I have vivid memories of getting excited about the Sunday edition of the Chicago Tribune as a kid. It listed every baseball stat for every player on every team. I think I spilled more Honey Nut Cheerios on sports pages than I actually ate. I liked playing baseball like any red-blooded American boy, but I think I liked it the same way Nate Silver does today, because it has more data points that just about anything in history.

I knew about Rotisserie baseball and how you could simulate your own games with friends, but damn if I was going to wait more than a week to get back results in the mail. I tried Strat-o-matic and Earl Weaver for Apple ][, but there was something about playing with a big group of friends that was most appealing about the potential of "fantasy" baseball.

I made up rules and manually scored a league for my friends early in high school. I can't say it made me the coolest kid in school, but it was fun. Don't worry, I still got chicks because I played trombone and was in advanced math.

League Zero

In college I met two things that changed me. The first was a dude on the track team named Jeffers who was named after a poet, was way more worldly than me, wore dog collars, and dyed my hair obscene colors on the regular. The second was the Internet.

In our college cross country coach's basement, we decided we needed something competitive (other than the running) to keep us busy for the winter. Jeffers wanted to start a fantasy basketball league from scratch. In 1994, the Internet wasn't much. But, we had "high" speed dorm connections and I'd had some luck building a joke (at first) Vlade Divac Fan Club that was featured on Yahoo!'s first What's Cool list and later on actual NBC TV.

Fantasy sports were coming into existence online, but only baseball and football were available, and they certainly weren’t mainstream. So we had to invent our own rules for basketball. We counted +1 points for the big categories (points, rebounds, assists, steals, blocks), let each team start one player at each position, have a sixth man, then two more count half-points. Two more on the roster could ride the pine. We added a one-point advantage/disadvantage for home-court. One catch: if a player didn’t play at all, the next in line would sub-in automatically (maybe the most important rule we added). If you play today, you’ll notice it’s a very crude version of the default game Sports.ws offers.

Rethinking Fantasy Sports

If you’re not versed in fantasy sports, you may wonder what the big deal is. Why so popular? Here’s my view: it’s the ultimate way to keep score. Take NFL Football and think about how many millions of hours are wasted spent watching games every Sunday. When friends watch together, the banter between plays usually revolves around what will happen next and postulating what the coaches, players, cheerleaders, trainers, and groundskeepers are thinking. There’s no score, it’s just argumentative fun. Fantasy sports is a way to keep track of this: winning is a proxy for a way to say “I’m a bigger fan than you.”

I think fantasy sports fans are underserved by mainstream rules. They’re just crappy and way too arbitrary. Let’s break down my six biggest gripes, and the guide I’ve used to create Sports.ws:

• Mainstream sites call game play “head to head” because they add up scores for a certain time period and score you against some else in your league. In fantasy football, you’re usually subbing out whoever’s not playing that week; for basketball you’re just picking players that play 4 games instead of 2. Actual good play isn’t rewarded enough.
• The default categories mainstream rules reward were seemingly selected because they’re available. In basketball, every major site counts shooting percentage for big points. You know who had a high field goal shooting percentage? Shaquille O’Neal: great player, one of the worst shooters in history. Add some value to stats if you use them. Kudos to any site that lets you customize point values, but offer meaningful stat values as a default.
• For leagues that draft, 99% of mainstream sites steer you towards doing them live, over just a few hours. I’d argue half the fun of a league is in the draft. It’s the one time of the year when everybody is equal, where anybody can win the championship. I’ve done live drafts, and they’re great, but why not at least offer the option to not rush bad picks? To make more trades mid-draft? To not force someone that can’t make it to choose auto-draft? Let leagues customize how they draft players.
• “Snake Order” drafting has been antiquated since it was invented. It’s a nice idea — give teams that get a first pick in the first round last pick in the next — but in every major sport, talent is not evenly distributed, and the first overall pick always has an even bigger advantage even with the added risk. The math is sound: Make the draft as fair as possible.
• How many sites let you have a league of more than 12 or 16 teams? Incredibly few. Don’t limit league size if you don’t have to.
• Team defense is still a thing? Seriously? Let game players identify with people, not teams or concepts.

94Feet.com

We had fun that first season, but it was a lot of work to score 82 head-to-head games manually. The next year I set out to automate things with more programming. Simultaneously, I was fortunate enough to forge a sports nerd camaraderie with Caleb, who used Pascal and Hypercard to code the first per-minute simulation. If you play today, you know that Sports.ws’ most unique feature is scoring of every minute of fantasy play.

Automated code meant it wouldn’t be much more work to host leagues for others, so I launched a public site at 94feet.com and tried to host multiple leagues. In short, it failed hard. It was absolutely crushed by the interest and I had to shut it down the first season even before it started.

It wasn’t long before I graduated, hated my first job, and quit to move in with Jeffers to try to go pro with a fantasy site.

Sports.ws

I got an offer to buy 94Feet.com in 2000 by a clothing manufacturer with the same name. I sold it for cash plus a huge assortment of amazing basketball clothing. Jeffers and I played around with the domains FinalBuzzer.com and Gametime.ws but ultimately won the Sports.ws domain from the newly minted TLD .ws registrar by convincing them “we are to fantasy sports what .ws is to .com.”

We launched a zillion mini-games, and portals for football, international soccer, hockey, and the olympics. We funded the site (and rent) with advertising dollars. Jeffers designed, our other roommate Hooshla coded some real backend stuff, and the rest of League Zero offered up insight and kept our league alive. The site took off and quickly amassed hundreds of thousands of users, but the basketball game was the most effective, successful, and we eventually narrowed the focus on that.

Things I learned quickly:

1) I had serious feature ADHD. I had some luck inventing a few games, but I should have stuck to a slimmer feature-set and communicated with users more.
2) I had no idea what I was doing from a business perspective.

Evolution, The Black Era

I had begun a hardcore job (in more ways than one) by 2001, and experimenting full-time wasn’t an option. Jeffers took another crack at a redesign and I coded the version of the game that exists today: 82-game schedule, up to 32 teams per league, per-minute scoring, playoffs with customizable brackets, awards, trash talk, trades, and voting.

By this time, our interests were to address all of the fantasy sports gripes above and to keep our own league (7 seasons in) alive. By focusing on basketball, we kept bugs at a minimum and grew faster than ever. By 2004, we were getting as much traffic and basketball teams as the biggest fantasy sites.

We call the era the black era because of the raw obnoxious black backdrop — it was a design that people complained about later saying “everybody thought I was watching porn at work.” But also, I was focused on so many other projects by then that it took a backseat at a time when it really could have taken the world by storm. In 2006, I turned it to auto-pilot for a season to focus on other things and because I couldn’t find an easy way to make a lot of money staying true to the mission.

The Renaissance

I had a hankering to inject some new ideas into the site for a while, and in 2009 overhauled it to function like it does today. I leveraged the powered of communal sharing to develop organic news feeds generated almost entirely from Tweets by journalists and players. I also broke down the wall between leagues. Chatter, sharing, and profile building kept users occupied between games. I also added one of my favorite features, the “props” buttons.

Gestures have long been a fascination of mine — user actions that require very little effort, unlike adding a comment. If you’re a user, you see these props available on draft picks, trades, and news items, but they’re built to be completely dynamic so that some day (cross fingers), you can choose your own amongst your leagues.

Exodus and The Good Point

The site has grown every year since 2008, but apart from playing with my own league, I don’t have the passion to support it the way it needs. Complicated rules mean complicated documentation, and that requires technical help. The millions of teams on the site need the TLC of someone that understands them.

I’ve had offers to buy the site to strip it for parts, mostly to mine the email lists for gambling offers. I’ve refused them to date because this is still the ultimate side passion project and the kid with the sports page would kick my ass if I killed that.

As of the first of this year, I’ve decided to sell to digital media juggernaut The Good Point. From their press release:

“What does it mean for regular Sports.ws users?
We all know that you’ve been loyal to Sports.ws for years. Now the site that you’ve been so committed to over that time will be evolving from the No. 1 fantasy basketball game on the internet, to the No. 1 most engaging fantasy sports community period. We have professional credentials here at TheGP, one of the many perks of working in news, and a team of 50+ writers both past and present delighted to give this site and this community the attention it deserves. The brilliant Patrick Butler and his team of former Sports.ws developers have created this gem that we’ve all grown to love, now we at TheGP have come together to accept the baton and complement that dream.”

Austin Kent and the staff of The Good Point have, first, been users themselves for many years. Second, the sensibilities of their content is right in line with the anti-establishment nature of the site. Lastly, the final piece of the puzzle to making the site really shine is good, original content and I’m excited to see what they can offer as I enter my role solely as end-user for the first time.

If you’re a user, I owe a huge amount of thanks to you for supporting and using the site, for sticking with it through experiments and tech hiccups, and for being willing to take a chance on the oddball rules before there was documentation. I don’t take that commitment lightly. In return, I’m hoping that handing it over to passionate people that will only improve upon it brings you solace.

I look forward to playing year after year… if just to take down Jeffers and his Manu Ginobili fascination.

One of my favorite parts of the Olympics is all the hypothetical pub-challenges I exchange with my friends.

“If you had 8 years to train and were executed if you didn’t medal, which event would you train for?”

“Which should be the first sport to go? And replaced with what?”

“If you had to sleep with a random Olympian in a certain sport, which sport would you pick?”

I like to arm myself with numbers when I come to a bar fight (go ahead, judge me), so I thought I’d try to settle the whole “Is Equestrian Elitist?“ debate.

For all summer medalists since 1988, here’s how much money the average person from the medal winning countries earn:

In my mind, these seem incredibly intuitive across the board. The more gear, luxuries, and training space required, the better richer athletes and countries do.

But it doesn’t stop there. Even within disciplines, the numbers are fairly unsurprising. Look at Track and Field:

There are a few outliers but if something requires stuff or special training, athletes from richer countries tend to do better.

So does this mean you can you compare one sport to another? One might look at these numbers and correlate race stereotypes with how difficult a sport is; I’d argue some countries do better because of the economics more than the body mechanics and genes. And in general, I think the lower the average medalist income, the more badass the medal is. Lower incomes mean less barriers mean bigger competitor pools.

Summary: If you’re starting from scratch and want a medal, don’t go into weightlifting. 10 of the bottom 17 spots are weightlifting events.

The process:

• To be clear, the averages come from macro-level country averages, not individual people.
• The income is not actually income but GDP per capita based on purchasing power parity (PPP) (source: CIA/World Bank, 2007-2011). Since accurate disposable income numbers aren’t readily available for all countries, I like this estimate for guessing how much buying power each potential athlete has to buy javelins, bikes, horses, and doping kits.
• The income is a mean, not a median, in the hopes uneven distribution does play a factor.
• Retired sports and retired countries were ignored.

Chicago was named 2011′s Most Moustache Friendly City in America. Civilians across Chicagoland can grow as much facial hair as their follicles support, now with less ridicule than ever.

If you’re a fan of Chicago sports, you may be aware of the amazing string of championships rung up by moustachioed coaches and managers. The last nine major sports championships won by Chicago teams were helmed with lip fuzz.

Exactly how much better are moustachioed managers? The breakdown since 1980, when moustaches emerged regularly:

Team
Bulls	545-193 W-L .738 pct Postseasons: 9 (100%) Championships: 6	792-1006 W-L .440 pct Postseasons: 12 (52%) Championships: 0
Blackhawks	370-294 W-L .557 pct Postseasons: 8 (80%) Championships: 1	721-728 W-L .498 pct Postseasons: 13 (62%) Championships: 0
Bears	146-118 W-L .553 pct Postseasons: 8 (47%) Championships: 1	119-121 W-L .496 pct Postseasons: 4 (27%) Championships: 0
White Sox	1151-1067 W-L .519 pct Postseasons: 3 (21%) Championships: 1	1447-1391 W-L .510 pct Postseasons: 2 (11%) Championships: 0
Cubs	569-619 W-L .479 pct Postseasons: 1 (11%) Championships: 0	1859-2018 W-L .479 pct Postseasons: 5 (21%) Championships: 0
City Total 1980-2011	2781-2291 W-L .548 pct Postseasons: 29 (33%) Championships: 9	4875-5264 W-L .480 pct Postseasons: 25 (26%) Championships: 0

Every team has performed better when led by moustache. The exception are the Cubs who do equally poorly in any condition, but they have Billy Goat, Black Cat, Vance Law, Steve Bartman and a whole bunch of other curses to offset the magic of the moustache.

While it’s obvious from the numbers that Moustaches are Moneyball v2.0, take a look how every Chicago sports champion did it:

Champion Team	Coach/Manager		Moustache?
2010 Blackhawks		Joel Quenneville The Walrus
2005 White Sox		Ozzie Guillen The Swamp Otter
1998 Bulls		Phil Jackson Coiffed Beard
1997 Bulls		Phil Jackson The Italian Plumber plus Soul Patch
1996 Bulls		Phil Jackson The Chevron
1993 Bulls		Phil Jackson The Lampshade
1992 Bulls		Phil Jackson The Selleck
1991 Bulls		Phil Jackson The State Trooper
1985 Bears		Mike Ditka Hurricane Ditka
1963 Bears		George Halas Clean
1961 Blackhawks		Rudy Pilous Clean
1946 Bears		George Halas Clean
1943 Bears		Hunk Anderson and Luke Johnsos Clean
1941 Bears		George Halas Clean
1940 Bears		George Halas Clean
1938 Blackhawks		Bill Stewart Clean
1934 Blackhawks		Tommy Gorman Clean
1933 Bears		George Halas Clean
1920 Staleys		George Halas Clean
1917 White Sox		Pants Rowland Clean
1908 Cubs		Frank Chance Clean
1907 Cubs		Frank Chance Clean
1906 White Sox		Fielder Jones Clean

And for fun, the top 50 Chicago teams since 1980:

Year	Team	Record	Pct.	Postseason	Coach/Manager	Hair	Stache?
1985	Bears	15-1	0.938	Won Super Bowl	Mike Ditka	Moustache
1996	Bulls	72-10	0.878	Won NBA Championship	Phil Jackson	Moustache
1986	Bears	14-2	0.875	Lost Quarterfinals	Mike Ditka	Moustache
1997	Bulls	69-13	0.841	Won NBA Championship	Phil Jackson	Moustache
1992	Bulls	67-15	0.817	Won NBA Championship	Phil Jackson	Moustache
2001	Bears	13-3	0.812	Lost Quarterfinals	Dick Jauron	Clean
2006	Bears	13-3	0.812	Lost Super Bowl	Lovey Smith	Clean
1998	Bulls	62-20	0.756	Won NBA Championship	Phil Jackson	Beard
2011	Bulls	62-20	0.756	Lost Eastern Conference Finals	Tim Thibodeau	Clean
1988	Bears	12-4	0.750	Lost NFC Conference Championship	Mike Ditka	Moustache
1991	Bulls	61-21	0.744	Won NBA Championship	Phil Jackson	Moustache
1987	Bears	11-4	0.733	Lost Quarterfinals	Mike Ditka	Moustache
2010	Blackhawks	52-22	0.703	Won Stanley Cup Finals	Joel Quenneville	Moustache
1993	Bulls	57-25	0.695	Won NBA Championship	Phil Jackson	Moustache
1990	Bears	11-5	0.688	Lost Quarterfinals	Mike Ditka	Moustache
1991	Bears	11-5	0.688	Lost Wildcard in Playoffs	Mike Ditka	Moustache
2005	Bears	11-5	0.688	Lost Quarterfinals	Lovey Smith	Clean
2010	Bears	11-5	0.688	Lost NFC Conference Championship	Lovey Smith	Clean
1991	Blackhawks	49-23-8	0.681	Lost NHL Division Semi-Finals	Mike Keenan	Moustache
1983	Blackhawks
1990	Bulls	55-27	0.671	Lost Eastern Conference Finals	Phil Jackson	Moustache
1994	Bulls	55-27	0.671	Lost Eastern Conference Semifinals	Phil Jackson	Moustache
2009	Blackhawks	46-24	0.657	Lost NHL Conference Finals	Denis Savard, Joel Quenneville	Clean, Moustache
1993	Blackhawks	47-25-12	0.653	Lost NHL Division Semi-Finals	Darryl Sutter	Clean
1984	Bears	10-6	0.625	Lost NFC Conference Championship	Mike Ditka	Moustache
2005	White Sox	99-63	0.611	Won World Series	Ozzie Guillen	Goatee
1983	White Sox	99-63	0.611	Lost ALCS	Tony LaRussa	Clean
1988	Bulls	50-32	0.610	Lost Eastern Conference Semifinals	Doug Collins	Clean
2002	Blackhawks	41-27-13	0.603	Lost NHL Conference Quarter-Finals	Brian Sutter	Clean
2011	Blackhawks	44-29	0.603	Lost NHL Conference Quarter-Finals	Joel Quenneville	Moustache
2008	Cubs	97-64	0.602	Lost NLDS	Lou Piniella	Clean
2007	Bulls	49-33	0.598	Lost Eastern Conference Semifinals	Scott Skiles	Clean
1984	Cubs	96-65	0.596	Lost NLCS	Jim Frey	Clean
1994	White Sox	67-46	0.593	Missed Playoffs	Gene Lamont	Clean
1996	Blackhawks	40-28-14	0.588	Lost NHL Conference Semi-Finals	Craig Hartsburg	Clean
2000	White Sox	95-67	0.586	Lost LDS	Jerry Manuel	Moustache
1990	White Sox	94-68	0.580	Missed Playoffs	Jeff Torborg	Clean
1993	White Sox	94-68	0.580	Lost ALCS	Gene Lamont	Clean
1989	Cubs	93-69	0.574	Lost NLCS	Don Zimmer	Clean
1989	Bulls	47-35	0.573	Lost Eastern Conference Finals	Doug Collins	Clean
1995	Bulls	47-35	0.573	Lost Eastern Conference Semifinals	Phil Jackson	Moustache
2005	Bulls	47-35	0.573	Lost Eastern Conference First Round	Scott Skiles	Clean
1995	Bears	9-7	0.562	Missed Playoffs	Dave Wannstedt	Moustache
2008	Bears	9-7	0.562	Missed Playoffs	Lovey Smith	Clean
1994	Bears	9-7	0.562	Lost Quarterfinals	Dave Wannstedt	Moustache
1995	Blackhawks	24-19-5	0.558	Lost NHL Conference Finals	Darryl Sutter	Clean
2006	White Sox	90-72	0.556	Missed Playoffs	Ozzie Guillen	Goatee
1990	Blackhawks	41-33-6	0.554	Lost NHL Conference Finals	Mike Keenan	Moustache
1992	Blackhawks	36-29-15	0.554	Lost Stanley Cup Finals	Mike Keenan	Moustache
1998	Cubs	90-73	0.552	Lost LDS	Jim Riggleman	Clean
2004	Cubs	89-73	0.549	Missed Playoffs	Dusty Baker	Moustache

If Theo Epstein and Jed Hoyer want to start winning, an obvious solution would be to hand over the 2012 Cubs to ex-Cub Warren Brusstar, but understandably there’s a transition phase.

