Behind the scenes at @NLLFactOfTheDay, Pt. 2

Yesterday, I described the sources of information I use for the @NLLFactOfTheDay twitter account. In this (much shorter) article, I discuss how I use all that information to create the facts that I publish.

Putting it all together

Now that I have all of this information, I start combing through for the actual facts. When Teddy Jenner interviewed me on the Off the Crossebar radio show, he referred to me as some kind of stats guru, and others have said similar things. While I greatly appreciate these compliments, let me set the record straight – I’m no savant. I don’t just know all of this stuff. I don’t have millions of stats memorized and running through my head for instant retrieval. At the risk of sounding immodest, I do have a pretty good memory for trivia and such, and I do have a Bachelor of Math degree (though in computer science, not statistics) so I understand the stats, but what I am good at is hunting for anomalies.

If I see a column of numbers that looks like “1, 2, 2, 1, 3, 0, 8, 1, 2”, I want to find out about that 8 and see what it means and if it’s interesting or not. I’ll sort one list by different values (eg. list of teams sorted by PP goals per game and then sorted by SH goals per game) and see if a team stays in the same position, or moves from top to bottom or vice versa. Maybe the league-leading team is in last place in some statistic, or the last-place team is leading in something. I look over the season records for repeated names. Maybe a player set two season records in the same season, or one person holds 8 of the top 10 records in some stat.

I look at the dates that significant events happened to see if there are any coincidences there. I look at the best seasons in different stats (eg. most goals in a season in the league – Athan Iannucci, 71) and for individual teams (eg. most penalty minutes in a season for the Roughnecks – Geoff Snider, 74). I look at career stats for the league (eg. most career goals in the league – John Tavares, 778) or a particular team (eg. most career assists on the Knighthawks – Shawn Williams, 491). I look at team stats (eg. most goals scored in home games – Philadelphia, 2151) and franchise records (eg. fewest goals scored by the Roughnecks in a game – 6, twice). I look at game, team, season, and league attendance records, both total and average.

Occasionally I think about a particular obscure statistic and look deeper into that. Recently, it was how often teams played two overtime games in a single weekend. I did some digging and came up with a few facts about that.

Once I’ve got a few facts I want to publish, I use a web app called HootSuite to schedule them. It allows me to write up the tweet, pick which account to use (mine or @NLLFactOfTheDay), and pick a date/time for the tweet to be sent out. I originally started tweeting at 11:00am, but then for some reason I have since forgotten, changed to 3:00pm.

Now that I’ve written it all down, it sounds like a lot of work, but it’s really not – now that I have the infrastructure in place. Whenever I come across an interesting stat, I fire up HootSuite and add it to the list of scheduled tweets – that takes a minute or two, tops. Once or twice a week, I make a point of spending 15-20 minutes adding new ones, so I usually have about a week’s worth done ahead of time. If I went off the grid right now, you’d still be seeing a tweet every day at 3pm for another 8 or 9 days. If something interesting comes up in the meantime, I can reschedule upcoming tweets to add in a more timely one. The hardest parts now are (a) try not to repeat any facts I’ve published before, and (b) to squash all the information down into 140 characters.

So that’s pretty much it – that’s how the magic happens. I say that facetiously, because there’s no magic here. I’m just a stats geek that enjoys sharing the facts that I find with others that might also be interested. For the cynics among you, I’m not doing this for the money – I haven’t made a dime off of this little project. In fact, I’m not sure how I could monetize it even if I wanted to. It’s just fun to come up with these things, fun to get replies from people asking questions about them, and fun to see that others enjoyed them enough to retweet them. If you follow, thanks, and I hope you enjoy reading these facts as much as I enjoy finding them.


