I’ve been interested in catch & shoot (C&S) jump shots for a while now, pretty much ever since I read this article by Stephen Shea. There is this idea that a C&S is better than a pull-up shot. And based on everything I’ve read and analyzed, this holds true. This lead me to ask “what is it about a C&S that makes it such a superior shot?”

To answer this question, I started with the NBA definition of catch & shoot, namely “Any jump shot outside of 10 feet where a player possessed the ball for 2 seconds or less and took no dribbles.” Let’s break this definition down a little.

– A shot past 10 feet is further from the basket, which I would think decreases the likelihood of success. However we can assume that when comparing to pull-up jumpers, the comparison group is past 10 feet as well. When the time come to analyze data, we will just have to restrict to shots past 10 feet. No problem.

– Taking no dribbles means a player doesn’t need to take time to collect the ball or fight the momentum of movement. It makes sense that not having to dribble would increase the chance of a making a basket.

– On the surface, possessing the ball for 2 seconds or less seems like it would give a player less time to set himself and shoot. Therefore I would assume short possession time would decrease the probability of success. However, a shorter possession means that the defense has less time to get in position as well. And this is where I get irked about the definition of catch & shoot.

When I began to look at C&S vs pull-up jumpers I hypothesized that the effect of the defense was confounding the effect of a C&S. I still thought a C&S shot would be better than an pull-up, but without controlling for defense, I was skeptical of how much better.

Let’s take a step back and define some terms. If we think of a C&S as a “treatment” ( my education and training are in public health so I often default to medical terms) compared to a “control” shot being a pull-up jumper, and shot success as the outcome, then our goal is to examine the treatment effect of a CnS on the outcome. We can also say that the type of shot is the independent variable, and success of the shot is the dependent variable. But shots are not randomized to be CnS or pull-up, so just looking at raw numbers won’t necessarily give us the full picture. A “confounder” is any variable that effects both the treatment and the outcome. Defense is very likely a confounder for any shot as a player is more like to take and more likely to make a wide open shot. Similarly, he is less likely to take and less likely to make a highly contested shot.

The NBA has a really great statistics site, http://stats.nba.com/. However, it doesn’t get to the granularity that I want. Thankfully www.nbasavant.com does. I went and pulled 50,000 shots from the 2014-2015 season. I chose that season because starting in 2016, defender data isn’t available. I also restricted to players who took at least 100 jump shots. I ended up with 50,000 shots (I assume this size is preset), which I then further restricted to 38,384 jump shots that were over 10 feet.

Of these 38,384 shots 21,917 had zero dribbles and a possession of 2 seconds or less and thus were defined as a catch & shoot. The remaining 16,467 shots were labeled as pull-up shots.

One more definition, for the purposes of this analysis (and future analyses), I consider any shot where the closest defender is more than 4 feet away to be an “open” shot. All other shots are considered “defended.”

Here are some basic statistics:

Counts | Defended | Open |
---|---|---|

C&S | 4049 | 17868 |

Pull-Up | 7468 | 8999 |

Percent | Defended | Open |
---|---|---|

C&S | 10.54% | 46.55% |

Pull-Up | 19.46% | 23.44% |

Most shots are open C&S. The majority of C&S shots are open, and the majority of open shots are C&S.

I could split this out further and look at 2pt shots vs 3 pt shots, but let’s skip that for now and put it into a simple logistic regression modeling the probability a shot is successful. We can include an indicator for whether or not a shot was a 3pt attempt:

Estimate | Std. Error | z value | Pr(>|z|) | |
---|---|---|---|---|

Intercept | -0.5467 | 0.0175 | -31.23 | 0.0000 |

C&S | 0.2955 | 0.0233 | 12.70 | 0.0000 |

3pt | -0.2727 | 0.0230 | -11.88 | 0.0000 |

Now let’s control for wether a shot was open or not:

Estimate | Std. Error | z value | Pr(>|z|) | |
---|---|---|---|---|

Intercept | -0.6305 | 0.0217 | -29.06 | 0.0000 |

C&S | 0.2597 | 0.0239 | 10.88 | 0.0000 |

open | 0.1626 | 0.0245 | 6.62 | 0.0000 |

3pt | -0.2927 | 0.0232 | -12.64 | 0.0000 |

Side note: I try not to pay too much attention to p-values, preferring to focus on effect sizes.

We see that C&S does increase the chance of success, though the effect is mitigated when we also control for whether or not a shot was open. I also fit the models with various interactions, but they had little impact (in fact the interaction between C&S and open was negative, though small).

This is why I am always a little irked by the definition of a catch & shoot – it doesn’t account for how open the player is. A player with a high C&S FG% who mostly takes open shots should be evaluated differently than a player with a similarly high C&S FG% who takes mostly defended shots. Yet raw C&S FG% or C&S EFG% obscures this difference.

We are just getting started here. After all, so far I’ve only presented simple regressions with some (confusing and hard to interpret) log odds ratio coefficients. In future posts I will get into how we can estimate the causal effect of a catch & shoot for only for raw field goal percentage, but also effective field goal percentage, which will give a bonus to three point shots.

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