Knowns and Unknowns: How Outliers Will Affect Milwaukee’s Growth

(Editor’s note: all stats are as of Monday, January 9th)

Well before the launch of the ill-fated 2015-16 season, Bucks GM John Hammond commented on Milwaukee’s strategy for improvement:

“We’re going to try to go from good to good,” (John) Hammond said, quoting (Jason) Kidd. “I think we were a surprise team this season. Nobody expected us to do what we did. Now, can we just be good again and gradually take that step. To go from good to great is often difficult. So we’re just trying to find some consistency, especially with our youth and build off that.”

As we know all too well, last year’s Bucks team did not go “from good to good.” Consistency was elusive as trusted veterans were replaced by untested youth, and instead of standing pat the team took a sizable step backwards.

So far this year, the Bucks’ improvement has both exceeded initial expectations while falling short of others. As it usually does in the NBA, credit for the improvement is largely given to the major strides made by the team’s foundational players: Giannis Antetokounmpo and Jabari Parker. And perhaps that’s good enough to make the team go from bad to good, but what will it take to go “from good to good?”

An obvious long-term answer is an infusion of talent elsewhere on the roster (either through internal development or an external acquisition), as well as a full recovery from fellow building block Khris Middleton. But in the short-term, I think that developing team-wide consistency can be reduced down to two things: containing knowns and controlling unknowns.

To elaborate, let’s talk about some things that we know about the Milwaukee Bucks. We know that Jabari will score points, and that Giannis will do a lot of everything. We know that the defense will try to generate turnovers to create transition opportunities, and that the offense will attack the basket while also taking more threes (on most nights).

But what about what we don’t know on a night-to-night basis? Will Matthew Dellavedova or Malcolm Brogdon take on significant playmaking duties, or play more reserved? Will Tony Snell, Jason Terry, or Mirza Teletovic hit all of the shots, or none of the shots? How involved will Greg Monroe be on defense, or how engaged will John Henson be on the boards? And on top of all that, how will the opponent’s knowns and unknowns play out during the game?

Breaking down team-level stats helps inform our knowledge of their W/L performance, but diving into individual-level numbers helps us see game-to-game outliers, which paint a more detailed picture. Consider this example: on October 29th Rashad Vaughn hit six threes and led the Bucks with 22 points in a 110-108 victory over Brooklyn. Giannis made the all-around contributions that we have come to expect from him, and Jabari Parker’s scoring was slightly depressed but not surprising. But it was Vaughn who stands out the most as one of the Bucks’ positive outliers, when considering how much he scored relative to his “expected” averages.

In this example, Giannis and Jabari’s box scores would qualify as “knowns,” and Rashad Vaughn’s heretofore unseen scoring explosion is obviously an “unknown.” Those unknowns are what I think the Bucks need to reign in for them to take the next step as a team. How many of these outliers have so far driven victories for the Bucks, and how many allowed outliers have caused losses?

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Before we dive in, let’s establish why outliers even matter in the NBA. As an example, as a coach planning a matchup against Cleveland, you can safely assume that limiting LeBron James to 20 or fewer points isn’t a realistic goal. Based on his ability and historical body of work, you can almost assume that a stat line somewhere around 27/7/7 is in the cards. It’s even called a “LeBron;” that’s what makes him a superstar.

If you were to ignore this assumption and set your LeBron-centric gameplan for ≤ 20 points, two outcomes are likely. First, you may or may not succeed in containing the known (in this case, stifling LeBron). This will largely depend on your defense’s execution and whatever counter-strategy the Cleveland offense employs. Good luck!

Simultaneously, because you are focusing attention in one area, there is a higher likelihood it lapses elsewhere, and you risk losing control of the unknown. This can manifest itself when Channing Frye scores 18, DeAndre Liggins scores 10, and you’re staring at a double-digit deficit in the fourth quarter. The reverse can hurt your team just as much. In focusing your strategy on controlling unknowns, you can definitely keep supporting cast members like Frye and Liggins at or below their averages...but you might compromise your containment of the known and watch LeBron slice through the lane for 45 points.

It is no revelation that the NBA is a star-driven league, which makes the task of containing the known all the more formidable, but also makes controlling the unknown more important. That’s how the Pelicans can lose a game in which Anthony Davis scores 50. If you can’t do either, your team gets blown out. If you manage to do both, then you get to rest your stars in the fourth quarter of a rout.

That’s the power of outliers. You can’t rely on them consistently, but they can combine with your team’s foundational players to create winnable situations. Outliers can turn a 2-point deficit into a 1-point lead when it matters. After all, even the worst NBA player is still one of the best basketball players in the world!

So why do outliers matter for the Bucks’ current outlook? In my view, it’s because they are part of the reason for their middling record (18-18 as of January 9th) despite boasting a top-10 Net Rating (ninth at +2.19 as of January 9th). From there, in looking at the state of the league, I would contend that limiting the impact of opposing outliers is how the Bucks will start to win toss-up games with regularity, which is the Bucks’ next step towards becoming a real NBA contender.