Behind the scenes at @NLLFactOfTheDay, Pt. 1

I started writing the Money Ballers column on IL Indoor in January 2012. While writing the articles that year (or while writing other stat-related articles for this blog), I frequently came across interesting statistics about the previous weekend’s games, and tweeted them with the hash tag #NLLStatOfTheDay (or something similar). I found that a number of people responded to them, whether to ask about them, or mention a similar one, or just to RT them. I figured if I worked a little harder, I could probably come up with one of these every day and if I could find a way to schedule them automatically, this would be a cool thing. So I created @NLLFactOfTheDay (originally @NLLStatOfTheDay but I changed it so I could post things that weren’t stats) in April, 2012.

Since then I have tweeted more than 200 facts, and collected well over 600 followers. This is far more than I have on my own twitter account (recently passed 400! Woo!), which I created three years earlier. I originally did this for NLL fans/stats geeks like me, but there are a lot of NLL players and executives following now, and most tweets are RT’ed at least once. It even got me interviewed on Teddy Jenner’s Off The Crossebar radio show (mine is the Feb 24, 2013 one). It’s safe to say this has become a fair bit more popular than I imagined.

I thought some people might be interested to see where the facts come from. So today we’re going behind the scenes and I’ll let you in on some of the secrets. Note that much of this qualifies as “Behind the scenes at the Money Ballers” as well. This article started to get kind of long, so I’ve broken it up into two. The first part describes where the information comes from, the second describes how I mine the information for the actual facts and post them.

I basically have four sources of information that I use for these facts: three databases and a web site. The contents of the three databases come from from three different sources. The web site is Wikipedia, and many of the lacrosse articles are those that I’ve written or contributed to myself over the years. I’ll start with an overview of the databases (a little bit technical) and then describe where I got the information stored in them.

The Technical Stuff

I have been a software developer for more than twenty years. I’ve been working at Sybase (now part of SAP) for over fifteen years, as part of the SQL Anywhere database server development team. So of course when I needed to create a database, SQL Anywhere was the obvious choice – and not only because I’m very familiar with it. SQL Anywhere has a built-in HTTP server (which I helped patent), which means that I can write procedures in SQL that directly access the data, and then easily create web pages that display the data in any way I want. When I started the Money Ballers, I decided that the best way to compile the points (i.e. easiest, fastest, and most accurate) would be to write a program to download the information from the NLL and then crunch the numbers. I wrote a python script to download the game sheet and parse it, then insert the resulting information into the database.

Extreme Game Detail

Here’s what I get from each game sheet:

  • Date/time of game
  • Home team, away team
  • Final score, winning team, losing team
  • Attendance
  • For each goal: who scored it, who assisted, what quarter, what time, whether it was power play, shorthanded, empty net, or penalty shot
  • For each penalty: who got it, what quarter, what time, what class of penalty (major, minor, misconduct, match, etc.), and what type (holding, high-sticking, fighting, etc.). This includes bench penalties
  • For each player: number of goals, assists, points, power play goals, shorthanded goals, loose balls, face offs won, face offs attempted
  • For each goalie: number of goals against, saves, minutes, shots on goal

All of this information is pushed into a database, and then I can view one of the many web pages that summarize it. With this information about each game, I can calculate pretty much anything: league standings, league or team scoring leaders, yearly records like most goals/assists/loose balls/etc. in one game, number of power play or shorthanded goals per team, attendance records, and of course the Money Ballers numbers.

There are a few things that the game sheet does not include. For example, penalty shots that are not successful, time spent on the floor, goaltender win/loss (or who started the game), forced turnovers, stuff like that. Another notable thing that I do not have is power play efficiency. I know when PP goals are scored, but I have not put in the work to determine how many power play opportunities there were. There’s more to it than just “a team is on the PP for two minutes after every opposing minor penalty”, since we can have coincident penalties for each team, penalties that overlap, penalties that end early because of goals scored, that sort of thing. I believe I have all the information to calculate it, I just haven’t done it.

Some game sheets are available from 2011 and previous seasons, but they are missing enough information that I don’t use them, so I only have this level of detail for the 2012 and 2013 seasons.

