I’ve been working on a project called Pinalytics and I’m sharing out Version 1.
Pinalytics mashes up pinball event and player data from the IFPA with geography information to create a regional profile of pinball stats.
Goals of the project are:
- Compile Relevant Player and Tournament Data
- Classify Players and Tournaments into interesting cohorts
- Add an extra layer to US Geography; instead of just using State lines we tie the ZIP Codes to a Designated Marketing Area centered around urban areas.
- Capture main metrics for an area for a rolling 48 month basis, which matches the time for which an event value counts towards ranking
@timballs created the Pinball Statistics Power Rankings which focuses on individual player performance which I took inspiration from for this project, but the focus for this project is a macro level for the overall “strength” of a region.
The result is coming up with a regional snapshot like this one for Philadelphia: Pinalytics - US - PHILADELPHIA- PinballSpinner
If you want to read about the metrics used: Pinalytics Metrics – PinballSpinner.com
If you’re interested in the tech stack used: Pinalytics Tech Stack – PinballSpinner.com
It’s to the point where basics are in place across the board to be able to make incremental updates over time. Right now the plan is to keep the data feed flowing the next few months as individual months roll along.
Potential Version 2 enhancements might include:
- Additional geography breakouts. Right now it’s only broken out for US Metro areas. I tried to find a Canadian breakout but the best I could find was this breakout (List of television stations in North America by media market - Wikipedia) but couldn’t find any connection to Postcode. The concept could be expanded to other areas, but I need other expertise on different regions.
- Additional metric sets. What are other good metrics for this data set? This is where I’ll presume the pinball community will chime in. Ideas I have are things like engagement scores of players based on how frequently they play, or “customer cohorts” based on how long they’ve been an active player for.
- Additional dimensionality and attributes. Right now the grouping of things like tournaments have been simple. But there’s more around event formats, Tournament Directors, or event text analysis of event descriptions.
- Additional datasets. Right now the data is just focused on the some of core items available, but there’s more to potentially do around integrating other sets like data from the MatchPlay.events API or the NeverDrains universe.
- Finish the API layer. This will make building future graphs easier, but it’ll also open the possibility to share the API out with others so that they can build their own applications or data analysis on top.
If you have any feedback, please drop it in the thread or drop me an email at email@example.com.