Friday, December 30, 2011

Podcast: No Hyperbole Allowed (Episode 7)

Last week Rich was sick and Colin was on vaca so apologies for the lack of podcast. Hopefully you didn't miss us too much. There's plenty of podcast this week to make up for it, I promise.

Unfortunately, Colin is sick this week (he should stop making out with Rich) so Rich had to find a last-minute replacement. Continuing the theme on the blog this week, Rich's friend and blog contributor, Adam Golub, is here to discuss Rich's top ten TV shows of 2011 list, in general, and four tv dramas, in particular.

There are some spoilers so pay close attention to the schedule below so you can skip ahead if necessary.

0:00 - Intro
5:00 - Top Ten TV Shows of 2011
16:14 - Adam Hates 'Revenge'
19:16 - Adam's Top 4 Dramas of 2011; We Discuss 'Boardwalk Empire'
35:32 - We Discuss 'Homeland'
51:22 - We Discuss 'Game of Thrones'
1:06:08 - We Discuss 'Breaking Bad'
1:36:29 - We Discuss 'Parks and Recreation' and 'Community'
1:37:42 - We Hand Out Some Awards for Acting and Best TV Episodes
1:50:23 - Closing

Top Ten TV Shows of 2011, Part 2

by richmin3000

today, we count down the top 5 tv shows of 2011. but before we get to the positive stuff, let's get the negative stuff out of the way.
worst tv shows of 2011:
drama: the killing (amc)
comedy: whitney (nbc)
neither drama nor comedy: entourage (hbo)

now, onto the top five:

5. homeland (showtime) - season 1
i never watched '24', but if the showrunners (same as homeland) built the tension half as well as they have in the first season of homeland then i may just have to check it out. i spent every episode waiting (not hoping) for the show to fall on its face. i blame my bad experience with 'the killing' and my general lack of faith in showtime shows. but, by the end of the season, i was finally secure in the knowledge that they had just completed one of the best first seasons in television history. the game of cat and mouse between agent mathison (claire danes) and sergeant brody (damian lewis) occupied most of the season, while the question of will he/won't he occupied the final few episodes. both plot lines were thoroughly engaging and were more than satisfactorily resolved through deft writing and acting. we'll see if they can keep this up in season 2, but at least we can enjoy this past season as a great accomplishment in television.
best episode: "marine one"

4. community (nbc) - season 2 and 3
some say it's smug and arrogant. i say those people are morons who lack the requisite taste and knowledge to appreciate this amazing sitcom. since season 1, i've been saying that this show is hit or miss, but that no show is quite this funny or smart when it hits. i know i'm right because critics echo this exact sentiment (i can't help being arrogant when talking about this show). the writers, usually through the character, abed, tackle and play off the mores and motifs found throughout pop culture, in a way that's often insulting, dismissive or condescending, but can sometimes be heartwarming (that's when they are at their best). but regardless, it's always witty and smart. whether the episodes imitate war films, mafia films, documentary films, mock glee, utilize clay-mation or just involve time-travel, the end result is always to add a new layer to the study group's dynamic. and in that vein, the show has succeeded in providing a novel approach to character development, which most sitcoms don't often concern themselves with.
best episode: "remedial chaos theory"

3. breaking bad (amc) - season 4
it's between 'mad men' and breaking bad for the most critically-acclaimed show of the last several years. bryan cranston, as walter white, has essentially owned the emmy for best lead actor (winning all 3 times he has been eligible). few shows can boast the quality of writing, directing and acting employed on breaking bad. and maybe no show elicits this intense level of reaction and scrutiny from fans and critics, alike. though, at times, i quibble with some of the plot twists and choices, the writers have impeccably constructed, developed and fostered the most colorful and unique characters on television. with 16 episodes left in the show's run, we're all going to be paying close attention to the final act in the story of mr. white aka heisenberg, high school chemistry teacher turned crystal meth impresario.
best episode: "crawl space"

2. game of thrones (hbo) - season 1
if a tv show was a drug, game of thrones could most aptly be described as crack-meth-oine (i just made that up). the show is based on the equally addictive series of books, 'a song of ice and fire' by george r.r. martin. after just its first season, game of thrones has successfully created a world so nuanced and fascinating that it's hard to imagine that it's not real (the appearance of dragons probably is a solid hint that it is, in fact, not real). more like 'the wire' or 'rome' than 'lord of the rings', game of thrones is all about power and politics. characters are treated as pieces on a chess board, to be moved, at will, by kings, as pawns try to elevate their status along the way. the usual and absurdly naive concepts of "good" and "evil" are quickly dismissed in this world as characters are never rewarded simply for making the "right" and honorable choice. as queen cersei states, "when you play the game of thrones, you win or you die. there is no middle ground."
best episode: "baelor"