An example

Here’s the “Game summary” section of the page for Philadelphia’s 10-8 win over Rochester on Feb 23, 2013. It lists each goal in order, separated by quarter. The pages are fairly plain; I’ve made no attempt to make them pretty. I don’t care what they look like as long as they’re functional. Note also that none of these pages are available on the internet – they are only on my computer. Click on the image to see a full-size version that might be easier to read.

Click to embiggen

The goals shaded in dark green are go-ahead goals, light green are tying goals, and red is the game-winner. The “special” column lists those three events as well as power play, shorthanded, empty net or penalty shot. You can see the current score, how far up the winning team is, how many goals in a row the team has scored, how long it’s been since the last goal, and how long it’s been since the last goal by the same team. The last four columns can be calculated given the rest of the information (if the score is 7-5, I know the difference is 2; I don’t really need the program to calculate it for me), but having it there makes patterns and extremes much easier to spot.

In this case, you can see the patterns in the goals – Rochester scored 4, then Philly scored 4, then Rochester scored 2, and so on. You can see that Rochester went almost 19 minutes between their 4th and 5th goal. You can see that Rochester didn’t score in the third and Philly didn’t score in the first, though I do have another chart that lists the number of goals in each quarter.

The idea here isn’t just to give me all the numbers, it’s to give me the numbers in such a way that I can see patterns or outliers. If I look at a game report like this and there are lots of dark and light green rows, then I know it was a close game. If there’s a green line at the top and a red line at the bottom, then one team led throughout the game. If the Special column is filled with PP and SH, then there were a lot of penalties.

All The Games

About a year ago, a reader of this blog sent me an email saying that he had a spreadsheet containing every game ever played in the NLL. He asked if I wanted it, and of course I said yes. I immediately put that into another database, and started creating web pages for that as well. With that, I can reconstruct the final standings of any season, show the entire win-loss history of any team, the head-to-head matchups of any two teams, all kinds of attendance figures, game score records, goal differentials, and even things like a team’s record on a particular day of the week.

All The Players

Between the 2012 and 2013 seasons, the NLL did some work on their web site. For a while, when you clicked on a player, there was a link to “Career stats” but rather than a page listing the stats for that player, the link gave you an Excel spreadsheet with the yearly stats for every player that has ever played in the NLL. I downloaded this spreadsheet and put that in a database as well. Not only can I search for any player and get their overall stats for any season (regular season and playoffs), but I can list all the players with their career stats, or list all the players who’ve ever played for a certain team, or display the final scoring stats for any season (Gary Gait led with 48 points in 1995), list the best seasons in any category, or combine seasons (eg. who scored the most from 1990-1999? Don’t fall over, but it was Gary Gait again, with 536).

I have since found that some of the stats are incorrect and some from the very early years of the league are missing. For example, there are no numbers from the 1987-1989 seasons, and apparently only five players were in the league in 1990. In the 2008 playoffs, did Cory Bomberry really take 360 face-offs in 3 games and only win 8 of them?

I looked at the top 20 single-season loose ball records and saw that Devin Dalep had 193 in 2002 and Erik Miller had 186 in 2003. I happen to know that those guys were goalies, but I notice that no other goalies are listed. In particular, there’s no Watson, no Eliuk, no Dietrich, no O’Toole, no Vinc. None of these elite goalies have had great loose-ball seasons since 2003? Strange. So I looked up their numbers. The same year Dalep had 193, Watson had 10. O’Toole and Eliuk had 11. Steve Dietrich had 0. I have a feeling Dalep’s numbers may not be correct.


A lot of people have a bad impression of Wikipedia. Since anyone can make changes to it, it’s got to be full of misinformation and crap, right? Well yes, there are a lot of pages that have rather non-encyclopedic content, and there are always pages with incorrect data, but for the most part I find things to be quite accurate. There are lots of people who edit Wikipedia as a hobby, and if any page they are interested in gets edited, they will know about it very quickly and correct any errors or vandalism within minutes. I used to edit Wikipedia quite a bit, and have created many pages on lacrosse players and teams. There is a page on every NLL team that has ever existed, as well as each NLL season since 1987 (and some pages for individual team’s seasons), each NLL award, the Hall of Fame, expansion; entry; and dispersal drafts, and so on. I created most of them.

