• jared@discsanddata.com
  • Siem Reap, Cambodia

These posts are the most datalicious...

Understanding the Metrics: Power Rankings, Threat Index and Strong Field Average Finish

Over on my disc golf data playground, www.discsanddata.ga, I have given visitors the opportunity to check out some metrics I have compiled using the disc golf data I have collected. The three metrics on the site look at tournament results from three different angles. Of course, the best players are represented in all of them, but the different inclusions and […]

How Much Is a Disposal Worth?

Here I work with play-by-play data through the Australian Football League’s week 21 (795,944 rows of data) to look at how much individual players’ disposals contribute to scoring.1 I take two different approaches. First, I look at how many points disposals translate into. For each player who has at least 400 disposals this year, how many points are scored, on […]

Which AFL Kicker-Marker Pairs Have Tallied the Most Forward-50 Marks through round 20?

In this post I am only answering the question in the title. If you are curious How I got to these answers, you can take a look at my notebook on my github (https://github.com/jaredcahners/Round-20-F50-Marking-Pairs). Here is the answer in dataframe form. ‘Count’ is the total number of forward-50 marks a pairing has achieved. Some games were left out of the […]

Pythagorean Expectation and Australian Rules Football

Pythagorean expectation, attributed to baseball statistician Bill James is a formula originally used to describe a relationship between the number of runs a baseball team scores and allows, and the team’s winning percentage. The basic formula is… Winning Percentage = (Runs Scored)2/((Runs Scored)2 + (Runs Allowed)2) For example1 , the 2002 New York Yankees scored 897 runs and allowed 697. […]