The Lakers recent NBA Finals victory got me thinking about how seemingly unusual their roster was shaped. The teams that win the championship almost always have at least a couple of high-end, All-NBA type talents on their roster (the 2011 Mavericks and 2004 Pistons are exceptions, but certainly not the norm). Still, this Lakers roster seemed especially top heavy, with LeBron James and Anthony Davis surrounded by a just solid crop of players. According to the newest Box Plus-Minus (BPM) model available at basketball-reference (the write-up of which can be found here if you are like me and want to get into the weeds), the third best player on the Lakers was JaVale McGee who was out of the rotation by end of the Western Conference Finals and did not play a game in the final round. Of the players in the rotation against the Heat, the third best player during the regular season was Danny Green, who added about half a point above average per 100 possessions during the regular season. McGee added 1.5; the difference between Davis (who posted a BPM of 8) and McGee was the second largest this century between the second and third best player on the NBA champion after the gap between the second and third best players on the 2012 Heat, Wade and Bosh. The gap between the Lakers this year and the third largest difference in the sample (incidentally between Kobe Bryant and Rick Fox on the 2001 Lakers) was the same as the difference between third and eighth of the list (which was the gap on the 2009 Lakers; the Lakers have won a lot of championships).
It seemed like there were a lot of worthy challengers for the NBA crown this year who boasted much deeper rosters than the Lakers, including the Clippers, Bucks, Celtics, Heat (who they vanquished in championship round), the Nuggets (who they beat in the semifinal round), and even the Rockets (who they beat in the second round). Yet the Lakers came out as the champion on the backs of their two superstars. We know the NBA is a star-driven league, much more so than any other sport excluding maybe the quarterback on a football team. So it begs the question: how much championship equity does a team going into the playoffs based on its best player and its best several players? To investigate, I took every team season since 2000 and pulled out the best player on each team and the three best players on each team based on BPM and a minimum of 500 minutes played in the regular season. I built two models: the first is a logistic regression where the target was whether or not a team won the championship and the variable was only the BPM of the best player. The second is another logistic regression where the target is whether or not the team won the championship, but the variables were the BPM of the best player, the second best player, and the third best player.
First, I took a look at the predicted championship probabilities of the model using data from only the best player. I will note I tried incorporating other data about the best player into the model, such as usage and shooting efficiency, but it actually made the model less accurate (based on AIC which gauges in-sample predictive power). For context, here is the distribution of BPM figures for the best players on championship teams versus all other teams in the sample:
Unsurprisingly, the teams that win championships have top players significantly better than the average team. The exceptions are Chauncey Billups on the 2004 Pistons, Dirk Nowitzki on the 2011 Mavericks, Kobe Bryant on the 2009 and 2010 Lakers, and Kawhi Leonard on the 2014 Spurs. Notable seasons on non-title winners were LeBron in 2009 and 2010 on the Cavaliers, Chris Paul on the 2009 Hornets, Steph Curry on the 2016 Warriors, Giannis Antetokounmpo on the 2020 Bucks, James Harden on the 2018 Rockets, Kevin Garnett on the 2004 Timberwolves, and Kevin Durant on the 2014 and 2015 Thunder. When you look at the output of the regression model, the best player has an outsized affect on a team's championship equity.
Interestingly, the marginal gains associated with your best player getting a little bit better have an outsized effect on your probability of winning a title at the higher-ends of the player production spectrum. For example, if a team had a best player that was about one point per 100 possessions better than average and added a player in the off-season who was about 6.25 points per 100 possessions better than average, that would would add about five percent of championship win probability in the average season this century (based on the model). The same championship equity would be added if the best player on a team went from about 8.75 points per 100 better than average to about 10. So on the upper end of the player ability spectrum, adding about 1.25 points is the same as 5.25 points when considering just championship equity.
When incorporating the second and third best players into the model, the model becomes more accurate (by AIC). This is not surprising because I am incorporating more information about each team's roster. Theoretically, adding in all of the players would be the most accurate, but there would probably be significant amount of diminishing returns after the seventh or eighth best players since rotations shorten in the playoffs. But in any sort of modelling process, there is a balance that needs to be struck between accuracy and the amount of information required to make a good prediction. A more complicated model that requires 20 inputs but adds only five percent more accuracy compared to a model with four inputs is not really a better model. Marginal gains in accuracy compared to monumental changes in inputs is not good process. Thus, I figured for now looking at just the three best players would suffice do to its simplicity and intuitiveness.
With the model trained, I could generate championship probabilities based on the play of the three best players on the team, just like the model that only incorporated the best player. My initial reaction was to look at the landscape of each team's top two players and outliers in championship probability.
