I was very high on Collin Murray-Boyles, ranking him third on my final big board before the 2025 NBA draft.

His rookie season was a bit bumpy, however. An elbow injury and a recurring left thumb injury forced him to miss 25 games and presumably made shooting more difficult.

But in this Raptors-Cavaliers series — featuring multiple All-Stars — he looks like one of the best players on the court, despite being just 20 years young.

What’s the special sauce that makes this unique rookie so potent in the playoffs?

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We released the results of my 2026 NBA draft model earlier this week, and you can find those here.

While the model has some of the usual suspects near the top, it also ranks several prospects significantly higher than you’ll find on most NBA draft boards.

Does this mean the model is overrating certain stats? Does it have blind spots for particular deficiencies?

Or are the NBA scouts and GMs still caught up in biases, such as a prospect’s ranking in high school?

Let’s check the tape for answers on six names that deserve a much closer look.

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Let’s dive into the results — which prospects my NBA draft model likes and dislikes, and why.

What the NBA draft model does

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We asked 12 leading NBA voices on Substack:

NBA playoffs: The stakes are highest for ...

Check out their answers and subscribe!

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While conceding that I’m probably not answering the question correctly and perhaps should be focusing on James Dolan announcing to the world in January that this a championship-or-bust spring for the Knicks — or fixating on Victor Wembanyama’s Spurs vs. Playoff Inexperience conundrum — I find myself thinking a lot about LeBron James’ future.

The Lakers realistically have little shot in their Round 1 series against Houston without the injured Luka Dončić and Austin Reaves, so it’s not like LeBron is expected to magically overcome their absences. Nor is there something to prove legacy-wise at this point. Yet there has never been more uncertainty about what happens next for King James than there is going into this series.

The leaguewide presumption is that the 41-year-old wants to play another season ... but where? Is there a pathway to stay with the Lakers when parting company would give the purple and gold much-needed flexibility to build a more well-rounded roster? Warriors, Cavaliers, Knicks, Nuggets ... do any of those destinations make legitimate sense when it is also widely presumed that LeBron does not want to leave Tinseltown? Or could this (gasp) actually be the last time we see him on the playoffs stage?

This much we know: We’ll finally starting inching closer to getting some answers when this Lakers season is over.

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Most to lose: Celtics. They don’t want just one ring in the Tatum-Brown-Queta era.

2nd most to lose: Rockets. If they can’t beat the disabled Lakers, why pay attention?

Most to gain: Nuggets. Nobody expects them to beat both OKC and the Spurs.

Something to eye: Detroit’s 3-point shooting. If it arrives, Pistons can win the East.

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The stakes are highest for … Victor Wembanyama.

I don’t see a plausible path to a fifth ring for LeBron James or a first for an injured Luka Dončić. Meanwhile, a second ring for Shai Gilgeous-Alexander, Nikola Jokić, or Jayson Tatum wouldn’t boost them that much historically.

Wemby, however, has a rare chance to join legends Bill Russell, Magic Johnson, and Tim Duncan as an early-twenties NBA champion. If the thrilling Spurs win it all, the 22-year-old French phenom would claim his first Larry O’Brien Trophy well ahead of Michael Jordan (age 28), LeBron James (27), and Stephen Curry (27). What better way to launch a GOAT candidacy?

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For the Cavs these playoffs feel existential. Perhaps a better framing: This group will decide whether it stays haunted or performs a group exorcism.

Some might say the Spurs and Wembanyama, but they get to exist in the bucolic realm of zero expectations and high skill. Then there’s the Pistons, the “prove it” team of this postseason.

Ultimately I’m picking OKC — and SGA. Repeating is always arduous, but this is a team that went from darling to target so fast, SGA included, that the eagerness to knock them out is palpable.

This was genuinely tough to narrow down; it’s good to have so many stakes!

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The Eastern Conference was regarded as wide open thanks to injuries to Damian Lillard, Tyrese Haliburton, and Jayson Tatum. This created a ton of pressure for the remaining healthy teams in the East. We’re already bracing for potential changes in Orlando after falling below the lofty expectations the Magic raised by trading for Desmond Bane. The Knicks could also be looking to modify their roster if they can’t get back to the East Finals.

But perhaps no team has as much pressure on them as the Cavaliers. It feels like anything short of a Finals appearance could result in a major shakeup. It could be in either direction: Do they consider offering Evan Mobley for a superstar like Giannis Antetokounmpo? Or does Donovan Mitchell ask for a trade? There’s too much at stake after trading Darius Garland for 36-year-old James Harden.

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Jake Fischer | The People’s Insider | Contributor to

I hate to pose as any grim reaper, but there are just too many good teams with high expectations to the point some club, maybe more, is inevitably going to find themselves at home after Round One and face some form of reckoning — whether that be changes to the roster, coach or front office.

The loser of Denver-Minneapolis, for example, very much entered this season with eyes on the title, let alone another conference finals trip. The same goes for the Lakers and Rockets. A lot of league personnel think Atlanta can topple New York.

What then?

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No matter how many angles I look at this from, I can’t see a bigger potential drama than an early exit (first or second round) for the Knicks.

On the positive side: Sure, if the Thunder win a second straight NBA title, it would elevate them because of their historic two-year dominance; if Wembanyama won with the Spurs, it would raise his global stature; for Boston, it would be a legendary season …

But any team or player has an excuse or a reason to survive not winning in 2026. Except for NY, obviously.

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The NBA desperately needs a fantastic playoffs for the Association’s own sake.

