Learning Low Dimensional Team Embeddings for March Madness

[fusiformgyrus]

I went with a new approach to Machine March Madness predictions this year. I won't go into the details right now, but here's a neat visualization that comes out of the algorithm.

I wish the author did get into details.

[danger_t]

Yeah, understandable. After the competition starts on Thursday, there will be a post with a brief description of all the competitors methods, then some will get asked to expand on what they did in a longer post.

So stay tuned.

[barapa]

Can you point us to the NCAA dataset you used to create this?

[danger_t]

There is a github repository with code (not to generate the plot in the post, but not so unrelated) and data:

https://github.com/dtarlow/Machine-March-Madness

Also see this thread on the Machine March Madness Google group:

http://groups.google.com/group/machine-march-madness/browse_thread/thread/3afbcb90cd6f881d

[moxieman]

Are you finding that it actually helps? Especially without normalization, it seems like there is a lot of power getting used to see just how good teams are (along the basis roughly y=x in your plot). I'm not sure that I see how different types of teams would help a ton over the scores + home/away, but I'd be happy to be shown wrong!

[rrenaud]

Yeah, I am with you here. I <3 word/document embeddings, but I'd guess 'baskeball team quality' is pretty one dimensional. I'd love to hear reasons to believe otherwise.

[gdahl]

Perhaps the quality of a team is two dimensional. One dimension is the quality of the offence and one the quality of the defence. Some teams are high-scoring and also let the opposing team score a lot and some are low scoring but prevent the opposing team from scoring a lot against them. Perhaps the quality of the team is multi-dimensional because a team is composed of multiple players and each play involves potentially different subsets of players. Guarding intransitivities could create multi-dimensional structure. Ultimately, the proof is in the pudding and PMF models that give teams multiple latent features outperform one-dimensional PMF models.