There used to be a web site called The Outsider’s Guide to the NLL which had tons of press releases, game summaries, stats, and so on from games back in the ’90s and early 2000’s. That site simply vanished one day without a trace, a huge loss to the lacrosse world. But before that happened, I got a fair bit of information from it for the Wikipedia pages, so at least some of the information wasn’t lost. Some of the player pages are unfortunately outdated since I no longer have time to maintain them, but there are others that are doing a great job of keeping things as up-to-date as they can.

Even without the Outsider’s Guide, there’s still a fair bit of information in those pages, so some of the non-stat-related facts come from there.

So now I have a ton of information including lots of historical data as well as incredibly detailed stats on every game in the past two seasons. Tomorrow, I’ll describe what I do with all this information.

Faceoff dominance: Does it help you win?

Over the last decade or so, a few players have stood out as excellent face-off men. Washington’s Bob Snider and his brother, Calgary’s Geoff Snider, are the cream of the crop right now, and in recent years Peter Jacobs, Jamie Hanford, and Jamison Koesterer have also made names for themselves in the circle. But does it really matter?

Logic says yes. When you win a face-off, you gain possession and in lacrosse, possession is everything. If you win 75-80% of your face-offs, as the Snider boys do with regularity, that’s 10-15 extra possessions per game for your team, and at least a few of those have to translate into goals, right? But do the stats bear that out? As we frequently do on this blog, let’s look at the numbers and see if they support something that “everybody knows”.


For those of you who don’t care to look at the actual numbers, here’s the “too long; didn’t read” version: Yes, but not by very much. Feel free to skip to the conclusion now.

The numbers

I only have sufficient stats for the 2012 season, so we’ll have to restrict the numbers to that season. There were 72 games played during the regular season, and therefore 72 winners. Three of the 72 games finished with a tie in faceoffs, so we won’t count those three. Of the 69 remaining games, the winning team led in faceoffs 39 times (56.5%). This means that in 30 of the games (43.5%), the winning team won fewer faceoffs than the losing team.

So it looks like winning the faceoff battle does give you a slight edge. But let’s look even further. If we look at games where one team really dominated the faceoffs, say winning 70% or more, we find the opposite. There were 28 such games last year, and the team that won the faceoff battle only won 13 (46%) of them. Of the 15 games where the losing team won 70% or more of the faceoffs, the teams break down like this: Washington 8, Philadelphia 3, Calgary 3, and Minnesota 1. The Stealth lost eight games (and won three) while winning 70+% of the faceoffs.

Of course, this is a strange case – the team with the best face-off man in the league and the worst record. This is also the record of one team over only 16 games. Calgary, for example, went 5-3 in games where they won 70+% of faceoffs. Even if we look at the season as a whole, that one team dominates so much that the numbers are too skewed to be meaningful. Not surprisingly, we can’t honestly say that winning 70% of the faceoffs means you’re less likely to win the game.


The conclusion to all of this is that during the 2012 season, teams won 56.5% of games in which they won more faceoffs than their opponents. I’ve done the calculations for the 2013 season as well (less than half over), and through 31 games (one game tied), everything is exactly 50-50 – winning teams have also led in faceoffs 50% of the time.

This tells me that winning the battle of the faceoffs does give your team a greater chance to win, but not by as much as you might think.

Veterans and goal reviews and Dave Pym is a smart guy

There have been a few things I’ve wanted to write about but they weren’t really enough for a whole post. So I came up with the completely original idea of combining them all into one post. I know, right? Brilliant!

What is a veteran?