The Terry Rozier and Chauncey Billups gambling scandals. The still-lingering Kawhi/Ballmer/Aspiration stench. The endless complaints about tanking and the 65-game rule. An unprecedented barrage of injuries. Unceasing debate about whether the league is thriving or dying.

Despite an excellent on-court product, the NBA has been almost completely overshadowed by one publicity crisis after another. You know how best to remind people that this sport deserves love and attention? By playing some good friggin’ basketball.

The play-in has been a fantastic start. Here’s hoping for two more months like this.

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I’m excited for these playoffs for two reasons:

1. The postseason signals the end of the droning on about the problem de jour. In this case, I won’t have to hear about tanking (at least until the draft lottery) — thank you to whomever you constitute as God.

2. Wemby, SGA, and Jokić are looking to stake their claim as the game’s best, with the latter two inserting themselves into conversations that cast them amongst the all-time greats — even if “ring culture” has made championships an overblown metric for evaluating individual greatness.

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The New York Knicks.

They fired Tom Thibodeau, a great coach who changed their culture and took them to the conference finals, because that wasn’t good enough. The new coaching staff inherited a roster with Finals-or-bust expectations, set not by media or rivals, but by the organization itself.

Jalen Brunson is elite. The supporting cast is deep. The Garden is electric. But New York doesn’t do moral victories. If this team doesn’t reach the NBA Finals, the roster may get blown up. No pressure, though. It’s just New York.

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One player who qualifies is not in the playoffs at all: Trae Young.

The Hawks have absolutely surged since removing Trae from the equation behind the efforts of Jalen Johnson and a career-best version of Nickeil Alexander-Walker.

And yet, nowhere will the Trae trade be brought up more than in the playoffs, when the Hawks face up against Trae’s old prey, the Knicks. If this Atlanta team can make a surprise run, it puts a lot of reputational pressure on Trae, who has been addition by subtraction lately.

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The Knicks’ entire starting unit will be on the wrong side of 30 before long. And their salary situation and lack of draft picks make it unlikely they’ll be able to add high-impact players without trading away core pieces.

Meanwhile, with Jayson Tatum not yet at full strength and the Pistons having limited playoff experience, this might be the best opportunity for New York to get out of the East.

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Like other advanced metrics, NBA draft models use machine learning methods to predict outcomes based on historical data. Most draft models help assign accurate weights to certain player actions. For example, a scout might know that an offensive rebound is more important than a defensive rebound, but how much more?

Draft models can easily internalize information on past prospects: Given that the entire point of the model is to make predictions about future prospects — while using past prospect data as a guide — a statistical model easily “remembers” which blend of player stats were a good predictor of success.

These models can also more easily adjust for age and strength of schedule, as we’ve shown here:

It’s no surprise then, that draft models can sometimes predict future stars — or busts — better than scouts and GMs.

But draft models aren’t perfect — human evaluators still retain advantages when it comes to evaluating fluidity, body language, injury proneness and other factors.

While it’s hard to estimate how much each NBA team is influenced by draft model input, recent selections indicate that some teams value it highly. For example, Reed Sheppard — a 6’3” guard who didn’t score much in the NCAA — probably doesn’t go No. 3 in 2024 without draft model input.

Today we’ll show what such a statistical NBA draft model looks like, where the pitfalls lie, and what model-design decisions can be made. We’ll also supply some of the data, so that potential readers can try things out for themselves.

What is a draft model trying to predict?

Key decision: Choosing a target variable

When building NBA player metrics, such as xRAPM or BPM, most, if not all, modern metrics have settled on trying to predict the same thing: a player’s influence on the team’s point differential when the player is on the court.

Doing it this way has several benefits:

• We already imply, in the design, that we’re looking for difference-makers, not those who simply accumulate counting stats.

• The scale of the results is intuitive to understand: A player with, say, a +4 rating would help outscore the opponent by 4 points — over an average player — if he were to play 100 possessions.

With draft models, this decision becomes a little more muddy. As a draft model creator, you’re faced with several design decisions. Choosing a sensible target variable is the first one:

Option 1: Predicting per-possession NBA impact

In this category, there are several metrics to choose from, including xRAPM and BPM.

My personal preference here is to use a metric that has a box-score and a plus-minus component, such as xRAPM. The box-score component increases stability and makes the metric less susceptible to noise. The plus-minus component allows us to identify under-the-radar difference-makers such as Franz Wagner or Derrick White.

Our NCAA dataset contains every season since 2009-10. We’ll thus want to compute a 2011-2026 NBA metric to serve as our “ground truth.”

Our custom NBA metric has the usual suspects at the top: Jokić, LeBron, Curry, and so on. But these players aren’t relevant to the model, as they didn’t play in the NCAA after 2009-10, if at all.

Among the best players relevant to the model are Joel Embiid, Shai Gilgeous-Alexander, Dereck Lively II, and Dylan Harper. Some of the worst-rated players are Nick Smith Jr. and Jordan Hawkins.

Our data setup would look something like this:1

Among other downsides, one issue with predicting an impact metric is that some highly touted NCAA players who failed — such as Jahlil Okafor, James Wiseman and Anthony Bennett, who all rated -4 or worse — rip quite a big hole into our projections. With the average outcome being relatively far in the red, the projections for new prospects remain fairly low.

The fact that some of these former high draft picks were this bad is also not really relevant to us: Any player below somewhere around -1 should not find himself in the regular rotation of a winning team. So, whether he’s ultimately rated as -1 or -4 is moot: From the perspective of a drafting team, neither player should be in its long-term plans, and drafting such a player would count as a failure.

