Welcome to Beyond the Basics!

My name is Zack Capozzi, and I run LacrosseReference.com, which focuses on developing and sharing new statistics and models for the sport.

The folks at USA Lacrosse Magazine offered me a chance to share some of my observations in a weekly column, and I jumped at the chance. Come back every Tuesday to go beyond the box score in both men’s and women’s lacrosse.

31%

We knew coming into this year that Rutgers was going to have a lot of holes to fill on offense. Last year, the top three guys were Connor Kirst, Adam Charalambides and Kieran Mullins. Collectively, they took 54 percent of the Scarlet Knights’ shots and accounted for 54 percent of their assists.

It’s been just two games, so don’t think that the Rutgers offense is a finished product by any means, but we can start to see the trends shake out. So far, Mitch Bartolo, Ross Scott and Ronan Jacoby have been the three players with the highest play share. Collectively, these three have 57 percent of Rutgers’ shots but just 31 percent of their assists.

The Rutgers offense has yet to reach the heights that last year’s team did, but its has been a bit more distributed in where the production is coming from.

0.82 POINTS PER GAME

Jacob Alexander put up an 8.95 EGA mark in the Bulldogs’ victory over Providence. We often see FOGOs with high raw EGA numbers because they get a chance to win a possession every time they step on the field. Aside from actually scoring, earning a possession for your team is about the highest EGA thing a player can do.

But that 8.95 EGA mark also included two goals and one assist. And this is the interesting part — Alexander is part of a trend. In 2022, there have been 0.82 points scored per game by faceoff specialists. Last year, there were 1.03 points per game scored by faceoff specialists. In 2020, the number was just 0.55. In 2019, it was 0.53.

Last year saw the most aggressive FOGOs since I’ve tracked this data. I’ll keep an eye on this trend to see how it plays out the rest of the season.

26.4%

At the start of every season, once all the schedules have been announced, I run my season simulator a couple thousand times. The goal is to establish a baseline for each team with respect to expected wins and postseason expectations. The nice thing about having a baseline is that as games get played, you can see who has improved their position the most.

Through three weekends, that would be Johns Hopkins. When schedules were released, the baseline probability that they would make the NCAA field was 8.4 percent. Not great. But after two wins and a loss against Georgetown, their NCAA probability is up to 26.4 percent. That increase of 18 percentage points is the largest of any Division I men’s lacrosse team.

13.4%

After UMass-Lowell scored a goal to go up by four with 2-plus left in the first half, the Bobcats’ win probability was just 13.4 percent. The third quarter, though, was all Quinnipiac. The period saw them score eight goals and give up just three to turn the four-goal deficit into a one-goal lead heading to the fourth quarter.

8.35 EGA PER GAME

Last year, Jared Bernhardt and Logan Wisnauskas were the top two players for the Terps offense, as measured by play share, which is a way of measuring how big of a role each player has within an offense. Together, they accounted for seven goals and nearly four assists per game. If you look at their overall production (measured by EGA), they accounted for 8.26 expected goals per game.

Fast forward a year and replace Bernhardt with Villanova transfer Keegan Khan, who has the second-highest play share behind Wisnauskas through two games. Using EGA again, the Wisnauskas/Khan pairing has generated 16.7 combined EGA, which works out to 8.35 expected goals per game.

With the departures, it seemed unlikely that the Terps offense could match last year’s unit. So far, though, they’ve exceeded the 2021 unit.

LACROSSE STATS RESOURCES

My goal with this column is to introduce fans to a new way to enjoy lacrosse. “Expand your fandom” is the mantra. I want you to walk away thinking about the players and stories presented here in a new light. But I also understand that some of these concepts can take some time to sink in. And part of the reason for this column is, after all, to educate.

To help this process along, I have several resources that have helped hundreds of lacrosse fans and coaches to internalize these new statistical concepts. The first is a Stats Glossary that explains each of my statistical concepts in more detail than I could fit here. The second is a Stats 101 resource, which provides context for each of my statistics. What is a good number? Who’s the current leader? That’s all there.

And last, I would love to hear from you. If you have questions or comments about the stats, feel free to reach out.