Welcome

Welcome to my basketball season data analysis. This page presents interactive visualizations and detailed data tables capturing team and player performance throughout the National Basketball Association (NBA) season. You can explore cumulative wins, point differentials, scoring trends, and advanced player metrics such as effective field goal percentage, player efficiency, and assist-to-turnover ratios. The charts and tables highlight team momentum, offensive and defensive balance, and individual contributions, providing a clear picture of which teams and players are excelling across the season.

All data are sourced from Basketball Reference and updated daily during the regular season, allowing you to monitor performance as the year progresses. Whether you’re a fan, analyst, or fantasy basketball player, these visualizations offer an accessible, data-driven perspective on NBA competition. I hope you find these visualizations and data tables helpful in understanding the current NBA season. Thank you for visiting the page.

Executive Summary¹

As the NBA season progresses, team performance data reveal distinct tiers of competition. The Oklahoma City Thunder (55 wins) and San Antonio Spurs (51 wins) have established themselves as the league’s top teams, holding the highest cumulative win totals as of March 18, 2026. Following them are the Detroit Pistons at 49 wins, with the Boston Celtics and New York Knicks tied at 46 wins apiece. An analysis of game-level statistics provides deeper context for these standings, illuminating not just which teams are winning, but the underlying factors associated with their performance on the court.

The first notable pattern is the consistent dominance of the league leaders, which is reflected in their point differentials. The Thunder not only possess the best record with a 79% win percentage but also lead the league with a median point differential of +9.5 per game. Similarly, the Spurs, with a 73% win percentage, have a median point differential of +9.0. These figures suggest that their high win totals are not a product of narrowly winning close contests but rather a reflection of consistently outscoring their opponents by a substantial margin. The standard deviation of their point differentials—14.71 for Oklahoma City and a lower 12.73 for San Antonio—indicates a relatively stable performance from game to game.

A second pattern emerges from the divergent trajectories of teams in the middle of the standings. The Los Angeles Lakers have seen a substantial increase in wins recently, securing seven victories between March 4 and March 18 to reach 44 total wins. This surge coincides with high-level production from Luka Dončić, who leads the league in scoring (33.0 points per game). In stark contrast, the Indiana Pacers have remained at 15 wins during this same period, indicating a prolonged losing streak. The Pacers’ performance is underscored by a median point differential of -9.0, which ranks among the lowest in the league and is consistent with their 21% win percentage.

Team data also point to varying levels of performance consistency across the league. For example, the New York Knicks (46-25 record) and the Boston Celtics (46-23 record) have nearly identical win totals. However, the Knicks’ point differential has a standard deviation of 17.52, one of the highest in the league, while the Celtics’ is a more moderate 15.22. This could indicate that the Knicks’ games have featured a wider range of outcomes, including larger margins of victory and defeat, compared to the Celtics. Conversely, teams like the Dallas Mavericks show a much lower standard deviation in point differential (11.90), which may reflect more consistently close games, even though their record is 23-47.

Finally, while individual leaderboards highlight standout performers like Shai Gilgeous-Alexander (31.5 points per game) and Nikola Jokić (10.5 assists per game), it is important to interpret these data with caution. Some statistical categories, such as 3-point percentage, can be misleading without additional context. Several players listed with perfect (1.000) or high percentages have likely taken very few shots, making their accuracy rate an unreliable measure of shooting skill compared to a high-volume shooter like Stephen Curry (11.5 three-point attempts per game). Furthermore, the appearance of players on multiple teams or with a “NA” designation is a result of mid-season trades, and their statistics reflect cumulative performance across the season.

Cumulative Wins

This figure presents cumulative wins by National Basketball Association (NBA) team during the current season. Each panel corresponds to a single team, with the x-axis representing the progression of the season by date and the y-axis showing the total number of wins accumulated to date. This display helps illustrate how quickly teams have been winning games relative to one another and provides a clear view of momentum, slumps, or sustained success over time. Because the plot updates automatically as new data become available, it reflects each team’s current position in the season at the time of the most recent refresh.

Graph Prepared By: Isaac H. Michaels, DrPH
Data Source: www.basketball-reference.com

Points Scored vs. Points Allowed

This figure plots points scored against points allowed for each National Basketball Association (NBA) team during the current season. Each panel corresponds to a single team, with individual points representing games. Points above the diagonal dashed line indicate games in which the team scored more points than it allowed (wins), while points below the line indicate losses. Points are colored according to game outcome to distinguish between wins and losses. Teams with a larger number of points above the line tend to outscore their opponents more consistently, reflecting stronger overall offensive and defensive performance. The figure provides a visual summary of each team’s scoring efficiency and defensive strength across all games to date.

Graph Prepared By: Isaac H. Michaels, DrPH
Data Source: www.basketball-reference.com

Point Differentials

Player Statistics