Option 2: Predicting “Wins”

The above metrics don’t take into account playing time — durability is not considered since the metrics try to predict per-possession impact.

If we wanted to account for playing time, we could use metrics such as Win Shares or VORP, derived from Box Plus-Minus (both can be found at Basketball Reference).

Both of these estimate how many wins a player has added to the team’s season total:

When I worked for the Phoenix Suns and Dallas Mavericks, my colleagues in the analytics department sometimes opted for this approach, but I think there is a significant drawback: Players on bad teams can get too much credit.

The issue is that some teams give their young players more playing time — look at the Washington Wizards, for instance. In turn, that leads to those players getting more credit, not because they are better or more durable, but just because of the opportunities they were granted. In some cases, the issue is the team is below average at drafting and/or developing players.

Should the model really boost players picked by losing teams like, say, the Wizards or the Sacramento Kings? It seems like we’d be introducing a bias, and in the wrong direction.

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Option 3: Predicting probabilities

Alternatively, we can try to predict a probability — for example, predicting whether a player will rate as above “0” in the impact metrics, such as xRAPM.

Cutoffs can be chosen at will, leading to different models and coefficients.

For the purposes of this article, I decided to predict four potential outcomes for each prospect:

1. Player impact below 0: This group is made up of players who shouldn’t be in a winning team’s long-term plans.

2. 0 to 1.5: Typically a starter on a good team or maybe the best player on a bad team.

3. 1.5 to 3: Often one of the three best players on a contender, peaking as a top-20 NBA player.

4. Above 3: High-impact players, including superstars.

I like this approach for a couple of reasons:

• It better encapsulates the variance of outcomes. Player A might be more likely than Player B to be above average, but B might be more likely to become a star.

• It solves the problem of extremely low-impact players in Option 1.

Does this approach imply that our target variable is just 1s and 0s — indicating whether a player’s impact has been above certain threshold, or below?

Machine learning algorithms tend not to love that particular data setup. It’s also not quite correct once we consider confidence in our estimates as measured by standard errors.

Take, for example, Cason Wallace, rated by the NBA impact model at +2.7. If we’re building a draft model that tries to predict if a player will surpass +3, then the target variable corresponding to Wallace would be a flat “0” when not considering standard errors.

But the +2.7 represents only our “best guess.” The level of uncertainty in that estimate is expressed by the standard error — in Wallace’s case the SE is 1.5 — creating a situation best displayed in the following graph:

Incorporating these standard errors, we attain a value for a probability that Wallace is above +3: around 43%.2

One further consideration with this model and Option 1 is what to do with players who have seen very limited time in the NBA.

Per-possession metrics might not be harsh enough on certain players such as Usman Garuba, a 2021 first-round pick of the Houston Rockets, who rates as a ±0 in our custom impact metric.

Should a player who totaled just 1,228 minutes in three NBA seasons really serve as a positive example? While the answer is probably “no,” the solution isn’t so straightforward and requires some arbitrary cutoffs. Do we consider a player with less than 500 minutes per season a failed draft pick? 700?

What variables do we want to use?

Draft models try to make predictions based on historical data. But there are, of course, different types of data.

Do we include NCAA per-game data? Per possession? Totals?

My personal preference is to use estimates for each player’s “true skill.”

There are also intrinsic variables we can add. Among those are:

• age

• height

• whether the player was born outside the U.S.

• whether the player’s dad (or another family member) has played in the NBA

And we can add variables such as the quality of the team and its strength of schedule.

Note that some of these statistics have to be normalized — for example, “z-scored” — while others should exist as simple dummy variables.

Another big topic of debate is to whether to include past “pick number” information in the model.

One reason to do that: For players like Anthony Edwards — who had so-so college stats — scouts can identify attributes that a draft model isn’t able to see. For the current draft class, the player’s current draft board (or mock draft) rank would serve as a proxy.

Incorporating past draft pick information would make this a bit less of a “statistical” draft model, since we would be essentially including past scout/GM rankings, and we will rely on similar draft board information to make predictions.

On the plus side, this makes the model more accurate when predicting future success, since not everything is captured by the stats, and scout/GM evaluations add valuable information.

Not including draft pick information means the model stays “pure,” but the predictions will be a bit less accurate.

Which route you want to choose depends on your goals. Here’s a (paraphrased) discussion between me and the Suns’ director of analytics in 2013:

Results

Which variables are most important?

Given that we are running three different models — for each group of players, based on their probability of becoming a star, a bust, and so on — we are getting three different set of coefficients.

Here are the most important ones:

• Being an international prospect is a huge plus in regards to having an NBA impact rating above zero, but its importance diminishes with higher cutoffs.

• Free-throw percentage and 2-point percentage are the most important shooting-related factors, and they remain relatively stable in importance.

• Assists and height remain important with any cutoff.

• Steals and blocks go in opposite directions:

  • Steals are important for becoming a >0 player, but they lose importance with higher cutoffs.

  • Blocks, on the other hand, gain importance with higher cutoffs.

• Lastly, draft pick number is extremely important and gains in relative importance with higher cutoffs, indicating that the draft model has an easier time identifying solid players than superstars.

Here are, according to the model, the strongest NCAA seasons since 2010.

These represent “in-sample” results: We are asking what the model’s output would be, using hindsight, if presented with the following players today.

The following chart shows how the group predictions can vary wildly from player to player. In this example, Karl-Anthony Towns and Cooper Flagg were “predicted” to have a similar chance of becoming a superstar. But KAT had a significantly higher chance of becoming a bust.

