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Nate Silver’s Grantland-esque website FiveThirtyEight debuted today. It includes an interactive graphic (utilizing seven different predictor variables) featuring every team’s chances to reach every round of March Madness, including their odds of winning it all. How do those odds stack up to the current (as of 11:59 pm Eastern Time) odds given by Sportsbook.com? Best of all, which teams make for the best bets, even if they are unlikely to win the championship, because Vegas is giving them even longer odds than they deserve? Find out below!

Positive Expected Value Bets to Win the NCAA MB Tournament

Team Sportsbook Odds-to-One Break Even Percentage FiveThirtyEight Percent Chance to Win Bet Expected Value
Arizona 8 11.11% 13.00% 1.89%
Villanova 30 3.23% 4.00% 0.77%
Ohio St 75 1.32% 2.00% 0.68%
Creighton 40 2.44% 3.00% 0.56%
Duke 20 4.76% 5.00% 0.24%
Michigan 35 2.78% 3.00% 0.22%
Kentucky 50 1.96% 2.00% 0.04%

For a bet of Arizona’s odds to be profitable (in the long run), it needs to cash 11.11 percent of the time; Nate Silver and his team estimate that the Wildcats’ true odds lie at 13 percent. That gap produces the largest positive expected value in the field. Which teams should you avoid putting money on to go all the way?

Worst Expected Value Bets to Win the NCAA MB Tournament

Michigan St 5.5 15.38% 6.00% -9.38%
Syracuse 18 5.26% 1.00% -4.26%
Iowa St 30 3.23% 1.00% -2.23%
UCLA 35 2.78% 1.00% -1.78%
Florida 5.5 15.38% 14.00% -1.38%
Wisconsin 22 4.35% 3.00% -1.35%
Wichita St 15 6.25% 5.00% -1.25%
Kansas 13 7.14% 6.00% -1.14%

Everyone loves Michigan St, and that is precisely why they are overvalued. The Spartans are good, and it is entirely possible they could win; it is even possible that Silver’s methodology has sold them short, perhaps by not accounting for Tom Izzo. But it is also true that at the five-and-a-half-to-one odds currently offered, the Spartans have to win 15.38 percent of the time for this bet to be profitable. Even if their true probability of a championship is around ten percent, or even 12 percent, it would still not be a good idea to put money on Michigan St. With the fabled winning streaks, Syracuse and Wichita St also make appearances on this list of worst bets in the tournament.

This is not to say that these teams are guaranteed to lose. But if you place bets with a negative expected value, while you may win one or two, over time you are guaranteed not only to lose, but to lose money.

Real quick, let us all remember three things that have happened in hockey, real or fictional.

First, this:

shot hits goalpost

Up 2-1 with less than two minutes left, Team USA’s clear to the empty net hits the post. Canada would score seconds later, and win 3-2 in OT.

Second, this:

The Mighty Ducks, starring Emilio Estevez & Joshua Jackson

It was, Charlie! It was so cool.

And lastly, remember this:

The United States and Canadian houses in Olympic village play an impromptu pickup game in a parking lot. They tied 4-4.

And read Katie Baker’s short, beautiful piece on Grantland about this absolute gem of a game. The game pictured above is why we play hockey, why we play sports, why we have Olympics. Not to have gold medals. (Though yeah, gold medals are nice.) To “have fun out there” does not even do it justice. “Teamwork”, “chemistry”, “bettering yourself”, and “friendship” might not do it justice either. But the moment itself does.

We should not tell the American women who lost today to be happy with their silver medals (though it is fine if they are). They practiced hockey for years, they played the game, their emotions–whatever they are–are legitimate. (Duh.) In fact I fully support Team USA in stabbing with their skates anyone who tells them to “just get over it” because “it’s just a game” or “you still got silver” or whatever. F@#$ those people

As a civilization of human beings, we have progressed from arguing whether it is okay for girls and women to play hockey to arguing whether cheering “Let’s go girls!” at a women’s hockey game is inappropriate and sexist.1 Which is cool, but still kinda missing the point.

