Ice Man wrote:Yes, it is.
Spaceman, you should start up a Chicago Bulls statistics thread and moderate the proceedings. You not only know your stuff, but you know how to communicate it too.
Generally I'm someone who hangs out on the PC Board, but I saw a thread on here asking about ESPN/Engelmann's Real Plus Minus and thought I'd clear some things up. Got a few requests to start and maintain/moderate an analytics thread, and I like the idea so I'm on board.
I'll use this thread to share statistics that I've come across and find useful, and to answer questions about the development, interpretation, and correct usage of statistics. Hopefully this thread can also serve as a discussion point for analytics as a whole, as I think there's something to be gained from the movement even amongst those who aren't statistically inclined.
Remember, analytics is about more than stats. It is about contextualizing, quantifying, analyzing, and most importantly understanding the game of basketball. Statistics are a big part of that, but so is watching film and deepening your understanding of the areas of the game that have nothing to do with numbers.
Everyone is welcome to participate, even if your knowledge of analytics is zero. Ask questions, share new things you've found, even develop your own stats if you're so inclined. Just keep an open mind is all I ask.
Where to start:
RAPM (Ridge Regressed Adjusted Plus/Minus)
Layman's explanation:
2015 Prior Informed RAPM: http://stats-for-the-nba.appspot.com/ratings/RAPM_2015.html
2008-2014 Prior Informed RAPM: http://www.gotbuckets.com/statistics/rapm/2014-rapm/
Doctor MJ's spreadsheet with Prior Informed data from 1998-2012: https://docs.google.com/spreadsheets/d/1h20JYcZJu2tGNIyOwVbNfez0-zXXy5ItLyXC4qTE5D8/edit#gid=0
A more technical explanation of RAPM: http://www.gotbuckets.com/what-is-apm/
Player Tracking, Play Type, & Lineup Data
http://stats.nba.com
These are "scouting report" type stats, found on NBA.com, and their utility is awesome.
Player tracking is awesomely useful for several niche type stats that would not otherwise be known. Rim protection, catch and shoot percentage, etc.
Play type is the old synergy. Most useful information is how many points per posession a player scores in isolation, post up, PNR handler, etc. Helps quantify those observations.
Lineup data is very useful for determining player fit and portability. Stray observations I've found on the Bulls this season: !. Gasol/Noah lineups generally do not play well 2. Dunleavy/Mirotic lineups are SCORCHING teams offensively 3. Mirotic and Brooks show up most often in Bulls best offensive lineups.
Player Tracking Plus Minus
A new development in 2015, this is the first statistical plus minus attempt to use player tracking data in place of the box score. Basically RAPM is "built on" by the player tracking data mentioned above.
https://docs.google.com/spreadsheets/d/1GtCDQw94kpcOw_kPhyH8F5cIjPT3QTsOGqvrX_hMCo8/edit?pli=1#gid=0
http://counting-the-baskets.typepad.com/my-blog/2014/09/introducing-player-tracking-plus-minus.html
Why is this a big deal? As illustrated here: http://apbr.org/metrics/viewtopic.php?f=2&t=8633&start=210#p23003 it is currently outperforming every single other type of metric by a gigantic margin. Nothing else comes close to being able to predict wins. If this continues, we could have by far the best all in one metric ever created in our hands.
We could eventually have a stat here that is able to capture the value of players that typically go unnoticed. Doctor MJ's take:
Thanks SideshowBob for introducing us to this: http://forums.realgm.com/boards/viewtopic.php?t=1374892.
WOWY
"With or Without You"
A method created by this forum's very own ElGee. Essentially this is a way to estimate players' impact by SRS (point differential) performance in games they've missed. He explains it better than I: http://forums.realgm.com/boards/viewtopic.php?t=1333570
Another way to estimate "impact", and this method also works with ORTG/DRTG on/off (can be found on basketball reference, I can talk more about that too if people are interested.
Final Notes
Here is a dropbox folder where I keep a bunch of spreadsheets you might find useful (and this will become more and more full over time): https://www.dropbox.com/sh/l9csmf8as5yjv1h/AADdREYub8D13pZUDqCblpgWa?dl=0
-When using impact stats, to reduce noise it is helpful to look at very large samples (ie. if one player is slightly above another in 2014 it might not be that big a deal, but if he is consistently better every year from 2007-2014 that's a very big deal)
-Look for impact stats to "agree" (ie. RAPM says a guy is huge impact, team posts terrible record without said player, team falls apart when player goes to the bench) If you have multople sources saying the same thing, believe it.
-don't use any single stat as an "all-in-one" that's not what they're designed for, that's not how it should be used
-don't discount an entire statistic because you don't like what it says
Anyway, this is all I got for now, but hopefully this can stir some useful discussion. Always like to see new minds come around to the analytics movement.