1. parks and recreation (nbc) - season 3 and 4
few shows are this funny. few shows are this heartwarming. few shows employ this level of character development. 'modern family' was once incredibly funny and heartwarming, but never chose to develop their characters in any meaningful way. 'arrested development' was incredibly funny but rarely heartwarming. perhaps not since 'the office' (where creator, michael shur, used to be a writer) has a show been this successful in combining all three aspects. and the office never felt as relevant or poignant as parks and recreation does. in 2011, we were lucky enough to have seen the entire 3rd season of parks n rec (because they didn't start till january 2011) and half of the current 4th season. in that time, characters settled into new and old roles (andy as leslie's new assistant, tom haverford as media mogul, april and andy as husband and wife), adam scott and rob lowe solidified their new roles in the cast after the city went bankrupt (relevant), and all this change served to enhance our appreciation of the interpersonal relationships between the various characters. what we've discovered is that this great ensemble cast, led by the eternally optimistic leslie knope (amy poehler), has built a bond of friendship and support that extends beyond their professional duties. and the writers have successfully developed the characters to this point in a way that feels neither manipulative nor forced. we've come a long way since the premier of the 3rd season where the parks department finally reopened after bankruptcy to the fall season finale where leslie knope's political career is resurrected with the support of the entire parks and recreation department. this show almost makes me want to become a resident of the fictional town of pawnee, indiana. almost.
best episode: "ron and tammys"

what i'm excited for in 2012 and beyond:
coming up in 2012, i am incredibly excited for the return of 'mad men' (which i consider the best show currently on tv) in march, the 2nd season of 'game of thrones' in april, and the new hbo drama, 'luck' starting in january, by david milch (deadwood), michael mann (heat) and starring dustin hoffman and nick nolte.
hopefully 'community' will return sometime in the spring on nbc and although we have to wait till 2013 (fingers crossed) for 'arrested development', it definitely won't be off my radar anytime soon.
finally, the true 2nd season of spartacus (entitled spartacus: vengeance) will begin on starz in january 2012 following the untimely death of andy whitfield from non-hodgkin lymphoma on september 11, 2011. whitfield played spartacus in the first season which aired in early 2010. after starz delayed production for the second season due to whitfield's illness, it later became apparent that he would not be able to continue in the lead role. the show is visceral fun and whitfield was great as spartacus. he will be sorely missed.

**to listen to a more in-depth review of some of the shows on this list, check out episode 7 of the no hyperbole allowed podcast**

Thursday, December 29, 2011

Top Ten TV Shows of 2011, Part 1

by richmin3000

this is my favorite time of the year... time for lists. top ten movies, albums... fleeting celebrities. this is where we separate the good from the bad, in a nice, neat package of ten (probably because we have ten fingers). i love lists... you love lists (here's why), so let's start listing... the top ten tv shows of 2011.

today, i will review the shows that ranked #6-#10. tomorrow, i will go through the top five.

i feel compelled to say that obviously it's impossible for me to watch everything on television so this list is composed solely from those shows that i did watch (and it was definitely a lot). over the past week or so, because of the holidays and being sick, i was able to catch up on a lot of critically acclaimed shows (like louie), but still there were a bunch i've missed. so on that note, the
two shows i wished i watched:
drama: justified (fx)
comedy: happy endings (abc)

and just so i can squeeze in a couple more shows without ruining the integrity of the top ten list, here are my
honorable mentions:
drama: revenge (abc)
comedy: modern family (abc)

onto the top ten:

10. enlightened (hbo) - season 1
created by mike white (freaks and geeks, school of rock) and laura dern (inland empire, recount), this show is typical of those on my top ten list - original and willing to take risks. it was thought that showtime had a trademark on half-hour dramadies with strong female leads (see weeds, nurse jackie and united states of tara), but hbo did them one better with this new series. laura dern brilliantly plays a woman in search of fulfillment (enlightenment is another word i guess) after a nervous break-down. it's the sort of character and story that could come across either very hokey or worthy of mocking, yet it's neither. at times, you can't help but feel repulsed by dern as the main character, amy, and at times you can't help but empathize. but at all times, you know you are watching an honest depiction of this character and in that honesty, judgment goes out the window.
best episode: "the weekend"

9. boardwalk empire (hbo) - season 2
this year in television was highlighted by a lot of great endings and courageous choices. none may have been better than the finale of the 2nd season of boardwalk empire. the season starts out with a great conflict between nucky (steve buscemi) and jimmy (michael pitt), the prodigal son of sorts. all the cards are stacked against nucky. and throughout the season, we discover the true nature of these two great characters as they are faced with difficult choices in the face of great conflict. the show, at times, can feel very superficial, especially considering the great production value the show has at its disposal, but when it counted, this season showed that it can test the mettle of its main characters with the best of them.
best episode: "to the lost"

8. masterpiece: downton abbey (pbs) - season 1
it's not tv, it's pbs. isn't that how it goes? no, well, maybe it should. the first season of downton abbey, a british tv show, is streaming on netflix so i urge you to check it out before season 2 premieres in january. this is what's great about critics' top ten lists - i've never even heard of this show until i saw it keep popping up on these year-end lists. so while i was sick, i watched all seven (yes seven, that's what they do in britain... it's like a miniseries year-after-year. though i think in the u.s., it was made into 4-90 minute long episodes) episodes in a two-day stretch. it was like watching a british version of an american soap mixed in with some mad men. but the execution... jeez louise, that shit was tight. the writing, the acting, the music, drama - it was captivating storytelling at its best. a lot of the shows on this list may be more original, but few, if any, were able to pull of storytelling at this level. it also has the distinction for being the guinness record holder for 'highest critical review ratings for a tv show'.
best episode: "episode 1"

