Building @SynergySST tools for coaches @Sportradar and sharing basketball data in fun and eye-catching ways.
Joined March 2016
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Last week I had the unexpected (and unbelievably neat) opportunity to chat with a former NBA coach about which analytics questions interest him. He mentioned that he was skeptical of the presumed infallibility of the end-of-quarter 2-for-1 strategy. So, I took a closer look.
The coach also has to consider who he has on the court to take the "rushed shot" and how this end-of-quarter strategy fits into his bigger goals of encouraging teamwork and sharing the ball, etc. So, the narrow margin for the 2-for-1 strategy might become more dubious.
For more thoughts on 2-for-1 you could check out my favorite article about it, here:
cleaningtheglass.com/two-for…
Or read about it in Seth Partnow's book The Midrange Theory:
triumphbooks.com/the-midrang…
Using player tracking data from @SynergySST to answer some questions about rebounding.
I really enjoyed hearing Bucks' coach Johnny Carpenter talk about spacing and decision making with Chris. Got me thinking, so I ran some queries and made this chart to describe the value of some of the options a player has when catching a kickout after a teammate drives.
Milwaukee Bucks head of player development Johnny Carpenter explains how CLA, offensive principles, advantage creation, decision-making and so much more, creates better player development than scripted repetitions.
basketballimmersion.com/the-…
The four highest-value outcomes of a pick are a shooting foul (1.57 points per), a rolling 2PA (1.31), a cutting 2PA (1.25) and a driving attempt at the rim (1.24); defenses that prevented those outcomes from happening tended to be successful last season.
The Big Data Bowl has an impressive track record — many past participants have gone on to work for NFL teams (or do other interesting things). If you want to work in sports analytics you might consider giving it a shot 🙌
With what type of screener(s) has he had the most success? How much does his production vary by screener? How dependent has his production been on working with a particular screener? What does his best pairing say about his potential on our team?
We're adding pick pair leaders in our new college player tracking tools. Mostly interesting from the perspective of "what's working for us?" and "what do we need to be prepared for?". But I think there's also some useful information for draft (or transfer) work.
Most Productive Pick Pairs (High Major Edition)
Most productive Ball Handler-Screener Pairs last season
via @SynergySST New Player Tracking
