Lacrosse Analytics

[laxreference]

Updated list of Live Win Probability Links

- Delaware vs Saint Joseph's
- Mount St Marys vs Towson
- Mercer vs Jacksonville

[1766]

We knew coming in to this year that Rutgers was going to have to fill a lot of holes on offense. Last year, the top 3 guys were Connor Kirst, Adam Charalambides, and Kieran Mullins. Collectively, they took 54% of the Scarlet Knights' shots and accounted for 54% of their assists.

It's been just 2 games, so don't think that the RU offense is a finished product by any means, but we can start to see the trends shake out. Through 2 games, Mitch Bartolo, Ross Scott, and Ronan Jacoby have been the 3 players with the highest play share. Collectively, these 3 have 57% of Rutgers' shots, but just 31% of their assists.

Something else to keep in mind; you might look at the efficiencies for the Scarlet Knights and think that they played better on offense against LIU. The efficiency in that game was 28.8%; it was just 26.0% against Marist. But that's why we adjust efficiencies for the quality of the opponent. After accounting for the defenses they faced, they actually ticked up today (28.9% adjusted efficiency vs 28.4% over the weekend).

I'd be curious what those analytics look like after St. John's.

[laxreference]

We knew coming in to this year that Rutgers was going to have to fill a lot of holes on offense. Last year, the top 3 guys were Connor Kirst, Adam Charalambides, and Kieran Mullins. Collectively, they took 54% of the Scarlet Knights' shots and accounted for 54% of their assists.

It's been just 2 games, so don't think that the RU offense is a finished product by any means, but we can start to see the trends shake out. Through 2 games, Mitch Bartolo, Ross Scott, and Ronan Jacoby have been the 3 players with the highest play share. Collectively, these 3 have 57% of Rutgers' shots, but just 31% of their assists.

Something else to keep in mind; you might look at the efficiencies for the Scarlet Knights and think that they played better on offense against LIU. The efficiency in that game was 28.8%; it was just 26.0% against Marist. But that's why we adjust efficiencies for the quality of the opponent. After accounting for the defenses they faced, they actually ticked up today (28.9% adjusted efficiency vs 28.4% over the weekend).

I'd be curious what those analytics look like after St. John's.

More balanced through 3 for sure. After the STJ game, those three now account for 48% of the team's shots and just 24% of their assists. Gallagher has been the big mover. He's got a full 20% of the Scarlet Knights assists now and the 5th highest individual efficiency rating on the team.

[1766]

We knew coming in to this year that Rutgers was going to have to fill a lot of holes on offense. Last year, the top 3 guys were Connor Kirst, Adam Charalambides, and Kieran Mullins. Collectively, they took 54% of the Scarlet Knights' shots and accounted for 54% of their assists.

It's been just 2 games, so don't think that the RU offense is a finished product by any means, but we can start to see the trends shake out. Through 2 games, Mitch Bartolo, Ross Scott, and Ronan Jacoby have been the 3 players with the highest play share. Collectively, these 3 have 57% of Rutgers' shots, but just 31% of their assists.

Something else to keep in mind; you might look at the efficiencies for the Scarlet Knights and think that they played better on offense against LIU. The efficiency in that game was 28.8%; it was just 26.0% against Marist. But that's why we adjust efficiencies for the quality of the opponent. After accounting for the defenses they faced, they actually ticked up today (28.9% adjusted efficiency vs 28.4% over the weekend).

I'd be curious what those analytics look like after St. John's.

More balanced through 3 for sure. After the STJ game, those three now account for 48% of the team's shots and just 24% of their assists. Gallagher has been the big mover. He's got a full 20% of the Scarlet Knights assists now and the 5th highest individual efficiency rating on the team.

Thank you. What was the team's adjusted efficiency?

It will be interesting to watch moving forward as Cameron gets more integrated and comfortable in the offense. Dante Kulas' numbers must be off the charts. I don't think he's taken a shot and not scored yet.

[wgdsr]

Duke looked good. The 34.4% offensive efficiency just edged out the mark they put up against RMU last year in their first game (32%). The difference is that the Blue Devils never made us nervous that the game was ever actually in doubt. In last year's matchup, RMU actually had a win probability above 50% with about 4 minutes left in the first half.

