Sacred Heart does rate a bit better than the other 2 defenses (24th vs 31st and 38th), but not to the degree that you’d point that as the obvious reason for the V. As with so many things, the data can point out something, but not give a satisfactory answer as to why.

So far this season, Hofstra is +8.3 possessions per game and their highest FO win rate since at least 2015. And with the performance, Patterson has jumped to 19th among active FOGOs in the faceoff Elo ratings.

Regardless of why there were so many offensive fireworks, the point of this list is to highlight the players that set them off and once again Dylan Pallonetti finds himself in that group. He didn’t shoot as well as he did against Sacred Heart, but he made up the difference in this one with the 3 assists and 5 GBs. There are many ways to generate expected-goals after all.

Olmstead led the offense in taking advantage of all those chances as they put up their best offense performance of the season (47.6% efficiency) against a Utes defense that rates in the top-15 on an opponent adjusted basis.

For his part, Olmstead’s role in the Hounds’ offense has continued to swell. He picked up a lot of the slack when Chase Scanlan left for Syracuse, and the has continued through to this year. The difference for Olmstead (by extension) and Loyola is that he’s been more productive with his chances this year. Shooting percentage is up from 22.0% to 26.9%. Assists/gm is up from 1.83 to 2.33. His expected vs actual goals ratio, while still not great, is up as well. The only downside is that his individualized turnover rate is a bit up this year from .76 to .87.

Olmstead may be the bellweather for the Loyola offense this year. So far, the signals are pointing in the right direction.

In 2019, his weighted play share was 9.9%; this year is it 6.4%. Again, not a huge surprise. But anything he may have lost in chances, he’s made up for in efficiency because he’s shooting at a higher percentage, converting more often than expected, dishing out more assists, turning the ball over less and generally playing better than he ever has.

Can’t speak for Joe, but I’m guessing it’s a trade he’d make again.

And in games where he didn’t play against the buzzsaw that is Georgetown’s defense, the numbers are really impressive (cherry-picking? of course).

The Hoyas did a good job limiting Campbell’s chances as he had just a 6.8% play share in that game. Against Marquette, that figure was 13.3%. Georgetown forced him into 3 turnovers and just 5 shots. Against Marquette, his stat line was clean and he was the primary option, taking 17 shots (most on the team).

For the season, Rutgers has a +3 per-game possession advantage. Last year, it was -5. Using a league average efficiency, a 8-possession margin should net you between 2 and 3 goals per game. Possessions has been an issue for Rutgers for years, but now with Dugenio in the mix, that may no longer be the case.

That’s not necessarily a bad thing. Assisted goals require more things to go right than unassisted goals. I’ve long thought (although yet to prove) that offenses that are reliant on assists are going to have a harder time over the course of a season than offenses that can just go get goals by having a single player make it happen. One possibly related benefit for Army is that their team turnover rate is down from 36.6% last year to just 30.2% this year.

It’s only 2 games, so we’ll see what happens with this, but it’s an interesting trend so far.

Of course, assisted goals, of which he had many, split EGA credit between the goal scorer and the assister, which is how Mike Robinson is only 9th on this list.

Lehigh has always been solid at the faceoff x, and while I don’t expect them to be +10 possessions every game, this was not an anomalous performance for the Hawks.