2015 NFL Preview

The 2015 NFL season is almost here.  That means it’s time for my NFL preview  (2014 NFL Preview2013 NFL Season Preview)!  Cheers.


Retro is based only on games played in 2013 and heavily weighs strength of schedule.  New Orleans is ranked number 1 based on this as they finished 11-5 and played 10 teams who finished 8-8 or better.

Prosp is based on 4 years of data weighted fro recency.  It’s based on expected points with New England ranked number 1 in this measure.

Both Retro and Prosp are framed in terms of the probability of defeating and average team.

Team Retro Prosp
DEN 67 77
NE 71 77
GB 73 72
SEA 45 70
SF 66 69
PHI 59 68
NO 40 63
CAR 48 60
HOU 53 56
DAL 49 55
DET 64 54
CIN 54 53
BUF 53 52
PIT 56 51
IND 62 49
KC 59 49
ATL 42 48
CHI 34 48
BAL 62 48
WAS 37 47
SD 53 46
MIA 47 43
NYG 41 43
ARI 61 43
MIN 51 42
NYJ 35 39
STL 41 38
CLE 46 36
OAK 38 28
TEN 28 27
TB 25 27
JAC 38 21
Team WinDivison MakePlayoffs MakeSuperBowl WinSuperBowl
ARI 0.20 2.20 0.10 0.02
ATL 5.70 22.22 1.40 0.36
BAL 45.60 64.62 6.90 2.40
BUF 4.28 18.66 1.50 0.52
CAR 10.76 34.56 2.72 1.30
CHI 3.22 9.50 0.64 0.14
CIN 37.68 56.50 6.40 2.28
CLE 1.36 2.94 0.10 0.00
DAL 24.78 41.04 3.94 1.56
DEN 96.52 99.38 38.24 23.60
DET 10.32 29.16 2.18 0.78
GB 85.98 92.52 19.74 10.80
HOU 43.46 62.52 8.10 3.00
IND 54.20 70.70 8.84 3.58
JAC 0.02 0.04 0.00 0.00
KC 1.36 24.02 1.20 0.46
MIA 9.10 32.66 2.48 0.74
MIN 0.48 1.84 0.04 0.00
NE 85.22 95.26 21.14 13.34
NO 83.46 92.72 20.62 9.08
NYG 4.48 8.88 0.64 0.08
NYJ 1.40 6.62 0.50 0.16
OAK 0.00 0.06 0.00 0.00
PHI 67.02 76.36 11.26 5.52
PIT 15.36 28.76 2.62 0.94
SD 2.12 32.58 1.68 0.60
SEA 62.90 94.14 22.38 11.54
SF 36.86 87.04 13.96 7.06
STL 0.04 0.70 0.02 0.00
TB 0.08 0.80 0.02 0.00
TEN 2.32 4.68 0.30 0.00
WAS 3.72 6.32 0.34 0.14

