NFL Picks – Week 4

My record last year

Total (weeks 1-4) – SU: 39-24 ATS: 30-33 O/U: 36-26-1

Week 1 – SU: 10-6 ATS: 10-6 O/U: 11-5

Week 2 – SU: 8-8 ATS: 9-7 O/U: 9-7

Week 3 – SU: 10-6 ATS: 2-14 O/U: 7-8-1

Week 4 – SU: 10-5 ATS: 9-6 O/U: 9-6

Baltimore at Pittsburgh

Prediction: Steelers 23-21 (55.5%)

Pick: Ravens +3

Total: Over 44

NY Jets at Miami

Prediction: Dolphins 22-18 (60.5%)

Pick: Dolphins +2

Total: Under 41.5

Jacksonville at Indianapolis

Prediction: Colts 26-17 (75.0%)

Pick: Jaguars +9.5

Total: Under 48.5

Atlanta at Houston

Prediction: Falcons 24-23 (53.5%)

Pick: Texans +6.5

Total: Under 47

Carolina at Tampa Bay

Prediction: Panthers 21-19 (54.1%)

Pick: Buccaneers +3.5

Total: Over 40

NY Giants at Buffalo

Prediction: Bills 22-20 (54.9%)

Pick: Giants +6

Total: Under 46.5

Oakland at Chicago

Prediction: Bears 24-18 (66.3%)

Pick: Bears +3

Total: Under 44.5

Philadelphia at Washington

Prediction: Washington Football Team 25-24 (51.4%)

Pick: Washington Football Team +3.5

Total: Over 47

Kansas City at Cincinnati

Prediction: Bengals 22-19 (58.4%)

Pick: Chiefs +4

Total: Under 44

Cleveland at San Diego

Prediction: Chargers 25-21 (62.4%)

Pick: Browns +7.5

Total: Over 45

Green Bay at San Francisco

Prediction: 49ers 23-21 (54.3%)

Pick: 49ers +9.5

Total: Under 48

Minnesota at Denver

Prediction: Broncos 28-20 (72.8%)

Pick: Broncos -7

Total: Over 42.5

Arizona at St. Louis

Prediction: Cardinals 22-18 (60.7%)

Pick: Rams +7

Total: Under 43

Dallas at New Orleans

Prediction: Saints 28-24 (62.2%)

Pick: Saints -4

Total: Over 46.5

Detroit at Seattle

Prediction: Seahawks 25-18 (67.4%)

Pick: Lions +10

Total:  Under 43.5











Is a career is academics a disaster? It Depends.

I read this article on Slate the other day about “Quit Lit” where “Soon-to-be former academics are taking their grievances public.”  Apparently, there are plenty of disgruntled academics, and their stories make good articles.  (Just see most of Rebecca Schuman’s writing.)  But these experiences don’t hit home for me at all.  I am a very gruntled (opposite of disgruntled, right?) academic.

But if all you read was Slate, you’d think that getting a Ph.D. was a disastrous, mentally scarring  experience, which then results in devastatingly disappointing lack of jobs and having wasted years (sometimes over a decade) of your life.  So the conclusion seems to be: A Ph.D. is not worth it.

So, Greg, should I get a Ph.D.?  Well, as I like to tell my students, the answer to all statistical problems is: it depends.  And that answer also applies here (and to nearly everything in life).  First of all, speaking about a Ph.D. as one thing is highly suspect.  Ph.D.s differ vastly by discipline and institution.  A literature Ph.D. is no where near the same thing as a statistics Ph.D.  A Ph.D. from Harvard is much different than a Ph.D from Phoenix University (yes, you can really do that!).

And as for the job prospects, those also differ wildly by discipline.  My friend who has a Ph.D. in English refers to the academic job market as a “lottery” where hundreds of applicants will apply for a single tenure track position (He did get a tenure track position though!!!).  In statistics, there are plenty of academic jobs.

I think one of the biggest problems with academic jobs is that there are a lot of people in those positions who don’t really want to be in them.  It was just the logical next step. (They are smart, go to college, work hard, go to grad school, do a post-doc, and…….then obviously an assistant professorship (tenure track only of course)).

