Turns out when I weight for the importance of the position, I can clearly see that I have a potential All-Pro QB sitting there, towards the top of my board.
We can make one final adjustment.
My team is going to be strong in some positions and weak in others.
If the draft pick comes down to a choice between a player at a position we need and one we don’t who have relatively the same grade, we should pick the player that shores up an area of weakness.
We can write another quick function for that:And then plot our new results for our final board:It looks like that last adjustment separated out OT Quisperny G’Dunzoid, Sr.
as the best option on our board.
Not bad for one week of training in Pandas and a few lines of code.
ConclusionThe above is not perfect by any means (for one, I never could quite get the visualization as I wanted in Plotly), but rather to show how relatively easy it is to make a simple visualization tool to aid in decision making.
With just a little more work, you could help GMs make decisions about whether a given trade is a good value or whether a given team is likely to take a player you’ve got your eye on.
Pandas and Plotly are simple, but powerful tools that could help an NFL team find the next NFL star.
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