7. louie (fx) - season 2
the fact that i have louie ranked much lower than almost everyone else this year should not be taken as a slight. in fact, it may be the most amazing thing on television right now. but, the show is so unique and such a singular vision (louie c.k. writes, stars, directs and edits the show) that it's almost impossible to rank such a show because how do you even begin to compare it to anything else. it's not a straight comedy though it is incredibly funny. it's not a straight drama even though it's incredibly moving. i've watched both seasons in last few days so i think i just need a little bit of time to let it soak in. i vacillated between ranking this show as high as number 1 and as low as number 9, finally settling on 7. i guess i've admitted that this isn't an exact science... sorry? in episode 12 entitled "niece" louie is trying to figure out a way to talk to his 13-year old niece after fellow comedian godfrey makes it seem so easy. louie asks him his secret to which godfrey replies, “You’ve just got to learn how to talk to people who aren’t like you. It’s called empathy, man.” louie is full of insights because it's obvious he's very intelligent - sometimes i just wish he would spend less time trying to tell people why they are wrong and just empathize. that's why my favorite episode this season is the hour-long one where he travels to afghanistan to entertain the troops... it's the least biting and the most heartfelt.
best episode: "duckling"

6. friday night lights (nbc) - season 5
no, i'm not crying.. i just have something in my eyes. we got five seasons (though we can sort of ignore season 2) of this amazing show so there's no need for tears. if this was a career achievement sort of list, fnl would shoot all the way to the top. as it were, this wasn't the strongest season, but it certainly provided my favorite finale of all-time. though the premise of this series (high school football in texas) does not sound so enthralling, between the acting and heartfelt writing, we were sent on an emotional rollercoaster from episode 1 through episode 60. i'm a huge cosby show fan and love claire huxtable, but coach eric taylor and tami taylor have set the new standard for the perfect television couple. it's not because they are perfect - it's because they respect and listen to each other in spite of their differences and imperfections. through their eyes, this show really let the viewer settle into this community of dillon, texas as we shared in their joys and sorrows... as we watched our favorite kids graduate and move on with their lives and watched freshmen turn into the next football stars. all under the watchful and protectful gaze of coach taylor. TEXAS FOREVER.

Tuesday, December 27, 2011

Review: Fall Television Season (Comedies)

by richmin3000

in anticipation of my top ten tv shows of 2011 list later this week, the following are my short reviews of the comedies that i watched this fall:

new girl (fox tuesdays)
you can read my earlier review of new girl, here. it's pretty clear that the show will live or die, at least for now, with your ability to tolerate the repeated attempts at being cute and adorable by zooey deschanel. luckily for them (or me perhaps), i'm not yet completely over her cuteness. however, this show clearly has no idea how to be original or create consistently funny characters. besides schmidt or jess (deschanel) the show has yet to find a steady voice for the other two roommates and the writers keep trying different things, in order to see what sticks. mostly it fails.

no hyperbole allowed: c+

suburgatory (abc wednesdays)
probably the most solid new sitcom i've seen this fall, the premise (city girl moves to the burbs) is pretty predictable in its jokes, but the execution is solid. suburgatory, unlike new girl, has shown an ability to progress their characters and their jokes. cheryl hines (curb your enthusiasm) is pretty hysterical as the poster child for the suburban housewife.

no hyperbole allowed: b

modern family (abc wednesdays)
modern family has not been the same since their fantastic first season. after struggling through an up and down second season, things have not improved much to start season 3. the big cast and flexible structure of the show allows them tons of options for laugh, but it seems like the writers continue to take the easy road with formulaic sitcom writing. the show is still incredibly funny at times and the ensemble is one of the best on tv, but it's really disappointing to see a show fail to live up to its potential.

no hyperbole allowed: b+

up all night (nbc wednesdays)
yes, i will watch a sitcom with will arnett (arrested development), christina applegate (married with children/anchorman) and maya rudolph (snl/bridesmaids). watching tv is as much about trusting those involved (showrunners, actors, studio) as it is about enjoying the show itself. you can get a good sense early on whether or not there is a future (it is like a relationship). up all night hasn't yet been consistently great, but it's slowly building into a solid comedy.

no hyperbole allowed: b

community (nbc thursdays)
o community, where art thou? who knows if we'll ever see this great comedy again, though nbc promises it'll be back. they are being temporarily sidelined as 30 rock makes it return in january. thursday nights on nbs is the mecca for comedies, with heavyweights, the office, emmy darlings, 30 rock, and current critical darlings, parks n recreations. but no show, at its peak, is as smart or funny as community. it's a risk-taking show that, when it hits, transcends the genre of situational comedies.

no hyperbole allowed: a-

parks and recreation (nbc thursdays)
the best sitcom this year. the best sitcom since the first season of modern family, the height of the office or even, dare i say, arrested development. is there a funnier collection of characters? leslie knope, ron swanson, andy, april, tom haverford, et al... they are so well defined by writer, michael shur (read his profile here).

no hyperbole allowed: a

the office (nbc thursdays)
this is what happens when you overstay your welcome. andy, the new boss, was never one of my favorite characters (in truth, my least favorite character) so suffice it to say i'm not happy he was chosen to replace michael scott (carrell). i wish he never came back from anger management many years ago. james spader has been pretty funny, but overall the show has lost its heart and its edge. this is sad for me to admit because i've never enjoyed a sitcom more than the office at its peak (all downhill since jim and pam's wedding... why does marriage ruin everything??!!!).

