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SUBMIT YOUR IDEAOn a spree to binge complete weekly challenges
Got bit confused with the later calculation. Had to check for hints.
A new type of challenge! Nice
The top 10 most relevant variables are straight forward.
I'd love to spend some more time understanding and fine tuning the models, although, I got some sensible results. Note: given the small number of records, I have used all values to fit the linear regression model.
I'd love to spend some time looking at solutions and predictions submitted by others.
I clicked reply from here: https://community.alteryx.com/t5/Weekly-Challenge/Challenge-18-Predicting-Baseball-Wins/m-p/79508/hi...
Hi Jamie,
I was having similar thoughts for a while, about the 161 games 🙂
It turns out the assumption for 162 games is used to calculate the predicted losses from predicted wins.
Steven
But it appeared at the end of the thread.
Wow!!! Such a nice and comprehensive explanation! Is for @samjohnson
Goes very well with
Without domain knowledge, building a practical model can be tough. I guess, this is part of reason why most people just used a linear regression by default.
Thanks!
Steven
Hi,
Here is my solution.
For the sake of curiosity, I added a stepwise after the modelisation with the 10 variables, in order the avoid some multi-colinearity issues. The number of variables dropped down to 5.
The predictions are quite the same, and the sum of least squares is better on the 6 cities considered.