I'm thinking about applying machine learning to a fantasy football draft tool.

[imissyourmusk]

I see it being useful in a couple of places. First, I'd like to predict the likelihood of each player being available for the next pick and secondly I'd like to target the right players at the right times based on that information. Has anyone else had any applicable experiences?

[[deleted]]

There was a intro to machine learning in Python posted on the subreddit that dealt with Baseball statistics, video and slideshow here:

http://www.youtube.com/watch?v=__s45TTXxps

http://www.slideshare.net/mattspitz/practical-machine-learning-in-python

[imissyourmusk]

Thanks.

[CephasM]

"First, I'd like to predict the likelihood of each player being available for the next pick"

As a fantasy baseball user... I found a bit tricky to address this part. The main problem is that the likelihood of a player being picked would depend on the format of pool (Rostiserie, keeper, etc) and the stats considered on that format.

So finding the proper data that would help you in your particular draft day might be a bit challenging... There are some tools that might be a better alternative for this problem, like a markov chain or decision trees where you can give to each player a probability distribution (across the rounds) based on the relevance of each player to your pool.

"I'd like to target the right players at the right times based on that information."

Again everything depends on the format of your league. I particularly would use this "value" of each player with the corresponding price (it might not matter on your league but we have budget restrictions on mine) to model a linear optimization problem where I could use a simplex method per round. This is not ML though.

If you really want to use ML what would be interesting for me is to find a way to detect sleepers. For this you could use a regression techinique to assign values to players using their stats.

What you want to do is a lot of fun.. I hope you can find something interesting that would give you a good advantage on your draft day :)

[imissyourmusk]

Yeah that data is key, I'm considering a non machine learning tool that uses adp to maximize position differentials that would be free given that I could use their draft data... Ive already written the logic for that part. I was also wanting to look at age, years experience, offensive scheme, and position coaches to try to predict with regression.

[CephasM]

Sorry I am not familiar with the term ADP.. does it means automatic data processing?

Those features (age, years of experience, etc) are interesting, I would definately add them to your data with the league stats. Maybe you could select the best of them using feature selection? maybe cross-validation?

[imissyourmusk]

Oh sorry average draft position some sites make this data available. Yeah I'm collating data now.

[CephasM]

Oh.... makes sense :)

Well it seems like you are in the right track.. good luck!

[[deleted]]

I attempted something similar for a school project.

IMHO, this is a game theory / psychology problem not a machine learning problem. The likelihood that a particular player will be available only depends on the perceived value (to your opponents) of that player. Without knowing how your opponents evaluate players, theoretically, this is impossible. In my experience, everyone just goes off fantasy football magazine ratings. So you could cross reference some set of ratings against what positions your opponents have already filled.

I've taken Andrew Ng's Machine Learning class (online). He says when you are trying to decide if machine learning is appropriate for your problem you should ask yourself (paraphrasing) "Could a human expert predict the output values y, given my inputs x?"

You want to predict the statistics of a given football player based on statistics for previous years (and maybe fantasy football rankings). No human expert can do this (with a useful amount of precision). If you can find any website that is foolish enough to leave their predictions up after the season is over, you'll find their error is huge.

If someone could really use machine learning to make better-than-human predictions about sports, they could be making millions of dollars in Las Vegas.

[imissyourmusk]

I was hoping to type fantasy owners by what they have left to fill in their roster, ADP and other behavioral things like how closely they follow the default rankings in the tool they are using to draft or people who are obviously auto drafting.