what kind of software you use for analytics

[Snedecor]

Hi,

I understand there are some analysis need to be done with special software and others metrics just need an spreadsheet.

Here is the question, what software are you using? I'm 'new' on these, and I would like to be more efficient in my results.

I used R and spreadsheet, get the data by box after match ends and start analyzing, in Europe we don't have by-by-play so some metrics are difficult to get, we don't have sportvu or synergy.

Any advice? Any tricks?

Thanks in advance

[eminence]

I guess I usually use matlab, but you can use pretty much any language when it comes to basketball, it's not too complex/specialized. Player tracking stuff might be an exception, I've never tried to do that myself.

[Snedecor]

For tracking players, there's any software for free or open source good to use?

There's not enough real time to watch 30-40 games per weekend or even in a week in best cases, so is a tedious task......

[DSMok1]

I use R and Excel for my needs. I'm not doing crazy complex math or dealing with giant datasets typically.

[shadow]

I use Python for both scraping and regression analysis.

[Rd11490]

I have a mix of python and scala for my data processing and analysis, and I store my data in a mysql database

[patrickj]

R and Stata.

[njworange]

I use Excel. It is enough for me because I don't have any tracking data and play-by-play in South Korea. However I hope I can analyze/calculate more metric with more language and raw data.

[pdevos]

To acquire (scrape, pull API) data, I used Python with libraries Requests and BeautifulSoup. I may use Pandas here as well to formulate the data as tabular data (e.g. CSVs).

To "munge" and clean the data I use primarily Pandas again and a SQL Database, I prefer Postgres (aka "PostgreSQL") where I use SQLalchemy & psycopg2 to connect to that Database. I will create tables (I have a background in databases) that are easy to join later for what I need, e.g. teams, players, coaches, advanced stats for each, etc.

I really like Jupyter Notebooks when I'm doing "data exploration" for data visualization and other aggregations. The data viz libraries I use most are Matplotlib, Seaborn, and Plotly for the most part.

To utilize regression, clustering, and classification algorithms, I primarily use Scikit-Learn, which enables you do do about 3-4 lines of code (once you've transformed your data into a tabular format that works with Scikit-Learn).

R has similar features. The basic descriptive statistics packages and models are "quicker", but I've found it's syntax is a little wonky for me and I enjoy Python for much greater versatility and job prospects, ergo, I use Python more than R.

For quick and dirty discovery analysis, I might still throw things into Excel if it's just a bit of "copy pasta" vs scraping.

If wanting to learn Python and Data Analysis in Python I'd recommend Jake VanderPlas' Data Science Handbook (https://jakevdp.github.io/PythonDataScienceHandbook/) and Chriss Moffitt's Practical Business Python blogs where he really helps Excel users make the transition to Python (https://pbpython.com/)

[rainmantrail]

Looks like I'm digging up some old threads here. But I'm also an R user. I use it at work daily, so that's my comfort zone. I also use Python occasionally, but I'm stronger in R so I usually stick with that. R has some pretty good web scraping capabilities now as well. I used to use Python with BeautifulSoup to scrape data, but now I pretty much use R exclusively, even for webscraping using the rvest and RSelenium packages.

[vzografos]

Python for the processing/modelling + scraping.

Some matlab if I want to do quick plots or try some quick ideas