NBC analyst and former USA Hockey player (and gold medalist in 1998) Natalie Darwitz remarked after the loss that it was a great game of and for hockey, not just “women’s hockey”. And she is totally right. It was a great game, a great championship. Someone had to lose. Unfortunately, yet again it was the United States. But God it was so cool. Let us all remember, and be proud, of that.


  1. Man [addressing hockey team of male adults]: “Alright, let’s go boys! Defense!” Woman: “Cool.”
    Man [addressing hockey team of female adults]: “Alright, let’s go girls! Defense!” Woman: “HOW CAN YOU BE SO SEXIST AND DEMEANING? THEY ARE INDEPENDENT WOMEN, NOT HELPLESS GIRLS, OKAY?” Man: “I, uhh…neither said nor implied nor thought that. (Thanks for projecting your gender stereotypes of men onto me!)” 

Yesterday Kirk Goldsberry, contributor at Grantland, put up an impressive, super cool, honestly just exciting piece about the recent developments in the use of big data in the NBA. The timing was perfect. In writing “DataBall“, Goldsberry essentially said, “Hey Colin! Feeling down without football? Need to be caught up with the awesome stuff happening in basketball, which you know much less about? Here is this great article about the current state, and future potential, of basketball analytics.”

Cool, right? This is a positively exhilarating time for the NBA, or at least for nerds that like basketball, or at the very least NBA executives and coaches that like winning. The spread of improved technology will make this season the source of the most data of any year in basketball history. Though a little daunting to work with, the data are useful, useful useful useful, in a practical sense, and can quantify essential, previously quantifiable player traits and skills.

A Game of Big Men, and Bigger Data

SportVU technology, of STATS LLC, tracks the movements of every player on the court. Constantly. Precisely. Using SportVU, one can make a replica of the play like the one below: Tony Parker’s assist to Kawhi Leonard’s game-winning three in a February 13th, 2013 game San Antonio played in Cleveland. Check out Goldsberry’s article to actually run the continuous animation; these are just screen shots.

Screenshot (91)

Screenshot (92)Screenshot (93)

Very cool. Very very cool. This animation exists because Cleveland was one of fifteen NBA teams to have SportVU cameras and what-not installed in their arena last season. But before this season, the NBA installed SportVU in every arena. This season there will be data on everywhere a player goes, every time he steps on the floor. Last season, in half the games, SportVU produced 800 million player locations. The academics Goldsberry speaks of, presenting at MIT’s Sloan Sports Analytics Conference later this month, used 93 gigabytes for their work, using only last season’s data.1 And there will be twice as much this year, and in years to come.

Future Discoveries of NBA Basketball

It is foolish to quantify a player’s talent with a single number, and equally foolish to think the league won’t learn a lot from this newfound data. Which players create the best possessions for their teams? No longer must this question be gleaned at from filtering assists, shot charts, player efficiency ratings, and whatnot. Using SportVU tracking, with over a billion player positions every season, different floor positions can be assigned probabilities for different outcomes. A player open under the basket with the ball has a high probability of scoring two points; without the ball, a slightly less probability depending upon his probability of receiving a pass; closely guarded but with the ball, a slightly different probability based on his shooting percentage, his defender’s prowess, etc.

Every game state–the location of all ten players and the ball, in relation to each other and their respective baskets–has an expected point value for both teams. If this sounds like how Brian Burke of Advanced NFL Stats determines his Win Probability Calculator, his Fourth Down Calculator, etc, that is because it is fundamentally the same analysis.2 But because basketball is a little simpler, having only ten players out there, and because each NBA team has around a hundred possessions every game, and plays 82 games a season, the analysis can become way more complicated awesome.

With a good estimate of the expected value of every game state in the NBA, breakdowns like the following become possible:

Leonard Options Expected Values

Staring at this graphic is just… enthralling. Look at it! Aaaauuuggghhhhhh!!! Who is the best passer in basketball? No longer is “Well, Player X has the most assists” or “Player Y has the lowest turnover rate” or “A team’s most points per possession come when Player Z mans the point” the best we can do. Now, we can say “Player A made a pass that maximized his team’s expected possession value on 94 percent of his passes, while Player B made a pass that maximized his team’s value on only 82 percent of his passes.” No, it does not have the same ring to it, but damn, is it sexy?!?