And they may feel that if they leave academics that they are a failure and that they couldn’t hack it. For some people this is surely true, but for many others, they definitely could do it, but would hate every minute of bring a professor.  Here’s the thing: People are different.  Academics isn’t for everyone.  Industry isn’t for everyone.  People are different.

I think one of the problems with academics is that the people you are getting advice from when you are doing a Ph.D. are people who chose the tenure track academic route.  So they often seem to encourage their students to pursue that path because it was right for them even if it’s not right for the student.  So I’m here to tell you, if you go and get a Ph.D. and decide not to go into academics you are not a failure by any means.  Academics may just not be for you.  And that’s ok.  People are different.

So should you get a Ph.D.?  It depends.  What field do you want to go into?  Why do you want a Ph.D.?  Do you want to be in academics?  Do you want to be in industry?  Is there a field that you love?  Can you handle 4-10 year of it?

If you want to know what it’s like to get a Ph.D. in statistics or biostatistics, check out “So you want a graduate degree in statistics?”


P.S. Here is a critique of Schuman’s writing from last fall by Charles Green who “teaches writing as a lecturer at Cornell University, where he asks students to do bicep curls on the first day so they can lift his syllabus”.

Your jaw may drop when you see Georgia Tech’s incredible new student apartment building

NFL Picks – Week 3

My record last year

Total – SU: 29-19 ATS: 21-27 O/U: 27-20-1

Week 1 – SU: 10-6 ATS: 10-6 O/U: 11-5

Week 2 – SU: 8-8 ATS: 9-7 O/U: 9-7

Week 3 – SU: 10-6 ATS: 2-14 O/U: 7-8-1

Washington at NY Giants

Prediction: Giants 23-22 (54.5%)

Pick: Washington Football Team +4

Total: Over 44

Pittsburgh at St. Louis

Prediction: Steelers 22-21 (52.5%)

Pick: Rams +2

Total: Under 47.5

San Diego at Minnesota

Prediction: Vikings 22-21 (51.6%)

Pick: Charger +2.5

Total: Under 45 PUSH

Tampa Bay at Houston

Prediction: Texans 23-18 (64.9%)

Pick: Buccaneers +6.5

Total: Over 41

Philadelphia at NY Jets

Prediction: Eagles 23-21 (54.5%)

Pick: Eagles +2.5

Total: Under 46

New Orleans at Carolina

Prediction: Saints 24-23 (50.6%)

Pick: Saints +6.5

Total: Over 44

Jacksonville at New England

Prediction: Patriots 30-17 (80.6%)

Pick: Jacksonville +14

Total: Under 48

Cincinnati at Baltimore

Prediction: Ravens 23-20 (57.2%)

Pick: Ravens -2.5

Total: Under 45

Oakland at Cleveland

Prediction: Browns 23-18 (63.1%)

Pick: Browns -3.5

Total: Under 41.5

Indianapolis at Tennessee

Prediction: Colts 24-21 (57%)

Pick: Titans +3.5

Total: Under 45.5

Atlanta at Dallas

Prediction: Cowboys 27-22 (61.7%)

Pick: Cowboys +2.5

Total: Over 45

San Francisco at Arizona

Prediction: 49ers 21-19 (53.7%)

Pick: 49ers +7

Total: Under 44.5

 Chicago at Seattle

Prediction: Seahawks 25-17 (71.1%)

Pick: Bears +15.5

Total: Under 43.5

Buffalo at Miami

Prediction: Dolphins 22-19 (58.7%)

Pick: Dolphins -3

Total: Under 44

Denver at Detroit

Prediction: Broncos 26-24 (53.5%)

Pick: Lions +2.5

Total: Over 45.5

Kansas City at Green Bay

Prediction: Packers 25-20 (65.0%)

Pick: Chiefs +7

Total:Under 49

Discretionary penalties in the NFL


Holding penalties in the nfl

Originally posted on StatsbyLopez:

As a former college offensive linemen, I’m well aware of the reputation that holding penalties have – ‘you could call one on every play’ goes the old adage.