no hyperbole allowed: b-

beavis and butthead (mtv thursdays)
apparently there are still music videos and apparently mocking them is a timeless art. beavis and butthead return after a 14-year hiatus and besides music vidoes, they are able to mock the bevy of ridiculous reality tv mtv has in their vaults... target #1: jersey shore. the jokes, as usual, are never laugh-out-loud, but i can't stop chuckling and that's good enough for me. the nostalgia is strong in this one.

no hyperbole allowed: b

Reviews: Fall Television Season (Dramas)

by richmin3000

in anticipation of my top ten tv shows of 2011 list later this week, the following are my short reviews of the dramas that i watched this fall:

the walking dead (amc sundays)
you can read my review of the fall season of the walking dead here. i gave the show a "b+" for its strong finish, but after some thought, i have to change my grade to a "b" simply due to the season's slow and boring start. the final 6 episodes of the 2nd season will air beginning on February 12, 2012 before making way for the 5th season of mad men.

no hyperbole allowed rating: b

pan am (abc sundays)
along with the cancelled 'playboy club', pan am attempts to put a female empowerment spin on the mad men sixties. with four strong female leads and great production value, pan am has debuted well this fall. while i'm skeptical over it's longevity and future, pan am, like any good period piece, conveys a a strong feeling of time and place.

no hyperbole allowed rating: b-

revenge (abc wednesdays)
you can listen to my review of revenge with ms. ana-mercedes cardenas, here. i think, at the time, i gave the show an "a-" rating for the season. now, after a few more episodes, i have to drop my grade down just slightly to a "b+" because the show has sort of dragged the last couple of weeks. after a really strong start, revenge is struggling to find ways to push the plot through a long network season - just one of the advantages of cable networks which require only about half the episodes per season. i feel confident though that the show will came back strong in the spring to close out a solid first year.

no hyperbole allowed rating: b+

american horror story (fx wednesdays)
this is objectively one of the worst shows i've ever seen, i think. however, i probably will never miss an episode as long as it's not cancelled. i'm a sucker for the horror genre and as bad as the show was this year, it still had its moments. the show hit a crescendo in it's penultimate episode then came crashing back down in the finale. luckily for everyone, we are essentially getting a do-over next year as the showrunners have announced they will explore a new horror plotline each season. good-bye mrs. taylor... here's hoping you get cast in a role where i don't have to hate you so very much.

no hyperbole allowed rating: c+

boardwalk empire (hbo sundays)
i assumed season 1 was the prelude to the story of nucky thompson (steve buscemi), but apparently this season served that role. much stronger than season 1, this season was set up to challenge nucky's resolve and character, showing his true color's by season's end. effectively, season 2 was more about jimmy darmody (michael pitt) than nucky himself, but at the end of the day, this is nucky's world and we're all just living in it.

no hyperbole allowed rating: b+

homeland (showtime sundays)
one of the best new shows on tv this year, this is already the best drama to ever air on showtime (especially after hearing the joke that dexter has become). claire danes plays cia agent carrie mathison, who suspects the recently rescued p.o.w. marine nick brody (damian lewis) of being a terrorist. the season plays out this game of cat and mouse and the writers are so effective at it by creating tension while never manipulating our emotions or prejudices. it's the class of the dramas to air this fall so i highly recommend to anyone who has showtime to watch it on demand.

no hyperbole allowed rating: a-

**tomorrow, i will be reviewing the fall season's comedies**
**later this week, look out for my top ten tv shows of 2011**

Monday, December 26, 2011

Beating The Spread: Statistical Models of NFL Power Rankings and Point Spreads (Part 4)

by Tim Rubin

**Information on this site is collected from outside sources and/or is opinion and is offered "as is" without warranties of accuracy of any kind. Under no circumstances, and under no cause of action or legal theory, shall the owners, creators, associates or employees of this website be liable to you or any other person or entity for any direct, indirect, special, incidental, or consequential damages of any kind whatsoever. This information is not intended to be used for purposes of gambling, illegal or otherwise.**
In Part 1 of this series I discussed the prevalence of statistical models in ratings systems for team strengths in things like Chess, Halo, etc.  In Part 2, I introduced a simple “Margin-of-Victory” style model for NFL game spreads, and I discussed how we “fit” the model, and then generated power-ratings with the model, using the results from this year up to week 13.  I then used these to generate predictions for the Week 14 games.  In Part 3, I discussed using normal distributions to model each team’s variation in performance.

In Part 4, we will look at how to model game outcomes and estimate win probabilities, both against the spread or straight up.  I know that in Part 3 it may have been unclear why I spent so much time talking about the normal distributions and bean machines, and where all of that was going.  This post should (I hope) clarify all that.

NOTE: I’m providing an Excel spreadsheet to accompany this post. You don’t need it to follow anything in this post.  But I’ll be demonstrating some things in Excel, and it may be helpful—especially if you are a hands-on learner—to go ahead and download it here so you can follow along (and see exactly what I did) when I refer to it later.

UPDATE: Here is an updated version of the spreadsheet.  It doesn't go along with the examples used in the post.  However I've updated it to correspond to a real-world scenario (tonight's game); it incorporate the home-field advantage and team-ratings (for the Saints and Falcons) from Sagarin's "Pure-Point model, the Vegas spread on the game, and more realistic variance estimates.
Part 4: From the Margin-of-Victory Model…
… To Estimating Win-Probabilities

The title of this post (and the corresponding figures) gives a rough outline of the starting and endpoints of what we’ll be covering in the post:  We will be starting with the simple Margin-of-Victory model that was introduced a couple posts back, and showing how this model can be used to estimate win probabilities for any matchup, either straight up, or against the spread.

