Beyond the Basics: Explaining the Gap Between the Top and Bottom Teams

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.

The gap between the best and worst teams is larger in Division I women’s lacrosse than it is for the men. It’s not often that you see a team with a greater than 90 percent estimated win probability heading into a contest, but it is much more common in the women’s game. To be exact, this year, 19.7 percent of women’s games started with the favorite having a win probability above this mark. For the men, it happens just 7.5 percent of the time.

That is actually down from the pre-COVID years. In 2019, 24.4 percent of women’s games featured these types of heavy favorites. But still, the gap between the contenders and the pretenders is wide. You might call it a chasm.

I was having a conversation last week about stats in women’s lacrosse and the question came up with respect to the top of Division I and the rest of Division I. What is the difference, statistically, between a top 20 team and the teams that are working to get into that group? To be honest, I haven’t given too much thought to this type of grouping before, but the question piqued my interest.

So, let’s answer it. Let’s see which statistical characteristics most separate the top 20 from the other 100 teams in Division I women’s lacrosse. (For this article, we are using the LaxElo ratings to identify who is included in the top 20.)

OFFENSE IS THE KEY

My first question was: what is more important, offense or defense? Put another way, is the gap between the best teams and the rest larger for offensive metrics or defensive metrics? In theory, if the gap is larger for defense, then if you were forced to pick, you’d rather make strides on defense than on offense. And this matters because teams have limited resources, namely time, to put toward any given area. Knowing where you get the most bang-for-the-buck is important.

But it’s actually offense that appears to be the key. To support this analysis, I looked at the average rating for all top 20 teams, and the average rating for all other teams across a range of metrics. As an example, let’s look at offensive efficiency (goals divided by possessions).

In 2022, the average top 20 team has an efficiency rating of 34.2 percent (they have scored a little over 34 goals for every 100 offensive possessions). The non-top 20 teams have an efficiency rating of 27.7 percent. A 27.7 percent efficiency rating would be the 67^th-best mark in Division I women’s lacrosse. A 34.2 percent mark would be 19th. That means that for offensive efficiency, in terms of spots, the gap between the top 20 teams and the rest is 48 spots. Putting it in terms of spots will allow us to determine how big the gap between the best and the rest is for each metric.

And to answer the initial question about offense-v-defense, this is a useful way to do it. We simply take the average gap (in spots) between the top 20 teams and the other teams for every offensive metric and every defensive metric. (I’m including shooting percentage, SOG rate, turnover rate, assist rate and saved shot rate.)

Across those categories, the average gap between the good teams and the not-so-good-teams is 38 positions for the offensive metrics and 28 positions for the defensive metrics. In terms of national rankings, you get more bang-for-the-buck from improvements on offense than improvements on defense.

OFFENSIVE SAVED SHOT RATE IS THE LARGEST GAP

That’s on average, but what about specifics? Which stat has the biggest gap between good teams and the other teams? The short answer is saved-shot rate.

A quick digression. Saved-shot rate is the inverse of save percentage for goalies. It’s the percentage of the shots you put on cage that are saved. Think about the universe of shots. Any shot can be missed (and likely backed up), a pipe or blocked (50/50 ground ball), a goal, or a save (virtually a turnover). A team’s shooting percentage is just the number of goals divided by the number of total shots, but not all missed shots are equal.

I find it useful to calculate a saved-shot rate because it separates out the missed shots (which typically result in a continuation of the possession) with the shots that typically end up going the other way. It’s a very underrated stat in my opinion. Case in point: saved-shot rate is the offensive metric with the largest gap between the top 20 teams and the rest.

For shots that are on-cage, the average top 20 offense sees the goalie make a save on 36.9 percent of the possessions (22nd). The average for non-top 20 teams is 43.8 percent (72nd). Compare that to SOG rate, where the average top 20 offense puts 78 percent (66th) of their shots on cage, versus 76.9 percent (44th) for the rest. So yes, good teams have higher SOG rate, but the gap is not nearly as wide.

The takeaway here is that the elite teams are finding the right balance between getting shots on cage, avoiding saves and scoring goals. The amateur economist in me is always interested when there are trade-offs to be managed. Saves are the most damaging outcome of any shot, and you could avoid saved shots entirely by never putting a shot on cage, but then you’d never score. You can’t score if you don’t put shots on cage, but too much focus on SOG rate leads to more saves (assuming shots-on-cage is something a team focuses on). The golden road is maximizing goals while minimizing saved shots. Also known as aiming for corners.