The Warriors show up a few times here, as they put together won of the most dominant stretches of play in league history. Interestingly, we also see the 2018 and 2019 Rockets with James Harden and Chris Paul at the helm. In 2018 specifically, the model indicated that the Rockets actually had a better chance at the championship than the Warriors. This is based on regular season performance and the Warriors after the Kevin Durant signing were known to loaf through games so the results should be taken with a grain of salt. Another couple of teams that unfortunately ran into the Warriors juggernaut were the 2015 and 2016 Thunder, featuring the aforementioned Kevin Durant and Russell Westbrook. The 2001 Jazz were the last hurrah for Stockton and Malone as serious playoff contenders. I also isolated teams that were similar to this year's Lakers, where the third best player was much worse than the top two. I filtered by teams with an implied championship probability of at least 20 percent and a third best player who was at best worth 2.5 points per 100 above average.
The "Heattles" show up here as do Durant's Thunder and the Stockton and Malone Jazz. Famously, Kevin Garnett carried a relatively barren roster (by contender standards) to the Western Conference Finals in 2004. The second and third best players on that team were Sam Cassell and Fred Hoiberg; this masterpiece from Garnett was one of the best seasons by a big-man in NBA history. Alternatively, here are some of the deepest contenders of the century (both the second and third best players were at least 3 points per 100 better than average):
Here we see different versions of great Spurs teams, an organization known for its depth and ability to develop players for most of the Greg Poppovich tenure. The earlier Spurs teams were led by the trio of Tim Duncan, Tony Parker, and Manu Ginobili while some of the later teams featured Kawhi Leonard and LaMarcus Aldridge. The Chris Paul and Blake Griffin led Clippers also make an appearance. After recent renditions of the Rockets and Thunder, these Clippers were the best group to not win a title.
When taking into account each team's championship probability every year since 2000, we can look at which teams exceeded or disappointed their expected championship output. First, all the teams that won at least one championship in the 21st century:
The Lakers lead the way in titles over expected. Some of this can be attributed to the Shaq and Kobe teams not taking the regular season seriously. The rest is attributable to the back-to-back championship teams in 2009 and 2010 not being especially strong title winners. Miami is second and for similar reason to the Shaq and Kobe teams they exceeded their expectations. Detroit and Toronto come out as surprising as "lucky" by this measure (a small ratio is expected to actual titles). For Toronto, they did not have much championship equity until they traded for Kawhi Leonard and in his lone season in Toronto Leonard often sat out regular season games to stay fresh. Detroit in 2004 was the worst champion by the model. Dallas had some great teams between 2000 and 2010 and oddly finally got a ring with Dirk when he was well past his MVP-level peak.
As I mentioned above, the Thunder, Rockets, and Clippers were very unfortunate given the players they employed this century. Milwaukee's misfortune is concentrated in the past couple of seasons where Giannis posted some of the best seasons of the century. Minnesota's misfortune was also concentrated in a couple of seasons, 2004 and 2005. Also showing up here is Orlando (2008 through 2010), the Suns (mostly due to the Seven Seconds or Less teams) and Utah (the tail end of the Stockton and Malone connection).
Finally, I looked at the strength of the top teams every season for the last 20 years. The model output does not consider other teams when computing championship probability. For example, the 2018 Rockets title chances are not affected by the fact that the 2018 Warriors existed. The model was not trained on individual seasons but the entire sample. Thus, the predicted probability is the probability of winning a championship given the players on hand in the average season in the 21st century. The issue for the 2018 Rockets is the average NBA season does not also include a team of the Warriors caliber. The top teams from each season have an outsized effect on the summation of title chances for all teams in a season. So when summing up all the title chances in each season, seasons over one can be considered top-heavy, while seasons under one have title contenders with worse players than the average contender. I call the sum of the probabilities the heavy weight index. The average heavy weight index in the 21st century is about one, which is not surprising. The landscape of the league, on the other hand, is noteworthy:
Recent seasons, especially 2015 through 2018, were especially top heavy. Those seasons were dominated by the Warriors, Cavaliers, Thunder, Spurs, and Rockets. These teams had some of the most impressive top-end talent the league has ever seen and just happened to exist in the same universe so the Rockets and Thunder missed out on titles. All of these seasons had heavy weight indices of at least 1.5, so they were at least 50 percent more top-heavy than average. Seasons in the first ten years of the sample featured much less impressive talent, especially in the years where Kobe's Lakers took how titles. These seasons were almost 40 percent less top heavy than average. With the break-up of the Thunder, Warriors, Cavaliers, and now Rockets, the league is starting to balance itself out again after some extremely top-heavy seasons. I think, subjectively, having an index a bit over one (the best teams are slightly better than the best teams in an average season), but not so dominant that they bowl over the competition offers the best entertainment product. With many top players hitting the free agent market after the 2020-2021 season, it will be interesting to watch how the contenders sort themselves out and if any team adds talent to the extent of some of the great teams of the last 20 years.
No comments:
Post a Comment