This weekend, Amy Winehouse joined the likes of Jimi Hendrix, Janis Joplin, Brian Jones, Jim Morrison, and Kurt Cobain in the tragic 27 Club. Her passing was unfortunate and inspired countless musings on the curse of being 27 and a rock star. Is there a curse? Is there even a pattern? Virtually every mainstream news outlet only offered up lines like this (from WaPo):

And then there was the “27” — rock-and-roll’s most dangerous number.

Dangerous, yes. Most dangerous? A few science blogs have looked at the reason we latch on to the coincidence of these deaths, but there’s virtually nothing on the arithmetic comparing 27 to other ages.

Here’s a breakdown (by age) of the deaths of pop musicians:

The process: I took every performance artist associated with the top 100 songs of each year since 1958, plus the top 30 of each since 1950. Just over 2700 names surfaced, of which 1920 have publicly available birthdays.

Conclusion: Even though the distribution of mortality isn’t normal, 27 doesn’t stick out at all. The 45 Club is even more prolific and includes Freddie Mercury, Nat King Cole, Marvin Gaye, Ricky Nelson, and Vicky Sue Robinson.

But the six that died at 27 were all very well known… perhaps their fame offsets the low count:

The process: I used the size of each artist’s Wikipedia bio as a rough estimate of their popularity. It’s messy, but not entirely inaccurate.

Conclusion: The 27 Club starts looking pretty unique. But, by every definition of outlier and accounting for kurtosis risk, the tragedy is still not a statistically significant blip.

This, combined with the fact that many of these artists may now be more famous because they died young or at 27, implies The 27 Club may be little more confirmation bias of something we want to believe. Tragic in any case.

As a bonus, here are the biggest Wikipedia articles (used in the second chart about) of deceased pop music artists. Interestingly, six of the top 30 were 27:

	Artist	Date of Death	Age at Death	Size of Bio (bytes)
1	Michael Jackson	June 25, 2009	50	678205
2	Larry Norman	February 24, 2008	60	567654
3	Elvis Presley	August 16, 1977	42	489759
4	George Harrison	November 29, 2001	58	473953
5	Perry Como	May 12, 2001	89	459965
6	John Lennon	December 8, 1980	40	430026
7	Jimi Hendrix	September 18, 1970	27	347781
8	Amy Winehouse	July 23, 2011	27	340050
9	Frank Sinatra	May 14, 1998	82	334649
10	Brian Jones	July 3, 1969	27	274835
11	Freddie Mercury	November 24, 1991	45	267406
12	Billy Preston	June 6, 2006	59	261701
13	Linda McCartney	April 17, 1998	56	242898
14	Marvin Gaye	April 1, 1984	45	235208
15	James Brown	December 25, 2006	73	235144
16	Nicky Hopkins	September 6, 1994	50	233422
17	2Pac	September 13, 1996	25	232234
18	Aaliyah	August 25, 2001	22	228570
19	Jo Stafford	July 16, 2008	90	221186
20	Dusty Springfield	March 2, 1999	59	216142
21	Notorious B.I.G.	March 9, 1997	24	213507
22	Johnny Cash	September 12, 2003	73	212470
23	Kurt Cobain	April 5, 1994	27	195379
24	Jim Jones	November 18, 1978	47	190806
25	Ray Charles	June 10, 2004	73	185208
26	Bing Crosby	October 14, 1977	74	182754
27	Frankie Laine	February 6, 2007	93	182509
28	Jim Morrison	July 3, 1971	27	181778
29	Janis Joplin	October 4, 1970	27	176099
30	Louis Armstrong	July 6, 1971	71	174824

 

Finding out where movies are filmed is easy. IMdB and others have comprehensive databases. But a huge proportion of movies are filmed in cities like Los Angeles, Vancouver, and Albuquerque and made to look like they’re set somewhere else. A database of movie plot locations doesn’t exist yet.

Here’s my estimate of how likely each major U.S. city is to be featured in a movie plot:

		Population	Major Movies	Score	Pivotal Movies
Washington, DC	20.3x	601723	466	79292016	Mr. Smith Goes to Washington, All the President’s Men, Independence Day, Minority Report, Wedding Crashers
Las Vegas, NV	12.5x	567641	308	46016257	Casino, Ocean’s Eleven, The Hangover, Fear and Loathing in Las Vegas, Rain Main, Con Air, Bugsy
Miami, FL	10.1x	433136	175	28334637	Scarface, Goldfinger, There’s Something About Mary, Bad Boys, Some Like It Hot, Ace Ventura: Pet Detective
San Francisco, CA	7.7x	815358	666	40801201	The Rock, Vertigo, The Maltese Falcon, Interview with the Vampire, Mrs. Doubtfire, Hulk, The Game
Los Angeles, CA	7.1x	3831868	1438	176257698	L.A. Confidential, The Terminator, The Graduate, Pulp Fiction, Die Hard, Beverly Hills Cop, Crash, Blade Runner
Boston, MA	6.6x	645169	229	27621078	The Departed, Good Will Hunting, The Town, Legally Blonde, Mystic River, Gone Baby Gone, The Boondock Saints
New York, NY	4.7x	8391881	4158	255981837	The Godfather, Taxi Driver, Spider-Man, I Am Legend, Midnight Cowboy, Shaft, Annie Hall, Marathon Man, Annie, Big
Baltimore, MD	3.1x	637418	77	12678744	Slience of the Lambs, Kiss Kiss Bang Bang, Enemy of the State, Hairspray, He’s Just Not That Into You
Seattle, WA	2.7x	616627	136	10791529	Sleepless in Seattle, Say Anything…, The Fabulous Baker Boys, The Ring, WarGames, 10 Things I Hate About You
Atlanta, GA	2.7x	540922	68	9325449	Remember the Titans, Mr. and Mrs. Smith, Driving Miss Daisy, Smokey and the Bandit, Drumline
Chicago, IL	2.6x	2851268	567	47411519	The Untouchables, Chicago, Blues Brothers, Ferris Bueller’s Day Off, Home Alone, Backdraft, The Breakfast Club
Memphis, TN	2.4x	676640	54	10511964	Walk the Line, The Blind Side, The Firm, Hustle & Flow
El Paso, TX	2.3x	620456	22	9205186	Kill Bill, No Country for Old Men, Traffic, Glory Road, Viva Villa!
Philadelphia, PA	2.0x	1547297	170	20442335	Philadelphia, Rocky, The Sixth Sense, Unbreakable
Detroit, MI	2.0x	910921	113	11678371	RoboCop, The Crow, 8 Mile, Gran Turino, Grosse Pointe Blank
Denver, CO	1.6x	610345	52	6233118	The Shining, Butch Cassidy and The Sundance Kid, How the West Was Won, Dumb and Dumber
Cleveland, OH	1.4x	431369	56	3914791	Major League, American Splendor, Duplicity, The Rocker
Sacramento, CA	1.1x	466676	30	3277451	Zodiac, Coach Carter, All About Steve, Pink Cadillac, Frankie and Johnnie
San Diego, CA	1.1x	1306300	101	9167137	Traffic, Anchorman, Old School, The Lost World: Jurassic Park
Omaha, NE	1.1x	454731	18	3134616	Up in the Air, About Schmidt, Election, Omaha
Albuquerque, NM	0.9x	529219	24	3100373	Easy Rider, Little Miss Sunshine, Sunshine Cleaning, Young Guns, Albuquerque
Oakland, CA	0.9x	409189	30	2313455	Romeo Must Die, Youth in Revolt, True Crime, Jack the Bear
Portland, OR	0.8x	566143	62	3087745	Goonies, Mr. Brooks, Elephant, Mr. Holland’s Opus
Fresno, CA	0.8x	479918	11	2596661	The Karate Kid, Par II, Thieves’ Highway, Shadow of a Doubt, The Gang’s All Here
Milwaukee, WI	0.8x	605013	22	2954139	Michael Clayton, Mr. 3000, American Movie, Love Actually
Dallas, TX	0.7x	1299542	65	6070769	Places in the Heart, Office Space, Boys Don’t Cry, The X Files
Tucson, AZ	0.7x	543910	35	2507418	Public Enemies, The Matador, Romy and Michele’s High School Reunion, Hamlet 2
Phoenix, AZ	0.7x	1593659	49	6777541	Raising Arizona, Away We Go, Psycho, The Savages
Austin, TX	0.6x	786386	54	2893518	Dazed and Confused, Grindhouse, Road Trip, The Life of David Gale
Louisville, KY	0.6x	566503	20	2037377	The Insider, Stripes, The Return of the Living Dead, Fear Strikes Out
Kansas City, MO	0.5x	482299	12	1554213	Sullivan’s Travels, Caopte, Mad Money, Superman
Houston, TX	0.4x	2257926	65	5395160	Apollo 13, Urban Cowboy, Terms of Endearment, The Right Stuff
Indianapolis, IN	0.3x	807584	19	1637714	Hoosiers, Close Encounters of the Third Kind, Now and Then, The Hudsucker Proxy
Columbus, OH	0.3x	769332	13	1343134	Bye Bye Birdie, The Mothman Prophecies, Traffic, Slience of the Lambs
Oklahoma City, OK	0.3x	560333	7	926619	Thelma & Louise, Elizabethtown, Dead Bang, Christmas on Mars
Nashville, TN	0.2x	605473	60	967440	Coal Miner’s Daughter, Nashville, Hanna Montana: The Movie
Long Beach, CA	0.2x	462604	16	452222	Blood Work, Cutter’s Way, The Star, Our Very Own
Raleigh, NC	0.1x	405612	4	295142	American Hardcore, Bandwagon
San Jose, CA	0.1x	964695	7	423050	Outbreak, The Social Network
Jacksonville, FL	0.1x	813518	5	295307	It Happened One Night, Cocaine Angel
Mesa, AZ	0.04x	467157	5	112190	Suspect Zero, The Marshal of Mesa City, Stage to Mesa City
San Antonio, TX	0.04x	1373668	30	316526	The Alamo, Like Water for Chocolate, San Antonio, Cloak & Dagger
Fort Worth, TX	0.02x	727577	9	98913	The Killer Inside Me, Fort Worth, Texas, Brooklyn & Heaven
Virginia Beach, VA	0.01x	433575	2	32715	The Baxter, The Trouble with Summer
Charlotte, NC	0.003x	709441	5	12827	Shallow Hal, Juwanna Mann

Washington, D.C., for example, is 20.3 times as likely the setting for a movie plot as the average movie (per capita).

The Process

1. For every non-TV movie, I parsed review sites, Wikipedia and IMdB listings, and tried to dynamically assess which cities were referenced or featured in the movie’s plot. Some parsing was easing: Wikipedia references cities like Omaha,_Nebraska and IMdB has a robust list of user-contributed and site-monitored keywords. Other implied references were made using crude natural language processing, trained using primary Wikipedia city and state articles (landmarks were common triggers). I ignored movies that didn’t have any reviews or plot descriptions: about 10,000 made the cut.

2. For each U.S. city/state that occurred in the text (verbatim or implied), I came up with a score for the movie-city or movie-state combo loosely defined by:

Quantity of Ratings

x

Quality of Ratings

x

Box Office Performance

x

Occurrences of City/State Name

x

Proximity of City/State Keywords to Important Article Words/Beginning of Article

x

Literal vs. Implied Reference to City/State

I wanted better, more popular movies to weigh more, but not overwhelm more quintessential classics. Rain Man was as critically and commercially successful as any movie, but didn’t top the Las Vegas movies because it took place in other cities too.

3. I added up the score for all U.S. cities with a population of 400,000 or more and sorted by score per capita. Foreign cities were harder to parse because of inconsistent language.

4. All the parsing and scoring was automated and no manual edits were made, so there’s plenty of fuzz in the estimations. The Pivotal Movies were the highest scoring movies associated with each city.

By State

State scores were more abundant, but less precise. A visual interpretation:

Cheering for the Bears requires living and dying by whatever Johnny-come-lately quarterback the franchise decides is the next big thing. New Chicago quarterbacks are common — as it turns out, more frequent than any other NFL franchise since 1980:

Team	Yrs	Most Played QB	Starts	Different QBs	QBs Per Year
Chicago Bears	31	Jim Harbaugh	60	29	0.94
Cleveland Browns	28	Bernie Kosar	91	26	0.93
Arizona Cardinals	31	Neil Lomax	92	27	0.87
Carolina Panthers	16	Jake Delhomme	71	13	0.81
Dallas Cowboys	31	Troy Aikman	165	25	0.81
Baltimore Ravens	15	Joe Flacco	48	12	0.80
Tampa Bay Bucs	31	Trent Dilfer	76	24	0.77
Atlanta Hawks	31	Chris Chandler	67	24	0.77
Washington Redskins	31	Joe Theismann	83	24	0.77
New Orleans Saints	31	Bobby Hebert	69	23	0.74
Detroit Lions	31	Scott Mitchell	57	22	0.71
San Diego Chargers	31	Dan Fouts	98	22	0.71
NY Jets	30	Ken O’Brien	100	21	0.70
Minnesota Vikings	31	Tommy Kramer	82	21	0.68
Kansas City Chiefs	31	Trent Green	72	21	0.68
St. Louis Rams	31	Jim Everett	100	21	0.68
Oakland Raiders	31	Rich Gannon	67	20	0.65
Philadelphia Eagles	31	Donovan McNabb	135	20	0.65
Miami Dolphins	31	Dan Marino	235	20	0.65
Denver Broncos	31	John Elway	230	20	0.65
Tennessee Titans	31	Warren Moon	139	18	0.58
Buffalo Bills	31	Jim Kelly	160	18	0.58
Indianapolis Colts	31	Peyton Manning	160	18	0.58
Houston Texans	9	David Carr	75	5	0.56
Seattle Seahawks	31	Matt Hasselbeck	124	17	0.55
New England Patriots	31	Tom Brady	143	17	0.55
Cincinatti Bengals	31	Boomer Esiason	118	17	0.55
San Francisco 49ers	31	Joe Montana	131	16	0.52
Pittsburgh Steelers	31	Ben Roethlisberger	98	14	0.45
Jaxsonville Jaguars	16	Mark Brunell	101	7	0.44
New York Giants	31	Phil Simms	148	11	0.35
Green Bay Packers	31	Brett Favre	237	8	0.25

According to the news, Charlie Sheen’s career is in shambles. He’s facing some unhealthy distractions that may cause him to miss some work, but he’s having fun too. I don’t doubt his short-term work will be limited, but I don’t buy that his long-term prospects are gloomy.

Is Charlie Sheen’s decline the worst in Hollywood? Let’s put the fact that Charlie’s still the face of the biggest sitcom on TV aside and just look at movies to find out.

Using some math, here’s a list of the the actors whose movies are sucking the fastest. Keep in mind that an actor’s “decline” implies they have somewhere to fall from — the better they originally did, the further they have to drop.

1		Vin Diesel Decline / Year: 4.88		Early Hits: 91 – Saving Private Ryan (1998) 97 – The Iron Giant (1999) 67 – Boiler Room (2000) Recent Misses: 36 – F&F: Tokyo Drift (2006) 7 – Babylon A.D. (2008) 28 – Fast and Furious (2009)
2		Damon Wayans Decline / Year: 4.23		Early Hits: 84 – Beverley Hills Cop (1984) 88 – Roxanne (1987) 87 – Hollywood Shuffle (1987) Recent Misses: 48 – Bamboozled (2000) 9 – Marci X (2003) 9 – Marmaduke (2010)
3		Amanda Seyfried Decline / Year: 3.67		Early Hits: 75 – Nine Lives (2004) 83 – Mean Girls (2004) 53 – Mamma Mia! (2008) Recent Misses: 33 – Boogie Woogie (2009 28 – Dear John (2010) 41 – Letters to Juliet (2010)
4		Lindsay Lohan Decline / Year: 3.22		Early Hits: 79 – The Parent Trap (1998) 88 – Freaky Friday (2003) 83 – Mean Girls (2004) Recent Misses: 13 – Just My Luck (2006) 17 – Georgia Rule (2007) 7 – I Know Who Killed Me (2007)
5		Barbra Streisand Decline / Year: 3.18		Early Hits: 92 – Funny Girl (1968) 100 – On a Clear Day… (1970) 91 – What’s Up, Doc? (1972) Recent Misses: 54 – Mirror Has Two Faces (1996) 38 – Meet the Fockers (2004) 10 – Little Fockers (2010)
6		Orlando Bloom Decline / Year: 3.10		Early Hits: 92 – Lord of the Rings I (2001) 77 – Black Hawk Down (2001) 96 – Lord of the Rings II (2002) Recent Misses: 20 – Love / Other Disasters (2006) 45 – Pirates of the Carr. 3 (2007) 38 – New York, I Love You (2010)
7		Billy Burke Decline / Year: 2.95		Early Hits: 86 – Under the Skin (1997) 78 – Without Limits (1998) 60 – The Independent (2000) Recent Misses: 15 – Untraceable (2008) 27 – Twilight 2 (2009) 50 – Twilight 3 (2010)
8		Macaulay Culkin Decline / Year: 2.88		Early Hits: 56 – Uncle Buck (1989) 71 – Jacob’s Ladder (1990) 47 – Home Alone (1990) Recent Misses: 8 – Getting Even with Dad (1994) 27 – Party Monster (2003) 9 – Sex and Breakfast (2007)
9		Julie Andrews Decline / Year: 2.70		Early Hits: 100 – The Amer. of Emily (1964) 100 – Mary Poppins (1964) 100 – Thor. Modern Millie (1967) Recent Misses: 41 – Shrek the Third (2007) 58 – Shrek 4 (2010) 17 – The Tooth Fairy (2010)
10		America Ferrera Decline / Year: 2.64		Early Hits: 83 – Real Wom. Have Curves (2002) 86 – Steel City (2005) 79 – Sis of the Traveling Pants (2005) Recent Misses: 11 – Towards Darkness (2008) 61 – The Dry Land (2010) 14 – Our Family Wedding (2010)
11		Derek Luke Decline / Year: 2.62		Early Hits: 79 – Antwone Fisher (2002) 84 – Pieces of April (2003) 81 – Friday Night Lights (2004) Recent Misses: 36 – Miracle at St. Anna (2008) 26 – Madea Goes to Jail (2009) 50 – Notorious (2009)
12		David Spade Decline / Year: 2.61		Early Hits: 94 – Light Sleeper (1992) 65 – Reality Bites (1994) 1994 – PCU (1994) Recent Misses: 12 – The Benchwarmers (2006) 18 – Grandma’s Boy (2006) 10 – Grown Ups (2010)
13		Chris Klein Decline / Year: 2.38		Early Hits: 92 – Election (1999) 59 – American Pie (1999) 63 – We Were Soliders (2002) Recent Misses: 20 – The Good Life (2007) 8 – New York City Serenade (2007) 10 – Stright Fighter (2009)
14		Diane Keaton Decline / Year: 2.10		Early Hits: 100 – The Godfather (1972) 100 – Sleeper (1973) 100 – Love and Death (1975) Recent Misses: 5 – Because I Said So (2006) 8 – Mama’s Boy (2007) 22 – Mad Money (2008)
15		Katherine Heigl Decline / Year: 2.07		Early Hits: 80 – That Night (1992) 96 – King of the Hill (1993) 60 – 100 Girls (2000) Recent Misses: 14 – The Ugly Truth (2009) 29 – Life as We Know It (2010) 11 – Killers (2010)
	Check it out, Carlos is only 35!
35		Charlie Sheen Decline / Year: 1.25		Early Hits: 81 – Ferris Bueller’s Day Off (1986) 86 – Platoon (1986) 78 – Wall Street ( 1987) Recent Misses: 36 – Scary Movie 3 (2003) 37 – Scary Movie 4 (2006) 40 – Due Date (2010)

