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
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 […]
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 […]
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 […]