I recently got into a conversation over Twitter with @IKnowLax (and he does know lax) after a blog posting he made where he referenced Rock forward Garrett Billings as a veteran. I said that Billings hadn’t been in the league long enough yet to qualify as a veteran, and he disagreed. So I asked my twitter followers what they thought – how do you define “veteran” in the context of the NLL? Is it strictly time played in the league, and how long? Does the player’s impact on the league change things? I used an example: Cody Jamieson is in his third season but has already led his team to a Championship – is he a veteran? I got a few varied responses:

  • IKnowLax – “a person who has had a long experience in a particular field” I’d say 2 years. You’re not considered new at a job after that. …a guy like Cody Jamieson I would consider a vet.
  • TimNThen – I’d say it’s more based on playing time and impact combined
  • HindaCozaCulp – time in the league
  • banditfan11 – I don’t think he [Billings] should be called a veteran just yet
  • StealthDragon – I think years. If you make it to your 3rd year you’re a veteran [He clarified later to say that he meant once you’ve finished 3 years, so you’re a vet in your fourth]
  • apmckay – To me a veteran is someone who’s been through enough he can guide others through it too.

I also asked on the IL Indoor forums and got a few more responses there:

  • Wings-4-Life – My cutoff is 5 full years. I do not consider Jammer or Billings to be veterans.
  • Laxwizz – I’d say 50+ games in the league. If you’ve been able to stick with the league that long you deserve to be called a vet. [50 games is a little over 3 seasons.]
  • Hollywood42 – 5 or more years IMO
  • @podcasterryan – For me it’s 3 full seasons. In your fourth year you are a vet.
  • poskid – I like four years. Might be because the Swarm are so young and lasting four years here means something.
  • swami24 broke it down even further: You are a veteran at the start of your third full year. You are a seasoned veteran in your 6th season and a grizzled veteran in your 10th.

So we don’t have a real consensus, but it looks like most people would consider a player a veteran after they had finished 3-4 years. Personally, I’d give it a couple more years – after five full years I’d probably use the term veteran. I might drop it to four if the player had had a significant impact, so I might give Jamieson that honour next year.

The magic number

Former Roughnecks coach and current Toronto Rock scout Dave Pym tweeted recently:

Unlike Spinal Tap the magic number is not 11. It is 12. If you can pop 12 then your winning % will be 75% and better. Probably a lot higher.

Of course, that made me curious, and I jumped into action. It’s not that I was trying to prove Pym right or wrong, I was just curious as to how true that was. It shouldn’t really be that surprising that Pym’s impressions after all of his years in lacrosse were pretty accurate.

Over the history of the NLL (not including any games from the 2013 season), the the average losing score was 10.6 (the average winning score was 14.4 in case you’re wondering). The median losing score is 10, so if you want to give yourself a >50% chance of winning, scoring 11 would do it, but just barely. Scoring 11 goals would have won you 51.6% of all NLL games. Scoring 12 would give you a much higher 64.6% winning percentage, and if you wanted to hit the 75% mark, you’d need to score 13 (which would actually give you 76%).

Automatic goal reviews

While watching the Calgary / Edmonton game a couple of weeks ago (not last week’s 9-8 nailbiter, but the 18-15 goal-o-rama game the week before), I thought I was watching a basketball game: there was lots of scoring and the last few minutes of the game took forever. I wasn’t the only one either; lots of people on twitter felt the same way. The reason for the frustration was the fact that there were three goals scored within the last two minutes of the fourth quarter, the time during which no goal challenges are allowed. Instead, the referees will automatically review every goal during those two minutes. This apparently includes included empty-net goals.

I get the idea of this rule. If a coach challenges a goal and the challenge is overturned, his team loses a timeout. If they don’t have a timeout remaining, they get a bench minor penalty. But if there aren’t two minutes left in the 4th quarter, the team can’t serve the whole two minutes, so there’s less of a risk to calling for a challenge. To avoid frivolous “nothing to lose” challenges near the end of a game, they simply take away that possibility and review every goal, negating the need for a challenge. But I expected that the rule was written to say that all goals in the last two minutes of the 4th were reviewed if necessary. If there is no question that the shooter was outside the crease, none of his teammates are near the crease, and the ball went in before the whistle, shot clock expiry or final buzzer, there’s no need to review it, right? Apparently not. Even worse, both of the empty-net goals were reviewed.