The Process

1. Rotten Tomatoes derives a score out of 100 for every movie based on the percentage of favorable reviews. They’re used for this exercise, unedited.

2. The decline per year and graphs were auto-generated. The decline is the slope coefficient for a standard linear regression, the graphs use a moving average for the trend line.

3. Only the top 500 actors are included (the 500 whose movies have earned the most at the box office), and actors must have starred in 10 movies.

Caveat

Since rate is a function of time, actors with a shorter career will have a much more volatile number. That means actresses like Julie Andrews and Diane Keaton have to work extra hard to sully all those 100′s from the beginning of their career.

The Extended List

The 50 Fastest Falling Rank Actor Fall per Year 1 Vin Diesel -4.88 2 Damon Wayans -4.23 3 Amanda Seyfried -3.67 4 Lindsay Lohan -3.22 5 Barbra Streisand -3.18 6 Orlando Bloom -3.10 7 Billy Burke -2.95 8 Macaulay Culkin -2.88 9 Julie Andrews -2.70 10 America Ferrera -2.64 11 Derek Luke -2.62 12 David Spade -2.61 13 Chris Klein -2.38 14 Diane Keaton -2.10 15 Katherine Heigl -2.07 16 Lena Headey -1.99 17 Marlon Wayans -1.89 18 Jackie Earle Haley -1.80 19 Ice Cube -1.76 20 Kate Beckinsale -1.75 21 Cher -1.63 22 Martin Lawrence -1.60 23 James Garner -1.57 24 Bette Midler -1.55 25 Jet Li -1.53 26 Breckin Meyer -1.50 27 John Corbett -1.49 28 Rufus Sewell -1.45 29 Nia Long -1.36 30 Amy Ryan -1.34 31 Carrie Fisher -1.33 32 Steve Coogan -1.29 33 Gael Garcia Bernal -1.26 34 Charlie Sheen -1.25 35 Randy Quaid -1.24 36 Ron Livingston -1.22 37 Paul Bettany -1.22 38 Edward Norton -1.21 39 Keira Knightley -1.20 40 Rutger Hauer -1.18 41 Arnold Schwarzenegger -1.18 42 Sarah Michelle Gellar -1.17 43 Seth Green -1.14 44 Jonathan Rhys Meyers -1.13 45 Jennifer Lopez -1.09 46 Sean Connery -1.08 47 Anna Paquin -1.06 48 Hugh Grant -1.05 49 Meg Ryan -1.03 50 Winona Ryder -1.03

The 50 Fastest Rising Rank Actor Rise per Year 1 Jeremy Renner 3.11 2 Jesse Eisenberg 2.4 3 Jason Bateman 1.97 4 Steve Carell 1.88 5 Vera Farmiga 1.62 6 Jonah Hill 1.59 7 Adam Scott 1.57 8 Tobey Maguire 1.56 9 Kristen Wiig 1.48 10 Piper Perabo 1.47 11 Zach Galifianakis 1.44 12 Chris Evans 1.42 13 Ashton Kutcher 1.37 14 James Franco 1.34 15 Abigail Breslin 1.25 16 Jason Schwartzman 1.23 17 Cillian Murphy 1.21 18 Johnny Knoxville 1.20 19 Kristen Stewart 1.16 20 Ryan Phillippe 1.02 21 Josh Brolin 1.02 22 Angelina Jolie 1.01 23 Ryan Gosling 0.99 24 Liv Tyler 0.96 25 Mandy Moore 0.92 26 John Krasinski 0.87 27 Guy Pearce 0.86 28 Rhys Ifans 0.86 29 Leslie Mann 0.86 30 Casey Affleck 0.85 31 Heath Ledger 0.84 32 Tea Leoni 0.83 33 Eva Mendes 0.81 34 Hilary Swank 0.81 35 Kal Penn 0.80 36 Viggo Mortensen 0.79 37 Jim Carrey 0.72 38 Jeffrey Wright 0.71 39 Paul Walker 0.71 40 Adrien Brody 0.68 41 Ryan Reynolds 0.67 42 Maggie Gyllenhaal 0.63 43 Taraji P. Henson 0.62 44 Thomas Haden Church 0.59 45 Clive Owen 0.54 46 Christian Bale 0.53 47 Mark Ruffalo 0.53 48 Dakota Fanning 0.49 49 Benjamin Bratt 0.48 50 Peter Sarsgaard 0.42

Boys’ weekend challenge: “We need a way to be able to bet on anything at anytime during this weekend’s games.”

Restrictions: we’re all flying to the same place, there are four conference semis to watch over two days, everybody needs to be able to participate at any level at any time, we’ve only got a five hour flight with in-flight Wifi to invent the game.

Challenge accepted.

292-1 payout for predicting an overturned penalty? Don’t mind if I do!

