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.
Another week, another top-EGA game for Jack Myers. Against UNC, Myers was primarily a distributor, with 2 goals but 7 assists. Against Harvard, he had the assists, but was able to finish as a high level too (71% shooting; 5 goals vs 33% shooting against UNC).
Myers has been the biggest reason why, compared to last year, the Buckeyes’ offense is scoring a lot more assisted goals. So far, they’ve assisted 60% of their goals, compared to just 49.6% last year. Look, it’s early, but I can’t help myself.
The 2017 team that made the finals ended with an adjusted offensive efficiency of 34.7%. This year’s team is at 37.3%. That teams adjusted defensive efficiency was 25.9%; this year’s team is 26.3%. That year’s team won an adjusted 65.1% of their faceoffs; this year’s team is at 80.4%.
Disappointing outcome for Coach Nadelen’s bunch, but we have to recognize the game (and season) that Nick DeMaio is having since transferring from College Park. He took more shots against Richmond than he did in 2 seasons with Maryland. He’s been the Tigers’ highest share player and has taken a whopping 30.6% of their shots on the year.
And the Tigers offense has undoubtedly been better than in recent years. Their adjusted offensive efficiency is 33.6% so far through 4 games, compared to 29.3% last year. That’s their best mark since a 33.7% in 2019.
Naso’s win rate in 2022 is 1.7 percentage points worse than in 2021 (61.2% vs 62.9% last year). He’s currently the 18th rated FOGO in the faceoff Elo ratings. Given who he’s faced this year, you could argue he’s having a small sophomore slump.
But against Delaware, you wouldn’t have known it. 27 faceoff wins works out to a 77% win rate, which was the 3rd highest of his career. We’ll see if Naso is able to build off that performance and become a dominant FOGO. In a conference with Petey LaSalla that could be “the” question for Duke.
To an extent, the leap is the result of a jump in his shooting accuracy. And I don’t just mean shooting percentage. Yes, his shooting percentage is 36%, up from 33% last year. But his shot-on-goal rate is actually down from 69% to 61%. Think about that for a second. In 2021, 48% of his shots that were on goal went in; 52% of his on-goal shots were saved. This year, 59% of his on goal shots went in. That means just 41% were saved. Saves function similarly to turnovers in a lot of ways. So a player who increases his shooting percentage while cutting out saved shots to that degree is adding a double dose of efficiency to his or her offense.
I won’t argue if you want to take that stance. Has anyone had more heartbreakers over the past few years than Towson?
Lance Madonna paced the Richmond offense, which has been a bit of a revelation this year. At 35% (opponent-adjusted), they are currently sitting pretty with the 15th best offense in the country. That puts them in the #1 spot in the SoCon.
Furlong’s game against Marist earned him the Offense Star for Saturday’s games. The 5GBs is what really put it over the top for me. Furlong had 2 GBs in their first 3 games. So yes, the 56% shooting is impressive, but I love to see players getting into the mix and winning possessions for their team.
A 7-point game is good; but to get to 7 EGA, it’s more than just the points.
#7 – Chris Gray (North Carolina) – 6.64 goals added
Feb 27 vs Johns Hopkins Opp ELO: 1627
In his first 2 seasons at UNC, Chris Gray was always a big part of the offense, but not the offense. His play share this year is back to where it was when he was carrying the BU offense on his back. So, sure, he’s generating a lot of offense; the 9 point effort against JHU is proof of that. But if the UNC offense is going to get back to where it was last season, they are going to have to figure out how to backfill the rest of the offense around him.
After finishing 4th and 1st in my opponent-adjusted offensive rankings in 2020 and 2021 (respectively), the Heels are currently sitting in 26th, last in the ACC.
10 points and 54% shooting. That’ll get you on this list most weeks. Zawada’s game against Canisius, with a 3.88 player efficiency rating, was his 2nd most efficient game of the year. And so far, the efficiency hasn’t fallen off with the increased role. Zawada has taken 18.2% of the Wolverines’ shots (most on the team) and has contributed 27.0% of their assists. And his year-long player efficiency mark is 2.89, which is, you guessed it, best on the team.
The trick for the Wolverines is, now that teams have 5 games of film on this offense, whether Zawada can adapt to what I’m sure will be novel ways to remove him from the offensive flow.
Same as last year, Duke is spending the month of February integrating new pieces into an offense. Lulley and Andrew McAdorey being the two most visible. You might look at 2 February L’s (out of a whopping 7 games) and wonder if it’s not going as well. But so far, the Duke offense has been a bit better than last year, at least if we look at raw offensive efficiency. Through 7 games, they have 116 goals on 308 offensive possessions (37.7%). Last year, in February, they scored 81 goals on 222 possessions (36.5%).
Perhaps there were some lessons learned from going through it last year. But whatever the reason, through one month of the season, the Duke offense has been even better than last year’s unit.
Duke’s fingerprints are all over this week’s list. But Montgomery is the only one to show up for his performance in the loss to Penn. One thing that I really like about Montgomery’s start to the season is his ball security. He’s averaging more turnovers per game (1.14 vs 0.82 last season), but because his play share is has grown more (5.7% vs 3.6% last year), his turnovers-per-touch number is better by quite a bit. As a result, his ball security rating is 72 (out of 100) this year; last year, he was a 22 on ball security. That’s a huge improvement.