The fact that these goals were reviewed at all is frustrating enough. But when the reviews take two minutes or more, we start to get into “ridiculous” territory. They showed the replay from a couple of different angles during the telecast, and there was no question that the goals were good. There’s no reason those reviews should have taken more than about fifteen seconds. I have no idea why they took so long. The fans watching on twitter, those in the stands at the game, and even the announcers calling the game were similarly stumped.

Some of the suggestions I saw on twitter were to limit the time that a review could take (i.e. if the ref can’t figure it out within 30 or 60 seconds, the call on the floor stands), or allowing the coach of the team who was scored on to waive the review. The problem with the second option is that if the losing team is gaining momentum in the last two minutes, the scored-on coach would not waive the review because he wants to kill any momentum the scoring team might have had. But momentum is being killed anyway, so they need to do something.

Thankfully the NLL announced a couple of days later that empty-net goals in the final two minutes of the fourth (or OT) are no longer required to be reviewed. While this is a great addition, I would have preferred the rule be changed to give the referees the ability to decide for themselves whether a review is necessary. A lot of people complain about the refs in the NLL, but the league has to show enough confidence in them to allow them to see a goal and make a judgment call as to whether it needs reviewing. Maybe they can err on the side of caution and say “If it’s close at all, review it”.

Either way though, kudos to the NLL for addressing this issue quickly.

Colorado’s goalies

Last year, I seem to remember some trepidation about Chris Levis being the Mammoth’s starting goalie. Was he up to the task? By the end of the year, however, there were no doubts, and Levis placed third in IL Indoor’s Goaltender of the Year race (even getting one first-place vote). It seemed that many people forgot about this over the summer, though, since I saw a lot of people remarking at the beginning of this season that Colorado’s biggest question mark was goaltending. Now maybe Levis had a bad summer in the MSL or WLA, or got injured while windsurfing or something, and I didn’t hear about it. But it seemed to me that if Levis was one of the best goaltenders in 2012, there shouldn’t be much of a question about him in 2013 before any games have been played.

Sure enough though, Levis didn’t have a great start to the season and was released. Matt Roik, who had been brought in during the off-season as Levis’ backup, was given the starting job. But when the Mammoth signed Dan Lewis to back up Roik, I saw tweets from people talking about how the Mammoth had solved their goaltending problems. Really? Dan Lewis is the answer? Nothing against Lewis, about whom I know very little, but his entire NLL career consists of nine minutes during which he allowed 4 goals and made 10 saves. That’s not much to go on.

That said, as of now Lewis has played 8 minutes. He’s got a 9.25 GAA and 87.5% save percentage. Not bad at all.


As they say, hyperbole is just the worst.

Over the 12 years I’ve been “involved” with lacrosse (involved is in quotes since I don’t play or coach – my sole involvement is watching it and writing about it), I’ve heard a lot of claims about the sport and the league. Some are true and some are not, but many are opinions presented as fact. Let’s take a look at some of them and see which ones actually stand up.


Bring someone to a lacrosse game once and they’ll come back again and again.

Truth: Nope. If that were true, attendance would continually be increasing. NLL attendance goes up and down just like every other sport, depending on the team’s success, the economy, other sports and entertainment offerings in the city, and a bunch of other factors. The Wings and Rock are drawing far less than they used to. Buffalo had huge crowds, then they dropped off significantly, and now they’re back up. This year, Washington’s first home game had an attendance of 7,023. About 2,000 of those were Native Americans for whom Rochester owner Curt Styres bought tickets. Washington’s second home game was seen by 3,766, a drop of almost 50%.

Some people see a lacrosse game for the first time and are immediately hooked, myself included. Many others are not.


Lacrosse players play for the love of the game and not for money, unlike greedy NHL/MLB/every-other-sport players.