Projected Records

Team – (Median wins) expected wins

AFC East

New England – (11-5) 11.31

Miami – (8-8) 8.06

Buffalo – (7-9) 7.20

NY Jets (6-10) 6.24

AFC North

Baltimore (9-7) 9.31

Cincinnati – (9-7) 9.00

Pittsburgh – (8-8) 7.80

Cleveland – (5-11) 5.35

AFC South

Indianapolis – (10-6) 9.53

Houston (9-7) 9.18

Tennessee (6-10) 5.54

Jacksonville (3-13) 2.73

AFC West

Denver – (12-4) 12.16

San Diego – (8-8) 8.10

Kansas City – (8-8) 7.70

Oakland – (3-13) 3.10

NFC East

Philadelphia (10-6) 10.25

Dallas (9-7) 8.74

NY Giants (7-9) 6.58

Washington (6-10) 6.29

NFC North

Green Bay (12-4) 11.54

Detroit (9-7) 8.38

Chicago (7-9) 7.03

Minnesota (6-10) 5.49

NFC South

New Orleans (12-4) 11.70

Carolina (9-7) 8.64

Atlanta (8-8) 7.99

Tampa Bay (5-11) 4.88

NFC West

Seattle (12-4) 12.05

San Francisco (11-5) 11.21

Arizona (6-10) 5.81

St. Louis (5-11) 5.09

Projected Playoffs


1. Denver

2. New England

3. Indianapolis

4. Baltimore

5. Houston

6. Cincinnati


1. Seattle

2. New Orleans

3. Green Bay

4. Philadelphia

5. San Francisco

6. Dallas

Projected Wild Card Round


Indianapolis beats Cincinnati  24-22

Baltimore beats Houston 23-20


Green Bay beats Dallas 27-22

Philadelphia beats San Francisco 23-22

Projected Divisional Round


Denver beats Baltimore 27-21

New England beats Indianapolis 29-23


Seattle beats Philadelphia 26-21

New Orleans beats Green Bay 27-26

Projected Conference Round


Denver beats New England 29-25


Seattle beats New Orleans 26-22

Super Bowl

Denver beats Seattle 25-23

Season Long Bets

Win Totals

Arizona Under 8.5 -105

Buffalo Under 8.5 -130

Denver Over 10.5 Even

Jacksonville Under 5.5 -115

Minnesota Under 7.5 +200

New Orleans Over 8.5 -145

New York Jets Under 7.5 -140

Oakland Under 6 -115

San Francisco Over 6.5 Even

St. Louis Under 8 -165

Seattle Over 11 -135

Win Division

Philadelphia +125

Green Bay -250

New Orleans +220

San Francisco +2000

New England -140

Baltimore +150

Houston +550

Denver -160


Cincinnati +2000

Denver +400

Houston +2200


New Orleans +2200

Crazy Long Shot Super Bowl Match-Up

New Orleans vs Houston +50000

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Deploy your Shiny App on AWS with a Function


Ramazon! #rstats

Originally posted on andrea cirillo's blog:

Because Afrausreceived a good interest, last month I override shinyapps.io free plan limits.

That got me move my Shiny App on an Amazon AWS instance.

Well, it was not so straight forward: even if there is plenty of tutorials around the web, every one seems to miss a part: upgrading R version, removing shiny-server examples… And even having all info it is still quite a long, error-prone process.

All this pain is removed by ramazon, an R package that I developed to take care of everything is needed to deploy a shiny app on an AWS instance. An early disclaimer for Windows users: only Apple OS X is supported at the moment.

How to use ramazon (2)

Using ramazon

As one would expect, using ramazon is a very pleasant experience, given that you just have to run a function, ramazon(), passing to it the EC2 instance public_DNS and…

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My list of interesting links from #jsm2015

I got back from #JSM2015 in Seattle yesterday.  While I was there I compiled this list of links of interesting things (talks, R packages, etc.) that I took away from JSM2015.  There are a ton of slides that I would love to add to this list (i.e. the rest of the talks from the session that @styatsbylopez organized), so if you have a link to anything like that, please send it my way and I’ll add it.


Interesting talks at JSM:

Interesting talks at JSM about Sports (with some R packages): 

Shameless self-promotion:

Interesting links from JSM:

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Jim Carrey, Please Shut Up About Vaccines

Originally posted on TIME:

Say this for the anti-vax clown car: it never seems to run out of new punchinellos to climb inside. If it’s not scientific fabulist Andrew Wakefield, he of the fraudulent study that got the whole vaccine-autism myth started, it’s Jenny McCarthy, she of the supposedly vaccine-injured son whose autism was cured in part by—yes!—a gluten-free diet because, um, gluten is bad, very bad.

After McCarthy, there was Saturday Night Live alum Rob Schneider—because when you’re looking for guidance on the wisdom of vaccines, who are you going to trust: the World Health Organization, the Centers for Disease Control and the National Institutes of Health, or the man who gave us Deuce Bigelow, Male Gigolo? I mean, hello, the movie was huge.


Now, to this group of board-certified jesters add Jim Carrey—the ex-Mr. Jenny McCarthy—who rose on July 1 in all his orange-wigged, floppy-shoed, seltzer-down-the-pants fury to condemn California…

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Battleship calculator

Originally posted on God plays dice:

C. Liam Brown has built a Battleship probability calculator, which (roughly speaking) works by finding the square which is the most likely to yield a hit given the set of hits and misses so far. You can play against it if you want. A lot of this might be said to be a web-friendly implementation Nick Berry’s analysis of the game, although analysis and implementation are two different beasts. (Funny, that keeps coming up in my day job…)

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An example of a statistically misleading MMR vaccine argument

I woke up this morning to a twitter comment about the “CDC Whistleblower Saga” from last year from one of my favorite twitter followers.  This obviously led to a conversion explaining to me that: vaccines aren’t effective, the idea of herd immunity has been debunked, they are making kids sick, and they cause autism (Vaccines Don’t Cause Autism). I should note that none of these claims have any scientific backing to them.  Other twitterers also told me that vaccines aren’t 100% effective (true; MMR is about 93% and 97% effective for 1 and 2 doses, respectively) and vaccines have side effects (also true, though side effects are rare).   But also, not a logical argument against vaccination.  I think we often forget (or in my case, never saw (thank you vaccines!)) how bad the measles really is (the measles are horrible).

And I know deep down, that no matter what I say, I’m not going to change someone’s mind on twitter.  So why do I engage in “discussions” with people like this.  I guess first, I can’t help myself.  If someone engages me first, and they are wrong, I’m going to tell them that they are wrong.  Though I’m not sure this is the best way to effectively deal with the anti-vaccine crowd (Here is how Jamelle Bouie of Slate suggests dealing with them), but I can’t help myself.  I do try not to insult or attack people, but rather their arguments.  But I find this difficult to do sometimes when I believe that these people are actively causing harm by trying to spread their anti-vaccines beliefs.  (So if I insulted you today, I apologize to you.  I should be better than that.  But I still think your ideas are pseudo-scientific cray-ball wackadoo stuff).

