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
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 […]
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. […]
Computing my disc golf power rankings is not a super complicated task, but it does have a few steps. I take a player’s placement in a particular tournament and give it a point value. I then adjust that value based on the tournament tier (major, national tour, ‘A’, ‘A/B’ etc. Excludes everything ‘B’ and below). I then alter the value […]
Each of the major disc manufacturers have attempted to put together a strong stable of players to get out on the courses (and social media) and promote their brands. Which of these teams have found the most tournament success? I totalled the prize money won (as of the end of May, 2021) by sponsored players and this is what I […]
Even though none of us outside of Santa Cruz could watch it live, Masters Cup caused some interesting movement in my world famous power rankings. (If you are a data science type, I used some beautiful soup, pandas, SQL… y’know. Stuff like that. And, no, I didn’t use them well. I tried though. You can check out what all I […]
Big weekend in disc golf. US Women’s Championships for the women, and watching the US Women’s Championships for the men. Some definite movement in my most glorious of power rankings. (If you are a data science type, I used some beautiful soup, pandas, SQL… y’know. Stuff like that. And, no, I didn’t use them well. I tried though. You can […]