I’ve enhanced my skills in working with CSV files in n8n, which has deepened my understanding of how data flows into the platform and what capabilities it offers. n8n is a powerful tool for creating automations, and mastering CSV imports has allowed me to streamline data processing for Fantasy Football projects. The key takeaway is how to structure CSV files, handle headers, and map data fields effectively to automate workflows. For instance, I learned to use n8n’s CSV importer to extract player stats, team records, and match schedules, then connect these to triggers like game outcomes or player performance metrics. This process involves identifying relevant columns, ensuring data integrity, and using n8n’s built-in functions to transform raw data into actionable insights. Looking ahead, I’m excited to explore integrating n8n with other data sources, such as APIs or databases, to build more complex automations. Additionally, I’m interested in experimenting with n8n’s advanced features like conditional logic or custom node development to further enhance my workflows.
My Additions
So, I have been really trying to figure out how to make an aggregator for NFL Fantasy news and additional information which links up with a Yahoo Fantasy Sports sheet to draft a daily fantasy team. There are a lot of components which I need to set up still, but this project is coming along now. The main idea is to get news, utilize that news to make an educated guess on how a player will perform, then use that possible information to find undervalued players at different positions and create a team that is still within budget.
This is a continuation of a previous project I did within Google Sheets, where I would create a spreadsheet and solve for the best possible team based on the projected/predicted points scored by the players. The difficulty was trying to project the points that a player would get any particular week, but I was able to create something that kind-of worked, though it was far from successful at picking the best team. My wife and I have utilized a lot of analysis of news stories for our recent picks, with some decent success. I had always wanted to utilize machine learning to “solve” for the best possible team, which is probably doable if we had all the possible variables at our disposal, though the “human element” is always a fickle problem.
Ultimately, this is a continuation of a really interesting side project which I am excited to see if it goes anywhere. For now, we will continue picking our weekly team using our own method, but maybe next season I will make another account and let that one be a “bot” account. Maybe the AI can do better? Or will we prevail? It’s an interesting experiment either way!
-Andrew
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