Leveraging Maching Learning & AWS to help you win your fantasy football league.
With the NFL season officially starting 8 September, we anticipate that at least a few of our classmates and/or colleagues are participating in Fantasy Football, either in a traditional league, daily fantasy, weekly pick'em contests, etc. As a point of reference it is estimated that Fantasy Football is a $18.6 Billion dollard market (https://www.sportsmanagementdegreehub.com/fantasy-football-industry/)
The overarching goal of this class (from our perspective at least) is to teach us the tools and skills needed to make smarter and faster data-based decisions. Whether this is looking at porfolio risk, stock tickers, or even football statistics these skill are certainly transferrable between various industries. In fact, we would be willing to make the argument that fantasy sports could be grouped into the larger fintech sector.
Our team has built a machine learning model to evaluate key statistics of player over time, and build a strategy that can be released via a Amazon Lex bot to help users draft their team, set their line-up and deliver a positive ROI from their initial investment.
Python 3.7 Streamlit App VS Code Jupyter Lab
This project leverages python 3.7 with the following packages:
- import streamlit as st
- import pandas as pd
- from sklearn.cluster import KMeans
- import matplotlib.pyplot as plt
- import plotly.express as px
- from PIL import Image
- pip streamlit
- pip pillow
- pip sklearn.cluster
- pip matplotlib.pyplot
- pip pandas
- pip plotly
To use the Place Sure Bet application from the file simply run in the command line *The recordings shows the command line prompt and running app:
running_app_command.mp4
Surebet_streamlit_app_Trim.mp4
Brought to you by:
- Sam Eberts (https://github.com/seberts12)
- Tracy Kellison Emory (https://github.com/emorytk)
- Jon Mitchell (https://github.com/JWM09)
- Chandler Schaak (https://github.com/schaakattack)
- Steven Shelton (https://github.com/steviej00)
Copyright 2022
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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