But, secondly, I am absolutely fascinated that people think this way.  It’s so foreign to how I think about the world.  I know people who are espousing these beliefs actually believe them in spite of the mountains of evidence against their claims.  To this end, The Atlantic wrote a really interesting article last fall about the psychology of anti-vaxers.  It’s a fascinating read.   And a bit sad with quotes like this: 

Dr. Douglas Hulstedt, a pediatrician in Monetery, California, shares Smoot’s preference for personal stories over scientific evidence. Hulstedt accepts patients who are not vaccinated. He goes even further, and recommends refusing vaccinations if a patient has a family history of autism, lupus, Crohn’s disease, or Type 1 diabetes.

“Why do I need a medical study?” he says. “If 80 percent of the parents of children with regressive autism in my practice say their child reacted after the MMR [measles, mumps, and rubella] shot, why do I need a medical study?” Hulstedt says that studies showing no link between the MMR vaccine and autism or showing that vaccines are safe and effective might have “fraud in the reportage.”

This is a medical doctor posing the question: “Why do I need a medical study?”.  That is absolutely appalling and evidence why I believe medical doctors need more statistical training before, during, and after medical school.  Statistics is a complicated subject.  Statistics is hard.  I find it is constantly difficult, and I’m supposed to be the “expert”.  But it’s just a difficult subject to tackle.  Statistics is hard.   But we need it as part of the scientific method to objectively answer medically important questions.  Like do vaccine work (Yes).

But it’s so easy to make mistakes.  As an illustration, let’s consider the plot below which shows measles deaths per 100,000 people over time.  This was sent to me by my favorite twitter follower with the (sarcastic) text:

As you can see #MeaselsVaccine [sic] is instrumental in eradicating #Measels [sic]

Screen Shot 2015-07-22 at 3.51.50 PM

They are arguing (I believe) that the death rate from measles was dropping for decades prior to the introduction of the measles vaccine, and the measles vaccine did little to lower the death rate of  measles.  So it follows that the measles vaccine isn’t as effective as science makes it out to be, therefore CONSPIRACY! #tinfoilhat #jadehelm

In all seriousness though, if you have no statistical knowledge, this might seem like a convincing argument.  And I’m sure there are a lot of smart people (and not so smart people) who could be convinced by this plot.  The problem with this is that this “analysis” is inherently trying to isolate the effect of vaccines on death rates without controlling for any other factors that are related to the death rate.  Medicine advanced quite a bit from 1840 to 1940 and the probability of dying from measles dropped considerably.  Even with no vaccines.  But that’s all this plot is demonstrating.  And it’s offering almost no evidence as to the effectiveness of the vaccine and is a case study in confounding.

I’d also argue the that graph is potentially misleading the viewer with scales.  By the time the vaccine is introduced in that graph, the line is so close to 0/100,000 that it’s hard to see the relative effect of the vaccine.  The death rate could have dropped 10% or 90% (It does drop some amount) and the viewer wouldn’t be able to tell .  The graph would be much stronger if it was zoomed in on the years 1948 to 1978.  But that doesn’t seem to be the narrative that is trying to be passed on with that graphic.

Screen Shot 2015-07-22 at 3.54.16 PM

To really get a handle on the effectiveness of vaccines, we should be looking at cases of measles rather than the measles death rate.  The graph below shows cases of measles in the US from 1954 through 2008.  The first vaccine was introduced in 1963 and a second version was released in 1968.  Notice the large and immediate drop from 1963 to 1969.  It’s possible that there could be some huge confounding effect that explains this drop, but I think it would be difficult to present a reasonable confounding effect here that would dwarf the effect of the introduction of vaccines.  The decline in measles cases was immediate and rapid.  So are vaccines effective in reducing disease?  Yes. Yes. Yes. Yes. Yes. and finally Yes.

Finally, I’ll close with this advice from the World Health Organization (WHO) an trying to persuade the anti-vaxxers:

How one addresses the anti-vaccine movement has been a problem since the time of Jenner. The best way in the long term is to refute wrong allegations at the earliest opportunity by providing scientifically valid data. This is easier said than done, because the adversary in this game plays according to rules that are not generally those of science.  This issue will not be further addressed in this paper, which aims to show how vaccines are valuable to both individuals and societies, to present validated facts, and to help redress adverse perceptions. Without doubt, vaccines are among the most efficient tools for promoting individual and public health and deserve better press.8

You can (and should) read the whole paper here.


Will 2015 be the Beginning of the End for SAS and SPSS?

Originally posted on r4stats.com:

[Since this was originally published in 2013, I’ve collected new data that renders this article obsolete. You can always see the most recent data here. -Bob Muenchen]

Learning to use a data analysis tool well takes significant effort, so people tend to continue using the tool they learned in college for much of their careers. As a result, the software used by professors and their students is likely to predict what the next generation of analysts will use for years to come. I track this trend, and many others, in my article The Popularity of Data Analysis Software. In the latest update (4/13/2012) I forecast that, if current trends continued, the use of the R software would exceed that of SAS for scholarly applications in 2015. That was based on the data shown in Figure 7a, which I repeat here:

Let’s take a more detailed look at what…

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