Small ball is no longer the wave of the future – it’s the wave of the present. The 80’s and 90’s featured brawny, bruising power forwards who could soak up punishment in the post, clean the glass, and protect the paint as weak side shot blockers. In a game dominated by giants, power forwards were the centers’ sidekicks. Even the beginning of the 21st century saw the San Antonio Spurs’ “Twin Towers” follow the same frontcourt structure that had held sway throughout the league’s existence.
Change, though, is persistent. Stylistic and strategic changes alter the face of the league regularly. In the last decade, those changes have eroded the physical profile of the standard power forward. Horace Grant has given way to Jerami Grant. As stretch 4’s have gradually replaced the 4’s of yesteryear, the function of power forwards on the defensive end has changed considerably. As with all changes related to lineup construction, the small ball revolution is an arms race. The more stretch 4’s there are who can shoot from outside, the more teams need their “power forwards” to defend on the perimeter. Today’s power forward must be able to stay in front of some opponents outside while still holding his own inside.
We’ve already established that defensive load has a greater spread for perimeter players than for big men, and have even posited some reasonable explanations. The point we need to make in this context is that being big and having less defensive flexibility is a double whammy:
- Big men have fewer scorers to match up against, since offensive talent tends to cluster on the low end of the size spectrum.
- Being limited in defensive flexibility also decreases a player’s defensive load. If a player can only guard players who are his own size, he has fewer opportunities to guard primary scorers.
For both of these reasons, defensive load for big men is likely to be close to the median.
There were only eight power forwards last season who ranked in the upper quartile of the group in load and also saved their teams at least 10 points per 100 possessions. Only these eight are in the table below. Remember that Shooting Defense is multiplied by .744 before it is combined with Non-Shooting Defense because 25.6% of the credit on possessions ending in a missed field goal goes to the rebounder, and only 74.4% goes to the defender.
|POWER FORWARDS||shoot def||PER 100||non-sht def||per 100||tot def||per 100||rel load||LOAD|
Smell test = failed. If we have reason to believe that the Matchup-Based Defense model accurately attributes credit to defenders, what are we supposed to do when it gives us results for a particular player that are surprising? There are three basic options for how we might respond to such a circumstance. We can 1) scrap or rebuild the model, 2) evaluate the model for biases, or 3) reevaluate our perceptions of the player at hand. Broadly speaking, option 1 teaches us something about building a model, option 2 teaches us something about the model itself, and option 3 teaches us something about the player.
Option 1 is only preferable if we have little or no confidence in the model. If we do have reason to think that the model measures something valuable, and measures it correctly, we should proceed on the theory that the model has a bias for or against certain teams/players, or that the player in question is inaccurately rated by other sources and the model does a better job of recognizing his value.
So why on earth does the model give Maxi Kleber so much credit? Other defensive metrics tend to depict Kleber as a well above average defender – he even ranked 23rd in the league in Defensive Box Plus/Minus last year. Defensive Win Shares classifies Kleber as a mid- to low-range starter, while ESPN’s Real Plus/Minus (which is based on Jeremias Engelmann’s xRAPM) measured his defensive impact at +2.1 points per 100 possessions in 2018-19, good for 12th among power forwards. Kleber clocks in at a very respectable 71.25 Defensive Points Saved according to NBA Math. There is no real consensus here, but other models seem to generally agree that Kleber is a decent-to-good defender.
Okay, So Why Does Matchup-Based Defense Like Kleber So Much?
Even if Maxi Kleber is an analytics darling who is over-appreciated by “stat geeks” and underappreciated by “eye testers,” that still does not explain why this model gives him more credit than other statistical models. The cause for this state of affairs is relatively simple to explain. Kleber had a load that was higher than the team’s average defensive load. Taking his role into account, Matchup-Based Defense infers that Kleber faced more difficult opposing players than an “average” defender might.
While this inference is normally reliable, it may not comprehend the unusual dynamics of the 2018-19 Dallas Mavericks. Although the model does handle players on both good defensive teams and poor defensive teams fairly, the Mavs were unusual in that they had little defensive depth, but managed to be a mediocre defensive squad in spite of limited options. Kleber furthermore played 943 of his 1502 minutes alongside of either Dwight Powell or DeAndre Jordan, making it necessary for him to cover the “stretch” big in most scenarios. After trading Harrison Barnes, Dallas had even fewer options to defend big forwards.
As a result, Maxi Kleber lands in the upper quartile of power forwards in Team-Adjusted Load despite having a raw Defensive Load of only 12.5 ppg. It certainly seems possible that the model has a bias against teams with highly unusual rotations, such as the one that pushed a 6’11” big man to the 4 spot to accommodate a thin rotation. In order to be certain of this reasoning, however, it is necessary to first establish that Kleber isn’t truly as good as the model says he is. Since the part of the model that supposedly overstates his impact is the Shooting Defense segment, can we be certain that Maxi Kleber does not save points for his team by preventing the players he guards from making shots?
Nonsense Stats, or Real Value?