Forget passing, which player is the best decision maker?3 In the above image, Leonard’s shot probability is tied for the most likely outcome, even though by expected possession value it is his worst option; passing to anyone would be better. And these numbers can be tailored to individual players! With substantial sample sizes for individual players over the course of thousands of possessions, we do not have to settle for “Shooters make X percent of open corner threes”, we can specify that “Player Y makes Z percent of open corner threes”. Which point guard best understands his teammates’ strengths and weaknesses, the differences between the starters and the subs, etc? Which big man has the most added value when getting the ball at the post?

The answers to these questions will not be 100 percent perfect; a single number, or even a combination of numbers is unlikely to completely quantify what a player brings to the floor.4 But we will know more than we do now, in really an unprecedented way. The moral of the story is: with football over, I will be watching more basketball now. What perfect timing.


  1. Ninety-three gigabytes is a lot of data. For some perspective: the entire Lord of the Rings trilogy, the extended editions, on BluRay at 1080p definition, is 12 gigabytes. The complete series of Breaking Bad is 40.3 gigabytes. Of course, the Library of Congress estimates that they add five terabytes of content a month, or 93 gigabytes every 13 hours or so. 
  2.  The Markov model, kids. Read about it. 
  3. My money is on Lebron James. Remember in the 2011 Finals when everyone shamed James for passing off to Wade in clutch moments? Maybe those passes were smart! Or, maybe they actually were terrible. From now on, with unprecedented objective data, we will have a much better idea. 
  4. Obligatory reminder: if a player sells out the house night after night, does his owner still care as much about his less than optimal expected possession value added? Probably not. 

Way back in early September, before the NFL season began, Robert Mays and Bill Barnwell, staff writers at Grantland, ran a podcast in which they made numerous preseason predictions for fun. At the suggestion of one of them during the podcast, I took down their predictions, but then never sent them in to Grantland, and the notes have just been sitting in my Gmail drafts folder for months. No more!

While Bill Barnwell posted an excellent feature about the Super Bowl champion Seattle Seahawks, quarterback Russell Wilson, and the best contract in football (click here for my own analysis of the best contracts in football; Wilson is certainly up there), I thought it would be fun to analyze Barnwell, and Mays, to determine who made the better predictions this season. Is one more expert than the other? Check it out!

Player Props

Adrian Peterson: 5.1 Yards per Carry
Barnwell: Under
Mays: Over
Result: Under (4.5)

Say it with me now: regression to the mean. Not just to the league average (about four yards) but to Peterson’s own. Peterson has now had two seasons over 5.1 yards per carry and five seasons under it; among those five seasons, even the highest clip is only 4.8.

J.J. Watt: 15.5 Sacks
Mays: Under
Barnwell: Under
Result: Under (10.5)

Regression scores again! J.J. Watt still put up the best season of any defensive player (highest graded by Pro Football Focus on the season), but 16 sacks is a lot for anyone, especially a 3-4 defensive end whose primary job is not rushing the passer.

John Abraham: 8.5 Sacks
Barnwell: Under
Mays: Under
Result: Over (11.5)

A surprisingly impressive season from the 35-year-old.

Andrew Luck: 4,200 Passing Yards
Mays: Over
Barnwell: No bet, agrees with logic, no strong feelings.
Result: Under (3,822)

This result is even more impressive given that Trent Richardson was so completely ineffective (averaged 2.9 yards per carry) this season.

Andrew Luck: 15.5 Interceptions
Barnwell: Over
Mays: Agree? Recognizes similar logic.
Result: Under (9)

The kid is good. Although he did rank 20th among 27 quarterbacks in accuracy percentage (per PFF). Maybe something to consider next season.