Kevin and I wrote a paper, recently appearing in JQAS, in which we looked at the rates of NFL penalties. Specifically, we wanted to address how rates fluctuate over the course of the game.

Quick summary: the rates of discretionary penalties in NFL games are hugely correlated with time.

Here’s my favorite plot, where, letting OHR be the holding rate on run plays, OHP the holding rate on pass plays, and DPI the defensive pass interference rate, we compare versus game minute (1 through 60). These rates are adjusted for play and game characteristics, and given per 1000 plays along with 95% confidence limits.

Model estimated penalty rates by game minute. DPI: defensive pass interference. OHP: offensive holding on pass plays. OHR: offensive holding on running plays Model estimated penalty rates by game minute. DPI: defensive pass interference. OHP: offensive holding on pass plays. OHR: offensive…

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NFL Picks – Week 2

My record last year

Total – SU: 18-14 ATS: 19-13 O/U: 20-12

Week 1 – SU: 10-6 ATS: 10-6 O/U: 11-5

Week 2 – SU: 8-8 ATS: 9-7 O/U: 9-7

Denver at Kansas City

Prediction: Broncos 25-22 (57.4%)

Pick: Broncos +3

Total: Over 42

Houston at Carolina

Prediction: Panthers 22-20 (56.8%)

Pick: Texans +3

Total: Over 40.5

Tampa Bay at New Orleans

Prediction: Saints 28-20 (71.7%)

Pick: Buccaneers +11.5

Total: Over 47

New England at Buffalo

Prediction: Patriots 25-22 (57.5%)

Pick: Patriots +0

Total: Over 45

Arizona at Chicago

Prediction: Bears 23-20 (56.8%)

Pick: Bears +1

Total:  Under 45

San Diego at Cincinnati

Prediction: Bengals 24-21 (58.9%)

Pick: Bengals -3

Total: Under 45.5 

St. Louis at Washington

Prediction: Rams 24-19 (64.5%)

Pick: Rams -3.5

Total: Over 41

Miami and Jacksonville

Prediction: Dolphins 22-19 (59.1%)

Pick: Jaguars +7

Total: Under 41.5

Tennessee at Cleveland

Prediction: Browns 23-19 (60.4%)

Pick: Browns +0

Total: Under 41.5

Detroit at Minnesota

Prediction: Vikings 22-21 (52.2%)

Pick: Lions +3

Total: Under 44

Atlanta at NY Giants

Prediction: Giants 25-23 (56.1%)

Pick: Giants +3

Total: Under 50.5

Baltimore at Oakland

Prediction: Ravens 22-19 (59.5%)

Pick: Raiders +7

Total: Under 43

Dallas at Philadelphia

Prediction: Eagles 27-24 (58.0%)

Pick: Cowboys +5

Total: Under 55

San Francisco at Pittsburgh

Prediction: Steelers 22-21 (50.8%)

Pick: 49ers +6.5

Total: Under 45

Seattle at Green Bay

Prediction: Packers 23-22 (51.6%)

Pick: Seahawks +4

Total: Under 48.5

NY Jets at Indianapolis

Prediction: Colts 24-19 (64.0%)

Pick: Jets +7.5

Total: Under 47

NFL Picks – Week 1

My record last year

Total – SU: 10-6 ATS: 10-6 O/U: 11-5

Week 1 – SU: 10-6 ATS: 10-6 O/U: 11-5

Pittsburgh at New England

Prediction: Patriots 28-23 (65.5%)

Pick: Steelers +7.5

Total: Under 52

Green Bay at Chicago

Prediction: Packers 25-23 (55.5%)

Pick: Bears +7

Total: Under 49

Kansas City at Houston

Prediction: Texans 22-20 (56.7%)

Pick: Texans +0

Total:  Over 41

Cleveland at NY Jets

Prediction: Jets 21-19 (55.7%)

Pick: Browns +3

Total: Under 40

Indianapolis at Buffalo

Prediction: Bills 23-22 (51.0%)