This year? No such luck. With a 10-3 first quarter, this game was over before it really ever got started. And you probably credit the Duke defense for that. Last year, they gave up 29.3% efficiency day to RMU. Tonight? 24.5%. And if you want to throw the ride in there too (9 failed clears), the defense never gave the Colonials a chance to put up a fight.

If you are on the Duke NCAA Champs 2022 train, I doubt you saw anything tonight to make you wonder whether you want to keep your seat.

Good analysis. On the defense vs ride how does that work into the number? Thought the ride was strong (and/or ROMO poor on clears) but 6 on 6 defense less strong. Could also be a factor of ROMO missing some clear dunks.

Failed clears do not count against an offense (or for a defense) in terms of efficiency. The denominator in the efficiency calculation is (total times they gained possession in the play by play - failed clears). So no, the 24.5% efficiency for the Duke defense is not affected by the number of failed clears for ROMO.

What happens when a team clear the ball over the mid-line and then gets stripped of the ball before getting it to the attack? With hard riding teams typically riding well into the offensive zone, I’m guessing there is grey area around a “successful clear” and a turnover in the offensive end.

That would count as a turnover for the offense and would count against their offensive efficiency. You are 100% right though that this is a grey area. Some teams actually adjust my data on the backend in situations where something is missed by the play by play.

lax ref,
how do you make your calculations for what counts as an offensive possession?
also, an example of a game and how many counted possessions each had?

[DMac]

While you're at it, what percent of goals were scored as a direct result of a face off win.

[wgdsr]

While you're at it, what percent of goals were scored as a direct result of a face off win.

or as the 2nd possession when each team had 1. thanks lax ref!!!

[laxreference]

Thank you. What was the team's adjusted efficiency?

It will be interesting to watch moving forward as Cameron gets more integrated and comfortable in the offense. Dante Kulas' numbers must be off the charts. I don't think he's taken a shot and not scored yet.

On an opponent-adjusted basis, they were 35.6% against St Johns. That brings their season average to 31.3% (28th nationally).

As for Kulas, his usage-adjusted EGA is 13.48, which means nothing to you. Obviously, he won't keep that up all season. The absolute most efficient players are going to be in the 2.5-3.5 range for a full season. But it does go to show just how much he produced with relative few chances against St Johns.

[laxreference]

Duke looked good. The 34.4% offensive efficiency just edged out the mark they put up against RMU last year in their first game (32%). The difference is that the Blue Devils never made us nervous that the game was ever actually in doubt. In last year's matchup, RMU actually had a win probability above 50% with about 4 minutes left in the first half.

This year? No such luck. With a 10-3 first quarter, this game was over before it really ever got started. And you probably credit the Duke defense for that. Last year, they gave up 29.3% efficiency day to RMU. Tonight? 24.5%. And if you want to throw the ride in there too (9 failed clears), the defense never gave the Colonials a chance to put up a fight.

If you are on the Duke NCAA Champs 2022 train, I doubt you saw anything tonight to make you wonder whether you want to keep your seat.

Good analysis. On the defense vs ride how does that work into the number? Thought the ride was strong (and/or ROMO poor on clears) but 6 on 6 defense less strong. Could also be a factor of ROMO missing some clear dunks.

Failed clears do not count against an offense (or for a defense) in terms of efficiency. The denominator in the efficiency calculation is (total times they gained possession in the play by play - failed clears). So no, the 24.5% efficiency for the Duke defense is not affected by the number of failed clears for ROMO.

What happens when a team clear the ball over the mid-line and then gets stripped of the ball before getting it to the attack? With hard riding teams typically riding well into the offensive zone, I’m guessing there is grey area around a “successful clear” and a turnover in the offensive end.

That would count as a turnover for the offense and would count against their offensive efficiency. You are 100% right though that this is a grey area. Some teams actually adjust my data on the backend in situations where something is missed by the play by play.

lax ref,
how do you make your calculations for what counts as an offensive possession?
also, an example of a game and how many counted possessions each had?

Total possessions are a) any time a team gains possession and b) doesn't fail to clear the ball. That means that failed clears do not count against offensive efficiency.