Geno Atkins: 9.5 Sacks
Mays: Over
Barnwell: Under
Result: Under (6)

Atkins went down on Halloween against the Dolphins and that was it for his season. He only played in seven games. Injury risk is always something to consider.

Greg Olsen: 775.5 Receiving Yards
Barnwell: Under
Mays: Under
Result: Over (816)

Curious. Prior to 2012, Olsen’s most receiving yards in a season were his 612 with the Bears in 2009. But with Cam Newton he has now gone over 800 twice.

Matt Forte: 1,000.5 Rushing Yards
Mays: Over
Barnwell: Under, later SWITCHES to Over
Result: Over (1,339)

A wise move as Forte put together his first back-to-back 1,000-plus yard seasons. Staying healthy, and amassing the most rushing attempts since his rookie season, certainly helped.

Charles Tillman: 4.5 Forced Fumbles
Barnwell: Under
Mays: No bet (“HOW DARE YOU?”)
Result: Under (3)

Injury cashes Barnwell in again, as Tillman went down only halfway through the season. But this merely underscores that a lot of things have to go right for a corner, or really anyone, to force five fumbles in one season.

Doug Martin: 8.5 Touchdowns
Mays: Over
Barnwell: Pressed by Mays, only says “8 or 9”
Result: Under (1)
Poor Doug Martin’s fate was sealed the instant I drafted him in the first round of my fantasy draft, as he went out for the season in Week 6. Still, a low total nonetheless.
Aaron Rodgers: 38.5 Touchdown Passes
Barnwell: Under
Mays: Over
Result: Under (17)

More injuries, more problems for the over bets. Although in the eight games in which he played more than a few snaps, he only threw 17, not quite on pace for over. Presumably offensive rookie of the year running back Eddie Lacy had something to do with this.

Robert Griffin III: 575.5 Rushing Yards
Mays: Over
Barnwell: Over
Result: Under (489)
Washington never seemed to recover from their opening day track meet against the Eagles, and Griffin missing the final three games while “sort-of-injured-but-healthy-enough-to-play-but-what’s-the-point” was pretty hard to predict.
Jason Babin: 9.5 Sacks
Barnwell: Under (Barnwell’s lock)
Mays: Under
Result: Under (7.5)

Barnwell’s lock comes through, although this must have been a little exciting as Babin came on and posted 5.5 in December.

Brian Orakpo: 7.5 Sacks
Mays: Over (Mays’ lock)
Barnwell: Over
Result: Over (10)

Mays’ lock comes through, as Orakpo went over on December 1st against the New York Giants. He is pretty good when healthy, it would seem.

Alex Smith: 3,350 Passing Yards
Barnwell: Over
Mays: Over
Result: Under (3,313)

This was about Andy Reid being allergic to running backs in Philadelphia and Alex Smith having Dwayne Bowe to throw to, and, uh, hold that thought…

***BONUS BET***
Dwayne Bowe: 1,000.5 Receiving Yards
Mays: Over
Barnwell: Over
Result: Under (673)

Ouch.

Dez Bryant: 92.5 Catches
Mays: Over
Barnwell: Under
Result: Over (93)

Ladies and gentlemen, put your hands together for Grantland staff writer Robert Mays! Really must have sweat it too, with Bryant needing eight receptions in Week 17 against Philadelphia, without Kyle Orton at quarterback. But he eked it out!

Danny Amendola: 950.5 Receiving Yards
Barnwell: Over
Mays: Over
Result: Under (633)

Ouch. Injuries, injuries, injuries… Amendola missed four games.

Tavon Austin: 7.5 Touchdowns (Rushing, Receiving, & Return)
Mays: Over
Barnwell: Over
Result: Under (6)

To be fair, Austin would likely have gone over if it had not taken the Rams coaching staff to realize that Austin was on their team (and/or the Rams special teams return unit had not felt the need to hold or block in the back on approximately 371% of their returns).

