Chapman Football Blog 🏈

Sometime in the mid ’90s, while I was playing high school football, I read an article in Sports Illustrated. The article was about a division III college football coach who was taking a non-traditional approach to coaching. The article is lost to me, as I could not find it during an internet search, but the premise stuck with me all this time. In the article, as far as I recall, the coach would encourage/require the players to make daily affirmations. He would stride through formation during pre-practice stretching, talking about how beautiful the day was, and that each and every player belongs there. He would make them repeat phrases aloud. The intent was clear, to me at least, that he was using those affirmations to prepare the players for the practice. To stay focused and build confidence. It seemed to have worked as well. The program was successful, winning many games, and stood in stark contrast to traditionally run programs. For example, the one I was playing for at the time. A top down dictatorship, with strict adherence to what the coaches commanded, all coming from a hard nosed head coach who would lose his temper and scream at people who made mistakes. Fear was one of their main tools of motivation. ...

TLDR: Some basic expoloritory analysis with 30 Rock Data. Introduction In this post we will explore some 30 Rock data. The data includes imdb ratings, viewership numbers, and writers of the episodes. We will take a step by step approach to creating plots for ratings and viewership over time. Libraries For this analysis we will be using the below Python libraries. Pandas for working with data frames matplotlib.pyplot for creating plots and adjusting features matplotlib.mdates for working with date formatting import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdates Create Data Frames The dataset from kaggle came in two csv files. The initial step is to read the csv files into pandas dataframes using the .read_csv method. It is important to know what type of data the dataframes contain. Using pandas’ .dtypes property shows the column name and what data type the values are. Comparing the results between the two data frames, it is clear that there is some overlap in the data. Both data frames have columns that hold values for season, episodes, title, and airdates. This information is important when the data frames are merged together. Another interesting thing that is shown by viewing the data types is that the column original_air_data is showing as on object; it might be more helpful for it to be a datetime data type. ...

TLDR: Create a plot that shows the win probability throughout the 2024 Rose Bowl Game using data from College Football Database API. You can click here to go to the full code. Introduction What follows is the step by step approach that I took to plot the win probability for both teams in the 2024 Rose Bowl Game. The task is pretty straight forward. The api provides the exact data needed for the plot and one could make this plot with one api call. However I did seek to add a little more to the plot that I believe enhances the representation. Things like using the team colors for the lines, adding the final score with logos, and using quarter endings as the x tick marks. To achieve this there was additional data needed and a little extra work that needed to be done. ...

Preamble The objective of this project was to provide a job description and resume to the OpenAI API. The API would then return a short paragraph stating whether the resume was suitable for the job. To accomplish this, a single webpage was created that makes a request to an AWS Lambda function which processes the request and returns OpenAI’s response. For brevity the code examples that follow will only contain code necessary to explain the concept being discussed. When available, relevant links to documentation, or helpful articles will be provided. ...