Meanwhile, Franz Wagner had a lower “bust probability” than Towns, but the model saw less upside for Wagner and suggested he would most likely end up in the “Starter” group.

While we will present results for the 2026 draft class in a separate article, here is a Google doc containing past and current data that you can use to build a draft model on your own, and make projections for the 2026 draft class.

For this redraft, let’s assume the situation is similar to an actual draft: Bad teams come first, and they value upside potential, even if it means making the riskier pick at times.

What’s changed is we have more info now, based on each player’s first NBA season. We know who has passed the eye test. And we can look at their advanced metrics — especially for players who have dug themselves a hole that will be difficult to climb out of.

For example, if we had defensive concerns about a prospect before the draft, and his NBA metrics peg him as one of the worst defenders in the league now, a lot of our initial optimism goes out the window.

On the positive end of the spectrum, players whose upside was assumed to be hampered by their athleticism, but who are breaking through that glass ceiling, will get a boost.

Here’s how I see this year’s rookie class:

The new top 10

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The San Antonio Spurs are the second-best team in the NBA — they’ve suffered just two losses in the last two months.

Yet, without Victor Wembanyama on the court, they are actually getting outscored by their opponents this season.

With his unique physical stature and playstyle, some proclaim Wemby is essentially breaking the NBA. That’s not crazy: He’s doing things no one has been able to do before.

Given that he’s just 22 years old, it would appear the rest of the NBA has a problem.

But let’s drill down to what’s really transformed Wembanyama into an MVP candidate and potentially the NBA’s best player — and perhaps the greatest defensive force basketball has ever seen.

Here’s the breakdown of where he is smashing NBA standards, based on advanced stats and video:

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You already know college stats play a vital role when it comes to the NBA draft — a prospect’s numbers indicate his strengths, weaknesses, and future NBA production.

What’s more, statistical draft models — built on college player and team stats — outperform the actual draft order in predicting future NBA impact.

But here’s the challenge: To make those stats work for us, we need systematic ways to compare players and their stats.

Let’s say two players sport almost identical stats, but one of them is a year older: How much extra credit should we give to the younger one?

And what if one player has played against a much tougher slate of opponents? We have to figure out how much of a boost he should get when projecting his future.

To answer these questions, today we will:

• Look at prominent members of the current NBA draft class and how their ages and strength of schedule compares.

• Explain how to design a model constructed from box-score stats, adjusted for age and opponents..

The results are rather surprising!

We’ll also note which 2026 draft prospects are most affected by these adjustments.

Cameron Boozer vs. Caleb Wilson

An example of why we adjust college stats

While the top prospects In the 2026 draft are freshmen, there are some sizable age gaps between them.

Take, for instance, Caleb Wilson and Cameron Boozer: Wilson was born July 18, 2006, exactly one year before Boozer.

While Boozer’s stats are exceptional to begin with, they are even more impressive when we consider that he’s projected to be the youngest first-round pick in this draft — he’ll be among the very few in the one-and-done era to be drafted before his 19th birthday.

Not only that, but Boozer has played against stronger opponents, given that Wilson’s season ended on Feb. 10 while Boozer is still going in the NCAA tournament.

But while it seems rather obvious that Boozer would likely have even better numbers if he were a year older, little research has been done to determine how much better, until now.

In other words, it’s unclear how large the effects of age and schedule really are, and how much exactly we should be boosting or dampening player stats as we evaluate their likelihood of success in the NBA.

But using regression methods, we can determine the size of the effect of age and the quality of opponents on box-score stats.

Today, I’ll explain how to design a model incorporating these effects.

The technical details

The regression design

In our series of technical articles, our most recent piece covered how to predict NBA player stats.

Here is a quick recap:

First, we create an X-matrix where the number of columns corresponds to the number of players. If we’re estimating a player’s 3-point percentage, the number of rows in X is the total number of 3-point attempts of all players.

For each 3-point attempt (=1 row), the corresponding column of the shooting player is set to “1”.

The results vector Y — which has as many rows as X — is set to “1” for makes and “0” for misses. For a player who made three of his six 3-point attempts, part of our X and Y would look like this:

Plugging (X, y) into a penalized regression setup, we get a more accurate projection for a player’s 3p%, in contrast to taking his current 3p% at face value.

Adjusting for aging effects

This regression setup is easily extended to account for aging effects:

1. We create additional variables in our X-matrix, denoting the possible (rounded) ages of the players.

2. For every 3-pointer taken, we activate the age variable that corresponds to the age of the player at the time of each attempt.

Say that our player’s six 3-point attempts came in different games: Three makes in a game early in the season when the player was (rounded) 18 years old, and three misses later when the player was 19.

Then the new setup looks like this:

Adjusting for strength of schedule

This adjustment works in a similar way:

• We first calculate each opponent’s strength.

  • This can be win percentage, point differential, or something similar to Basketball Reference’s Simple Rating System (SRS), which adjusts for strength of opponent.

    • We “bucket” the various opponent strengths — for example, we can round to the nearest integer.

• We add a new set of dummy variables to X, one for each bucket.

• For each shot, we activate the dummy variable which denotes the strength of opponent in that game.

Suppose the opponent in the first game was rated +5, while the second opponent was rated +6.

With this setup, we can estimate aging curves and the effect of opponent quality, for each of the box-score stats.