Richard Sherman: 4.5 Interceptions
Barnwell: Under
Mays: Under
Result: Over (8)

An incredible result. Among corners who played half or more of their teams’ snaps, Sherman was targeted only 58 times in the regular season, the sixth-fewest. He led the league with eight interceptions. Sherman grabbed a pick every 7.25 throws into his coverage, easily tops in the league. Goodness.

***Mays’ Prediction***
Jonathan Banks leads the league in interceptions.

Very, very difficult to predict; Banks finished tied for 15th with several players, having recorded three interceptions.

Chris Long: 10 sacks
Mays: Over
Barnwell: Over
Result: Under (8.5)

Maybe next year; PFF awarded him 10 sacks, as they do not punish players by awarding only a half-sack when another teammate also gets to the quarterback. Also Long’s 46 quarterback hurries were tied for fourth at his position this season. He generated pressure, but sometimes it takes a little luck (or a bad opponent quarterback) to get the sack numbers.

Josh Freeman: 16.5 Interceptions
Barnwell: Under
Mays: Under
Result: Under (4)

What a year for Freeman, in all the bad ways. Ugh. And he actually was right about on pace, throwing one in every game he played.

Clay Mathews: 11.5 Sacks
Mays: Over
Barnwell: Over
Result: Under (7.5)

Injuries, oh the injuries…

Russell Wilson: 3,400 Passing Yards
Barnwell: Over
Mays: No bet
Result: Under (3,357)

Yeeesh. Perhaps if Percy Harvin had played more than 40 snaps…

Division Winners & Playoffs

First Pick in 2014 Draft
Barnwell: OAK
Mays: OAK
Result: HOU
AFC East
Barnwell: NE
Mays: NE
Result: NE
AFC North
Barnwell: PIT
Mays: CIN (PIT last!)
Result: CIN (PIT actually 2nd, 8-8 and ahead of the 8-8 Ravens)
AFC South
Barnwell: HOU
Mays: HOU
Result: IND
AFC West
Barnwell: KC
Mays: DEN
Result: DEN
AFC Wildcards
Barnwell: DEN, CIN
Mays: KC, BAL
Result: KC, SD
NFC East
Barnwell: NYG
Mays: DAL
Result: PHI
NFC North
Barnwell: GB
Mays: GB
Result: GB
NFC South
Barnwell: TB
Mays: TB
Result: CAR
NFC West
Barnwell: SEA
Mays: SF
Result: SEA
NFC Wildcards
Barnwell: SF, DET
Mays: CHI, SEA
Result: SF, NO
AFC Champion
Barnwell: DEN
Mays: DEN
Result: DEN
NFC Champion
Barnwell: SEA
Mays: GB
Result: SEA
Super Bowl Champion
Barnwell: SEA
Mays: DEN
Result: SEA

Ladies and gentlemen, I give you Grantland staff writer Bill Barnwell! Correctly predicting BOTH conference champions AND the Super Bowl champions! Barnwell would be the very first one to tell you that this result is due to his prodigious SKILL and not at all due to luck…oh right, he is Bill Barnwell. He is not foolish.

Player & Coach Statistical Leaders and Awards

Defensive Player of the Year
Barnwell: Clay Matthews
Mays: Geno Atkins (15 sacks!)
Result: Luke Kuechly

To be fair, Kuechly totally did not deserve this award at all. (Maybe more on that later.) But then with injuries, neither did their selections.

Passing Leader
Barnwell: Peyton Manning
Mays: Andrew Luck
Result: Peyton Manning
Rushing Leader
Barnwell: Trent Richardson
Mays: LeSean McCoy
Result: LeSean McCoy
Receiving Leader
Barnwell: Calvin Johnson
Mays: Dez Bryant
Result: Josh Gordon (in only 14 games!)
First Pick in 2014 Draft
Barnwell: Jadeveon Clowney
Mays: Teddy Bridgewater
Result: TBD
Offensive Rookie of the Year
Barnwell: Tavon Austin
Mays: Eddie Lacey
Result: Eddie Lacey
Defensive Rookie of the Year
Barnwell: Kenny Vaccaro
Mays: Alec Ogletree
Result: Sheldon Richardson
Coach of the Year
Barnwell: Andy Reid
Mays: Greg Schiano
Result: “Riverboat” Ron Rivera
Most Valuable Player
Barnwell: Russell Wilson
Mays: Aaron Rodgers
Result: Peyton Manning

