It’s amazing how much further from the box score advanced stats are taking NBA fans these days.
The guys over at Hickory-High.com have been writing about a stat called “Expected Points,” which basically calculates the number of points an average NBA player scores on shot attempts of differing distances and compares it to players’ actual output from those distances, as Zach Harper wrote about on TrueHoop on Friday.
Here’s Hickory-High’s explanation of the stat:
Another way to think of Expected Points Per Shot, is the average number of points scored on a shot attempt. For example, over the last 4 NBA seasons, factoring in makes and misses, a field goal attempt at the rim was worth an average of 1.208 points.
Using numbers from Hoopdata’s Shot Location Database, and the Expected Points Per Shot from Lyu’s post I was able to calculate what I am calling Expected Points per 40 Minutes (XPts/40). I began by calculating each players XPts/40 from each area of the floor. To do that I took each player’s per 40 minute field goal attempts from each area of the floor and multiplied it by the expected Points Per Shot for that location. Adding these categories together results in XPts/40. Another way to think about this is, given a player’s per 40 minute shot selection, how many points would he score, shooting the average percentage from each location. The numbers I borrowed from Lyu are below:
- At Rim – 1.208
- <10ft. – 0.856
- 10-15ft. – 0.783
- 16-23ft. – 0.801
- 3PT – 1.081
- I needed to include Free Throws, so I used 0.759 the league average for last season.
The Hickory-High guys have determined how each individual team did in comparison to how many points they should have scored based on their shot attempts.
It should come as no surprise that the team that led the NBA in offensive efficiency and true shooting percentage last season also was the squad that far and away scored more points than expected.
Yes, the Phoenix Suns scored 7.264 points per game more than one would expect based on the shots they attempted. The next most efficient team was Toronto, scoring 4.218 more points than expected.
These numbers are not pace adjusted, but still it’s just one more measure that shows how efficient Phoenix’s offense was last year as 11 more teams are within the 3.046 points per game that No. 2 Toronto trails the Suns by in this measure.
The Suns were most efficient on their long balls, scoring 3.350 points per game more than expected on those shots, which makes sense considering their league-leading shooting percentage from distance.
It doesn’t take John Hollinger to tell you that Nash himself led the league (just ahead of old friend Dirk Nowitzki) by scoring more than 3.333 more points per 40 minutes than expected himself, and he’s likely a big reason that every returning Suns rotation member boasted a positive figure last season. and were slightly in the negatives last season playing without Nash.is a big reason for this.
Last season Amare Stoudemire ranked in the top 10 in the league in this department, scoring 2.226 points per 40 minutes more than expected based on his shot selection (guys like Kyle Korver, Kevin Durant and Chris Paul were also in the top 10, so this passes the eye test).
was next on the Suns at 1.882 more than expected per 40 after scoring 1.517 more points than expected on long balls.
The real value in this stat is the fact that it breaks down how many points more or less than the average NBA player that a guy is scoring from a variety of different distances as explained above. Armed with this knowledge, you can tellthat he’s scoring 1.608 points per 40 minutes less than the average guy on 16-23-footers, so maybe he should either become proficient from that range or stop taking that shot as frequently.
So while the holistic view is interesting, really we already knew that the Phoenix Suns and Steve Nash in particular were an amazingly efficient basketball team last season.
What could be most salient going forward is the distance data, stuff that many NBA teams likely already track themselves. That information can tell a team where to force a player on the floor as well as show an individual player what areas of his game he needs to improve upon.
This kind of straightforward data should even appease the kind of fan who will never give stats like Wins Produced a chance.