The Story So Far

Let’s review everything we’ve covered so far as quickly as possible, just to refresh everyone’s memory.

This is the equation that I gave in Part 2, to describe our Margin-of-Victory model:

PointsHomeTeam – PointsAwayTeam   = RatingHomeTeam– RatingAwayTeam + HomefieldAdvantage + Error 

Essentially everything you need to understand about the model can be captured by a couple simple graphs, which are shown below.  

Note that to keep things from getting too messy, I’m only showing 4 of the 32 teams here (furthermore, these aren’t the true estimates of these teams’ ratings; I chose these values for illustrative purposes).

Team Power Ratings (top graph)

Our model assigns each team a rating.  This rating is a single real-numbered value.  Teams with positive ratings (Rating > 0) are better than an average team; teams with a negative rating are worse than an average team. 

Ratings are on the same scale as points.  What this means—in terms of understanding a game outcome—is that a team with a rating of +6 is expected to beat a team with a rating of +1 by 5 points.  All team ratings are constrained so that the average rating is 0.  This gives the team ratings some inherent meaning outside of specific matchups; a team with a +6 rating would be expected to beat an average team by 6 points on a neutral field.

So, in the image above, the distance between any pair of teams would give an estimate of the outcome of a game between the two teams (on a neutral field).  And the Packers and 49ers are above-average teams, while the Seahawks and Vikings are below-average teams.

Our model also learns a homefield advantage, which is on the same scale as ratings and points, so can simply be added to expected game outcome, expressed in terms of: PointsHome – PointsAway.  So if the value of homefield equals 3 points, a team rated +6 would be expected to beat a team with a +1 rating by 8 points at home, 5 points on a neutral field, and 2 points on the road.

Team Performance as normal distributions (bottom graph)

To account for the fact that each team’s performance will vary across games, we model each team using a normal distribution. This is illustrated in the bottom plot of the figure above.

The mean (or center) of each team’s normal distribution is equivalent to its rating.  For each team, I’ve shown the team rating using a dashed vertical line, and the teams’ rating distribution (which capture the variability in the team’s performance across games) using a solid line; this solid line corresponds to a normal distribution.

This is how we use normal distributions to model team ratings:

Sunday, December 25, 2011

Merry Christmas!

Rich was sick this week; Colin was on vacation. So please forgive us for the lack of a podcast, but we want to wish everyone a Merry Christmas and Happy Holidays!

And we'll be back next week!

Tuesday, December 20, 2011

Unfine Dining: The McRib

by AJ Golub

Hello. My name is Adam Golub, and I am an addict. I have been addicted to fast food since I was 14 months old. It was a tender, formative time; My communicative skills were rounding into form. I would request a Happy Meal with a series of grunts comprehensible to only my poor mother, which she begrudgingly relented to on more occasions than she feels comfortable admitting. In 12-step speak, she is my addiction’s classic enabler.

In the near three decades since those early mother-son bonding moments, I do humbly believe that my palate has evolved a great deal. Quite frankly, my addiction has demanded it. I have been told by many of my peers that my refined tastes and encyclopedic knowledge of fast food offerings has made me an untapped resource ready to explode upon the world. (In reality, only our esteemed editor Rich holds this view of my potential. I have tried to convince myself that he isn’t the only one.)

Seeing as how I would like to believe I possess knowledge of subjects beyond fast food, and given that I have been barred from adding insight into any subject aside from fast food herein, I am going to write this column as if I am the Hemingway of the Hamburger. The Friedman of the Fry. The Nietzsche of the Nugget. The Ebert of the Egg McMuffin. Or something like that.

Just keep this in mind as you read this column: I know how every fast food restaurant dresses their sandwiches, by name and by type, and can recite them at the speed by which an idiot-savant multiplies two 5-digit numbers. I can order a full meal from any fast food chain with items not appearing on the regular menu. I know how frequently Burger King changes the oil in their fryers. I am special.
In this premiere edition of Unfine Dining, I would like to discuss what is widely considered the Holy Grail of fast food items: McDonald’s transcendent McRib sandwich, which has recently been reintroduced for a limited time. The McRib has taken on great cultural significance among fast food connoisseurs and laymen alike. Urban Outfitters sells a McRib t-shirt right along side one’s bearing the slogan “More Cowbell” and other popular hipster prints. There is a McRib tribute site (, a McRib twitter account, and a McRib locator app. In their latest commercial announcing the McRib’s triumphant return, a man is sent reeling when, on his honeymoon, he receives a text message from his buddies that they are en route to McDonald’s, for their beloved McRib has returned. Much like when the plumber comes knocking in a porno scene, we know where its going to go from here - Honeymoon over, wife only slightly displeased as she smiles wryly, McRib in hand. But perhaps this advertisement isn’t so far fetched. According to the Wall Street Journal, McDonald’s sees an increase in revenue of approximately 15% nationwide during the McRib’s run. Without going into a whole discussion of micro economics, that’s, like, a serious increase. Best to change those honeymoon plans now, ladies.

