pybaseball VS boxball

pybaseball and MLB-StatsAPI are the go-to python wrappers for the official MLB API.

I think pybaseball includes spray angle in their statcast() data frame! And if not, there definitely is the (x,y) location of where the ball landed, so you could probably do some trig to calculate that too.

Check out the docs here: https://github.com/jldbc/pybaseball/blob/master/docs/playerid_reverse_lookup.md

I think a lot of what you're looking to do though can be accomplished a bit easier by utilizing pre-existing Python libraries like Pybaseball.

most folks without direct access to mlb's api scrape baseball savant's data api. packages like baseballr or pybaseball can help with this. remember, this is in the open on a trust model: no commercial use, and don't hammer the api.

And if you’re using Python, it’s pybaseball I believe. https://github.com/jldbc/pybaseball

I haven't worked with BI tools, but I'll make the same recommendation for Retrosheet data that I always do: check out the Boxball distributions, specifically the CSV files (or the parquet files if you prefer). You can load those right in, rather than futzing with Retrosheet's event files and processing them manually, etc., just to get to the same spot.

The original data source is the Baseball Databank (aka "The Lahman Database"). I use the Boxball distributions, which include both retrosheet and Baseball Databank data.

You know, I thought about that after posting, that the dailies might not be available directly from retrosheet. I use the Boxball distributions, which take the retrosheet data and process it into database images. These distributions include a "daily" table with the daily logs. So apparently Boxball is generating that table.

I used to think the same thing, and then I found the Boxball pre-built images. You still have to be a bit handy to get them working, but it's way easier than starting from scratch with raw retrosheet data. I initially used the postgres image and worked in SQL, and then switched to python/pandas and the parquet files.

I've always been good with data, math, Excel, etc., but decided to take it to another level during the pandemic. The most difficult thing about Retrosheet data is getting into a usable format. Luckily, the boxball project has created a number of ready-to-download images with all of the data, for different database technologies.

For Retrosheet data, I've been using pre-built images from Boxball, rather than going through the whole process of downloading and converting the files from retrosheet. The Boxball images are awesome. I mostly use the Parquet files for Pandas, but they have other formats (e.g., postgres Docker images, CSV, etc) there.