and the numbers that love them...
and some other numbers...
These posts are the most datalicious...
This was my final project for the “Applied Plotting, Charting and Data Representation” course (part of the “Applied Data Science with Python Specialization” on Coursera). Not the most exciting piece of work, but it was cool I got to use the olympic data I hadn’t really worked with since my SQL specialization. Here is a basic bar graph looking at […]
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 […]
I know have a place where you and I can go play with my disc golf data. Discs and Data REMIX! (and I made it in Flask!) Currently I only have a player head-to-head tool, but more is coming… https://www.discsanddata.ga
It was a normal morning in red zone Cambodia. I heard that police checkpoints were becoming more numerous, even stopping food delivery drivers. I would need to think about this soon. Especially as I was short on mixers. Cocktails by the pool weren’t as nice when your cocktail was a coffee mug of cheap vodka. I also should have done […]
So I was chatting in a Clubhouse room the other day and someone said the craziest thing… (BTW I love me my Clubhouse) He said that he had read somewhere that there was actually no home field advantage in football (soccer). There may be some tilting in results toward home teams, but… p-values… not statistically significant… yadayada… This I found […]
Sometimes you can’t just Python to Python. Hopefully you’re studying Python (or another computing language) because it connects to the way you think in some way. But you shouldn’t always Python just to Python. There needs to be something more. Python is a language, like any other language you would study. It can be very frustrating. You look at some […]
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. […]