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So what results have outliers contributed to this season? The answer (as it often does) lies within the box score of every Milwaukee matchup. To briefly summarize, I took each box score from all 36 Bucks games, excluded Giannis and Jabari, and compared each players’ scoring total to their season average. Then I pulled the amount scored (minimum threshold: 8 points) for any player who also exceeded their personal scoring average by one standard deviation (which is roughly ≥ 33%), then sorted based on the outcome of the game. This methodology is specifically aimed to highlight the impact of outliers of Milwaukee players for when the Bucks win, and that of opposing players for when they lose.

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Bucks’ Outliers

As mentioned at the top of this post, one hallmark of a competent NBA team is consistency. Consistent teams are able to set the pace, play the style that suits them, and generally dictate the terms of engagement rather than react to them. The Bucks have, for as long as I’ve followed them, lacked this trait. At this moment, however, we are seeing that change.

When running each Buck’s box score through this model, we confirm what many have already noticed: the team largely depends on Giannis and Jabari for the bulk of the scoring, and they have continued to improve their ability to be dependable. Out of the 71 games that Parker and Antetokounmpo have both played in (36 for Parker, 35 for Giannis), they have met or exceeded their expected scoring averages in 80.3% of them.

Being able to depend on two players for 40+ points on most nights allows for your supporting cast more flexibility, as it does not force anyone into the “third banana” offensive role. Greg Monroe (the only other double-digit per-game scorer) is the closest fit this year, and it’s widely assumed that Khris Middleton will slide into this role upon his return from injury. Adding a third reliable scorer will promote further “spreading of the wealth” for everyone else on the roster, allowing players to focus on taking “their” shots rather than taking just any shot, which will both improve individual efficiency and the team’s overall scoring output. As the chart below shows, the Bucks are already reaping the benefits of this setup:

Who are the most common outliers, you may ask? The obvious top performer in this exercise is Greg Monroe, who scored enough above his average in 10 out of 35 possible games. Following him is Michael Beasley (7 games out of 30), Malcolm Brogdon (6 games out of 36), and Tony Snell (6 games out of 35).

This information may not be revolutionary, but is worth taking into account when considering the Bucks’ reputation, and for predicting how opposing teams will design their defenses against Milwaukee. If opponents want to contain the Bucks’ knowns (Giannis, Jabari, and eventually Khris), they run the risk of letting the Bucks’ unknowns (everybody else on the team capable of hitting a shot) get hot enough to compensate. This speaks to the importance of maintaining a competent bench going forward, and could help explain some of the decisions the Bucks have made in free agency over the past two summers. But if opponents overcompensate towards the other extreme and try to limit the bench...well, have three teammates ever scored 25+ in a game before? Because the Bucks’ Big 3 is more than capable of such a feat.

Opponents’ Outliers

As always, there’s another side to the story. Milwaukee can try to score all they want, but we’ve seen how bad defense can submarine a team before. Inconsistency in this department is probably the biggest area of opportunity for the team this season, and improvement here would be a massive positive to build off of going forward.

The inquiry here is the same. How many players did the Bucks allow to exceed their personal scoring average by a significant margin? As it turns out, most losses this season saw a non-Milwaukee player (and in most cases, two players) accumulate enough points to cause serious problems.

In putting this piece together, I realized that this may be a symptom of what many consider a massive flaw in the Bucks’ defensive strategy, and it may also be a reason for the Jason Kidd backlash after losses. Because of the level of activity, flexibility, and communication Kidd’s blitz-heavy, switch-all-the-things scheme requires, the margin of error for Milwaukee defenders is razor-thin. And with the overall lack of consistency on the roster, defenders are more prone to making mistakes, which makes Milwaukee uniquely vulnerable to the scoring punch of opposing outliers.

Going forward, this weakness reiterates the aforementioned necessity of maintaining a competent bench going forward. Bucks role players need to be able to take what the defense gives them on offense, but it seems even more important that they understand and can execute the scheme on defense. This is the case to be made for retaining a player like Tony Snell, who may not offer consistency on offense but can be relied upon on defense.

So we come back to the question: should the Bucks defense focus more on stopping outliers, or limiting the impact of stars? Both approaches have merit, but the current scheme seems to be the best option for long-term success when considering the defensive strengths (and flaws) of each of the Bucks’ cornerstone players. For my part, I think that the organization has demonstrated their belief in Jason Kidd’s system, and over the 2.5 seasons he’s been in charge, the defense has been an overall-positive for nearly 1.5 of them. And during that timeframe, the defense is at its best when other teams’ supporting role players aren’t able to get into a groove, limiting their impact overall.

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More than anything, putting together this article marks a significant shift in our approach to understanding the Milwaukee Bucks. For years, the prospect of landing a top-tier player who could be relied upon was a pipe dream. As it stands now, we have two of those, with a possible third returning from injury. While invigorating, the shift also highlights the importance of building a strong bench, who are going to often be the difference between big wins and disappointing losses going forward. The conclusions in this piece are just the ones that I came to myself, but what do you think? Let me know how far off the mark I was in the comments!