A quick note: All the players used in this analysis were eventually drafted. This creates a biased sample, and the results would probably look slightly differently if we included all NCAA players. On the plus side, the results will probably be more accurate when predicting stats of future drafted players.

The surprising results

For points per possession, the effect of aging on college players looks like this:

Unsurprisingly, the graph points upward, denoting that NCAA players tend to score more points with each additional year of age.

The surprising part is this:

We might intuit that younger players make bigger leaps, year to year. Any parent or coach has seen how quickly young people can improve in any facet of life.

But for college players, we don’t see that.

Instead of the big improvements coming early in college and then the line looking flatter for older players, the improvement shown is remarkably linear, suggesting that the effect stays constant through at least age 23: A player is expected to score 3 points more (per 100) with each additional year of age.1 2

Here are the aging curves for assists, steals, blocks, rebounds and 3-point percentage:

Interestingly, blocks don’t see any increase with age — perhaps that is because this is the stat most dependent on raw athleticism, which might peak around age 18. Another explanation could be that players learn to contest shots by standing or jumping straight up, which can stymie opponents while creating fewer fouls — and fewer blocked shots.

For the other stats, the analysis suggests that with each year of aging, an NCAA player tends to gain:

• 3 more points (per 100 possessions)

• 0.5 assists

• 0.1 steals

• 0.4 rebounds

• 1% of increased 3p%

And here’s a chart denoting the improvement, in percentage, over the baseline (18 years old).

Points and assists rise almost 60% from age 18 to 23.

Rebounds, steals and 3p% rise by around 15% over the same timeframe, while block rate sees a small decline.

For the effects of opponent quality, the graphs looks like this:

As expected, all of these stats decline when playing against stronger opponents.

Playing a team like Houston or Duke — compared to an average opponent — will reduce stats by these amounts:

• Points by around 5 per 100 possessions

• Rebounds and assists by 2

• Blocks and steals by 0.5

• 3p% by 2.5%

Armed with these results, we can answer this basic question: What would everyone’s stats look like if they were the same age and played against the same quality of opponents?

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How this impacts Boozer and Wilson

To make these adjustments, we first have to calculate each player’s (possession-weighted) average age, and their individual strength of schedule.

For Boozer and Wilson, it looks like this:

• Boozer — Average age: 18.5. Strength of schedule: +8.0

• Wilson — Average age: 19.5: Strength of schedule: +5.8

Here are the projected stats for Boozer and Wilson if both players had been exactly 19 years old for every game and were playing average NCAA opponents each night.

As you can see, Boozer gets a decent-sized boost in his “Resulting Stats” line, while Wilson’s age- and schedule-related adjustments mostly cancel each other out.3

... and some other prospects in this draft class

If we use these adjustments for projected first-rounders of this current class, these are the players who get the biggest bump:

• Darryn Peterson has played the toughest schedule, with an average opponent SRS rating of +9.8. His points per 100 possessions would thus see a bump of around +2.

• On the other end of the spectrum, Bennett Stirtz’ opponents are rated at only +5.5, followed by Joshua Jefferson’s (+5.7) and Caleb Wilson’s (+5.8).4

• Cameron Boozer, Chris Cenac and AJ Dybantsa would see the biggest positive age adjustment.

• Yaxel Lendeborg — with a possession-weighted age of 23 — would see his 27.4 points/100 drop to around 15 using the above method.

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The 2026 NBA draft is set up to be a very exciting, top-heavy one, with three prospects who would all go No. 1 in a weaker draft.

But these three players are extremely different — in body type, playstyle and stats.

The teams that win the No. 1 and No. 2 picks will have a tough, risky question on their hands: Who should they take?

Let’s answer that question here.

Strengths

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Player stats are fairly volatile — more volatile than fans realize — as they depend on many external factors and lots of random chance.

Some statistics stabilize quickly, such as 2-point percentage and rebounding rate. Others, like 3-point percentage, can take years to settle.

Naturally, when creating advanced NBA metrics, we want to determine a player’s true skill level. Estimating this accurately is the key to building metrics from box-score statistics, similar to Box Plus-Minus (BPM) and some other all-in-one stats.

Today, I’ll cover three methods based on statistical and machine-learning techniques for estimating a player’s true skill level for virtually any individual statistical category.

We can use these methods for percentage-based stats — 3-point percentage is an example — or for per-possession stats, such as assist rate.

Additionally, these methods allow us to create confidence intervals — essentially a range in which we’re relatively certain the player’s skill resides.

An example

Imagine a rookie coming into the NBA and making 5 of his first 10 attempts from 3-point range. Obviously, if we are projecting his performance forward, it’d be unwise to expect him to continue to make 50% of his 3s.

But what’s our best guess? And how narrow can we make our prediction of his true skill?

When it comes to 3-pointers, previous analysis has found out that 750 3-point attempts are necessary before signal outweighs noise (random chance).

But that is far from saying the first 750 3s provide no information: We can say more about a player who shot 10-for-20 than about someone who shot 5-for-10.

For Part 1 of our series on creating advanced stats, click here:

Prior information

Even before a new player enters the league, we have enough data to predict the likely range of his stats — for example, we know that NBA players hit about 36% of their 3-pointers overall, while virtually no players hit more than 50% consistently.

Plotting the distribution of NBA player 3-point shooting in a histogram, we get the following picture, showing a realistic range of 22% to 45%.

For any player, it makes sense to assume that his skill lies somewhere within the above distribution, with the extremes on the left and right being less likely: It’s more likely for an NBA player to be a 36% shooter than a 31% or 41% one.

