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 […]
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 […]
Your first reaction when hearing this question will probably be “of course, duh” (or “What is AFL?” if you misclicked). You have to think that the team coming away with the ball the most would score the most points in general. With this project I wanted to see if the data supported what seems to be kind of obvious. The […]
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. […]