# What happens during crunch time for the entire NBA

Click for a full-sized interactive version.

After my last post about the Houston Rockets crunch time woes, Bluemars from the forums asked if I could do a similar analysis for all NBA teams. That seemed like a pretty neat idea, and the above chart is the result. The chart shows all teams’ offensive efficiency plotted against their assist ratios during various crunch time moments, defined as being down by five points or less (or tied) with a certain number of minutes remaining. Basically, it’s an attempt to show which teams have heroball tendencies and what results those tendencies produce. For extra fun, the chart also shows each team’s win percentages during their crunch time minutes.

Technical information

On the x-axis is assists per 100 possessions. On the y-axis is points per 100 possessions. The grey lines show the averages of each measures for all teams.

You can, and should, use the slider in the upper left hand corner to change the time period of analysis. There are seven. The first is an entire game, which is how each team behaves on the two measures of interest throughout the course of an entire game. The next six are all crunch time moments. They show how each team behaves when they’re down by five points or less (or tied) starting from five minutes remaining to 30 seconds remaining in a game.

The size of each team’s circle indicates that team’s winning percentage during that time period. Larger circles indicate higher winning percentages. You can mouse over a circle to see the complete data for that team during any time period. So, for example, with four minutes remaining and down by five or less, the enormous black dot on the far right of the graph is the ethereal San Antonio Spurs. They have a ridiculous winning percentage of 63.6%, pass more, and score more than any other team in this situation. I hate them for being so good.

In order to track the progress of a specific team, you can either click on its circle or its name in the lower left hand corner to highlight its circle. That circle will remain highlighted as you adjust the time period using the slider.

Important limitations

• Because not all games are close, the sample size of the games (and possessions from those games) as one moves closer to the end of games becomes very small.
• Each average is an unweighted average. Each team’s performance is considered equally when calculating the average, regardless of how many crunch time possessions and games the teams played.

Observations

Heroball is the king of crunch time

Move the slider to the entire game time period. Look at where the average lines intersect. Now move the slider through each time period chronologically. Notice how the intersection of the average lines steadily moves downwards and to the left. That means as crunch time becomes, uhh, “crunchier,” teams are passing less and scoring less. The Houston Rockets are pretty representative of what the entire league seems to do. Highlight them and slide through the time periods. You’ll see that they start off reasonably well, but with between 4 and 3 minutes remaining the ugly specter of heroball emerges. By the time 30 seconds are remaining, they’re firmly at the bottom and on the left of the chart, meaning they don’t pass and don’t score.

Passing the ball is better than not passing the ball

This observation seems so plainly obvious I almost hate having to write it. But clearly it needs to be shouted from the rooftops because not all teams seem to get it, especially during crunch time. If you look at the general dispersion of the teams, you’ll notice that it trends to the right and upwards. In other words, the more assists a team has, the higher that team’s offensive rating tends to be. This isn’t an optical illusion. In fact, I wanted to know the exact value of this trend, so I plotted it.

Click for a full-sized interactive version

I took out the bells and whistles from the initial chart and just plotted each team as a blue dot. That red line you see is the trend line. It shows the general direction of where teams are plotted on the chart. As you flip through the time periods, you’ll see that offensive rating increases as assist ratio increases in every single period, including the entire game.

The lowest slope (the grade) of any trend line from any chart is 1.69. The highest is 3.05. In normal person speak, that means that an additional assist per 100 possessions (or playing in a way that might produce an additional assist) correlates to between 1.69 and 3.05 additional points per 100 possessions. PASS THE BALL.

Unless your name is Kevin Durant

The upper left quadrant is interesting. Teams in this quadrant need to have high offensive efficiency but low assist ratios. Not surprisingly, there are practically no teams in this quadrant in any of the charts, because, as we just discussed, passing the ball leads to scoring. While the Portland Trailblazers flirt with this quadrant a couple of times, the only team that is strongly in this quadrant in all time periods is the Oklahoma City Thunder. I also hate Kevin Durant.

Teams that pass more, win more

As you scroll through the time periods, take a look at the dispersion of circle sizes. Generally speaking, larger circles are to the right of the assist ratio average line, especially in the 3 and 2 minutes left charts (afterwards it probably becomes much harder to win, period). I don’t think this is a coincidence.

Crunch time teams I like

This doesn’t necessarily mean they win a lot of close games, but it means these teams tend to deviate less from a “normal” offensive strategy during crunch time (aka, they heroball less)–Golden State Warriors, Miami Heat, Atlanta Hawks, Memphis Grizzlies.

Also worth mentioning are the Toronto Raptors. They are the anti-Rockets. They are the only team I found that moves completely against the grain. Their normal offense is quite selfish and inefficient but they become more unselfish and efficient as crunch time progresses. Track their circle. It’s interesting.

About the author: Richard Li is an independent researcher and consultant. He likes numbers and pictures.

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