In our previous post we tackled, at length, how John Hollinger’s Player Efficiency Rating (PER) is calculated and identified how different aspects of a player’s game is weighted. This leads us to question certain components of PER. Should I prefer a scorer or a rebounder? How do these terms interact? What about team affiliation? In this post, we tackle a few of these components to help understand what PER **truly says about a player** when combined with their traditional statistics; of which PER is obtained.

For example, every rebound is reduced by the league rate of rebounding. For the 2016-17 NBA season, this amounted to reducing all defensive rebounds by 76.7% and reducing all offensive rebounds by 23.3%.

## Offensive Rebounders are Better.

Consider two players who are identical in every way. Same number of minutes, same number of points scored with identical field goal (two’s and three’s as well) and free throw percentages, same number of steals, same number of steals, and same number of rebounds. These two players **should** have the same PER right? **Not a chance**.

### But First: Teams Dictate a Portion of the Overall PER or…

### Klay Thompson is Better Off on the Warriors than the Suns

First off, if these two players are on different teams, then they are affected by their team’s performances. There are two parts of PER, which I coined the “What the Heck” factor given by

For these two identical players, the league assists, field goals made, and free throws made totals are going to the be the same. However, suppose one player is on the **Golden State Warriors** and the other player is on the **Phoenix Suns**. Let’s denote the **percent of team field goals that came off assists as **

Applying a little algebra and we see that the difference between these two players **with identical stats across the board** has a difference of unadjusted PER (uPER)

First we see the difference between Golden State and Phoenix’ **assist to field goals made ratio** washing out as a difference between these players. To give insight, **Golden State had 70.5266% **of field goals come off assists. Alternatively, **Phoenix managed only 49.0520%**. This difference sits at **.214746**. This means the Warriors player will have a boost of the first term by twenty-one percent. We call this a boost, only if the first term is indeed positive.

For reference, the 2017 NBA season witnessed **55,600 assists**, **96,061 made field goals**, and **43,883 made free throws**. This results in

This is **only positive if the player makes 2.518625** **more field goals than free throws**. For the 2017 NBA season, this is only true for **238 of the 486 players**. The most common type of players on this list are three point specialists such as **Kyle**** Korver (6.2895), Danny Green (6.5185), **and** Wayne Ellington (6.2432)**; mid-range shooters who rarely draw fouls such as **Meyers Leonard (4.1714), Leandro Barbosa (4.3000), **and **Boris Diaw (5.6154)**; and terrible free throw shooters such as **Thaddeus Young (8.0444), Sam Dekker (5.3412), **and **Andre Drummond (3.5255)**.

This means if the players we are comparing score more field goals than free throws than the 2.51 required rate; then the **Golden State Warrior will obtain a higher PER just by being on the Warriors**. The amount difference? Let’s suppose this player is **Klay Thompson**. Then the Golden State Warrior bump in uPER is **2.4945**. This amounts to roughly an extra field goal made; which will result in roughly **an increase in 0.0769 in PER**.

What this means is… if a Phoenix Suns player replicates Klay Thompson’s 2016-17 NBA season; they are docked nearly 0.08 PER point due to playing on the Phoenix Suns.

### Now How About Those Rebounds

Now that we have seen just by playing on a certain team, a player’s PER can be affected by roughly 0.08; let’s see the impact of rebounds. In this exercise, we return to the identical players who have the identical stat lines, **except for breakdown of rebounds**. In the 2016-17 NBA season, there were 256 of the 486 NBA players with 164+ rebounds (2 per game). Our example of Klay Thompson above yielded 285 rebounds on the season. Thompson’s rebounds break down as 49 offensive rebounds and 236 defensive rebounds. The amount of contribution to uPER for rebounds is given by

The **estimated points per terminated possession**, *v*, is a league average calculated points scored per estimated number of possession. While the estimated number of possession is grossly off, despite corrections given by Basketball-Reference (still off by a few hundred possessions) and Squared 2020, we calculate the **league points per possession factor **as 1.062323 (259,753 points scored over 244,514.2 estimated possessions).

The **defensive rebound rate**, *d*, is the percentage of league rebounds that resulted in defensive rebounds. This factor is the simplest of the three to compute in the PER calculations and is given by 82,109 divided by 107,046 for a value of **0.767044**.

This means that Klay Thompson’s rebounds contributes to an extra **98.3403** uPER. In our article breaking down PER, we noted that all rebounds are down-weighted by the league average. Using a little algebra and ignoring *v*, we saw that this is equal to **defensive rebounds minus league-average-weighted-defensive rebounds** **plus offensive rebounds minus league-average-weighted-offensive rebounds**:

What this means is that **Offensive Rebounds are more important than defensive rebounds!** Let’s check this calculation. Let’s assume that Phoenix Suns player who has identical stats to Thompson exists. Furthermore suppose that this Suns player has instead of 236 defensive rebounds, they have 250 defensive rebounds. This yields only 35 offensive rebounds. This will result in a uPER contribution of **90.3984. This is another 8 solid points less than Klay Thompson’s uPER attributed to rebounding**.

This means that **Klay Thompson gets an extra ~0.33 PER points from being a stronger offensive rebounder, weaker defensive rebounder, and playing on the Warriors instead of the Suns. **

This indicates approximately a 10 player jump in PER **despite having same summary statistics except a 15 rebound change in OREB/DREB. **

## Volume Shooters are Rewarded

### And you don’t have to be that good…

Next we look at how field goal percentage is taken into account. The terms involving field goals made (FGM) and field goals attempted (FGA) are taken into account. To understand how FGM and field goals missed (FGA – FGM) are rewarded/punished, we look at “breaking even” in field goal shooting percentage. This percentage would then indicate how well a shooter must shoot before seeing their PER increase.

To start, we take a look at the terms associated with FGM and FGA. These are given by

Note that the factor, *f*, is the what the heck factor which is merely **0.600493. **While league field goals made appears twice in the denominator of *f*, the contribution of a player is fairly negligible. For instance, consider the **worst field goals made players**: Aaron Harrison, Ben Bentil, Danuel House, Gary Neal, Patricio Garino, and RJ Hunter. All these players **scored zero field goals and admit zero contribution to these denominators**. On the flip side, **Russell Westbrook **lead the league with 824 made field goals. His contribution will affect this factor by .001 and **a****ffects field goal percentages by less than .0001. **This means that we can effectively treat this factor as a constant.

Next, we see that FGM appears for a player embedded in **Team FGM**. This is a place where a player can change the field goal percentage. The largest percentage, of which, is again Russell Westbrook with 824 of this **Oklahoma City Thunder’s **3237 field goal makes. This is over a quarter of field goal attempts taken. This will actually affect field goal percentage necessary for finding a zero contribution to uPER. In this case, **the maximum, we find the change in FG% is less than 1.5%**. This means that if we treat this percentage as a constant for each team, the field goal percentage will effectively be within one percent.

### Time for Heavy Duty Math…

Now if we write the above model to being **zero contribution** (Break Even) to uPER and **eliminate constant terms that attribute to effectively plus-minus one percent of resulting FG%**, we obtain the model

The function F(*t*) is the factor, *f*, times the assists per field goals made for each team. This will be a function of a respective team. Now, let’s break down **FGM** by its definition **FGM = 3PM + 2PM**. Similarly, we break down **FGA** by its definition **FGA = 3PA + 2PA**. We then obtain an equation in terms of **3PA, 2PA, 3PM, **and **2PM**. We can collect like terms to obtain

Now we perform the trick of multiplying by one. This will eliminate two variables (more so will hide them cleverly) and give us two equations with two unknowns; **hidden as one equation**. I will build off the previous two steps to show where the work comes in:

This last line is actually two equations two unknowns. The rationale for this is due to the fact that **no player can attempt less than zero three point or two point attempts**.

Hence, the only way this quantity can be zero is if **both** **3PA ****and 2PA are zero **or **the coefficients are zero**. Let’s assume the latter, as we are interested in finding field goal percentage that results in a net zero gain. Let’s isolate this equation and look at each coefficient.

This equation is the important one. **First, **if the field goal percentage for each type of field goal attempt is above the “break even percentage” **then the uPER increases by the number of attempts of that player**. This directly shows that **PER favors volume shooters provided they shoot above a “break even” percentage.** Note from the equation that this is team-dependent.

**Second**, this equation yields the break even value for each team. Anything above this percentage results in positive PER. The better the field goal percentage, the higher the PER **per attempt**.

Solving for both three point and two point percentages, we obtain

Calculating this for every team for the 2016-17 NBA season, we obtain the break even field goal percentages for every team:

What this shows is that **a team effectively needs to shoot better than 33% from two-point field goal range and better than 23% from three-point field goal range. **These are terrible numbers! Anything above these percentages will result in positive PER **per attempt**.Let’s drive this point home.

Suppose a player on the New Orleans Pelicans shoots 35% for two’s and 25% for three’s. This player will obtain a multiplier of 1.05 per two-point field goal attempt and 1.0634 per three-point field goal attempt. Now if the player takes **Russell Westbrook** type attempts, say 26% of the offensive load, then the player will attempt 1,853 attempts over the season; 571 of which are three point attempts. **This results in a uPER increase of 1953 uPER**. Consider this compared to **Klay Thompson’s uPER due to scoring of 1010.3107**. This means this Pelican’s player is nearly twice an efficient scorer as Klay Thompson (as Westbrook played in just a little more minutes than Thompson on the season).

By the way, this results in the Pelicans player scoring 428 points on three’s and 897 points on two’s for a total of **16.16 points per game from the field. **That means he’s pushing his team to scoring roughly 64 points from the floor as he commands over 25% of field goal attempts. But yet, he’s nearly twice as efficient as Klay Thompson. This absurdity is highlighted by Thompson’s 19% of the offense on the floor that results in 1556 points for an average of **18.98 points per game**. And hold on… this number is lower than actual since Thompson played in 78 games instead of 82. That said, Thompson is pushing his team to score roughly 95 points per game from the floor. This is **150% more effective than the hypothetical Pelicans player**; **who currently is viewed as twice as better than Thompson according to PER scoring**.

## Conclusions

In three careful examinations of PER, we find that PER benefits players on teams that pass well, make more than 2.5 times as many field goals than free throws, and tend to gather more offensive rebounds than defensive rebounds. We also identified that PER heavily benefits volume shooters and if they manage to be at minimal terrible shooters, that can use that **to inflate their numbers to look better than their All-Star and All-NBA quality peers.**

This doesn’t show that players with high PER are terrible, however. What we do say is take this measure with a grain of salt and surround it with qualifying metrics. We quickly see that this measure favors perimeter shooters, as well as poor free throw shooters who score a sizable amount of points from the field. The above explanations capture these rumblings.

How else do you see PER?

Reblogged this on Advance Pro Basketball and commented:

Another great post by Justin Jacobs @Squared2020

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