The sequel has been around forever. It’s natural for an audience to want the stories they like to continue. When storytelling became marketable through print in the 18th century, sequels became a reliable stream of income. When movies emerged a century ago, the sequel seemed like a logical fit, both as a storytelling device and as a money-maker.

So how many movies made are actually sequels? Here’s my raw estimate:

Here’s how that compares to the number of all movies:

Sequel-making is on the rise, but it’s not keeping up with overall movie releases. The cost of production is dropping, so new movies are being released all the time. More and more aren’t sequels. Yet — people are watching sequels more than ever.

Originality in 1984

The Top Grossing Movies of 1984 (Domestic) 1. Beverly Hills Cop Original $235M 2. Ghostbusters Original $229M 3. Indiana Jones and the Temple of Doom Sequel (2) $180M 4. Gremlins Original $148M 5. The Karate Kid Original $91M 6. Police Academy Original $81M 7. Footloose Original $80M 8. Romancing the Stone Original $77M 9. Star Trek III: The Search for Spock Sequel (3) $76M 10. Splash Original $70M In 1984, original screenplays dominated the box office. Even Indiana Jones and the Temple of Doom and Star Trek III were based on original screenplays despite being sequels. These are stories that aren’t based on anything except the imagination of the screenwriters’ and producers’ imagination. Not books, not comics, not video games, not old TV shows (except Star Trek). The top 10 isn’t an anomaly either. Other hugely successful originals in the top 25 included Purple Rain, Amadeus, Tightrope, Revenge of the Nerds, Breakin’, Bachelor Party, Red Dawn, the Terminator, the Killing Fields, and Places in the Heart (among others).

Originality in 2007

The Top Grossing Movies of 2007 (Domestic) 1. Spider-Man 3 Sequel (3) $337M 2. Shrek the Third Sequel (3) $323M 3. Transformers Based on TV $319M 4. Pirates of the Caribbean: At World’s End Sequel (3) $309M 5. Harry Potter and the Order of the Phoenix Sequel (5) $292M 6. I Am Legend Based on Book $256M 7. The Bourne Ultimatum Sequel (3) $227M 8. National Treasure: Book of Secrets Sequel (2) $220M 9. Alvin and the Chipmunks Based on TV $217M 10. 300 Based on Comic $211M Fast forward 23 years. Not a single movie in the top 10 is based on an original idea. Every one is the continuation of a movie franchise, or an adaptation of a book, an amusement park ride, or a reboot of a TV show.

Looking at Sequel Money for All Years

For lots of reasons — built-in audience, existing marketing streams — sequels make more money than non-sequels. Even if they take up a smaller percentage of overall movies made, they’re a much safer financial bet for studios than original movies.

The shift between 1984 and 2007 is dramatic, but is it a continuing trend? To see exactly how often these studios are going the “safe” route, I looked at revenue for the top 50 grossing (domestic) movies of each year since 1980. (The top 50 seemed like a good cut-off since 65% of 2010′s movie revenue has come from the top 50 movies. That number has been as high as 80% in recent years).

The Process – Instead of manually tabulating what movies are sequels and what aren’t (that would take forever), I set up semantic structures based loosely on IMDb‘s and Wikipedia‘s movie pages, then analyzed a combination of keywords, similar web links, and related metatags (Wikipedia, for instance, allows entries to have a “next” or “previous” in the series). A spot-check revealed a very accurate categorization — even with the subtleties — although I’m sure it’s not perfect. An example: Batman Forever was categorized a sequel, but Batman Begins a remake.

Broken down by percentage:

For another time: the yellow original section in the last five years is more than 70% book, comic, TV, and video game adaptations.

What’s Wrong with the Creep?

Sequels are profitable. Embracing this new incrementalism makes sense for studios, and you can’t fault them for sticking with what works. The ubiquity of this logic was even the basis for the satire of Hamlet 2.

There are more movies than ever to see. So what’s the problem? If I want to want some schlocky big-budget sequel, I should be able to do so without derision, right? There’s nothing wrong with going to see whatever I want!

Wrong. The internet is making it easier to stream obscure movies that weren’t available a few years ago. But there are only a limited number of full-sized theaters. The movie-going experience is something unique that hasn’t been fully replicated at home yet. Until it is, the movies that make money will flood the theaters first, and it will be more and more difficult for you to find original movies to see on the big screen and be seen in their intended original art form. Will there ultimately be a backlash and shift? Not until the market dictates it. And you can bet that Disney will continue to pump out hundreds of millions in marketing to guarantee their expensive investments like Pirates of the Caribbean are seen before anything else. Some say that sequels can be original but I think they’re recognized with a lot of awards because there’s a lack of mainstream alternatives.

One scary scenario: studios start behaving like pharmaceutical giants. 10-15 years ago, Big Pharma started making super profits from its blockbusters in a very similar way to movies do today. Pharma in one of the most profitable industries in the U.S. has spawned two trends: more money is being spent on marketing than R&D and government lobbying is growing like crazy. (I’d argue that, although government involvement is to blame for some of this; it’s a result of lobbying and not proactive anti-trust measures).

Liken Pharma R&D to movie originality, and lobbying to media promotion and studio PR. If in 10 years, the movie industry mirrored the pharmaceutical industry of today, we’ll all be slaves to monster sequels and 3D eye-rapings.

The size of home TVs and entertainment systems is going up and the cost is going down (whether that’s a good thing is worth another post). So at least the original stuff will find an audience eventually, even if it’s not on a 50 foot screen. Social media could play a role in helping indie gems bubble up to popularity. I personally think the most likely catalysts to derail the sequel gravy train are activist studios with less financial focus like Participant Productions.

But until then, there are unofficially 86 big budget movie sequels in production and no end in sight.

At least there’s a good chance I’ll get to see There Will Be Blood 2: The Legend of H.W.’s Black Gold.

A friend and I wanted to settle a stupid bet.  He said alumni from our alma mater Wesleyan had created more active TV shows than any other academic institution.  I argued it was probably USC.  Turns out we were both wrong.

Here’s the breakdown for TV program creators (developers where appropriate) by undergraduate education:

Notes:

• Only active non-reality, non-game shows are counted
• Fractions come from shows with multiple creators or creators with multiple places of origin
• Grad schools are not counted (USC ran away with this one)
• Some shows don’t make the cut because info isn’t widely available

And just for grins… by home state, province, or country:

The sample set is pretty small, but some of these shows are clearly influenced by the stereotypes of their creator’s school or home.