Going into this NBA season, there has been a lot of talk about how much more evenly matched the league has become. With the departure of Kevin Durant from the Warriors, the leaps and bounds made by the Clippers over the offseason, and the general improvements made by many of the league’s least successful teams, there seems to be much more parity than there was in the previous year. The eye test is certainly a strong indicator, as well as the expert opinions, but there are some challenges approaching this question from a statistical angle. Considering that, how can we see—through statistics—whether there actually is more parity this year?
Traditional metrics are more indicative of style of play than of competitive balance. A team with a high pace of play could appear better than they actually are in many areas such as rebounds, steals, points, and assists, all of which would be inflated by the team’s high pace. At the same time, a team that is smaller and more focused on shooting will likely generate fewer rebounds, even if they are one of the best teams in the league.
Advanced statistics, like player efficiency rating or box plus minus (a per 100 possessions metric that uses box scores and team overall performance to estimate a player’s level of impact), allow us to see a more complete picture. In particular, these statistics are less influenced by the individual style of the team.
There is one additional problem in setting up this data: rookies. Rookies are one of the most unpredictable factors in the NBA. There is no prior relevant data that can be easily transferred to the NBA to make predictions about their performance, since many players’ college stats and first-year stats look wildly different in regards to both traditional and advanced metrics. Considering that, I have made the decision to exclude rookies and their potential numbers from the team averages. This will have an impact on teams such as New Orleans, who are planning to feature Zion Williamson as their star player from the beginning, but most teams will not be significantly impacted by this exclusion. This is even more true in the case of high-level playoff teams, where rookies rarely see meaningful minutes.
To find statistical evidence of an increase in parity, I decided to use last year’s data organized into two data sets.
The first set was the final rosters at the end of last season. The second set was last year’s data, reorganized using this season’s rosters. Then, I took the standard deviations for the advanced stats for each roster. In order to try and account for differences in playing time, I took one standard deviation using all players and a second using only players that played over 400 minutes played last season. In theory, the year that has more parity will, in general, have lower standard deviations when comparing the group of all teams in the NBA, demonstrating a possible way to demonstrate whether or not there actually is more parity in the league this year.
The advanced statistics I will be focusing on are VORP (value above replacement player), PER (player efficiency rating), offensive, defensive, and overall BPM (box plus minus), offensive, defensive, and total WS (win shares, a box score based calculation that estimates how many wins that player was responsible for over the course of the season), as well as WS/48 (win
shares per 48 minutes). As a note, offensive win shares are also sometimes referred to as GA-SW.
When comparing the statistics using all players on the 2018 and 2019 rosters, there is variation, but a general trend indicates that there is in fact more parity this year compared to last.
In the “All players” category, standard deviation was lower in 2019 than in 2018 in PER, DWS, WS/48, OBPM, DBPM, and BPM. Standard deviation was lower in 2018 in OWS, WS, and VORP. In the “400+ minutes played” category, standard deviation was lower in 2019 in OWS, DWS, WS, WS/48, OBPM, DBPM, BPM, and VORP. Only PER had a lower standard deviation in 2018 than in 2019.
Overall, and especially in the “400+ MP” group, there seems to be more parity this year than last. There are exceptions, most notably PER as the single outlier in the 400+ group, but not enough to reverse the trend. While it is essentially impossible to measure the comparative power of each advanced statistic, the overall strength of the trend suggests an increase in parity.
That being said, an increase in parity does not mean that every team is now competing on the same level. For example, although the standard deviation for VORP is lower in comparison to last year, when comparing the average team VORP’s from 2018 and 2019, Cleveland remains significantly behind the majority of teams. In all four data groups, Cleveland has a negative VORP, and is the only team in this situation in either of the 2019 groups. While the top of the graph, included below, has balanced out considerably, Cleveland remains in the same negative position as last year.
Cleveland’s case is also indicative of one of the potential flaws in this analysis, borne from using last year’s advanced statistics with this year’s rosters. As a team in the lottery, Cleveland’s goal is likely not to compete for an NBA championship, but to develop the players on their roster and the team as a whole. This means current statistics used as projections may be completely different from the numbers they actually put up. The roster may not even look like it currently does come the end of the season. This same issue is also true for teams with playoff aspirations, but it is less salient as development is less of a focus for them.
Additionally, many of the teams with a similar emphasis on development, such as the Knicks or the Grizzlies, have high-level rookie players. The impact of players like RJ Barrett and Ja Morant cannot be predicted with any accuracy and have therefore not been included in the calculations. This factor makes these rebuilding teams less likely to perform in accordance with their statistics. That being said, while the amount of variance may be greater for these teams, it should not be so much that the numbers used are irrelevant. Many players have semi-consistent levels of production across their careers, especially developing players who hit their ceiling. As such, the overall variance should not be so overwhelming that the overall accuracy is undercut.
Overall, several statistical indicators show more parity in the NBA than last season. 2019 has more statistics with lower standard deviation than 2018, and 2019 almost completely sweeps the “400+ MP” group, which is meant to show heavier weighting to players who will be more involved in the season. Parity is not perfect—there is still a large difference in many categories between playoff contenders and teams in the middle of a rebuild, but by the metrics used these distances are smaller than they were last year. So how will this look to fans?
Hopefully, fans see closer games, higher intensity, and more underdog wins. The Phoenix Suns so far have been outperforming expectations, and the Warriors will probably not find themselves winning by 20 points going into the fourth quarter nearly as often as they did last year. Only time will tell, but it looks like this season will be more intriguing than last season.