[wgdsr]

Duke looked good. The 34.4% offensive efficiency just edged out the mark they put up against RMU last year in their first game (32%). The difference is that the Blue Devils never made us nervous that the game was ever actually in doubt. In last year's matchup, RMU actually had a win probability above 50% with about 4 minutes left in the first half.

This year? No such luck. With a 10-3 first quarter, this game was over before it really ever got started. And you probably credit the Duke defense for that. Last year, they gave up 29.3% efficiency day to RMU. Tonight? 24.5%. And if you want to throw the ride in there too (9 failed clears), the defense never gave the Colonials a chance to put up a fight.

If you are on the Duke NCAA Champs 2022 train, I doubt you saw anything tonight to make you wonder whether you want to keep your seat.

Good analysis. On the defense vs ride how does that work into the number? Thought the ride was strong (and/or ROMO poor on clears) but 6 on 6 defense less strong. Could also be a factor of ROMO missing some clear dunks.

Failed clears do not count against an offense (or for a defense) in terms of efficiency. The denominator in the efficiency calculation is (total times they gained possession in the play by play - failed clears). So no, the 24.5% efficiency for the Duke defense is not affected by the number of failed clears for ROMO.

What happens when a team clear the ball over the mid-line and then gets stripped of the ball before getting it to the attack? With hard riding teams typically riding well into the offensive zone, I’m guessing there is grey area around a “successful clear” and a turnover in the offensive end.

That would count as a turnover for the offense and would count against their offensive efficiency. You are 100% right though that this is a grey area. Some teams actually adjust my data on the backend in situations where something is missed by the play by play.

lax ref,
how do you make your calculations for what counts as an offensive possession?
also, an example of a game and how many counted possessions each had?

Total possessions are a) any time a team gains possession and b) doesn't fail to clear the ball. That means that failed clears do not count against offensive efficiency.

do you have a one game example of 2 teams' total possessions?

[laxreference]

do you have a one game example of 2 teams' total possessions?

Every game has all the summary stats listed on a dedicated page

[wgdsr]

do you have a one game example of 2 teams' total possessions?

Every game has all the summary stats listed on a dedicated page

thanks. my formula for possessions is successful clears, successful rides and faceoffs.
looking at the hp - uva game:
you have
hp 38 possessions
uva 44 possessions

i have
hp 34 possessions
uva 39 possessions

i do wonder what's a more accurate way to do it. with mine that's even lower than yours, i feel like you can be double counting possessions (faceoff goes backwards for example, needing to clear).

[laxreference]

Every way of doing it, short of watching the film for every game, is going to have trade-offs. But I think that misses the point; efficiency only has meaning relative to other efficiencies, whether it's your team vs your opponent, your team vs the league, or your team vs your team in other games.

As long as the methodology is consistent, I think any logical way of doing it is fine. It's the comparisons that are the important output, not the absolute efficiency numbers.

[wgdsr]

Every way of doing it, short of watching the film for every game, is going to have trade-offs. But I think that misses the point; efficiency only has meaning relative to other efficiencies, whether it's your team vs your opponent, your team vs the league, or your team vs your team in other games.

As long as the methodology is consistent, I think any logical way of doing it is fine. It's the comparisons that are the important output, not the absolute efficiency numbers.

i understand that (in theory).
but it can apply to, say, value of faceoffs as an example. and turnovers.
also -- one school's vernacular and habits in typing out play by play (or e.g. recording clears) can differ from others.

[1766]

Thank you. What was the team's adjusted efficiency?

It will be interesting to watch moving forward as Cameron gets more integrated and comfortable in the offense. Dante Kulas' numbers must be off the charts. I don't think he's taken a shot and not scored yet.

On an opponent-adjusted basis, they were 35.6% against St Johns. That brings their season average to 31.3% (28th nationally).

As for Kulas, his usage-adjusted EGA is 13.48, which means nothing to you. Obviously, he won't keep that up all season. The absolute most efficient players are going to be in the 2.5-3.5 range for a full season. But it does go to show just how much he produced with relative few chances against St Johns.

Thank you. Interesting data...

