The regular season for the ALeague in Australia concluded with Perth’s victory over Wellington at HBF Park (link).
Of the 135 games played, 113 of these were not lost by the team scoring first (84% of the games). 90 of these game were won outright by the team scoring first (67%) and 23 were drawn by the team scoring first (17%).
16 games were lost by the team scoring first. Three of these games were lost by the team that led by two goals (Brisbane in week 11, Central Coast in weeks 12 and 17). In week 17, Brisbane became the only team in 1000 games this season (six European leagues and the ALeague) to lose after leading by 3 goals.
I have a record of 420 goals scored in the regular season: 192 first half; 228 in the second half. 283 of the goals were scored by game winners, 80 by losing teams, and 57 in draws.
Earlier this week, Avinash Kaushik wrote about Responses to Negative Data (link). Shortly after his post was published, I found a link to a Turing Institute blog post, written by Franz Kiraly, What is a data scientific report? (link).
Both posts have helped me to think about the why, what and how of sharing observations, analyses and insights.
Franz, the author of the Turing blog post suggest that a stylised data report is characterised by:
Topic. Addresses a domain question or domain challenge in an application domain specific to a data set.
Aim. Data-driven answers to some domain question.
Audience. Decision-makers or domain experts interested in ‘evidence’ to inform decision-making.
Franz suggest five principles that inform good reporting:
Correctness and veracity
Clarity in writing
Reproducibility and transparency
Method and process
Application and context
Whilst there are some issues I take with Avinash’s and Franz’s posts, I do think they both raise some fundamental issues for us as we contemplate sharing our data-informed stories. I am particularly interested in how the curiosity and openness Avinash describes meets Franz’s five principles.
As I was concluding this post, up popped a link to Samuel Flender’s post How to be less wrong (link). This will be an excellent companion to the two posts discussed here. It also gives me an opportunity to extend my interest in Bayesian perspectives.