Truth: Tough one. Yes and no. It’s definitely true that lacrosse players make far less money than in the NBA, NHL, MLB, or NFL. While we see NHL players flying first class and staying in 5-star hotels and fighting for $8 million per season instead of a measly $7½ million (and baseball players fighting over double that amount), lacrosse players seem happy to take vacation days from their real jobs to fly coach (or take buses), play a game, and then travel home again before going back to work, all for an average of less than $15,000 per year.

At the same time, it’s not like there have been no labour issues in the NLL:

  • The 2005 season was almost cancelled because of labour difficulties, and last-minute negotiations resulted in a deal that saved the season.
  • The 2008 season was cancelled because of a players strike (not a lockout) over money. Again, some last-minute heroics saved the season.
  • That 2008 strike cost the league the Arizona Sting.
  • They are playing the 2013 season under the previous (expired) CBA because they couldn’t negotiate a new one in time for the season to start. Kudos to both the league and the PLPA for agreeing to do this.

It sounds like more hyperbole, but because of that strike in 2008 we almost lost the season and I’m not sure the league as a whole could have survived it. This was pre-Twitter, but those of us on the NLL message boards were quite convinced that we’d seen the last of the NLL. That event really soured me on the whole “players play for love of the game” thing, and whenever I hear someone say that, I immediately think “well mostly, but…..”


NLL teams that play games on back-to-back nights do better in the second game.

Truth: Nope. Last season, I looked at the back-to-back game stats over the history of the league (up to 2011 since 2012 was in progress at the time) and did the math. The numbers tell us that there is no pattern – playing two games on consecutive nights is no different from playing two games a week apart.

I think the confusion here is that it seems like common sense that a team playing on back-to-back nights would be tired on the second night and so one might expect that they lose those games more often. Since teams don’t have a terrible record in those games (on average, their records in those games aren’t any better or worse than their records in any other games), people think “they win more often than they should”, and that ends up translating to “they win more often than they lose”.


Lacrosse is the fastest-growing sport in North America.

Truth: Yes and no. I’ve been hearing this claim for ten years, and if lacrosse had been the fastest growing sport every year for 10 years, it’d be freakin’ huge by now. And I was right, sort of. Lacrosse has not been the fastest-growing sport in each of the last ten years; between 2007 and 2009 it was rugby.  But over the last ten years as a whole, lacrosse is indeed the fastest-growing sport in the US. From that article, “Lacrosse participation is up 218.1 percent over the last 10 years.

Note that all the stats I could find were American, and were likely talking about field lacrosse.


Offense wins games, defense wins championships.

Truth: Mainly false. Again, I’ve done the math and if we look at all of the champions in the 25-year history of the NLL, more often than not they’ve ranked higher in the league on offense than they did on defense. The Les Bartley era in Toronto (1998-2003) was a six-year anomaly.


Do you know of other similar statements about lacrosse? Want to know if they’re true? Post a comment and let me know.

Home field advantage

As I’ve mentioned before, I have a database of every NLL game played between 1987 and 2011 (now 2012). When I started looking over the data, the thing that jumped out at me the most was when I looked at individual game score records such as highest scoring games (total score), lowest scoring games, biggest goal differential, that sort of thing. I looked at the top ten of each, and three of them had a very obvious pattern. At first blush, one of these didn’t seem to match the other two, but I’ll get to that later.

The first two that I saw were these:

  • Of the top ten games with the biggest goal differential, nine of them were won by the home team.
  • Of the top ten games with the most goals by one team, eight of them were won by the home team.

Why would this be? Could it be that the crowd is indeed the extra man on the floor and their cheering really does provide a boost to the home team, like the players always say in interviews? I have always assumed that players say that because that’s what they have to say, but in truth they are so focused on the game itself that they are able to tune out the fans, whether cheering or booing. I’m sure that’s at most partially true, since it’s hard to tune out 10,000 people cheering for or against you. (Indeed, Edmonton Rush player Jarrett Toll admittedyou don’t hear “normal” crowds but the loud ones make an impression every time‘.) But if your team is scoring tons of goals and the fans are really loud, it appears that this can push you to keep scoring, even if the other team is scoring a ton as well.