Sunday, December 18, 2011

Podcast: No Hyperbole Allowed (Episode 6)

After much delay, Colin's interview with Yoshi, his friend from Japan, is finally here. They discuss the aftermath of the tsunami and other aspects of Japanese culture. In addition, Rich and Colin talk about smartphones and read the first fan email.

If anyone would like to donate to the Tsunami relief in Japan, there is a link to 'Americares' on the upper right hand corner of the blog.

0:00 - Intro
1:47 - Smartphones
10:54 - Reader mail
14:07 - Colin's Interview with Yoshi about the Japanese tsunami

Friday, December 16, 2011

Beating The Spread: Statistical Models of NFL Power Rankings and Point Spreads (Part 3)

by Tim Rubin

**Information on this site is collected from outside sources and/or is opinion and is offered "as is" without warranties of accuracy of any kind. Under no circumstances, and under no cause of action or legal theory, shall the owners, creators, associates or employees of this website be liable to you or any other person or entity for any direct, indirect, special, incidental, or consequential damages of any kind whatsoever. This information is not intended to be used for purposes of gambling, illegal or otherwise.**

In Part 1 of this series I discussed the prevalence of statistical models in ratings systems for team strengths in things like Chess, Halo, etc.  In Part 2, I introduced a simple “Margin-of-Victory” style model for NFL game spreads, and I showed the power-ratings that this model learned, as well as it’s predictions for the Week 14 games.

In Parts 3 and 4 (which will be posted today and tomorrow), we will be looking at the Margin-Of-Victory model in a little more detail, so that we can understand how this model can be used to predict (in addition to the margin of victory), the probability of different outcomes (e.g., the probability that the home-team wins, both straight up and against the spread).  We will also be looking back to see how accurate the model’s predictions were for last week’s games.

After we have looked under the hood of this model, we will take a step back in order to look at some of the pros and cons of the model, as well as ways in which we can improve upon it.
The simple Margin-of-Victory Model (Brief Review)
In part 2 of this series, I introduced the simple Margin-of-Victory model we’ve been using.  As a reminder, this is the basic idea of the model:

PointsHomeTeam – PointsAwayTeam = RatingHomeTeam –  RatingAwayTeam + HomefieldAdvantage + Error  
When applying this model to the NFL, this model consists of 33 parameters:  A “rating” for each of the 32 teams, and a “Homefield Advantage” which we assume to be equal for all teams and games.

After fitting the model last week, I showed what the model’s week 13 power-ratings were for each team, and the model’s prediction’s for week 14.1

Some Nice Features of this model

At a later time, we will do a more critical analysis of this model, looking at the pros and cons of it, etc.  But for now, I just want to point out a couple of nice properties of this model.  In particular, the “parameters values” for the model (i.e., each team’s Rating, and the “Homefield Advantage”), have an extremely intuitive, real-world interpretation. 

First, this model expresses team ratings on the same scale as the game score.  For example, on a neutral field (i.e., with no team having a home-field advantage) a team with a +10 rating would be expected to beat a team with a +5 rating by 5 points.  The value of the “Homefield-Advantage” in the model—let’s call it h—has similarly intuitive interpretation; it can be treated as adding h to the rating of the home-team.

A second nice feature is that we can arbitrarily “shift” all team ratings up or down as we please, since the only thing that matters is the difference between team ratings.  For example we could add +100 to each teams’ rating, and it wouldn’t change our predictions.  In fact, as I mentioned last week, the Sagarin “Pure Point” ratings are extremely similar to this model’s ratings, except that he “shifts” all of the team ratings so that the average rating is 20.  To illustrate this, I’ve created a plot here, in which I’ve aligned the Margin-of-Victory model’s ratings through week 13 with Sagarin’s week 13 ratings (by setting the average rating of our model to equal 20).  You can see in the plot that the ratings generated by the two models are generally quite similar.

For our Margin-of-Victory model, I chose to set the average rating to 0, since I think this leads to the most easily interpreted set of ratings (by far).  Specifically: any team with a positive rating is better than average, and any team with a negative rating is worse than average.  The more positive the rating, the better, and vice versa. 

Even better, our ratings then have a real-world interpretation of their meaning.  Specifically, each team’s rating corresponds to how we would expect them to perform vs. a league-average team, on a neutral field (i.e., with no team having a home-field advantage).  For example, using the power-ratings that were fit last week:  the Packer’s rating of +12.2 means that they would beat an average team by about 12 points on a neutral field (or by about 15 at home).  Which seems like a fairly reasonable statement.

Now that you hopefully have a pretty clear intuition for how to interpret this model, let’s take a closer look at some of the underlying details.  If this sounds intimidating, don’t worry.  I will literally make no assumptions about your background.  If anything is unclear, feel free to leave a comment and I’ll do my best to either give you an answer directly or clarify the issue in the post itself.

The Margin-Of-Victory Model, In Pictures

Let’s take a step back, and consider one key thing: uncertainty.  In our model, we express uncertainty using the “error” term on the right side of the equation.  But where does this error come from, and what does it mean? 

