Each week, we will look back at the games that were to see which players had the largest individual performances. I say largest because the contributions that we can measure (from play by play) tend to be things that are easy to count. This includes, goals, shots, assists, turnovers, penalties, etc. We can’t measure a defender who shuts down an opposing player so completely that he doesn’t even touch the ball. Still, it is interesting to be able to identify the players that really filled it up each weekend and give them a shout out here.
For a bit of background, in order to rank single game performances, we needed a way to condense box score stats to a single number for each player. In order to do this, we relied on our expected goal values methodology, which assigns a goal value to each type of play depending on how often it leads to a goal in the next 60 seconds. By adding up all the expected goals added for each player, we can get to that single number and these rankings.
We have also tagged each performance with the opponent’s ELO rating. The higher the number, the stronger the opponent. This should help to give some context for each performance. Did the player feast on the dregs of D1 or did they put up these numbers against a quality opponent?
Click on any player’s name or the PRO logo () and you’ll head straight to the detailed breakdown on their LacrosseReference PRO page. As opposed to last year, all players appearing in the weekly rundown are unlocked and the information on their page is available to all readers.
Michigan’s offense definitely turned some heads this weekend. 22 goals against Bellarmine worked out to a 40.7% efficiency start to the season. No one is suggesting they’ll be that good all year, but if they can improve on the 29.8% efficiency that they put up last season, then this a team, thanks to Nick Rowlett’s success at the X will be tough to catch from behind.
Over his career, Josh Zawada has become more of an accomplished distributor. Saturday’s 5 assist performance is the continuation of a trend. His assist-per-touch rating went from 66th percentile in 2020 up to the 80th percentile last year. Given his usage-rate, his individual assist rate on Saturday was 1.56, which would be top percentile for a full season.
Tre Leclaire and Ryan Terefenko aren’t in Columbus anymore. That makes Jack Myers “the guy” this year for the Buckeyes. I’m not going to read too much into a win over Detroit (final adjusted efficiency rank in 2021: 42nd).
Last year, Myers’ best categories were related to his shooting (93rd percentile for shooting percentage). And OSU would love to improve their shooting percentage from where it was in ’21 (29.6%). Myers taking a larger share of the team’s shots should help there.
The second half of what was a dynamic duo for the Wolverines against Bellarmine, Michael Boehm will be looking to expand on a highly successful freshman season last year.
And he’ll be looking to help Michigan continue a very positive trend on offense. 2021 was obviously a year in which the Michigan offense played better than average defenses because of the all Big Ten Schedule. It’s also the reason that I “adjust” a team’s efficiency to account for the quality of their opponents.
Using raw stats, the Michigan offense fell from 34.7% to 29.8% last year (4.9 percentage points). But when we adjust for the strength of the defenses they faced in ’21, the drop-off is actually just 2.9 percentage points (33.5% to 30.4%). So yes, it was still a down year for their offense, but don’t be surprised to see them back up into the mid 30’s again now that the schedule isn’t going to be as tough.
In his freshman season, Brennan O’Neill was great, and his particular strength was his shooting. Granted, his assist rate and ball security ratings were still above average, if only slightly.
I think the question for O’Neill as he continues to develop is what happens if (and I mean if) he runs into a team with a system or singular defenseman that is able to prevent him getting his shots? Can he further develop the side of his skill set that leads to offense for others? Given that he’s just played his 19th game of his college career, my guess is that he’s going to have lots of chances to find out.
When I wrote about the Blue Hens over the offseason, I focused a bit on what happens to a shooter (Robinson) when a team loses their primary initiator (Charlie Kitchen). The worry is always that when the source of the good looks dries up, the goals do as well. Look, a high assist rate is not a prerequisite for an effective offense, but it can be a signal that losing a player has changed how an offense functions.
It’s just one game against NJIT, but keep an eye on what the Blue Hens’ assist rate looks like in March.
Strong candidate for the best game of the opening weekend.
In ’21, Sullivan wasn’t a primary option for the Pios. The impact he had was primarily as a distributor. His assist rate was in the 79th percentile nationally, while his shooting rating was 14 out of 100 (I use a Madden 0-100 scale). Small sample size, but 4 goals on 6 shots, to go with 2 assists against Utah suggests an off-season of shooting practice has paid off.
It’s one game, so let’s not get too carried away, but my sense is that this offense works best with Jack Hannah creating offense rather than being the finisher. If Sullivan’s shooting really is improved to where he could be a reliable option there, this offense could be scary.
I measure individual player efficiency using my usage-adjusted-EGA metric. It takes my EGA (expected goals added) stat and adjusts it to account for the fact that some players have more chances to do things than others. The result is something akin to production-per-touch, also known as efficiency.
Individual efficiency tends to improve as players get more experience, but you don’t often see such consistent improvement. Through one game, Bartolo was able to limit the turnovers, which has been as issue for him in the past. He’s going to occupy a larger role in the Rutgers offense than any he had at Penn, so the increase in efficiency is doubly important as his leverage increases.
I mentioned this on Twitter, but Sean Goldsmith’s evolution from sharp-shooter to well-rounded offensive weapon has been impressive. In the shortened 2020 season, he was an 85th percentile shooter and a 4th percentile assister. So of qualifying players, 96% of them had more assists-per-touch than he did.
Last year, his assist score was 95, meaning just 5% of eligible players generated more assists-per-touch than he did. Through 2 games, his assist rating is 75 and his ball security rating is 61. As a whole, this offense is generating assisting on 70% of their goals so far.
I have tweaked the formula for this list this year. It used to be straight EGA, whoever generated the most production make the list. The issue is that EGA doesn’t penalize a FOGO for losing a faceoff, so you tended to end up with a very FOGO heavy list. And it’s not just that there were too many FOGOs, I truly think that because FOGOs don’t lose EGA for a faceoff loss, the model was truly over-valuing their performances.
So going forward, the model is going to combine raw EGA and usage-adjusted EGA to get to a composite score that better reflects the value of a player’s outing. Using usage-adjusted-EGA means that all those faceoff losses do get incorporated into the calculation. As a result, it’s going to be harder for a FOGO to make this list this year.
Not impossible though.
And Jake Naso’s game against Robert Morris is proof of that. 24 faceoffs wins, 2 goals on 2 shots, and just one turnover. It was a nice start to the year for Naso and the Blue Devils.