What does Marquette’s Synergy Shot Quality tell us?

Earlier this week, Synergy (the preeminent basketball video and stat site for college basketball) released a new feature that had me in a tizzy. As a big soccer guy that uses xG (expected goals) probably too religiously, I’m obsessed with measuring the efficiency of shots, regardless of the result.

If you take an open corner 3 and miss it, that should be noted and patted on the back more than taking a contested dribble jumper and making it. Instilling a good process will yield greater returns in the long run than rewarding lower probability results. And now, Synergy Sports has given us just that.

Basically, it has taken the millions of data points it collects from games at every level, and assigned values based on distance, defensive pressure, the play type and the time left on the clock. Here’s a lengthier explanation if you are so inclined to dig into the weeds: https://synergysports.com/explaining-synergy-shot-quality/.

I still think it is missing a few components adjusting for individual ability (a Markus Howard step-back should not be given the same value as anyone else on this planet save Steph Curry), but for now, this gives us a great starting point to measure not just end results, but the process that goes into those shots.

Mapping the Big East

What better way to do so than plotting how each Big East team is currently looking based on SSQ (expected points per shot) and PPS (actual points per shot).

You can see in the upper right quadrant, Marquette is tied with UConn for the highest expected shot value, and tied with Xavier for the actual shot value. All of which is to say Marquette’s elite offensive results are not indicative of a team that is “lucky” or filled with tremendous shooters. It takes great shots and has seen great results.

Check out how this tracks against TRank’s eFG% and AdjORtg results:

The results won’t be identical, as Synergy is simply looking at individual shots, not TO or ORebs, which also impact offensive efficiency, but in general, we see a fairly similar pattern appear on both charts. Taking good shots is and will always be vital.

All comes back to growth

To see exactly where those good shots were coming from I made a table with Marquette’s players, and added a space for the 2021-22 season numbers in order to be able to gauge what had changed the most. The results were fascinating and all point to a single thing.

Take Tyler Kolek for example. Last season, he could barely hit rim on some of his shots, and despite being given a vote of confidence from the staff both in public and in practices, the results were pretty poor. Using this Synergy tool, you can see that even though he was expected to hit 0.97 points per shot last season, he actually only hit 0.8, a huge -0.17 underperformance.

This season, his shot selection/quality has actually gone down to 0.94. However, the results are head and shoulders ahead of where they were last season, making an additional 0.21 points per shot, for a turnaround in Synergy Shot Making of +0.24, the largest on the team.

And look at that quality chart again. One of my worries going into the season was that without Justin or Darryl to carry the offense at points, this group wouldn’t have the ability to increase their usage efficiently against more keyed in opponents. And the shot quality does tell us that on the whole, this group of returners has been taking “tougher” shots this year compared to last, with a median SSQ drop of -0.03.

But, as we highlighted over and over at the end of 2022, the growth this team has shown in ability is remarkable. Despite taking lower quality shots (in much higher minutes and volume) the actual production has increased by +0.11 points per shot, for a turnaround in shot making of +0.16. That’s a lot of numbers, but think of it this way, the returning players have been able to collectively improve their shot making ability by 16 points per 100 possessions.

It isn’t one player all of a sudden finding their 3-point stroke. Or another just going on a heater at the rim. The “worst” jump from year to year was Kam Jones’ +0.08, and yet that’s still a 66% increase from his freshmen year.

There is a lot of season left and we’ll make sure to run this analysis again once all the games have been played, but for now, this fancy new tool just goes to confirm that this offense is no fluke, and the individual leaps taken by each player have yielded some amazing collective results.