A year ago today, I was celebrating Leicester City’s Tactical Insights day.
I thought it was a delightful adventure in sharing and exploring data.
Tonight, under the leadership of Sam Robertson, it is the Western Bulldogs’ opportunity to be innovative.
The football club and the City of Ballarat are hosting a weekend #hackathon.
There is a website to provide information.
Some of the data used at the event will not be in the public domain and attendees will sign a non-disclosure agreement.
The fun starts this evening at 6pm at the Ballarat Library.
I am hoping it has this kind of energy found in Ballarat.
I have spent much of the day compiling a bicycle data resource.
I am hopeful this will be a helpful micro-content resource for the OERu course Sport Informatics and Analytics.
Some time ago we used New York Citi Bike data as a practice for creating a neural network in R. I am keen to revisit this work and today’s research has been part of the project.
The new content includes:
I do think these data will be of interest for generic and domain specific data science activities. I found a 2014 paper by Jake Vandeplas a good place to start. He writes “this post is as much about how to work with data as it is about what we learn from the data” (original emphases).
The Bicycle November Project 10 & 11 (Mike Logsdon)
There were 200 goals scored in the 2016-2017 W-League Competition.
191 in the regular season
9 in the Knockout games
Goals By Time Interval
In six time intervals (15 minutes each):
The Canberra v Melbourne City game involved two periods of extra time. Melbourne City scored in the second period of extra time.
Goal Scoring Data
I have compiled details about the goals scored in a Google Sheet.
My record includes an ordered list of when each goal was scored. There are hyperlinks to the official W-League website to each game within this table to assist with ant secondary data analysis.
Before the Final (Westfield W-League on Twitter)
Champions (Westfield W-League on Twitter)