Depending on the statistic in question, the player distribution can vary greatly. Here is a histogram for blocks per 100 possessions:

Three different approaches

1. Penalized regression and machine learning

Most machine learning algorithms — from regression to neural networks — require us to transform the data into an X matrix and a “results vector” called y.

In our 3-point shooting example:

y: Holds the outcomes of each shot (makes and misses), coded as ones and zeros.

X: Holds the variables. As with Adjusted Plus-Minus (APM), we use “dummy variables” to denote a player’s presence on the court, also represented by ones and zeros.

The number of columns in the X matrix equals the number of players in the dataset, with each column corresponding to one specific player. Each individual 3-point shot — whether a make or a miss — is an “observation” and gets its own row.

For a player who goes 5-for-10, the X-matrix and results vector would look like this:

Since the X-matrix is even sparser than the one used in APM — it contains only a single “1” per row — it makes sense to use sparse matrices if your programming language supports them.

If we used a standard least-squares regression, the coefficient for our example player would be 0.5. With no constraint put on the coefficient, it ends up fitting the data perfectly. But we know the 50% figure is not a good prediction of that player’s true skill.

To get more accurate predictions, the methods below use penalization. Through internal cross-validation, they determine how strongly to pull coefficients toward the league average, and whether they should penalize strong deviations linearly, quadratically, or a mix between the two.1

clf_el = linear_model.ElasticNetCV()
clf_lasso = linear_model.LassoCV()
clf_ridge = linear_model.RidgeCV(alphas = [..])
clf.fit (X, y)

Note that, for many of the ML algorithms to perform better, we need to put everyone’s attempts into X and y, even if we’re interested in the skill of just one player. This will allow the algorithms to more accurately determine the optimal penalization.

Using the above Ridge Regression setup, we get the following estimates for some select players:

Employing the technique laid out by Justin Jacobs allows us to also compute the 95% confidence intervals.

Plotting attempts against the uncertainty of each player’s skill estimate, we see how a larger number of attempts leads to less uncertainty but that the downslope flattens out with more attempts.

Initial attempts help us narrow down the player’s true ability more than attempts when we already have a decent sample.

Optimizing with weights

To save memory and processing power, we can take advantage of the fact that many rows in our matrix are identical. Instead of repeating them, we can weight the rows by the number of occurrences.

Python’s sklearn’s Ridge function allows for weighing observations like this:

clf.fit(X, y, sample_weights = [.., 5, 5, ..])

Or we can weigh them “manually:”2

for i in range(0, numpy.shape(X)[0]):
  X[i] *= sqrt(occurances)
  y[i] *= sqrt(occurances)

With huge amounts of space saved — instead of thousands of rows per player, we need only two — we can now also use the method for per-possession statistics. To do so, we replace the numerator and denominator from the 3-point analysis — makes and attempts — with blocks and possessions:

As an example, let’s use a player with 20 blocks in 1,000 possessions:

The initial X-matrix stays the same, but the weights change to 20 and 980, with 20 representing the number of possessions with blocks, and 980 representing the possessions without a block.

If we do any of the above regression techniques, we then have to calculate new penalization parameters, since these are going to be different for every statistic.

Two ways to further improve projections

(1)

In our technical article explaining RAPM, we showed how to incorporate priors. Priors represent an initial first guess about our variables. Depending on the statistic, this can be a helpful tool.

Take, for instance, block rate. If we create two histograms — one for the taller half of the NBA, and one for the shorter half — we can easily see that the distribution appears to be dependent on height.

Other stats, like steals or points, might show distributions dependent on age.

So, rather to have an implied “zero” prior for everyone, it seems intuitive to create individual priors based on player attributes.

Some considerations have to be made regarding how to model these relationships, such as deciding on the size of the buckets.

(2)

A technique that aims to estimate a player’s skill level “in a vacuum” (as if he were playing for an average team) is to adjust for impact of teammates.

Most people would agree, for instance, that it’s easier to sport a good shooting percentage when playing with Nikola Jokić or Luka Dončić compared to playing on this year’s Wizards team. And playing alongside Victor Wembanyama might cost his Spurs teammates some defensive rebounds.

To adjust for this, we can introduce a second set of variables to our X-matrix.

The first set denotes the player’s own ability.

The second set of variables has the same size: the total number of players. But the dummy variables within this subset denote whether a specific teammate was on the court.

The coefficients of these new variables denote the size of each player’s teammate effect.

Using cross-validation, we can compute different penalization values for each variable type.

When we run this analysis for points per shot, we see several high-usage players at the top when it comes to positively influencing their teammates’ efficiency:

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2. Using combined probabilities

For this method, we are going to use the information represented in the 3-point percentage histogram above.

We can easily compute the average and standard deviation for the above player population using traditional methods, but let’s use a probabilistic method — Monte Carlo simulation — to compute these two numbers, as the method will come in handy later:

Using a (uniform) random number generator, we draw a number on the histogram’s X-axis and create two lists:

1. Contains the 3-point percentage drawn (the number on the x-axis)

2. Contains the probability of that 3-point percentage, according to the histogram (the number on the y-axis). This second list can be viewed as weights.

We then calculate a weighted average and standard deviation. This will lead to the exact same results as usual methods, assuming we draw a sufficient number of times.

But so far, we haven’t made use of the 3-point data of the player in question. To do so, we have to modify the second list:

For every number drawn from the x-axis, we compute the probability of this number leading to the exact number of makes, given the number of attempts.

