The Evolution Of Shooting Stats

Way back when shooting stats were very straightforward. Points and field goal percentage were king. Fast forward a few decades and the addition of the three-point line created a problem with FG%. A player could make fewer shots but produce more points if the majority of their makes came behind the 3-point line. In the year 2024, FG% is basically useless. Steph Curry is taking 12 three-pointers a game and 8 two-pointers. His FG% is 45% which is five percent worse than league average. Obviously, this isn’t accurately capturing how efficient he is.


This leads us to the first step of the shooting stat evolution, Effective Field Goal Percentage. This stat correctly views three-pointers as being more valuable than two-pointers. One three-pointer made is worth 1.5 two-pointers made. Steph’s eFG% is 57%, considerably higher than his FG% (45%). FG% says Steph is five percent worse than league average efficiency while eFG% says he’s 5 percent better. This is a massive change.


The next step in the evolution of shooting stats is True Shooting Percentage. It correctly values two and three-pointers like eFG% but also factors in the most efficient shot in basketball, the free throw. Jimmy Butler is a good example of why this stat is valuable. He has a very high free throw to field goal attempt rate. He sports a career eFG% of 50% which isn’t very impressive (four percent worse than league average over that span), But his TS% is 59% (five percent better than league average over that span). Jimmy attempts fourteen field goals and eight free throws a game. Not including free throws in his overall efficiency is a massive oversight.


Another step down the efficiency rabbit hole leads us to Points Per Possession. This takes everything incorporated in TS% and adds turnovers to the equation. Throwing the ball out of bounds is the same thing as missing a shot from an offensive possession standpoint because both result in zero points for the offensive team.


Now that we covered box score efficiency stats we are going to take a much larger leap, think standard to high-definition TVs. Basketball Index has three shot making stats that use shot quality to evaluate how a player is performing relative to expectations. The first metric is Shot Making Efficiency, this metric works by finding the expected eFG% for a given shot based on its openness, location, and difficulty. Then using the player’s real eFG% on those shots it measures how much they over or under performs relative to expectation. A good example of how this is useful is when looking at a big that scores the majority of their points via assist near the rim. Their raw efficiency numbers are going to be high because of their high shot quality. But if the majority of bigs post even higher efficiency numbers on similar shots this player would be below average in Shot Making Efficiency. This metric is also padded with a specific number of league average attempts to enable higher predictiveness and combat the problem of small sample sizes. 


Next is Shot Making, it works the same way as Shot Making Efficiency but it has a shooting volume component that looks at how often a player scores per 100 possessions. This is helpful because the ability to scale efficiency on higher volume is an extremely valuable trait.


Our last metric and the current edge of the shooting stat frontier is Overall Shooting Talent. This metric works the same as Shot Making with an additional weighting for self-created shots. It’s important to make the distinction between volume and shot creation, a player who can’t self-create can have their volume limited by their teammate’s lack of playmaking and the opponent’s scheme. Shot creation is much harder to suppress and therefore more valuable. These self-created scoring chances drive higher attention by the defense, which is valuable for an offense through enabling more open shots for teammates (as long as the player finds those teammates). There’s inherent value to this element that is accounted for with this metric.


All of the Shot Making/Shooting Talent stats can be distilled down to measure certain things on the court. You can isolate how a player performs at the rim, the midrange, and from 3. You can also look at halfcourt vs transitions (Half Court Shot Making & Transition Shot Making) and more.


Here is a recap of each stat:


FG%: Field Goal Percentage is a simple metric that records how many shots a player takes and how many shots a player makes. 

Formula – FG%= FGM/FGA


eFG%: Effective Field Goal Percentage is FG% that correctly values a 3-pointer as being more valuable than a 2-pointer.

Formula – eFG%= FGM + (0.5*3P)/FGA


TS%: True Shooting Percentage correctly weights 2-pointers and 3-pointers while also including free throws to better capture a player’s overall scoring efficiency.

Formula – TS%= PTS/2(FGA + (0.44 * FTA))


PPP: Points Per Possession takes into account a player’s 2-pointers, 3-pointers, free throws, and turnovers to capture their overall scoring efficiency.

Formula – PPP= Points/(FGA + (0.44 * Free Throw Attempts) + Turnovers)


Shot Making Efficiency: This metric measures how much a player over or under-performs relative to expectation. It does this by looking at the difficulty of a shot, creating an expected eFG%, and then comparing it to the player’s actual eFG% on that shot. This metric is also padded with a specific number of league average attempts to enable higher predictiveness by combating the problem of small sample sizes.

Shot Making: This metric works the same as Shot Making Efficiency but also takes into account shooting volume per 100 possessions. This means this metric is a hybrid between efficiency and scoring volume. 

Overall Shooting Talent: This metric works the same as Shot Making with an additional weighting for self-created shots. The ability to self-generate shots is one of the most valuable skills in the game.

The evolution of shooting stats is very much a story of standing on the shoulders of giants. These stats build on each other to better explain what is happening on the court. As the game evolves so does the way we analyze it.

Have questions? Reach out on Twitter/ @taylormetrics

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