SOME SURPRISES

There were some stats that I was curious about and some that surprised me. As an example, take offensive assist rate (i.e., what percentage of your goals are assisted). You might think that better teams would have higher assist rates. And that’s true, but not to the degree I was expecting. The top 20 teams had an equivalent assist rate to the 29^th-best team in the country; the other teams were around 57th on average. That 28-spot gap was much smaller than some of the other metrics.

A stat that was more important than I’d expected was shots-per-possession. The top 20 teams took 72 shots per 100 possessions (equivalent to 31st); the other teams averaged 67 (equivalent to 69th). I think that this probably ties in with the saved shot percentage we discussed earlier more than the ability of good teams to back up shots. The best teams get multiple bites at the apple because they aren’t having as many shots saved. This in turn leads to higher efficiency and more wins.

And while, on average, the offensive categories seemed to be more telling of which group a team would be in, there were some where the defensive variant was more predictive. We talked about SOG rate being a smaller part of the story on offense, but on defense, the gap is much larger. The top 20 teams allowed a SOG rate of 74.5 percent (equivalent to 30th); the other teams averaged 77.8= percent (equivalent to 73rd).

For an offense, it seems that putting shots on goal is less important than avoiding saves. For a defense, the opposite is true; the gap between the best and the rest is much larger on SOG rate than it is save percentage. The fact that these two stats are so different whether you are thinking about offense or defense is intriguing. It suggests that to become an elite team, your shooters need to be comfortable enough to pick at corners and your defense needs to keep teams uncomfortable enough that they have a hard time getting shots to the cage at all.

THE REST OF THE DETAILS

For those who have made it this far, here’s the rest of the detail. I’ll show the metric, then the average, for the top 20 teams and then the average for the other teams. The equivalent ranking will be shown in parentheses.

For the Clearing/Riding categories:

Clearing Rate: 96% (25) / 93.7% (69)
Opponent Clearing Rate: 93.5% (40) / 94.1% (53)

For a team looking to get into the top 20, it looks like you’d rather get better at clearing than riding.

For the offensive categories:

Offensive Efficiency: 34.2% (19) / 27.7% (67)
Offensive Saved Shot Rate: 36.9% (22) / 43.8% (72)
Offensive SOG Rate: 78.1% (44) / 76.9% (66)
Offensive Shooting Percentage: 47.4% (19) / 41.5% (65)
Offensive Turnover Rate: 28.7% (22) / 36.7% (68)
Offensive Assist Rate: 46.5% (29) / 39.5% (57)

For the defensive categories:

Defensive Efficiency: 23.6% (16) / 29.8% (73)
Save Percentage: 45.9% (32) / 41.7% (68)
Defensive SOG Rate: 74.5% (30) / 77.8% (73)
Defensive Shooting Percentage: 38.6% (29) / 43.6% (64)
Defensive Turnover Rate: 37.1% (38) / 35.3% (52)
GB Win Rate: 53% (23) / 49.6% (63)
Defensive Assist Rate: 39.5% (57) / 41.7% (71)

It is interesting to see the difference in turnover rates versus shooting stats. The top 20 separates themselves not by forcing more turnovers, but by limiting their opponents’ shooters.

For the free position categories:

Offensive Free Position Utilization: 68.1% (62) / 67.9% (62)
Offensive Free Position Rate: 17.8% (56) / 17.7% (56)
Defensive Free Position Rate: 17.4% (57) / 17.6% (62)
Offensive Free Position Efficiency: 32.1% (40) / 29.4% (58)
Defensive Free Position Efficiency: 27.6% (46) / 30.1% (58)

There really aren’t gaps between the groups in terms of how often free positions are earned/given or how often they are used.

For the pacing/possessions categories:

Draw Control Win Rate: 54.1% (25) / 49% (69)
Possession Advantage: 4.9 (13) / -0.9 (70)
Average 1st Shot Time: 38.6 (66) / 38.7 (64)
Shots-per-Possession: 0.72 (31) / 0.67 (69)

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.