A couple of other similar numbers that I found later:

  • The home team has won 53.4% of regular season overtime games (87-76)
  • The home team has won 70% of playoff overtime games (7-3)

Former NLL player, coach, and serious dreadlocks owner Tom Ryan (pictured at right) wrote a piece on IL Indoor last year about Home Field Advantage in the NLL, where he did a lot of work analyzing how teams performed at home vs. on the road. His conclusion was that over the last three seasons, Toronto and Minnesota have enjoyed the best home field advantage.

The outlier?

The one pattern that was obvious from the data but didn’t seem to match the rest was this:

  • Of the top ten games with the lowest total score, seven of them were won by the away team.

But when you think about it, the crowd could explain this as well. If the home team has only scored 3 goals in the game and it’s the fourth quarter, the barn is likely to be pretty quiet. For the home team, not hearing the crowd just reminds them that they’re not playing well, while being able to silence the crowd in your opponent’s barn is likely a huge confidence booster for the other team.

Long story short? If you’re a fan, cheer loudly for your team. It really can help.

Success vs. attendance

This one should be obvious. If a team is winning, what happens to their home attendance? Goes up, right? In general, yes. But how much?

I was having a conversation with someone about attendance at lacrosse games, and he said that attendance had dropped at games in Philadelphia ever since the league started cracking down on hitting and fighting. It certainly hasn’t been eliminated from the game, but many think it’s down from where it used to be. He said that this is a bad thing for the league and this could be seen by looking at the attendance numbers. I pointed out that the fact that Philadelphia has had a playoff team only twice in the last decade may have something to do with declining attendance, so it’s pretty close to impossible to say that the drop in attendance was due entirely (or even partially) to the drop in hitting.

Hitting is something we don’t have accurate stats on, so we can’t really do any kind of analysis on how that correlates with attendance. But we do have won-loss records and attendance numbers, so let’s look at those.

What we’re looking for is how a team’s attendance correlates with that team’s success on the floor. To measure attendance (and factor out the number of games per season), we’ll use the average attendance at home games. To measure success, we’ll use the winning percentage, number of wins divided by number of games played. In this case, we are ignoring playoff games. I then calculated what’s called the correlation coefficient for each team. I won’t describe the math since if you know what it is you don’t need the description, and if you don’t know what it is you likely don’t care. Suffice it to say that a value of 1 means the attendance always goes up as success goes up and drops when the team is less successful. A value of -1 means it’s exactly backwards – attendance goes up as success goes down and vice versa. The closer the number is to 1 or -1, the stronger the effect – a value of 0 means that attendance and success are unrelated.

To avoid small sample sizes, we’ll only look at teams with 10 or more seasons in the NLL. The teams involved are the New York Saints, Baltimore Thunder, Philadelphia Wings, Colorado Mammoth, Calgary Roughnecks, Toronto Rock, Rochester Knighthawks, and Buffalo Bandits.


What this tells us is that the New York Saints attendance numbers were very dependent on their success – as their win-loss records started to decline, their attendance dropped. This effect was similar in Philadelphia, Rochester, and Colorado. The rest of the teams had much smaller coefficients, meaning that their attendance didn’t depend very much on their success on the floor.

Calgary’s value was negative, implying that as Calgary’s numbers go up, their attendance numbers actually go down. But this is a bit misleading – especially since I tweeted about it saying that it was depressing. The actual value is –0.019, which is close enough to zero that it’s fair to say that Calgary’s success on the floor is unrelated to their attendance numbers. The numbers for Toronto and Baltimore are slightly higher but still low enough to imply no correlation, and Buffalo is right at the bottom end of “low correlation”.

The definition of “bandwagon jumpers” or “fairweather fans” would be those who show up to support their team when they’re doing well and abandon the team when they’re not. Would it be unfair to refer to the numbers for the top four as being indicative of this? I’ll leave that determination as an exercise for the reader.