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For my Honors Thesis, I decided to create an Artificial Intelligence Project to predict Fantasy NFL Football Points of players and team's defense. I created a Tensorflow Keras AI Regression model and created a Flask API that holds the AI model, and a Django Try-It Page for the user to use the model. These services are hosted on ASU's AWS service. In my Flask API, it actively gathers data from Pro-Football-Reference, then calculates the fantasy points. Let’s say the current year is 2022, then the model analyzes each player and trains on all data from available from 2000 to 2020 data, tests the data on 2021 data, and predicts for 2022 year. The Django Website asks the user to input the current year, then the user clicks the submit button runs the AI model, and the process explained earlier. Next, the user enters the player's name for the point prediction and the website predicts the last 5 rows with 4 being the previous fantasy points and the 5th row being the prediction.
In the basketball world, perhaps one of the most sought-after feelings is that of momentum. Basketball players, coaches, analysts, and fans alike are all too familiar with the idea that a “team has momentum” during a stretch of time, or that the team needs to do something to “generate their own momentum”. In a game that appears to be an accumulation of independent possessions, what exactly does momentum really mean? My goal was to see if there is a way to quantify momentum in an NBA game, particularly by looking at the Phoenix Suns 2021-2022 NBA season.