Now, one way to think about the model, is that each team has a true rating corresponding to how good the team is and that each team’s performance always corresponds exactly to what their true rating is.  And that any deviation in a game’s outcome from what our model predicts (i.e., any deviation from our equation:

PointsHomeTeam – PointsAwayTeam = RatingHomeTeam –  RatingAwayTeam + HomefieldAdvantage

is due to randomness inherent to football games.  However, this is probably not the right way to think about things.  Although there certainly is a lot of randomness in football games, the notion that each team always plays at the same rating-level seems like a stretch.  One good example for why this idea is problematic relates to injuries: in the NFL, key players have to sit out for a series, a game, or multiple multiple due to injury, all the time.  And so each team’s starting roster is constantly in flux.  It seems highly unlikely that with all that personnel change going on, the true rating of a team stays constant.

A more realistic way to think of the model is as follows:  each team has a true rating corresponding to their team strength, but the level at which the team actually performs will fluctuate from game to game (i.e., some of the time they play worse than their rating indicates, some of the time they play better, but on average they play at the level corresponding to their true rating). 

Modeling Variations in a Team’s Performance

A good way to model the random variation in the performance of each team is using our old friend, the normal distribution.  In other words: we assume that each team has some underlying “rating”, but their performance across games varies according to a normal distribution centered about their rating.
The normal distribution can be specified using two parameters: a mean (indicated using the greek letter mu, μ) and a standard deviation, (indicated using the greek letter sigma, σ).  It is often more convenient to talk about a normal distribution in terms of its variance, which is simply the standard deviation squared, or σ2.
Here's what the two parameters of the normal distribution do:
- The Mean (μ): This parameter Controls the "Location" of the distribution.  The mean of a normal distribution is also it's median and mode (the peak of its curve).  In our model, each team's rating is equal to the mean of the team's distribution.
- The Standard Deviaton (σ) /  Variance (σ2):  This controls the "spread" the distribution.  As the standard deviation or variance of a distribution gets larger, it becomes much more likely to observe numbers that are further from the mean of the distribution.
The shorthand notation that is helpful in describing a normal distribution is: "normal(μ, σ2 )", which denotes a normal distribution a mean equal to μ  and a variance of  σ2.   
So for example, a normal (0, .1) is a normal distribution with a mean of 0 and variance of .1 (which is very small).  A normal(0,.1) distribution will mostly generate numbers very close to zero, and almost all of the numbers will fall between -1 and 1.  A normal with a mean of 0 and standard deviation of 100, on the other hand, will generate a huge range of numbers (very few of which will fall between -1 and 1, despite this range containing the "peak" of the distribution)
The image below gives a nice feel for how likely different values are, given the parameters of a normal distribution: About 70% of all numbers generated by a normal distribution will be within 1 standard deviation (1 sigma) of the mean.  About 95% of numbers will fall within 2 standard deviations, and nearly all numbers will fall within 3 standard deviations.  This general idea is known sometimes as the 3-sigma rule.

Probabilities of different values for a normal(μ,σ2). Courtesy of Wikipedia, 
In our model, we have 32 team “ratings”, each of these corresponds to a specific team’s average performance across cames.  Since we will model each team's performance across games using a normal distribution, and using each team's “rating” to describe the team's average (a.k.a. mean) performance, we will say that the “rating” for team i is μ.
To write this using the shorthand notation above: we say that for each game that the ith team, plays, they “sample” their game-performance from a normal distribution with parameters: (μi , σ2).

Don’t worry if this isn’t totally clear yet.  The pictures below will help a lot in terms of understanding this idea.

Bean Machine Prognostication
After the AFL/NFL merger, Sir Galton's infamous Bean Machine Prognosticator simply became too cumbersome for practical use.

Monday, December 12, 2011

Legal Analysis: Arizona SB 1070 (Immigration)

by richmin3000

it's going to be an interesting and influential 2012 for the united states supreme court. having already agreed to hear a constitutional challenge regarding healthcare reform, the supreme court today has announced that it will decide whether to uphold certain provisions of arizona's controversial immigration law, entitled 'support our law enforcement and safe neighborhoods act' or 'arizona sb 1070'.

the issue-at-hand is whether the arizona law conflicts with federal law and is therefore preempted and invalid. pursuant to the united states constitution, article vi, clause 2 (the supremacy clause), the united states constitution, federal laws and treaties "shall be the supreme law of the land".

the united states justice department's legal challenge of arizona sb 1070 is the first of many to run the federal court gamut necessary to reach the supreme court. governor brewer signed into law arizona sb 1070 in april, 2010; on july 6, 2010, the justice dept filed a motion for preliminary injunction which was granted in part on july 28, 2010 by the united states district court for the district of arizona. the united states court of appeals for the ninth circuit heard oral arguments on november 1, 2010 and rendered a decision on april 11, 2011, affirming the lower court's decision.

**a quick federal court primer: there are 3 levels of federal courts - the district court, court of appeals and the supreme court. the district courts are the trial courts (with 1 judge per case) and there are 89 districts in the united states. the circuit court of appeals is the first level of appellate courts (3 judges hear your appeal) and there are 13 circuits. finally, we have the supreme court and there is only 1 of those with 9 justices (not judges) who sit in on every case (unless recused).**

as with the challenge to the healthcare reform laws, we will continue to monitor the developments in this case and we will offer some new insights into the legal arguments on both sides. one of the more interesting aspects of this case is the attorney arguing on behalf of arizona, mr. paul d. clement, a bona fide superstar in the legal world. as solicitor-general under president bush, he argued on behalf of the united states in some of the more controversial cases before the supreme court (think guantanamo, torture, enemy combatants). he is also the attorney handling the legal challenge to health care reform also before the supreme court. and just for fun, he also was one of the lawyers advising the nfl and nba during their most recent labor disputes earlier this year. he's on a short list of potential republican nominees to the supreme court and he used to clerk for justice scalia back in the day. it's not a bad resume (objectively speaking that is).

