As Devin Booker and Trae Young lead their respective teams to the 2021 NBA Conference Finals, it's time to retire the phrase "good stats, bad team."
Both Booker and Young were tagged with the immaculate label early in their careers, when they accumulated impressive per-game figures for lottery-bound teams. This notion may have also helped keep Young away from this year's All-Star team, when the Atlanta Hawks began the season 13–18, while dealing with injuries to key starters before the reserve was announced. (The Hawks only went 28-13 the rest of the way.)
Now, Atlanta's upset series pairs with Young Powers as a low seed, and Booker takes the Phoenix Suns to the Western Conference Finals — without star guard Chris Paul — on Tuesday's Game 2, we must learn from their examples and forget a concept that always makes more sense in theory than practice.
The clumsy relationship between individual and team success
I can understand where the concept of "good statistics, bad team" originated. Back when players were evaluated primarily based on their per-game stats, and especially points per game, it was easy to confuse volume scoring for performance that translated into wins.
Without the evaluation framework provided by Advanced Statistics, the team record was a shortcut to tell fraudsters from the actual article.
Consider a group of about 1,000 players since individual turnover was first tracked in 1977–78, while at least two-thirds of their team playing games averaged at least 20 points per game. Of these, a little over a third came from teams with below-.500 records. Conventional wisdom is correct to an extent. There is a correlation between a high scorer's team record and his or her own performance, as measured by my player's net rating on a per-capture basis.
On average, my net ratings for players on teams that finish .500 or better (plus-4.5 points per 100 assets) are more than twice as good as those on below-.500 teams (plus-2.1). Yet individual performance for a high scorer still only accounts for 20% of the variation in team records.
Essentially, judging a player's potential by his team's performance is a blunt measure. Modern statistical analysis gives us more surgical tools that can better differentiate individual performance. And although those tools indicate that most high scorers on lottery teams are actually worth less than those in the playoffs, that's not always the case.
After all, there are four other players on the court at all times, not counting the minutes. One player is on the bench. So that's how the lowest-rated player by my metrics in this group of 20-point scorers (Jeff Malone with the 1991–92 Utah Jazz) could go from 55-27 to play on a team that reached the conference finals, Stars. Thanks to Karl Malone and John Stockton.
At the other extreme, Anthony Davis's 2018-19 performance ranked in the top 25 of this group, the highest for any player on a below-.500 team. (Apparently, Davis played sparingly after publicly requesting a trade, but only after the New Orleans Pelicans were out of the playoff race.) A year later, Davis' trade with the Los Angeles Lakers After doing that, he helped them win the championship. Turns out he certainly wasn't an example of "good stats, bad team".
Better support for Booker and Young
Booker & Young did not need to solicit trades to put them in a better position to succeed. Their own teams achieved this by eventually centering a series of lotteries on young talent and skillfully manipulating those pieces through trades in free agency.
Weighted by minutes played, the Hawks have the third youngest rotation among playoff teams, while the Suns are the sixth youngest. This is in contrast to their opponents in the conference finals, the Milwaukee Bucks (the third oldest) and the LA Clippers (the oldest). In addition to Young, the Atlanta native includes recent first-rounders John Collins, Kevin Huerter and the injured D'Andre Hunter, while Phoenix has a pair of No. Mikal Bridge.
For those groups, both teams made major pickups last season. For The Suns, that was All-NBA point guard Chris Paul, whose veteran example helped Booker and other young Phoenix players to an undefeated run in last year's seeding games to a full season of success, as well as propel Jae Crowder forward. is of. The Hawks weaponized their cap space to add starter Bogdan Bogdanovic and top reserve Danilo Gallinari, already adding Clint Capela to trade on the 2020 trade deadline.
Due to newcomers, both Booker and Young saw their scoring averages drop this season. Young's drop-off of 4.3 ppg is particularly noteworthy. Young was one of 28 players in the league to see his scoring decline dramatically at an age where most players' figures are rising. This happened partly because Atlanta's pace slowed dramatically, but Young's assist rate also increased 10% on a per-capture basis, while his use declined. It should come as no surprise that Young was willing to rely on better teammates.
Given that Paul took over the primary ballhandling duties, and the Suns had a point guard on the court after using Booker in that role several times last season, his assist rate actually decreased, while his use increased. Gone. But the improved depth allowed Phoenix to rest Booker more, as his minutes per shot dropped from 35.9 to 33.9. As a result his score fell.
How both stars have evolved their games
Although the change in approach at Hawks & Sons has more to do with the quality of their rosters, they have also benefited from the inevitable improvements of Young (age 22) and Booker (24). This is particularly evident on the defence, a more legitimate criticism of both players earlier in their careers.
During 2019-20, Young's minus-1.8 defensive RAPM (regularly adjusted plus-minus via NBshotcharts.com) was ranked in the bottom 10 of the league when accounting for teammates and opponents who was on the court with him. At minus-1.3, Booker was also in the bottom 25. While the defense remains a weakness, both Booker (minus-0.7) and Young (minus-0.9) have improved outside that range.
We have also seen Booker reach a whole new level of shot making during this playoff run. Second Spectrum's QSI (Quantified Shooter Effect) metric measures how many players outperform the expected effective field goal percentage for an average player on the same shot attempts based on the location, type and distance of nearby defenders. Booker has also excelled in this category, finishing 59th in the league with a plus-7.1 QSI during the regular season (minimum 100 field goal attempts). In the playoffs, they reached plus-12.0, the sixth best in the same group.
In Young's case, playmaking has taken the forefront. He has assisted nearly half the field goals of his teammates while on the court in the playoffs, according to Basketball-Reference.com data, the highest estimated rate in a run of more than 10 games since Russell Westbrook in 2016. According to Stathead.com, Young is on track to join Westbrook (twice), James Harden, LeBron James (four times) and John Wall as the fifth player since 1977–78, with an assist rate of 40%. and the utilization rate is more than 30%. playoff
At this point, Booker and Young have proven that their statistical output can translate into team success. For the next generation of similar players, we shouldn't wait for playoff runs like this to evaluate individual performance rather than their teams' records.
Let's stop using the phrase "good stats, bad team" and instead use stats that better reflect winning contributions than points per game.