In the “Star War” film series, stormtroopers are the fictional space marines of the Galactic Empire. They became notorious over the years for their subpar blaster aim, which constantly allowed the heroes of the stories to escape them unharmed. It’s gotten to the point that Star Wars itself pokes fun at the overall poor shooting ability.

Since May 4th is unofficially Star Wars Day — “May the 4th be with you,” being the tagline — The Athletic decided to honor the series by presenting awards for the NHL’s own version of stormtroopers: the worst-shooting players and teams in the league.

This is a deep, fun exercise because while identifying the league’s worst shooters sounds like a fairly straightforward task, there are actually several ways to look at it. Finishing/efficiency — measuring how good you are at burying the chances you get — is the component most closely associated with shooting ability, but there are other factors as well.

Whether the sport is hockey, basketball or soccer the basic formula behind scoring is the same. Goal-scoring is ultimately driven by volume (can you create a high number of quality shooting opportunities) and efficiency (can you bury those chances). On the side, you might consider shot selection (are you a smart shooter who takes high-percentage shots, or are you shooting from everywhere which presents an opportunity cost for your teammates?).

Let’s dig into some numbers to objectively measure which NHL players and teams stand out as some of the worst performers in various aspects of shooting.

Who are the worst finishers in the NHL?

The best finishers in the league can beat goalies from distances and angles that other NHLers simply can’t. Conversely, the worst finishers can even struggle with Grade-A chances.

How do we analyze this skill in an objective way? To establish how much better or worse each shooter is, we need to start by establishing a baseline for average shooting ability. For this, we can lean on expected goal models.

Expected goal models run through hundreds of thousands of past shots as a reference point and then assign a value to every shot based on their distance from the net, angle, whether it was off the rush or a rebound and so much more.

To put it simply, expected goal models assign a statistical probability for how likely a shot attempt is to become a goal. A shot from the left point, for example, might hypothetically be worth 0.03 expected goals (three percent chance of going in) while a shot from the inner slot might be assigned 0.20 expected goals (20 percent chance of going in).

Expected goal values are assigned based on the assumption of average finishing. These models and values obviously aren’t perfect but it creates a reasonable baseline for us to compare shooting efficiency among players.