Basketball Analytics, aka, different ways to tell us information that we already sort of know. From PER, to WS48, to plus/minus, to real plus/minus, to shot charts, to shot charts with smaller dots, to David Berri, to John Hollinger, to whoever the latest dude is who figured out you could enter basketball data in R and get it to spit out stuff. It’s all been quite fun and worthy of a few clicks here and there, but when are you guys actually going to tell us something useful? I mean, actual useful information that can be actioned and not just casually and sparingly glanced over in a long-form article only to be forgotten a week later.

Here are a few basketball problems that need addressing, or at the very least, should be talked about more:

Risk of Injury

Tell me when a player is likely to be injured, and tell me early. Figure out what his bone density is, what force (mass times acceleration) his joints can take, and then relate that information to his on-court movements to project how long he’ll last. For example, you can tell that the way Derrick Rose players he’s prone to injury – quantify that for every player, preferably before he’s drafted.

Effort Measure

Come up with a mathematical way of measuring effort without falling back to vanilla statistics like distance ran and speed. Figure out what the player’s “capacity” is using tests, and then determine how much of that capacity he’s using during different parts of the game. Pro Tip: If Andrea Bargnani isn’t dead last in this category, your work is wrong.

Grading the Coach

We measure players a lot, we don’t measure coaches enough. Much like how baseball is a game of probabilities, so is basketball, the math just happens to be a little more complex then pitching a lefty against a lefty. At the very least, there needs to be a binary indicator of whether the coach made the statistically correct move in late-game situations. For example, given the inbounder, the four other offensive guys on the court, the side at which the ball was being inbounded, the time on the clock, the score in the game, the venue, is it a statistically correct decision to not pressure the inbounder and sag?

Quantifying High Basketball IQ

We got an IQ test, how come we don’t have a basketball IQ test? All the data is there and it’s a question of sifting through it. Any decision on the court can be – based on all the other happenings on the court – retrospectively graded as right or wrong. A player with a highest basketball IQ might make the highest percentage of good decisions, as opposed to bad ones. I want to see if Landry Fields is what he’s cranked up to be. Or Matt Bonner for that matter.

Floor Stretchability

This idea that having John Salmons camp out by the fire in the corner means he’s stretching the floor needs to be validated. How much does a person’s reputation and past/in-game performance influence the defense’s opinion of him? How many threes does James Johnson have to hit before he causes a shift in the defense? By what factor does a particular player stretch the floor and how does his in-game play change that factor? I would think this is a straightforward one to figure out.

What is the Perfect Free Throw?

Based on a person’s height, what’s the ideal release point, force, angle, trajectory? Figure it out for every player and give them their particulars – watch them shoot 90%+. Seriously, I’m shocked that there are professional basketball players that shoot 50% from the free-throw line, that’s got to change.

Strategic Technical Fouls

Profile a player to determine what series of events cause them to pick up a technical. Then try to replicate that sequence in hopes of throwing him off his game. This can be extended to essentially influencing a player’s mental state negatively through pure basketball play. And poking them in the right places when nobody’s watching.

Figuring out Full Court Pressure

Teams get burned by full court pressure all the time. In the hopes of forcing a turnover, they end up creating a hole in the defense which ironically puts the offense at an advantage. Given a set of offensive players, their approximately positions, and the inbounder and ball-handler, there has to be an optimal defensive configuration that is most likely to cause a turnover while minimizing risk of defensive breakdown. Figure that out. As a bonus, come up with suggested defensive configurations which increase the chance of causing a turnover, while understandably increasing risk of defensive breakdown.

Optimizing Minutes Played

What is the ideal rest pattern for a player so that they can last an 82-game season and the playoffs? If they’re extended in one game for 44 minutes, how should that influence their playing time in the next game so as to not cause harm over the course of, say a roadtrip. Obviously, a player’s body needs to be analyzed, then correlated with the type of movements on the court, the pressure exerted, the effort expended, and likely a host of other factors. Put them all together, shake the box, and out you get a number telling you how many minutes DeMar DeRozan should be playing on the second night of a back-to-back having played 42 minutes the night before.

Psychological State

Players have money, money gets you shiny things, it also brings distractions and problems, which can influence a player’s on-court state of mind. Remember Keon Clark? Yeah, money did him no good. There’s got to be a way to measure a player’s psychological state before a game, and then see what, if any, impact that has on his game. Once you got that, adjust lifestyle to tune psychological state, thus improving his game. I’m talking girl problems, alimony, size of posse, everything. That all affects a player’s psychological state.

So there you have it – some questions that I always ask myself, knowing that very well that I can’t answer them, so maybe #analytics can?