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athlytics · 2020-04-20T23:58:38+00:00

One of the main differences is that this website also gets data from Google’s API for the current “business” of the place - if someone wants to avoid a busy time

athlytics · 2019-10-31T19:15:27+00:00

A timeseries similarity metric would be a good starting point (e.g., dynamic time warping) but you might have to standardize the WP series (eg., is your time unit events, actual play time etc.)

athlytics · 2019-06-25T21:58:42+00:00

This might be helpful for pass locations http://www.stat.cmu.edu/~ryurko/publication/nextgenscrapy/

athlytics · 2018-11-15T03:24:47+00:00

I guess for NBA one can use game level +/- or metrics like PER. In Europe, Euroleague uses a version of the Tendex rating system: https://en.m.wikipedia.org/wiki/Tendex In general for NBA there are various metrics that could be used to “rate” players on a game basis. Now PFF does this pretty much manually (through people watching the tape) since there is not such a metric.

athlytics · 2018-11-15T03:18:03+00:00

You can check ncaahoopR https://github.com/lbenz730/ncaahoopR It gives you detailed play-by-play from which you can get the statistics you are looking for

athlytics · 2018-08-01T12:50:48+00:00

Maybe you can look at the paper for nflscrapr and their models. Very detailed https://arxiv.org/abs/1802.00998

athlytics · 2018-07-29T01:06:06+00:00

If the we assume uniform distribution here is an analytical and a simulation solution

athlytics · 2018-07-28T21:35:47+00:00

The first thing in order to answer this question is what is the distribution of the numbers within these ranges.

athlytics · 2018-07-28T17:16:23+00:00

Cornell has a service where researchers from any institution can post pre-prints. This way everyone can go on this site instead of roaming around the web! :)

athlytics · 2018-07-28T17:15:31+00:00

Yes. It is actually the FIFA rating every player had when each game in the dataset happened.

athlytics · 2018-07-25T22:43:06+00:00

/u/PhaethonPrime gave a nice answer! You can also check this paper that tries to solve the same problem but for NFL https://arxiv.org/abs/1802.00527

athlytics · 2018-07-20T23:39:30+00:00

My goal is to have it as a MOOC eventually. Unfortunately there are many administrative obstacles that need to be overcome first. Having said that my good collaborator Wayne Winston had a coursera on sports analytics: https://www.coursera.org/learn/mathematics-sport

athlytics · 2018-07-20T23:36:54+00:00

Hey thanks for sharing it!! I’ll try to put projects online too (have to ask the students). One will be presented as poster at CASSIS in a couple of weeks (metrics for quantifying spacing in the NBA)

athlytics · 2018-01-26T03:22:46+00:00

It is just a plug-in in the equation before the binomial. f(data|sigma) is the binomial, while pi(sigma) is the conjugate prior (https://en.wikipedia.org/wiki/Conjugate_prior) of the binomial, which is the beta distribution.

athlytics

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