Systems, Simulation and Offensive Basketball
(Note: reposting this from a thread on PGs.)
Thanks for commenting on my analogy of client/server vs. multinodal internet, for thinking about offenses. At first, I just thought about it as a metaphor, but the more I think about it the more I think computer networks may actually be a good model for thinking strategically, tactically and operationally about offensive basketball.
In all networks you are looking at bit flow rates of the system. About certain subsets of the network using certain amounts of resources and other parts of the network using other amounts of resources and about varying loading.
Then there are the concepts of distributive computing and parallel processing that might be robust concepts for thought about offensive basketball.
Offensive basketball, especially the way Self plays it, is about optimally redistributing system resources as the opponent “adjusts” to what is being done; i.e., as the opponent varies available bandwidth for one player (i.e., one node) and so gives greater band width to another and so on.
I haven’t really thought this through much beyond what I am relating here, but if offensive basketball were thought about in this way different kinds of statistics might begin to be measured that better capture team’s abilities to redistribute their team resources to meet adjustments, and differing types of opponents, and so coaches might begin to think more systematically about how to make the tweaks to enable the redistributions, and might think more systematically about the kinds of skill sets a player has and how they mesh (or fail to) with other nodes (players).
I know this may sound bizarre to many, but I have a hunch that a basketball team modeled this way could then be subjected to a modified finite element analysis that would find the weak and strong dynamical links among the multimodal system that is a basketball team.
Likewise, a defensive coach could look at the same offensive statistics, especially strong and weak dynamic linkages among the nodes/players and find weak points to attack.
Imagine being able to do what I am conceptualizing here and plug in various recruits in a simulation to see which one produces more net benefit and which recruit produces less as preparation to decide which prospect to sign.
Great coaches probably do a lot of this by “feel” and heuristics developed through years of experience, just as great engineers and designers used to be able to build great cars, or trucks, or planes, before computer modeling. And the feel and heuristics are still important to engineering and design. But computerized simulations allow testing of ideas and exploration of systems to find points that can be altered to optimize the system.
I have a hunch that right now this could be done.