In any part of life, you can use data and analytics to judge performance but if you lack context you can’t get a complete picture. Basketball is no different. Building back on Part 1 of Basketball Analytics, it is clear that TS% and PTS are important and much better indicators of efficiency than FG% and other basic stats. While TS% and PTS are important in judging a player’s scoring value, we still face the flaw of lacking context. If we just used these two statistics, our evaluation of a player would be severely lacking and we wouldn’t get the complete picture.
We have two more major factors that go into assessing a player’s scoring value. First, we need context in terms of the player’s teammates. Surrounding a player with elite horizontal spacing, vertical spacing, and advantage creation (adv. creation) will give a player a much easier time scoring. Shooters and lob threats make it easier to score. Considering this, SGA (55.7 TS%) has a very impressive TS% because the spacing, finishing, and adv. creation around him was poor. The second piece of context is the level of creation the player himself is doing. What makes SGA’s TS% more impressive is only 13.7% of SGA’s 2P FGs were assisted and 30.3% of his 3P FGs were assisted and he had a 27.7 Iso Frequency% (third in the league). A very high percentage of his shots were unassisted which means he was self-creating most of his shots. The ability to create shots for yourself is not only important as a scorer but also as a playmaker. Another reason SGA was so valuable was the ability to score at all three levels. Although mid-range shots are generally not efficient, players who self-create need the ability to score from all levels. SGA was at the top of the league in terms of drives per game so he was applying pressure on the rim, which is worth a lot when it comes to playmaking. Going back to Harden, we see some historical self-creation numbers as less than 10% of his 2P FG were assisted and less than 30% of his 3P FG were assisted. A large number of these points came not only unassisted but from a standstill without any previous advantage. Especially when trying to project young players, this is all important information to understand. A lot of times, young players will take a lot of self-created shots and will be inefficient, but they shouldn’t be written off because of this. All in all, TS% is a massive step up from FG% but it doesn’t do the whole job when trying to understand someone as a scorer. Understanding shot diet, self-creation, and team context are also much-needed statistics when comparing scorers.
In 2011-2012, Rajon Rondo averaged 11.7 assists per game, a very high number, but how valuable was Rondo as a playmaker and passer? The answer is not very valuable, he was very overrated. When someone says assists, it has no set definition as not every assist is created equally. James Harden can break down a defender, draw help, and set up a wide-open C&S 3, and he can miss. Rondo can dribble the ball while his defender sags off, give a pass to his teammate, and the teammate takes a dribble and makes a difficult shot. Harden added a lot more value on that specific play but Rondo gets credit and Harden doesn’t. In another situation, Curry breaks down the defense, passes out to Poole, and Poole throws the extra pass to Klay who shoots. Poole would get the assist but Curry is the one who created the advantage. Curry would still get the secondary assist but if the ball were passed a few more times Curry wouldn’t receive any credit even though he created the advantage. An assist gives no understanding of how much creating the player did. People started to realize two things with Rondo, the first being he wasn’t doing much as he was being set up for success and not creating that success himself, and second, in some ways, he was hurting the team with a lack of spacing.
After understanding why assists are flawed statistics, we start to get into stats like modified assists, which do a good job of giving players value while adjusting for teammates. All of this leads to the ultimate goal of quantifying playmaking as a whole. Modified assists are a step in measuring the passing part of playmaking but quantifying playmaking as a whole would include passing (quality of passes, ability to find high-value shots), finishing (pressure on the rim, proficiency at the rim), shooting (range, accuracy, volume), and off ball ability (moving off the ball effectively). Playmaking is built on these pillars and great playmakers are players that can create shots for themselves and leverage these into open looks for others. Another way to measure playmaking, or at least part of playmaking, is through adv. creation. SIS Hoops has been working on some amazing statistical work with adv. creation. They have attempted to measure advantages created on a per-minute basis. This is a huge step forward, but they have taken another step with role players and maintaining these advantages.
TS: Attempts to measure scoring efficiency as a whole, takes into account how much each shot is worth, and gives a possession value to a free throw. ((PTS)/(2 (FGA x (0.44)FTA)))
TS+: Percent above league average TS%. 100(TS%/League Average TS%)
Modified Assists (From the Flarescreen): A player’s assists adjusted for league average conversion rates and weighted for 3-point makes being statistically 1.5x more valuable than 2-point makes, using a model similar to eFG.