Taking our example player who went 5-for-10 on 3s:

If we first draw “37%” on the x-axis, we compute the probability of a 37% shooter going 5-for-10, and so on. The probability of drawing 37% multiplied by the probability of a 37% shooter going 5-for-10 is one of the new weights in our second list.

The first part of the weights stems from our knowledge about the population, while the second part of the weights stems from the data of the player in question.

With the new weights, we can once again compute a weighted average — a best guess for the player’s true 3p% — and a standard deviation to derive a confidence interval.

3. Using the PyMC library

The aforementioned Ridge Regression implicitly assumes a prior of zero (the league average) for every variable, and tends to force a Gaussian distribution on the coefficients.

PyMC — a probabilistic programming library for Python which can be used for Bayesian statistical modeling — allows for a wider variety of prior distributions, which we’ll have to explicitly specify.

For example, the case has often been made that NBA player impact doesn’t follow a Normal distribution, but rather a Half-Normal distribution (that is, only the right-hand side of a Normal distribution).

In PyMC, we can specify a 3p% model like this:

with pm.Model():
  p = pm.Normal(”p”, mu = mean, sigma = stdev, shape=(1,))
  y = pm.Binomial(’y’, n = attempts, p = p, observed = makes, shape=(1,))
  idata = pm.sampling_jax.sample_numpyro_nuts()
print (idata.posterior[’p’])

In this setup:

• y represents the likelihood, which follows a binomial distribution, defined as “a discrete probability distribution of the number of successes in a sequence of n independent” yes/no experiments.

• p is our prior. While a Normal distribution was chosen above, there are lots of probability distributions to potentially choose from. The PyMC function find_constrained_prior can help in finding reasonable priors.

Compared to the other methods, this approach is far more flexible, also allowing for hierarchical modeling (e.g., putting priors on priors). The downside is that the runtime can be many times longer than that of a method like Ridge Regression.

Today we’re bringing back a metric I introduced two years ago — one that captures the gap between how much a player appears to contribute and how much he actually contributes to winning NBA games:

It’s the All-Fluff Team — the Empty Calories All-Stars.

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If we were guessing the NBA players with the best net rating this season, we’d probably point toward Shai Gilgeous-Alexander and Nikola Jokić.

But the top two are actually OKC guard Ajay Mitchell and a rookie — Boston Celtics wing Hugo González, the 28th pick in the draft.

What’s more, in the past three decades, only eight players have posted a better net rating than González.1

Is González anywhere close to this good? Or is he just getting lucky?

Let’s go to the tape and figure out how González is doing it.

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I’ve done deep dives on nine of the top NBA prospects, from Darryn Peterson to Kingston Flemings.

Let’s take a closer look at two of the most exciting draft prospects: Keaton Wagler and Mikel Brown Jr., who are battling for a mid-lottery spot this June.

Here are my evaluations of these two guards, using stats and video:

Keaton Wagler

Illinois
Freshman
Shooting guard
6’6” | 185 lbs
Age on draft night: 19.4 years

What works for Wagler

Five weeks ago, I covered the incredible quality at the top of this draft class:

The five freshman who made the list had a Box Plus-Minus (BPM) of 12 or better. Keaton Wagler didn’t make it then — but he would now. That’s how strongly he’s progressed this season as a draft prospect.

Wagler’s BPM has seen significant improvement, all the way up to 13. As a reference point, that puts Wagler ahead of AJ Dybantsa in BPM, although not on most draft boards.

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Last week, Nate Silver published his “Future of the Franchise” rankings — his semiannual feature covering all 30 teams and how they rank in NBA championship equity over the next decade.

Nate asked me to provide my rankings once again, and to write up about a dozen teams.

So today we’re presenting my rankings below. And I’ve written up the other 18 teams as well.

Nate provided the full background at his Substack — Silver Bulletin — but here’s his short version from a previous edition:

The sole criterion for these rankings is a team’s expected number of championships over the next 10 NBA seasons (2025-26 through 2034-35). We hope these rankings are well-informed and analytically-minded, but ultimately they’re subjective opinions. The voters are me, Silver Bulletin Assistant Sports Analyst Joseph George, and Jeremias Engelmann, the developer of Real Plus-Minus and a former analyst for the Mavericks and Suns, who writes at the newsletter 5x5.

My rankings:

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Today we’re taking a close look at two impressive point guards — Kingston Flemings and Darius Acuff Jr., both ranked in the top 10 on NBA draft boards.

What do stats, video, and similar players tell us about their chances of succeeding in the NBA?

Kingston Flemings

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Darryn Peterson entered his freshman season at Kansas as the nation’s No. 2-ranked player, according to RSCI. Since then, he’s climbed into the first spot on almost everyone’s draft board, even though Cameron Boozer is recording some of the best freshman stats we’ve ever seen, with AJ Dybantsa not far behind.

But should Peterson really be the clear-cut No. 1 prospect?

Let’s check the tape and the stats — and look at the major concerns surrounding Peterson — to determine where he belongs among this year’s elite draft prospects:

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The Charlotte Hornets have been the hottest team in the NBA, winning nine straight before falling in a contentious affair to the Detroit Pistons on Monday.

In calendar year 2026, the Hornets been the NBA’s second-best team by scoring margin, at 9.8 points per game.

And at the forefront of their success is the No. 4 pick in last June’s draft, Kon Knueppel.

Rookies usually don’t fare well in advanced metrics, but Knueppel is a breakout star at age 20.

What makes him a historically special rookie? Is he destined to be one of the elite players in the NBA?

And where does he rank among the greatest rookies of the past three decades?

