that I have corrected these data 30 September.)

 

In terms of “all other things being equal“, this gives me a three goal advantage to Adelaide (18 points) (Note I revised this figure pre-game after reviewing my data). I will not be watching the Grand Final but, post event, I will be interested to learn about:

  • The contest in the 1Q
  • Whether Richmond stayed with Adelaide in the 2Q
  • If Richmond lifted in 3Q

All of which would mean it would come down to which team had the legs and tactical nous in 4Q.

Otherwise we do have a predictable outcome (other things being equal), an Adelaide win. A win for Richmond will lead to fascinating conversations about readiness to perform.

Whoever wins, the day for me, in large part, will be spent remembering Phil Walsh.

Photo Credits

Grand Final Parade (AFL, Twitter)

Phil Walsh (Wikipedia, Fair Use)

Introduction to R and ggplot2 with Scottish Hill Race Data

A photograph of a hill race in Scotland on Kirk Craigs

I have been fascinated by the impact 35 records of Scottish hill racing from 1984 have had following their publication by Anthony Atkinson in 1986.

I have produced a Google Doc to use these data as an introduction to R and ggplot2.

I thought this might act as a microcontent resource for the OERu course in Sport Informatics and Analytics, particularly in regard to the pattern recognition (Using R) and audiences and messages (Visualising data) themes.

Photo Credit

Kirk Craigs Christmas Cracker (Ross Branigan, CC BY-NC 2.0)

Spring cleaning #OERuSIA ready for #Abbotsthon17

A frame grab of the landing page for the Sport Informatics and Analytics (#OERuSIA) course on WikiEducator.

I have been getting ready for #Abbotsthon17 in October in Dublin at the HPX 2017 Knowledge Exchange Conference.

As part of the day’s workshop, I have planned an autoresponse pre-workshop sharing of information about Sport Informatics and Analytics. I am using an OERu course to do this (#OERuSIA).

I have spent the last week Spring cleaning the course and making sure I am using the appropriate WikiEducator pedagogical iDevice templates to structure course content.

An example of the IDevices used in WikiEducator

The process has enhanced my interest in the open sharing of microcontent. I am looking forward to learn how the #Abbotsthon17 participants have enjoyed the experience.

The WikiEducator course template includes the opportunity to list an outline of the course with all the sections listed.

The Sport Informatics and Analytics outline can be found here. As of today there are 100 mocrocontent parts of the course.

As an open resource licensed under a Creative Commons license CC BY-SA 3.0, I think these microcontents could contribute to an infinite collection of resources focused on performance in sport.

Postscript

Shortly after posting this, Wayne Mackintosh was writing on the ICDE blog about micro-credentials in open online courses. In his post he notes:

The OERu assembles open online courses from OER and open access materials designed for independent study. Learners can study OERu courses online for free from anywhere in the world. Learners only pay for assessment, if and when they are ready for it. OERu partner institutions award transcript credit for assessed learning. OERu partners have developed a system for transnational credit transfer that operates within existing institutional policies. Successful learners can have their credits recognised towards designated qualifications based on credit transfer and credit accumulation agreements between OERu institutions.

I see enormous opportunities in this approach for learners with different amounts of time to invest in their learning journeys.