[laxreference]

Just one game on the men's slate today: Navy vs Hofstra

[laxreference]

Updated list of Live Win Probability Links

- Air Force vs Colgate
- Marist vs Richmond
- Mercer vs Cleveland State
- Denver vs Duke
- Villanova vs Yale
- Lehigh vs Hobart
- Georgetown vs Penn
- Robert Morris vs Stony Brook
- Marquette vs Jacksonville
- Providence vs Siena
- St. Bonaventure vs Binghamton
- Canisius vs Bellarmine
- NJIT vs Harvard
- St. John's vs Bucknell
- Merrimack vs Dartmouth
- Army vs Rutgers
- Virginia vs Towson
- Drexel vs UMBC
- Monmouth vs Princeton
- Hampton vs Mount St Marys
- Holy Cross vs Michigan
- UMass-Lowell vs UMass
- Sacred Heart vs Lafayette
- Wagner vs Fairfield
- Albany vs Cornell
- Loyola vs Johns Hopkins
- Ohio State vs North Carolina
- Penn State vs Saint Joseph's
- Quinnipiac vs Brown
- Bryant vs Boston U
- Detroit vs Notre Dame

[laxreference]

Updated list of Live Win Probability Links

- Air Force vs Colgate
- Marist vs Richmond
- Mercer vs Cleveland State
- Denver vs Duke (Recap Analysis)
- Villanova vs Yale
- Lehigh vs Hobart
- Georgetown vs Penn
- Robert Morris vs Stony Brook
- Marquette vs Jacksonville
- Providence vs Siena
- St. Bonaventure vs Binghamton
- Canisius vs Bellarmine
- NJIT vs Harvard
- St. John's vs Bucknell
- Merrimack vs Dartmouth
- Army vs Rutgers (Recap Analysis)
- Virginia vs Towson
- Drexel vs UMBC
- Monmouth vs Princeton
- Hampton vs Mount St Marys
- Holy Cross vs Michigan
- UMass-Lowell vs UMass
- Sacred Heart vs Lafayette
- Wagner vs Fairfield
- Albany vs Cornell
- Loyola vs Johns Hopkins
- Ohio State vs North Carolina
- Penn State vs Saint Joseph's
- Quinnipiac vs Brown
- Bryant vs Boston U
- Detroit vs Notre Dame (Preview Analysis)

[laxreference]

Here's a quick rundown of some of the games from yesterday

Duke vs Denver

It's time to start worrying about the Denver defense. When they played Duke last year, a 12-10 loss, they held the Blue Devils to a 30% offensive efficiency and just .75 shots per possession. In that game, Duke shot 40% and turned it over on 25% of their possessions.

Fast ... Read More

Rutgers vs Army

Another game, another feather in the cap of the Scarlet Knights' defense. Army's 14 goal output against UMass earned them the 2nd best adjusted shooting percentage of any team in Division 1. Today, Rutgers held the Army offense to just 21% shooting.

Now, shooting defense is always tough to break ... Read More

Johns Hopkins vs Loyola

At the start of today's games, the Johns Hopkins NCAA Probability estimate stood at 18.7%. In less than a quarter of my simulations, they ended up as one of the names called on Selection Sunday. And there was the small (6.7%) chance that they would end up hosting 1st round ... Read More

North Carolina vs Ohio State

In 2021, after adjusting for the quality of the all Big Ten slate, the Ohio State defense finished the year ranked 49th out of 65 teams. Their goaltenders finished the year ranked 63rd with a 42% save percentage.

As with most things, where they'll settle is likely somewhere between the extremes ... Read More

[laxreference]

As always, here is the top individual EGA performances of the past week. The full post is here, but here are the highlights...

Ashton Wood (MER) ended up with a 9.05 EGA mark after 26 faceoff wins and a goal against Jacksonville.


Here is how the rest of the top 10 shook out:

Nick Crovatto (BUCK) - 7.03 EGA
Matt Bohmer (SJOE) - 6.46 EGA
Jack Myers (OSU) - 6.10 EGA
Max Waldbaum (JAV) - 5.51 EGA
Darian Cook (BRWN) - 5.75 EGA
Brennan O'Neill (DUKE) - 5.46 EGA
Chris Brown (PRIN) - 5.62 EGA
Chris Gray (UNC) - 5.78 EGA
Brian Minicus (COL) - 5.79 EGA