Final Scorecards

Overall, Mr. Mays went a respectable 15/44, 34% on his picks. In pure props he was 6/20, while going 2/9 on individual awards and statistics and 7/15 on team predictions. Mr. Barnwell edged him slightly, going 17/46, 37%. Barnwell went 9/22 on player props, 1/9 on individual awards and statistics, and 7/15 on team predictions. When both Mays and Barnwell agreed, they went 8/21, 38%; 5/15 on props, 0/1 on awards, and 3/5 on teams.

The lesson? Predictions are not easy, and your gut feeling will not take you very far, even if you know a lot. Consider that among their player predictions, designed to have a 50-50 chance, both Mays and Barnwell did worse than a coin flip. This is not because they do not know about football (they know a great deal), but because this stuff is hard, and luck plays a bigger role than anything else. Nonetheless, one can see why a comprehensive examination of numbers might come in handy.

If you see a supposed pundit make a prediction, remember to think twice before buying in. Okay, that is not news. But remember to ALWAYS think twice (and a third time, a fourth, etc), even when the pundits are quite knowledgeable, even when the predictors tell a story that you find logically sound, and perhaps most importantly, even when you already agree with them (and especially when they are not being 100% serious, à la Mays and Barnwell). Or at the very least, think twice before you put any money down.

Reading one of Andrew Sharp’s whimsical #HotSportsTakes yesterday on Grantland (which I still agreed with in parts), I discovered this tweet from Detroit Tiger’s ace/2011’s American League Cy Young winner/Kate Upton’s “on-again” boyfriend Justin Verlander1:

Just a quick aside: Verlander’s current profile-description-about-me thing on Twitter reads: “My house smells of rich mahagony and I have many leather bound books! -Anchorman”. Hold on, I have to go follow Justin Verlander on Twitter. Back. Wait, I have to tell Justin Verlander that he’s misspelling mahogany.

Okay, back. Hang on, that’s not even the quote, Ron Burgandy mentions the leather-bound books first… one sec.

Okay, all set. Remember this?

It’s David Ortiz, at home in the playoffs, hitting a game-tying grand slam off Tigers’ closer Joaquin Benoit with two outs in the eighth inning. What if after circling the bases, Ortiz had screamed this into the cameras:

I’M THE BEST HITTER IN THE GAME! WHEN YOU TRY ME WITH A SORRY PITCHER LIKE BENOIT, THAT’S THE RESULT YOU GON’ GET! DON’T YOU EVER TALK ABOUT ME! DON’T YOU OPEN YOUR MOUTH ABOUT THE BEST!

Questions to consider: Would baseball be better or worse? How quickly would Ortiz be forced to apologize (if at all)? Would people like to see him suspended? Would people be concerned he was taking performance enhancing drugs that also affected his behavior? (And wouldn’t people find this outburst just f#!%ing bizarre?)

Setting aside those questions, one thing is clear: if Ortiz had said that, Verlander, and presumably other Tigers pitchers, would throw 95+ mile-per-hour fastballs at Ortiz’s head.2 Baseball has a built-in corrective mechanism for such antics. There is a league office to fine players, the risk of ejection, and rarely a beaning will start a full-scale brawl, but players learn to keep their showboating to a minimum, lest they spend the rest of their at bats fearfully ducking for cover.

This got me thinking about other sports. As a fan, my general perception is that the NFL and NBA have more rude, childish behavior than the NHL and MLB. Perhaps this has more to do with the physical consequences–both their magnitude and their ease of execution–players can inflict on one another.

Such physical dangers are relative to the baseline for the sport. Football is quite physical already. The little catfights NFL players get into, while perhaps drawing a 15 yard penalty, do not pose any additional pains. Basketball has a lot of contact, although less forceful. Shoving matches and the occasional punch are more or less on par with the physicality in the game itself.

