Select Team

A New Level of Analysis: Intro To Advanced Stats

The way that the game of basketball is analyzed – from front offices, to coaches, to players, to media and fans – continues to improve as more data and advanced metrics become available.

For years, the statistics provided in the traditional box score – points, rebounds, assists, steals, blocks and shooting percentages – formed the basis for most conversations about the women’s game.

Of course, we had the eye test and could discuss what we saw on the floor, but when there are numbers available to back up (or refute) what we see when we watch the games, then it makes our analysis even stronger.

The launch of advanced stats for players, teams and individual games on is a step towards allowing us to analyze the game with a different lens.

Want proof? Let’s look back at some of last year’s numbers to showcase the importance of these new metrics. If we want to look at which teams had the best offenses last season, and only had traditional stats to use, we would look at which teams averaged the most points per game. Here is that list.

1. Chicago (82.9)
2. Atlanta (77.8)
3. Tulsa (77.7)
4. Indiana (77.7)
5. Minnesota (75.5)
6. Phoenix (75.2)
7. Connecticut (75.0)
8. New York (74.4)
9. Los Angeles (73.6)
10. Washington (73.6)
11. Seattle (70.4)
12. San Antonio (68.1)

Using per-game statistics is not necessarily the most accurate way to compare players and teams because not all teams play the game at the same tempo. The key component that has to be accounted for is pace, which is the number of possessions a team averages in a 40-minute game.

If a team plays at a faster pace and has more possessions to utilize, then they have more opportunities to score and increase their points per game numbers. Their offense could be less efficient – lower shooting percentages, more turnovers – and yet they could still put a points per game number that ranks rather high.

In order to make a truer comparison between teams, we have to put them all on the same level when it comes to possessions. By accounting for each team’s pace of play, we can measure their offensive production based on 100 possessions rather than per game. Let’s see what happens to these rankings when we use offensive rating (points per 100 possessions) rather than points per game.

1. Chicago (104.3)
2. Tulsa (101.1)
3. Indiana (99.5)
4. Minnesota (99.3)
5. Los Angeles (98.8)
6. Phoenix (98.3)
7. Connecticut (97.9)
8. Washington (97.8)
9. Atlanta (97.4)
10. New York (96.5)
11. Seattle (92.3)
12. San Antonio (88.7)

When we compare the two lists, there are some teams that match up in the rankings both in per game and per 100 possessions – Chicago leads in both and Seattle and San Antonio are at the bottom two on both lists. But let’s look more closely at the teams that are bolded on both lists – Atlanta and Los Angeles – as their ranking vary greatly between the two lists.

The Dream’s offense goes from second when measuring with points per game (77.8) to ninth when measuring with offensive rating (97.4). Part of the reason the Dream scored so many points per game is because they led the league in pace at 79.2 possessions per 40 minutes. By using more possessions, the Dream were able to overcome an inefficient offense to still be among the top scoring offenses in the league. But when we even out the number of possessions, we see the Dream’s offense was in the bottom third of the league, as was their finish in the standings.

On the other hand, the Sparks played at the league’s slowest pace (74.1), so they ranked ninth in the league in points per game (73.6) despite having the fifth-best offensive rating (98.8).

The use of pace, offensive and defensive efficiency ratings is just one of many ways that advanced metrics help give us a better understanding of the game than traditional stats can provide.

Throughout the season, we’ll delve into each topic to showcase the new statistics and offer new insights into how players and teams are performing through a new lens of analysis.

Stay tuned each week for new stats content, and in the meantime, dig into the numbers on the site to build on your own analysis. Here are some useful links to get you started: