Archive for the ‘Ramble On’ Category

Finding Hockey on the Frozen Tundra

Posted: January 2, 2013 in Ramble On
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or: Why USHL Scoreboard Operator is the Best Job in the World

gambler tee shirt 2

Although I live in Southern California, I was born and raised just outside St. Paul, Minnesota. I went to college in Eau Claire, Wisconsin where I met my lovely and talented girlfriend, Sam. She’s intelligent, funny, clever, and beautiful, but she grew up in Green Bay, Wisconsin where she attended Vince Lombardi Middle School. I’m a Vikings fan and she’s a Packer fan, which has been the main source of friction in our relationship.

I starting writing this from “Titletown, USA” where we visited her family after spending Christmas with my family. While Minnesotans love to brag about the State of Hockey, Wisconsinites are famously and justifiably known for having one of the most successful and storied NFL franchises in history. As I write this, the wall opposite me in the living room has a framed share of Packer stock, a photo from the 1997 Super Bowl parade, and a collage of ticket replicas and photos from each of the Packers’ four Super Bowl victories. Sam’s dad has two boxes of Lambeau Field grass from when they re-sodded the field a few years ago, they cut up the old stuff and sold it. One box just wasn’t enough, but three would have been too many…

Lambeau grass

One afternoon, as Sam and her mom were getting ready to host a baby shower for a friend, her dad asked me if I wanted to see a hockey game, and of course, I quickly agreed. I had heard of the local team, the Gamblers, but didn’t really know anything about them, so in the couple hours before puck drop, I did some research. The Green Bay Gamblers have played in the USHL since 1994, and their alumni include a number of big-leaguers like Stu Bickel, Justin Braun, Adam Burish, Ryan Carter (pride of White Bear Lake, MN,) Matt Greene, and Blake Wheeler.

The snow had been coming down steadily all day by the time we left for the arena, and though it was light and powdery, the accumulation would end up at 6.5 inches when it was all said and done. The Gamblers’ arena is literally right next door to Lambeau Field, and we parked at one of the Packers’ practice facilities, Ray Nitschke field. It was a short walk to the arena and I saw a good crowd had make it out to see the Gamblers skate with the Chicago Steel. I saw a ton of Packer shirts, hats, and jackets, but there was a lot more black and yellow Gambler swag than I had anticipated (plus a North Stars hat, a toddler in a Wild jersey, a number of Wisconsin Badger sweaters, and a guy with a Pavel Datsyuk jersey so crisp it had to be a Christmas gift.)

The arena was less than a third full, partly because of the snowstorm and partly because the city was focused on the upcoming Vikings-Packers game. We had great seats, second row from the glass right on the blue line. I had not been that close to the ice before except for Bantam games coached by my brother-in-law, so it was great to take in a game from that vantage point.

The game moved pretty smoothly, with no penalties in the first period and not a single icing call. There was very little hitting, and almost no extra-curricular activity after the whistle. There were hardly any odd-man rushes, and not a lot of setting up in the offensive zone…it was a lot of carrying the puck in and getting a shot and maybe a rebound. Not sure if that’s par for the course for most USHL games or if the guys were just a little sluggish after Christmas.

Having been involved in zone entry tracking for quite a while now, I tend to view any game through that prism and  subconsciously count controlled vs. uncontrolled entries. These teams would chip the puck in deep for a line change here and there, but other than that you could literally count on one hand the number of times both teams attempted a dump-and-chase. Again, since I don’t have a frame of reference I don’t know if this is normal or if these two teams just play more of a carry-and-shoot style.

Midway through the second period, I looked up at the scoreboard and noticed that despite the score being 3-1 Gamblers, the Steel were credited with 21 saves on 21 shots. I thought to myself, “that’s a little strange but the scoreboard guy must have just made a mistake.” Then in the second intermission, as fans were taking part in the Puck Chuck attempting to throw bright orange foam pucks into a coffee can at center ice for some reason, I looked up and saw that the scoreboard was off by more than a couple shots–for  both teams.

I mentioned this to Sam’s dad, saying in as many words that the scoreboard guy must be watching a different game than the rest of us. At this point, a 20-year old kid with an extra-large cubic zirconium earring and a fur-lined Aeropostale coat turned around and explained (not unkindly) that they counted some missed shots as shots on goal. “Like if he misses the net but it’s pretty close, they’ll count that as a shot.” I was pretty sure there was no way that was correct, at least not for the official stats…but I didn’t argue with the diamond earring kid because he was nice enough and it didn’t seem like the right time or place to get into a fancy stats debate.

For the entire third period I watched the scoreboard more than the game and tried to wrap my head around the “some missed shots count as shots but some don’t” theory. I know there is a hazy differentiation between shots and scoring chances, both by the NHL and Elias, but I  was unable to come up with even a working model that explained the sloppy scoreboard-keeping (the three craft beers I had before the game and the 32-ounce Miller Lite from the concession stand didn’t help.)

So the game ended and the fans went home happy because the Gamblers won 6-3, earning everyone a free hot dog from the bar next door for scoring 5+ goals and free nachos for scoring a shorthanded goal (this one game with an empty net, but as the Lonely Island taught us, STILL COUNTS!)

I snapped a picture of the scoreboard before we went home so I could compare to the official score:

GB Gamblers Scoreboard

The official line for Gamblers netminder Michael Rotolo is 26 saves on 29 shots, good for a .897 Sv%. The scoreboard guy has the correct number of saves but an inflated number of shots, which would lead me to believe that he was in fact counting near misses as shots or perhaps scoring chances.

But the numbers for Steel backstop Alex Sakellaropoulos are completely out of whack: the official scoresheet has him 34 for 39 (.872) while our friend working the scoreboard again has the right number of saves but either had some acute narcolepsy or a faulty Shot button, because by his count, Sakellaropoulos made 34 saves on just 29 shots, good for a 1.172 Sv%. I’d say that’s prettaaaay, prettaaaay good.

To sum it all up, here’s what I learned: 1) in addition to a rabid NFL fanbase, the good people of Green Bay, Wisconsin have some darn good ice hockey fans, and 2) if this stats thing doesn’t work out for me, I’m going to become a scoreboard operator, because as long as you get the final score right, only nerdy guys like me will notice if you botch the rest of it.

gamblers action

Nate Silver and the Changing Statistical Zeitgeist: Brace Yourselves–Big Data Is Coming

Posted: November 8, 2012 in Ramble On
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After correctly predicting 49(!) states in the 2008 presidential election and 50(!!) in 2012, Nate Silver is the King of Stat Nerds. He sits on a throne made entirely of TI-83 graphing calculators and he wears a gold-played Casio calculator wristwatch. His fivethirtyeight blog was recently pulling in a staggering 20% of the New York Times web traffic, and while the rest of the pundits were reporting a dead heat in the polls, Silver’s model ended up at more than 90% for Obama leading up to election night.

Silver cut his teeth in baseball sabermetrics before moving to politics, and the backlash he faced in recent months strongly parallels the anti-stats sentiments that are still going strong in the sports world. But now Silver has been completely legitimized, and his recently released book (currently number 17 on Amazon and rising) is going to propel this whole “math” thing into the mainstream conversation, the likes of which we haven’t seen since Freakonomics and Moneyball.

Furthermore, the concept of “big data” will gain steam as the stories are written in the coming weeks about how the Obama administration campaigned more efficiently with less money and mopped the floor with the Romney camp. The zeitgeist is changing, and the legitimacy of data mining, reliance on sophisticated computer models, and quantitative analysis in general is going to become much more accepted in all parts of society. Fancy stats are certainly nothing new in sports, but I genuinely think that with Silver’s domination of the political prediction game, sports stats can hang on for the ride. I wanted to share some of my thoughts on the recent goings on because I really think we are at a crossroads with the way big data and stats are going to be received.

OBJECTIVITY: The Numbers Don’t Care Who Wins

Try to imagine for a minute what would have happened if the election was flipped–if Romney was leading in Silver’s model going into the election and then won. The reception in the media surely would have been different, but I wonder if the classic “I don’t like your numbers, therefore they’re untrue” argument would have been as obvious, or if the ad hominem attacks would have flown so freely.

Silver faced so much backlash because his forecast directly flew in the face of the pundits, and not just the Republicans. The narrative coming from talking heads on both sides of the aisle was that the election was “Razor Tight” (their words, not mine) and right up to November 6th, they were saying it was anybody’s election. Of course, it was in their interest to push that narrative, as they all have airtime to fill and quotas to fill for their magazines or newspapers or blogs. The fact that Silver was making an objective forecast was upsetting because it was so opposite of the status quo. The fivethirtyeight model simply input poll results, weighted them appropriately, and output a prediction of what the electoral votes would be. Silver didn’t set out to create a model that would show Obama was winning…he set out to create a model that would reflect the truth. 

Everything in the above paragraph is directly applicable to sports writers and sports researchers. The Old School is mad at the New School because they have made their living on their experience and their “gut calls” and now those things are being invalidated by numerical models. It has been said that the difference between researchers and pundits is that one starts with a question and constructs an argument based around the answers to that question, and one starts with an argument and searches for facts that support the argument. The sports and political worlds both have become so reliant on pundits who until recently have been using just the most basic of stats that this whole “objectivity” thing is still very new, and therefore, scary.

But over time, both political stats and advanced sports stats will gain legitimacy as they persist in the mainstream consciousness. And as long as they are good stats, they will persist (more on this in a bit). And obviously Silver is CRUSHING IT for his part, so there’s no reason to think he’s going anywhere.

TRANSPARENCY: Hey, come over here and look at what’s behind this curtain!

With the Old School way of doing things, the pundits don’t have to be concerned with transparency…they say what they think, they explain it, and boom, there’s your transparency right there. Here’s my opinion, and by definition it’s unfalsifiable so…we’ll just keep going round and round because there is no right or wrong, it’s just all conjecture. With the New School data-driven approach, there is now a need for transparency that didn’t exist before. Without transparency, it’s all too easy to try to discredit stats by calling them biased (see above section on Objectivity.)

Sports researchers (and Nate Silver) walk a fine line when it comes to the transparency of data and formulas (formulae?). They must reveal enough about their methodology so that others can understand what they did, other researchers can help develop the measures, and laypeople can get what the numbers mean. But let’s be really real right now, the way to make money off these kind of things is to withhold enough of the nuts and bolts so that nobody else can figure them out. Silver was incorrectly slammed for tinkering too much with his machine, oversampling Democrats for example, or generally just using his “Magical Formula.” But he does in fact go into great detail about his methodology. I guess what it boils down to is that research should be as transparent as is necessary, but each person will have different levels of comfort when it comes to divulging all the secrets. For Silver’s part, I have been impressed with how open he has been and how much of the nitty gritty he gets into.

It Helps (A Lot) to be Right

It’s hard to imagine Silver’s model being wrong, given that his forecast reached over 90% for Obama. If it was in fact a closer election and it was, say 55/45 then there would have been a much different discussion surrounding his forecast. But over the last two presidential elections, he is batting .990, which is obviously a stellar showing. The nature of Silver’s forecast is such that he puts himself out on quite a limb, predicting each state and the overall winner. Yes, he does in fact give probabilities for each state so if he was wrong he would be able to defend the forecast. But he’s NOT wrong, which is very good for him.

In hockey research I keep coming back to the whole Minnesota Wild thing from the 2011-12 season. Long story short, when the Wild were number one in the NHL, the advanced stats community was vehement about their falling back to earth, based mostly around the PDO statistic. And they were RIGHT, the Wild did regress in a big way. The stats crowd will always have that one in their pocket, and if we ever get a season again, that stat will have a very established place in the stats conversation, not just on the blogs and on Twitter, but I think with the legitimacy gained from last year, it will creep into the mainstream conversation. Just as Silver’s model has been shown to be profoundly correct, the PDO analysis has proven correct and useful.

Final Thoughts

The quote from Moneyball that stuck with me the most (I can’t remember if it’s in the book too) is: “The first guy through the wall always gets bloody.” Nate Silver took a lot of shots in the media this year, but he has come through the other side squeaky clean. His forecast has been amazingly accurate, and I think it’s going to go a long way toward the legitimization of Big Data and statistical analysis not just in politics, but in sports and all other areas of society. I have been very impressed with the way Silver handles his work, and conducts himself in public and in the media. I hope these ramblings have made some kind of sense, I am still forming what I think are the lessons to be learned from his unprecedented success. Let me know what you think about whether and how the fivethirtyeight model’s success will help usher in the Big Data movement and what lessons the sports stats community can learn. Don’t forget to follow me on Twitter @Hashtag_Hockey, and thanks so much for reading!