baseballr VS hoopR

A basic understanding of R should be enough if you install the baseball r package. From there you can scrape off of Baseball Reference or Fangraphs for custom date ranges to get stats on whatever time frame basis you would like. Then you can export/copy/whatever to excel if you want, or do the analysis right in R.

Source: MLB Stats API via baseballr.

I like this idea, too! I use baseballr all the time. It is a godsend.

In order to make it, I need to get every lineup from every game in the season. I am using the baseballr package to get the game_pk number. Each game has a game_pk number, and each lineup is tied to that game_pk. So I need to create a dataframe (all_games_list) for each game with its game_pk number in it, and then use the game_pk numbers to create a new dataframe (lineup_all) that contains the lineup for said game_pk.

I just remembered there is also this R package: Acquiring and Analyzing Baseball Data • baseballr.

To get all the data, I would suggest checking out baseballr if you are familiar with R. https://billpetti.github.io/baseballr/

Visualization originally posted on my blog - I built the boxplot using R and ggplot2, and was fortunate to be able to use the excellent baseballr package to query MLB game information for the runtime source data!

baseballR (baseball)

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.

If you're using R, this is the gold standard package to use for getting baseball data. This helps you scrape data.

Thankfully, I've made use of the hoopR package which lets me get data that is up to date, primarily by pulling info from ESPN's APIs. While that does most of the work, there's tweaking that I need to do (most particularly for streamed game broadcasts that aren't ESPN3/ESPN+) but it usually goes fairly swimmingly.

hoopR (college and pro basketball)

https://github.com/saiemgilani/hoopR Digging around through here will probably help you find a csv which is easy to convert to excel

Next, we’ll look at a few measures of performance on the court to see whether certain signs are better or worse suited to particular aspects of the game. For each of these, I took player box score statistics (sourced from https://github.com/saiemgilani/hoopR), grouped them by the player’s astrological sign, and calculated their average numbers through the 2020-21 season. First, we’ll look at scoring -- the chart below shows each sign’s average points per game.