If you’re a hardcore follower of this blog, you know that one of things I have frequently complained about is the failure of NBA play-by-play data to include the shot clock. It’s so obviously important and—relative to other play-by-play data—so easy to track, that it’s a complete mystery to me why doing so isn’t completely standard. OTOH, I see stats broken down by “early” and “late” in the shot clock all the time, so someone must have this information.
In the meantime, I went through the 2010 play-by-play dataset and kluged a proxy stat from the actual clock, reflecting the number of seconds passed since a team took possession. Here’s a chart summarizing the number and outcomes of possessions of various lengths:
The orange X’s represent the number of league-wide possessions in which the first shot took place at the indicated time. The red diamonds represent the average number of points scored on those possessions (including from any subsequent shots following an offensive rebound, etc).
We should expect there to be a constant trade-off at any given time between taking a shot “now” and waiting for a better one to open up: the deeper you get into a possession, the more your shot standards should drop. And, indeed, this is reflected in the graph by the downward-sloping curve.
For now, I’m just throwing this out there. Though it represents a very basic idea, it is difficult to overstate its importance:
- Accounting for the clock can help evaluate players where standard efficiency ratings break down. Most simply, you can take the results of each shot and compare them to the expected value of a shot taken under the same amount of time-pressure. E.g., if someone averages .9 points per attempt with only a couple of seconds left, you can spot value where normal efficiency calculations wouldn’t.
- Actually, I’ve calculated just such preliminary “value-added” shooting for the entire league (with pretty interesting results), but I’d like to see more accurate data before posting or basing any substantial analysis on it. Among other problems, I think the right side of the curve is overly generous, as it includes possessions where it took a while to get the clock started (a process that is, unfortunately, highly variable), or where time was added and the cause wasn’t scored (also disappointingly common).
- Examining this information can tell you some things about the league generally: For example, it’s interesting to me that there’s a noticeable dip right around where the most shots actually take place (14 to 16 seconds in). Though speculative, I suspect that this is when players are most likely to settle for mediocre 2 point jumpers. Similarly, but a bit more difficultly, you can compare the actual curve with a derived curve to examine whether NBA players, on the whole, seem to wait too long (or not long enough) to pull the trigger.
With better data, the possibilities would open up further (even moreso when combined with other play-by-play information, like shot type, position, defense, etc). For example, you could look at the curve for individual players and impute whether they should be more or less aggressive with their shot selection.
So, yeah, if any of you can direct me to a dataset that has what I want, please let me know.