This post should be thought of more as a tool than a prediction, although I do give predictions at the end of each section. The tables and treemaps below show “expected scoring” for each series. Expected scoring is simply defined as the average rate (%-total plays) and efficiency (PPP) between the offense of one team vs. the defense of the other team (and vice-versa). The data comes from Synergy, of course. Continue reading
Of course, not. But…
Jayson Williams, freshly released from prison, was quite a prolific offensive rebounder. If you don’t believe me, look at this table of the top offensive rebounding seasons (by ORB%) in the 3-pt era: Continue reading
Some more stats to throw at you today using my new distance metric, which judges scoring based on both efficiency (measured by TS% ) and volume (measured by USG%).
Here are the rookies in 2012 with greater than 300 FGA attempted. Recall that 1.0 is the greatest DIST a player can have, 0 is what an average player would have, and -1 would be very bad. I’ve also standardized the rating according to how rookies peform. That’s given in the STD column. You can see that Kyrie Irving has been a very, very good scorer. He is 2.5 standard deviations above an average rookie. Klay Thompson (yes!) and Isaiah Thomas have also been quite good. Continue reading
I created a Tableau visualization of all 10,000+ TS-USG data points, which includes all player-seasons since 1980 with greater than 100 FGA in a season. Allow me to give you a virtual tour, before you go off and play with the data on your own (be careful!).
The red line represents what I have proposed is the “efficient frontier” (to borrow terminology from finance).
Think of the big blob of data points roughly as a clock. Let’s go around counter-clockwise. When you explore the Tableau viz, you can zoom in on data points, and click on individual points to see more details, such as the year, team, and age of the player. Continue reading
An age-old question — see what I did there? — among APBRmetricians is trying to understand how aging affects players. Consider this post my first contribution to the discussion.
I calculated the distance metric that I introduced in a recent post for the 10,000 or so player seasons since the 3-pt shot was instituted. I then divided these seasons into four groups by age, as follows:
- “very young” (18-21)
- “young” (22-25)
- “prime” (26-29)
- “old” (30+)
That title ought to have gotten your attention. :)
In an effort to look deeper into the (hypothesized) tradeoff between usage and shooting efficiency, I went to basketball-reference and compiled a list of every player-season of >100 FGA since the 3-pt shot came into effect. There are roughly 1800 unique players in the list and a little over 10,000 seasons (each represented by a row of data). I also captured the player’s age, which you’ll see in the plots that follow.
With these data in hand, I used the function lmer() in R (part of the lme4 package), to create the following linear mixed-effects model:
ts.lme<-lmer(TS.~USG. + Age | Player,data=usage_big,weights=FGA)
In my last post, I introduced the idea of a “convex hull” for the usage vs. efficiency relationship. If we agree that the upper-right edge of that relationship represents “greatness” (in terms of scoring), then it is a simple matter to quantify the relative greatness of all other player-seasons by measuring the distance from each point to that edge (see the plot below).
The distance between a data point and the edge of the USG/EFF relationship is a measurement of relative offensive value.