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#NBA Schedule Density App
--
A couple of years ago I created this #shinyapp to explore the density of the schedule in the NBA.
I just published an update to account for some of the schedule challenges due to COVID.
Live App: josedv.shinyapps.io/NBASchedule/
For more background ๐
1. Purpose
-
My goal was to come up with a way to explore the stress of the schedule for both, teams and players, using only publicly available data, mostly box scores.
Understanding periods of higher / lower schedule stress could help coaches manage work and plan accordingly.
2. Schedule Stress
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There are many ways to look at this. In this case, I was constrained to only public data, so I focused on the interaction of various geographical and time related factors such as:
- Location
- Time Shifts
- Travel Direction
- Time between games
- etc.
3. Game Density Index
-
I won't go into calculations here (the code is available for those interested). Long story short, the combination of time + geographical factors results in a game density score (or index) for each game. A number between 0-100 applied equally to all teams.
4. Rolling Index
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Along with the game index, a rolling index over n days can help evaluate the density of the schedule over a period of time, which may be practically more useful than a single game index.
6. How much weight?
-
Users can change the weighing factors for all different schedule metrics, for example, I may score crossing two time zones as a 3, but for someone else it may be a 2.5
I left it open so users can explore. Also, some of the articles above may help with this.
7. From Excel to Shiny
-
Eventually I turned that into an #rshiny app to be able to look at multiple teams over various seasons, dynamically adjust schedule factors, look at teams & their opponents, etc.
I'll drop a few screenshots of key parts of the app in the next few tweets
8. Team Season Report
-
This is basically the translation of the excel table above into a datatable in shiny.
9. Player Report
-
Looking at the combination of schedule factors + minutes played. The app displays individual loads for players in various ways.
10. League Density Heatmap
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A visual to look at the density of the schedule for all 30 teams. The image below is for the 2019-20 season. You can see the "bubble", in green on the far right.
11. League Wide Player Report
-
Another report looking at individual player loads for all teams. There are filters to look at different factors.
12. Research / Articles
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I added a table with ~174 research and media articles on anything schedule and basketball. This is available for download.
I'd like to think this is one of the most extensive resources on this topic out there.
I try to update this table every 6 months
13. Limitations
-
There are always many limitations, even more if we are limited to only public data.
But I hope this is helpful for those like me, that are curious about this topic and have no access to team proprietary data.
14. Predicting Outcomes
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This comes up often. I didn't create this index under the assumption that it could predict outcomes/injuries, but rather, I wanted a bird's-eye view at the beginning of the season of easy / hard schedule windows for planning purposes if I was a coach.
16. Access Schedule Data
-
For those interested in downloading NBA schedule data, I created {airball}.
An #Rstats package that makes it really easy to download various schedule related metrics in a tidy way.
-
Available here: github.com/josedv82/airball
Finally,
-
If you want to check the code behind the app or if you spot any issues, it is all available on my GitHub page here: github.com/josedv82/NBA-Game-Density-App