Let’s get into it.

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We asked 11 leading NBA voices on Substack:

Your take on the 2026 NBA trade deadline?

Check out their answers and subscribe!

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Delightfully busy ... assuming you’re into trades.

Even as we get deeper into the NBA’s Aprons Era, action prevailed. Numerous recent All-Stars were traded and we went from only one consummated deal as of last Friday (Trae Young to Washington) to 25+ deals (official count TBD).

I’m sure it will be chronicled as a Dudline in some corners because Giannis Antetokounmpo, Ja Morant and Domantas Sabonis stayed put. The counter: James Harden, Darius Garland, Jaren Jackson Jr., Nikola Vučević and Anthony Davis all got traded within the last four days. Washington (Davis and Young) and Utah (JJJ) saw trade opportunities that enabled them to take big swings now rather than preserving their projected salary cap space for free agency.

The Aprons have certainly complicated team-building in a lot of ways, but Trade Season — for the third straight winter under this new CBA — was robust.

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Harden/Mitchell in the same backcourt? Bulls unloading Dosunmu — don’t you have to build something before you rebuild? Clippers trading Zubac just as they’re beginning to win? 76ers letting McCain go? Mavericks officially confirming they traded Dončić for nothing?

I ask myself these questions. Meanwhile, Celtics get Vučević, which might just make for Championship No. 19.

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The 2026 NBA Trade Deadline was predictably unpredictable.

Sure, there were some moves that we may not have expected — AD to Washington, Zu to Indy, JJJ to Utah, and of course, the Harden-for-Garland swap — but the logic behind all of those transactions was understandable in retrospect.

Even teams like the Kings and Bulls that tend to come out on the lesser end of the transaction game were operating within the logical (if poorly executed) confines.

Maybe it’s just the residual shock of the Luka trade one year later, but this deadline felt incredibly tame compared to the norm.

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Jake Fischer | The People’s Insider | Contributor to

This year was another example of just how prevalent “pre-agency” has become in the NBA.

It’s not just for players who want to find a new team that will deliver a payday they wouldn’t have gotten from their incumbent team — like James Harden and Trae Young. It’s also for teams looking to add players they otherwise wouldn’t have been able to afford with cap space this summer — see the Timberwolves’ addition of Ayo Dosunmu.

The Utah Jazz filled their expected cap space this summer by prying Jaren Jackson Jr. from the Grizzlies. The Wizards followed up their Young acquisition with Anthony Davis to use up their own summer spending power.

Is free agency dead? Not necessarily. The Lakers will look to be real players in the summer. The Nets have a ton to spend. The Bulls loom with all kinds of wonky optionality.

But teams are working ahead of a schedule more than ever before — as evidenced by Indiana’s aggressive move to add Ivica Zubac now, so that he’ll be ready to help Tyrese Haliburton contend for a title in 2027.

Pre-agency has arrived.

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When it comes to managing assets, intelligence is vital to build a viable project in the medium- and long-term, even if players change teams incessantly.

Four examples from this trade deadline:

1. The Clippers are preparing for the future without tanking.

2. The Pacers replaced Myles Turner, but at half the salary.

3. The Cavs cleaned up their payroll while doubling down to win the 2026 NBA title.

4. The Wizards are transforming themselves without sacrificing their future.

Some teams play chess, not checkers.

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My take on the trade deadline is that no one should ever say again that it will be slow or boring or that the CBA has made trades too hard.

This was the third deadline with the current CBA, and each has featured blockbuster trades. As Jurassic Park taught us, “Life finds a way.”

NBA teams are motivated by a lot of factors (loading up for a run, rebuilding, saving tax money, etc.), and the trade deadline never disappoints with action.

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Everyone hates offense-first guards. Nobody wanted Ja Morant. Nobody wanted Cam Thomas, not even his incumbent team, who dumped him. Washington was willing to take Trae Young if the price was a moldy bag of chips and some couch change. Coby White didn’t return a first; Darius Garland’s eight good toes returned, uh, James Harden.

One curious exception didn’t get the memo: Chicago, who zagged by adding a quartet of the least fashionable archetype in the league. I respect the commitment to the bit.

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I’m old enough to remember when all the chatter was that the new CBA had killed the trade market. So much for that! This trade season felt as wild and active as any I can remember.

What the new CBA has done is severely deflate the value of players who are in line for extensions, making some exciting young talent and big names eminently gettable at relatively paltry prices.

As a fan of chaos, I’m here for all of it.

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Second apron or not, trades find a way. Since the signing of the current collective bargaining agreement, we were warned that the restrictions could slow down the trade deadline, but we’ve continued to see a high volume of trades regardless.

Even if they are smaller ones, trades always find a way … especially with James Harden. He always finds a way, too.

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One particular trade caught my eye: The Pacers/Clippers Zubac deal.

To get Zubac from the Clips, Indiana gave up both their unprotected 2029 first and this year’s first, with a strange protection range: 1-4 and 10-30. In other words, they’re taking a risky gamble here that we rarely see.

Indiana has top-3 odds in the lottery at the moment, but with Zubac, they could end up being too good too late, making the weird 5-9 range a distinct possibility.

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Ray LeBov | Basketball Intelligence Newsletter

Fascinating fact: The published grades by knowledgeable analysts for the Wizards’ side of the Anthony Davis trade ranged from A to F, with virtually every grade in between present and accounted for.

I can’t recall ever seeing anything remotely similar to that sort of nearly all-inclusive range for any trade in any previous year.

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