Sunday, December 11, 2011

Podcast: No Hyperbole Allowed (Episode 5)

After a delay of a couple of days, Rich and Colin are back with special guest Ally Simons ( to talk about the Victoria's Secret Fashion Show, 2012 fashion trends, high-end labels at affordable prices, Macy's and the transgendered, John Huntsman, Salons v. Barbershops, and the Walking Dead/Homeland dilemma.... listen to it here.

We apologize for the long delay... apparently we still have to "work" to pay the bills. We will be back in a few days with another new episode.

0:00 - Intro (Bad Boy Remix)
5:19 - Victoria's Secret Fashion Show
17:07 - Latest Fashion Trends
21:50 - Macy's is pro-transgendered
33:20 - John Hunstman/Salon v. Barbershops
38:17 - The Walking Dead/Homeland Dilemma
42:50 - Closing (Death Row Remix - not really)

Friday, December 9, 2011

Beating The Spread: Statistical Models of NFL Power Rankings and Point Spreads (Part 2)

by Tim Rubin

**Information on this site is collected from outside sources and/or is opinion and is offered "as is" without warranties of accuracy of any kind. Under no circumstances, and under no cause of action or legal theory, shall the owners, creators, associates or employees of this website be liable to you or any other person or entity for any direct, indirect, special, incidental, or consequential damages of any kind whatsoever. This information is not intended to be used for purposes of gambling, illegal or otherwise.**
In Part 1 of this series I did a general introduction to statistical modeling for team ranking systems.  In Part 2, I will be introducing a simple “Margin-of-Victory” style model for NFL game spreads.  After a brief discussion of the model, I will show the team power ratings that the model learns, as well as its predictions for Week 14’s games.*
* Saying that a "model learns" is something of a misnomer.  What this really means is that we infer the parameters of the model that best fit the data.  Since in this case, the parameters of the model include the "Rating" of each team, it can be helpful to just use shorthand and say that "the model learns the team ratings".
An Extremely Simple Margin-of-Victory Model for NFL Game Outcomes.

First, let me walk you through a simple Margin-of-Victory model for NFL game outcomes.  We can then look at some things that we can do with this model, such as generate NFL team power rankings, and predict the outcome of upcoming games.

Here, on one line, is the essence of the model:

PointsHomeTeam – PointsAwayTeam = RatingHomeTeam –  RatingAwayTeam + HomefieldAdvantage + Error  
Pretty simple.  On the left-hand side of the equation, we have the game outcome, expressed in terms of point differential.  A positive value on the left-hand side indicates a victory for the home team, while a negative value indicates a loss (i.e. a victory for the away team).  On the right-hand side of the equation, we have the model parameters.  The model parameters are the "Ratings" for both the home and away teams, and the “homefield advantage”.

In plain English, this model states that for any NFL game, the outcome (in terms of margin of victory or defeat for the home team) is equal to the "Rating" of the home-team, minus the "Rating" of the away-team, plus the homefield advantage.

Finally, the "+ Error" that I added on right side of the equation captures the amount of error made by our model’s predictions.  This indicates that the predictions made by our model will vary in their accuracy (and will almost never be exactly correct).  But this does not indicate that it is a bad model.

In any model for NFL point differentials—no matter how good it is—there will always be some error, simply because there is a large amount of randomness that goes into the final outcome of every NFL game.  Football is a high variance sport, plain and simple.  It is a sport in which a game can be decided on a play in which a quarterback somehow eludes a mob of 300 pound men swarming around him, wildly hurls the ball down the field to a mediocre receiver that will never catch another ball in his career, who manages to make one of the greatest catches in NFL history by trapping the ball against his helmet two inches off the ground. NFL games often turn on strange, lucky, and flukey plays.  That is a big part of what makes the NFL entertaining.  But—from the perspective of predicting outcomes—that is something we call variance.  And the NFL has a lot of it.

As a general rule: if a model for NFL games—or more generally, for anything involving human behavior—doesn’t include something equivalent to an error term, then something is wrong with that model (or the person who formulated the model is delusional).

With our model, it is easy to see why we will have to have some errors, when we think about fitting the model, in the next section.

Fitting the model (intuitively)

Let’s think about how we would fit our model to the data, and what it even means to “fit” a model…

Note that, in the equation above, there are three model parameters: a “rating” for the home and away team, plus a value for the “home-field” advantage.  If we want to predict the outcome for a single game—between one home team and one away team—then we only need these three parameters.   However, to make this model apply to all games that are played in the NFL, we will actually need a “rating” for all 32 NFL teams, plus the value of the “homefield advantage”.  Once we have learned those parameters, we can then use the model to predict a point-spread for any possible matchup.

Ignoring the “Homefield Advantage” component for now, our model has 32 parameters: One “Rating” for each of the 32 NFL teams.  Given our 32 team ratings, our model makes a prediction for all of the games that have happened so far this season. 

So, suppose we start with a random numerical value assigned to each of our 32 ratings.  We could then go and look at what the predictions are for each of the game outcomes given our current parameter-values (i.e., ratings).

Example: Team Ratings and Game Predictions