Baseball and hockey are different. In MLB, physical contact is very rare, while pitchers can easily brush off opponent hitters. Hockey has a lot of hitting, though it’s often more fluid than in other sports. A hockey player is a scarred player, but longer-term tears and breaks are less common.

Like baseball, hockey has a built-in mechanism for players who show off, taunt, and are generally just dicks. Enforcers and fighting are ingrained in hockey, and the two-minute penalties that come with them are frequently off-setting. NHL fighting penalties are usually not worse than any other penalty, and the players who receive them are usually less skilled. The NHL and MLB have milder deterrents for hitting back.

Is there actually less needless, immature, look-at-me, plain obnoxious behavior in MLB and the NHL than in the NFL and NBA? It’s hard to say. An exhaustive study would take a lot of thought and work. Googling a few things and drawing sketchy conclusions, however, is not too hard.

The table below shows the number of Google hits for some particular search terms, as of earlier this afternoon, January 23rd, 2013. The search terms are on the left; for example, the NFL search terms were “nfl”, “nfl football”, “nfl playoffs”, “nfl taunting”, “nfl taunts”, “nfl trash talk”, and “nfl insults”.

Trash Talk by Sport, Google Hits, 1/23/2014

[league] + “…” NFL NBA NHL MLB
[league only] 118,000,000 186,000,000 52,300,000 105,000,000
[league + sport] 553,000,000 360,000,000 189,000,000 136,000,000
Playoffs 126,000,000 98,000,000 61,400,000 87,700,000
~([league + sport] – Playoffs)~ 427,000,000 262,000,000 127,600,000 48,300,000
Taunting 515,000 241,000 147,000 132,000
Taunts 533,000 295,000 162,000 189,000
Trash Talk 13,800,000 11,600,000 956,000 1,050,000
Insults 2,710,000 1,900,000 392,000 296,000

Neat-O! While “taunting” and “taunts” did not yield much difference, there are many times as many hits for “trash talk” and “insults” in the NFL and NBA than in the NHL and MLB. Might that be conflated by the fact that some leagues are more or less popular than others? That is why I have included baseline numbers for each league. How about the fact that MLB is in the off-season currently, while the NBA and NHL are in full swing, and the NFL’s popularity is likely peaking as Super Bowl XLVIII nears?

Those are valid concerns, also this is not a scientific study in any way. To maybe-sorta-kinda get an idea, here are the Google hits for each sport’s “trash talk”, as a percentage of the playoffs-adjusted number of Google hits for [league + sport].3

Trash Talk by Sport, Google Hits Percentage, 1/23/2014

[league] + “…” NFL NBA NHL MLB
Trash Talk 3.23% 4.43% 0.75% 2.17%

There you have it! Football and basketball have to put up with more of this nonsense than hockey and baseball because it is easier for hockey and baseball players to punch back, with more bite, and fewer punishments from their leagues’ offices. From an individual (or microeconomic) perspective, running your mouth is more costly in the NHL and MLB than in the NFL and NBA.

As much as I might respect Sherman as a football player, and loath his (un)professional conduct, I have got to hand it to the Stanford communications major. He is really good at what he does. In the span of just a few hours he gave us this:

NFC Championship - San Francisco 49ers v Seattle SeahawksAnd this:

Screenshot (89)The adage “If you don’t have anything nice to say, don’t say anything at all,” is there, if you want it. But in this case, I choose another old favorite: hate the game, not the player.


  1. Tagline for this already-extensively-titled post: “the intersection of Andrew Sharp, Cy Young, Kate Upton, and Justin Verlander”. Catchy, right? 
  2. If not immediately, in the midst of a tight playoff game, then later in the series during a game that was in hand, or certainly in a game this coming season. 
  3. Ie, the number of hits for “nfl football” minus the number of hits for “nfl playoffs”. Why this number? It scales better than other figures to the number of hits for “trash talk” and “insults” across all four sports, and more importantly, WHY NOT
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