This is where it gets interesting: when we evaluate shooting defense using other methods, it becomes increasingly difficult to maintain the thesis that Kleber is overvalued by the model. The raw difference in Defensive FG% against Kleber and the normal FG% of the players shooting against him is -3.9, meaning that opposing players’ FG% is 3.9% worse when shooting against Kleber than it is normally. This figure ranked Kleber 14th in the league last season. FiveThirtyEight’s DRAYMOND metric, which relies heavily on this variable, has Kleber as the fifth-best shot defender in the NBA last year. I have already explained why the raw difference overstates the contributions of big men compared with guards and wings. If we instead use the percent change on shots taken against the player compared with all shots taken by the shooter, we still see that Kleber has a noticeable effect on opponent shooting. Eliminating players who appeared in fewer than half of their team’s games, Kleber is 26th in the league with an 8.57% decrease in opponent shooting.
Thus, it looks like we don’t know for sure that Maxi Kleber doesn’t provide value for his team by preventing opponents from making shots. Although the 2018-19 Dallas Mavericks were certainly an unusual squad, it is not certain that their unusual roster and rotation are responsible for Kleber appearing on the leaderboard. Maxi Kleber might actually be (gasp) a good defender despite not making any flashy or splashy plays.
But What Does the Film Say?
Statistics are fine, I can hear some of you saying, but film don’t lie. What does the film have to say about Maxi Kleber. This season against New Orleans, watch Kleber stay in front of a guard and force him across the lane, then clean up the shot:
Here in a game against Atlanta last year, we see Kleber open his hips toward the dribbler’s off hand, stay on a guard’s hip, then deflect the shot to a teammate.
What Kleber lacks in foot speed he makes up for in a variety of ways: good initial positioning, attention to oncoming screens, good instincts for the timing opposing player’s cuts, and some good old-fashioned defensive IQ are among them. He can chase a wing on a loop around a screen step-for-step:
He has strong feel for the PnR against a duo with a lot of experience running it, and is able to cut off both the pass and the drive.
But of course, whenever you’re gearing up as an analyst to praise a player, he has to go and do this:
Maybe nobody is going to concede that Kleber is a good defender after the “Hammer Heard Round the World,” but the fact of the matter is that both the stats and film say Maxi Kleber is a good defender. Every player in the NBA makes mistakes on defense. Kleber makes fewer mistakes than most, though this particular mistake came at an extremely inopportune moment.
Who Else Was Among the Best?
P.J. Tucker saved the most total points of anyone in the group, surprising exactly no one. Despite carrying the second-highest load in the group behind Al-Farouq Aminu, Tucker was remarkably effective. Despite his lack of size, Tucker has proven himself a world-beating stopper.
The small ball 4’s show out quite well in this analysis, as a matter of fact. The high load, high effectiveness subgroup is at least ¾ small guys (or more, depending on how you count Tucker and Aminu). James Johnson and Aaron Gordon are similar to one another insofar as both have more ball handling responsibility than normal for power forwards. In addition, both players depend on terrific Shooting Defense for the majority of their defensive value, as neither player forced even 2.0 turnovers per 100 possessions.
Even smaller are Michael Kidd-Gilchrist and Joe Ingles, the next two players on the list. Neither of these two reached 10 points saved per 100 possessions in Shooting Defense, though both ranked highly in Non-Shooting Defense. MKG was second in the group to Thaddeus Young by forcing 3.63 turnovers per 100 possessions.
What About Everyone Else?
There are also a number of high-load power forwards who are not as effective as the group above. The table below includes all high-load power forwards, regardless of performance.
High-Load Power Forwards
|POWER FORWARDS||shoot def||PER 100||non-sht def||per 100||tot def||per 100||rel load||LOAD|
The rest of the power forwards in the league fall into one of the other three quartiles in defensive load. Several notable defenders appear in the table below due to falling in the “Medium Load” group. Building on our previous discussion about the differences in defensive load between position groups, it appears that big men (4’s and 5’s) who are good help defenders are underrated by Matchup-Based Defense. These players often make high-value plays that prevent players other than their man from scoring. Since Matchup-Based Defense is keyed to recognize a player’s value in forcing turnovers and preventing his opposite number from scoring, it does not pick up on some of the value contributed by players like Draymond Green and Giannis Antetokounmpo. These players were among the very best defenders in the league according to my Tracking-Based Defense model. Matchup-Based Defense, however, does not recognize all of their value.
|Player||Shoot Def||PER 100||non-sht def||per 100||tot def||per 100||rel load||LOAD|
|Jaren Jackson Jr.||350.9||11.8||64.7||2.2||325.7||11.0||1.0||12.0|
|Harry Giles III||116.2||6.5||34.6||1.9||121.1||6.8||0.9||11.0|
Other notable defenders outside the upper quartile in load are Jonathan Isaac and Jaren Jackson, Jr. These two athletic young forwards are widely regarded as strong defensive prospects. As such, it is worth watching to see if either is able to move up to the high-load class and maintain the same level of effectiveness.
Who do you think is the best defensive power forward in the league? Leave your takes in the comments, then click “Subscribe” to receive an email notification when the list of best defensive centers in the league comes out.