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 faceoff. Should we ban it? (I’m kidding.)

Still, no aspect of men’s lacrosse is as divisive as the faceoff. There are those who think that the faceoff puts too much importance on specialists who have one job and one job only. The data generally suggests that the impact of having a dominant faceoff guy is overrated. After all, in roughly 90 percent of all Division I men’s lacrosse games, the team with the better per-possession efficiency is the team that wins.

But these questions push us too close to emotional debates in which data and statistics have no role. I’d rather spend my time with you this week getting smarter about faceoff stats. “Stats, you say?” Yes. Stats — plural.

Today, we are going to go beyond faceoff win rates to get at the heart of what it means to be a good faceoff team.

FACEOFF WIN RATES

If you’ve seen any faceoff statistics, chances are you’ve seen a faceoff win rate. “Player X has won 58 percent at the stripe this year.” Nothing could be simpler: total faceoff wins divided by total faceoffs taken. So far this year, the leading FOGOs by this metric are:

Zach Cole (St Joseph’s): 73.8%
Mike Sisselberger (Lehigh): 68.9%
Luke Wierman (Maryland): 67.5%
James Reilly (Georgetown): 66.9%
Nathan Laliberte (Bryant): 65.7%

With any new statistic, you have to weigh the benefit of additional complexity with the costs associated with trying to explain it. When you apply that logic to faceoff win rates, additional complexity is worth it; basic win rates are just too simplistic. And there are two reasons. First, faceoff win rates do not account for how good the opposing faceoff unit is. Second, winning the faceoff isn’t the end of the story. Let’s take those two issues one at a time.

FACEOFF ELO

My biggest issue with faceoff win rates is that, like so many stats, it is not adjusted to account for the strength of the opponent. How do you compare a 58-percent win rate against a bunch of strong FOGOs with a 66-percent rate against the bottom of the barrel? Today, we might just assume that the 66-percent player is the better faceoff specialist, and we could easily be very wrong.

Those that have followed my work will know that I’m a huge fan of Elo models. When you have individual, discrete events between competitors of uneven strengths, they provide a simple mechanism to convert win rates into a true strength metric. (Elo models were originally developed for ranking chess players by Arpad Elo).

So, we have misleading faceoff win rate stats, and we have my love of Elo models; what do you think happened? You are right! The Faceoff Elo model was born.

The link above goes deep into the development of the model, but the core of the mechanism is that for every faceoff, the winner takes rating points from the loser. The amount of points transferred is dependent on the relative strengths of the FOGOs coming into the faceoff. In general, the more surprised you are by a faceoff win, the more it moves the Elo ratings. Here are the top-five rated FOGOs in Division I as of Monday morning. (1500 is average.)

Mike Sisselberger: 1738
Luke Wierman: 1701
Zach Cole: 1676
Alec Stathakis: 1672
Thomas Colucci: 1669

By raw faceoff win rates, Stathakis is No. 24 nationally and Colucci is No. 12. And that is why you should account for the opponents faced.

One more note about faceoff Elo ratings: they are actually a career metric as opposed to one that resets every season. But the most recent faceoffs have the most weight in the model, which means it has a good mix of incorporating history while still being nimble enough to approximate a player’s true skill level. The highest rated FOGOs in my data, which goes back to 2016, are:

Trevor Baptiste: 1801
Alex Woodall: 1767
Jon Garino: 1761
TD Ierlan: 1761
Mike Sisselberger: 1738

FACEOFF CONVERSION

So, we’ve covered the fact that faceoff win rates do not account for how good the opposing FOGOs were. But remember, there is a second issue with faceoff win rates; they assume that once the faceoff is “won,” the job is done. But as any coach will tell you, winning the faceoff doesn’t mean you’ve actually gotten the ball to your offense. If you win the faceoff, but you turn it over before you run your offense, then did you really accomplish anything?

We need a new stat to complement win rates. Let me suggest Faceoff Conversion Rate. Of the faceoffs that you win, how often do you get into your offense without a turnover? I’ve defined a faceoff conversion as any time that, after a faceoff win, one of these things happens

• Your team has the ball for at least 20 seconds

• You get a shot off

• Someone other than the FOGO commits a turnover

So, what do the numbers say? Which teams are best at turning their faceoff wins into offensive possessions? Are there any teams with high win rates that lose some of that advantage because of a poor conversion rate?

Here are the top five teams in Division I by faceoff conversion rate. These teams turn their wins into actual possessions at the highest rate:

Notre Dame: 100%
Cleveland State: 100%
Army: 100%
Marquette: 99%
Bryant: 98.9%

That’s right, three teams have yet to lose a faceoff win before they were able to start their offense. In this way, we can see another parallel between faceoff conversions and clearing: the gap between the best and the worst teams is not as large as something like efficiency or actual raw faceoff win rate. So far, the worst team in Division I with respect to converting wins into possessions is Cornell. Their rate is just 88.2 percent.

DEFENSIVE FACEOFF CONVERSION

Now the interesting thing about faceoff conversions is that they go both ways. Just like a save requires a clear to become an offensive possession, a faceoff win requires a conversion. And as teams with effective rides can steal possessions (and create transition opportunities), some teams are better than others at salvaging possession from a faceoff loss.

The top-five teams in terms of defensive conversion rate are:

Princeton: 88.9%
Bucknell: 90.1%
Hobart: 91.8%
Boston U: 92.2%
Saint-Joseph’s-pro: 92.2%

Boston U is the No. 1 riding team so far this year. Princeton is No. 10. But it appears that being a successful team in preventing faceoff conversions is different than being a good riding team. Hobart is just No. 24 on the ride; Bucknell is No. 32; St Joe’s is No. 53. My guess is that it’s a matter of emphasis. You can choose to be aggressive in winning back faceoffs in the same way that you can decide to run a 10-man ride. As with everything, there are trade-offs involved. (Hence why we need stats to balance them!)

TRUE FACEOFF WIN RATE

It has all led up to this: True Faceoff Win Rate. Take your faceoff wins, subtract the ones that you didn’t convert into offensive possessions, add the ones that you stole from your opponents. What you end up with is a rating that describes how many of the faceoffs in a game end up as offensive possessions for your team.

At the end of the day, I would argue that this is the stat we really care about when we cite faceoff win rate. So, who are the leaders in True Faceoff Win Rate? By and large, it’s the same teams that are at the top of the faceoff win rate rankings. Ohio State and Lehigh swap 2nd and 3rd place, but the rest of the top five is the same.

What’s interesting about True Faceoff Win Rate is less at the top of the table and more about how some teams see their true faceoff performance ranking change quite a bit from their raw ranking. Here are the teams that saw their standing change the most when we switch to True Faceoff Win Rate:

Princeton: 46th (raw) to 28th (true)
Cleveland State: 64th to 55th
Mercer: 29th to 38th
Delaware: 56th to 47th
Army: 34th to 27th

I have said that I think one of the big use cases for advanced stats is giving you a better idea of where to spend your time and energy. If I’m a team like Princeton, you might look at your raw faceoff win rates and think that winning more faceoffs should be a point of emphasis. But when you look at true faceoff win rate, all of a sudden, you are in the top-half of Division I and maybe there are other areas of focus that are more pressing.

So, remember, next time you hear any sort of faceoff win rate stats cited, ask if they are telling the true story. We have better stats! Let’s use them.

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.

And last, I would love to hear from you. If you have questions or comments about the stats, feel free to reach out.