Pick: Bills +2.5

Total: Under 45

Miami at Washington

Prediction: Washington Football Team 23-22 (53.1%)

Pick: Washington Football Team +3.5

Total: Over 43.5

Carolina at Jacksonville

Prediction: Panthers 22-18 (61.3%)

Pick: Panthers -3

Total:  Under 41.5

Seattle at St. Louis

Prediction: Seahawks 22-18 (61.3%)

Pick: Rams +4.5

Total: Over 40.5

New Orleans at Arizona

Prediction: Saints 24-23 (53.9%)

Pick: Saints +1

Total: Under 48.5

Detroit at San Diego

Prediction: Chargers 23-22 (52.3%)

Pick: Lions +2.5

Total: Under 46

Tennessee at Tampa Bay

Prediction: Buccaneers 21-18 (57.2%)

Pick: Titans +3

Total: Under 41

Cincinnati at Oakland

Prediction: Bengals 22-19 (59.5%)

Pick: Bengals -3

Total: Under 43

Baltimore at Denver

Prediction: Broncos 27-21 (66.9%)

Pick: Broncos -4.5

Total: Under 49

NY Giants at Dallas

Prediction: Cowboys 25-21 (61.9%(

Pick: Giants +7

Total: Under 51

Philadelphia at Atlanta

Prediction: Eagles 25-24 (50.3%)

Pick: Falcons +2.5

Total: Under 55

 Minnesota at San Francisco

Prediction: 49ers 24-17 (70.0%)

Pick: 49ers +2.5

Total: Under 41

Open peer discussion: An alternative to closed peer review by Peter Hoff, University of Washington

Originally posted on :

 Journals and their alternatives

Our current journal publication system has traditionally served two purposes: dissemination and peer review. Regarding dissemination, the journal system arguably does a good job with some things (special issues, discussion pieces, copy-editing), but does a poor job with others (open-access, time to publication, article updating). Happily, we have alternative ways of disseminating our research. For example, the arXiv provides an updatable, open-access article platform that appears publicly within days of article submission. Furthermore, posting an article on the arXiv does not preclude it from being published in a journal. In this way, the arXiv can be seen as a complement to the journal system, rather than just an alternative or replacement.

The standard argument against replacing the journal system with something like the arXiv has been that the journal system provides peer review. However, the journal peer review system is deficient in many ways and, unlike…

View original 726 more words

A reflection of my first year on the tenure track

I have now officially lived in Chicago-land for over a year, and I’m beginning my second academic year at Loyola University Chicago.  After one full academic year, I can say that I still absolutely love it:  I love the department, I love the students, I love Chicago.  I’ve also learned quite a bit in the past year both academic and non-academic.  So here it is.  My advice to someone and thoughts on the first year of the tenure track:

  • Find out who can help you with administrative stuff.  Every university everywhere is going to have some arcane system of paper work for getting reimbursed or when you want to purchase something.  Then you have to send it to the right person.  And you’ll always send it to the wrong person.  I’ve worked at two state schools (Umass and UConn) and, while it’s better at a private school, you’ll never escape the paper work.  So find out who knows what they are doing and let them help you.  We have an amazing administrative assistant (I love you Agnes!) who helps me with all of my reimbursements and purchasing paperwork. I didn’t realize this for about 6 months, which is why (at least in my mind) I got nothing accomplished the first semester.  Paperwork is the worst.
  • Advertise what you do for research to students.  No matter what you do, some student will be interested in it (well maybe not all areas of research, but most).  And the good ones will come ask if they can help.  Just because they are interested.  Let them help.  It’s a win-win for everyone as long as the student is good.  The real keys are (1) figuring out which students are good and (2) picking an appropriate project for the level the student is at.  These are skills that you won’t learn in grad school or at a post-doc.  You just sort of have to figure this out as you go along (like so many other things in academics.)
  • Go to as many department/college/university events as you can.  Even if you think some of these events are corny (and some of them will be), just go.  Go and just meet people.  You never know who you’ll meet at these things.   Or who you’ll be introduced to.  In my day-to-day life in the department there are countless professors who I never get to interact with for whatever reason.  They may have a totally opposite schedule than me, they may be in an entirely different field (i.e. analysis, number theory, any of the other math fields), they may just be trying to avoid other people.  But this might be your only opportunity to meet the really interesting people in your department.  And everyone likes meeting really interesting people.  I met a collaborator who I am currently writing papers and a grant with at a joint math/anthropology department event called “bacon and booze”.  (Science seems to be based on booze.  Data and booze. Quote me on that: The two most important ingredients in science are data and booze.  Is there an event called “Data and Booze”.  If not, I’m starting one. #ramblingOver)
  • Where ever you end up, find the local R/python/etc. users group and attend meetings.  For me, this is the Chicago R Users Group (CRUG or ChicagoRUG).  I’ve been to a handful of meetings and I’ve met a bunch of really interesting people from both academics and industry.  And I’ve learned a lot about R.  I’ve even been asked to present twice (#linesForTheCV).
  • #lifeAdvice “I can’t do that, I’m not [blank]” This doesn’t really have to do with my job, but it’s something I learned in the last year.  I remember when I was a kid, I’d always think “I can’t do that, I’m not [blank]”.  The blank could be a baseball player or a musician or a skateboarder or a programmer or a business major.  But the thing is, no one IS anything.  If you want to be something, just do it even if you aren’t that thing.  For me this is most relevant in my art in the last year.  Since I’ve moved to Chicago I’ve been submitting my art to shows and I keep getting accepted.  I was even invited to do an entire show of my work, which was up for a weekend this past summer.  So I’m an artist because I say I’m an artist.  And whatever you want to do, you are that just because you say you are that (I mean don’t be delusional about this, you’re not the president of the USA.)  But if you want to be an author, don’t let the fact that you aren’t an author stop you from being an author.  If you want to be a musician, don’t let the fact that you can’t play music stop you.  If you want to be a statistician, don’t let the fact that you don’t have a statistics degree stop you from doing that.  The internet will teach you everything you need to know.  You just need to practice.  Everything is made up.  Be whatever you want.
  • I had the opportunity of being on two search committees my first year.  I probably wouldn’t advise this if you had a choice, but I was grateful that I was able to participate on two search committees in my first year.  I was on a computer science search committee and a statistics search committee.  Being involved in such important decision making in my first year really made me feel like my department (and the CS department) valued my opinion.  I also cherished the opportunity to be involved in having input into the direction the department would move in in the next decade.  There are currently 4 tenured/tenure track statisticians in the department and three of them have been hired in the last two years.  This is an incredible opportunity for me to really have a lot of input in the direction that a statistics program will move in next few years.  (I hope I don’t screw it up.)  But I immediately have the chance to try and fix all of the things that I thought were wrong or not perfect about my undergraduate/graduate experience (which overall was excellent!)  Usually a new professor does not have these opportunities to change a program untiul many years into their academic careers.  I’m really excited to have this much influence at the beginning of my career.  (And again, I’ll try not to blow it.)
  • Your are going to be tired on Friday night.  When I was a grad student, time was absolutely unlimited.  At least that’s how it felt.  Want to spend a week learning about web scraping?  No problem!  That dissertation can wait!  As a post-doc, I had a little bit less time as there was a project that always needed to be worked on, but I also didn’t have any teaching or service related responsibilities.  Now, as an assistant professor, I still have to do research, but just throw teaching two classes a semester and departmental service (like two search committees!), and you can see that time is not on only no longer unlimited, it’s almost non-existent.  (I’m writing this in a rare free moment during the semester).  Mike Lopez (a.k.a @statsbylopez) had this to say about being tired: “My major issue was getting enough rest, especially the first semester. If I had to do things over again, I would’ve recognized that you can’t keep up the same research pace you had when you were in grad school or a post-doc. As a grad student or a post doc, you have the time nearly every day to devote to major projects.  That time just doesn’t exist when you also have to teach and advise. Time for faculty should be a zero-sum game; roughly fix the hours, and let things fall where they fall. To interrupt sleep, social life, or family responsibilities is a major mistake that many make in their first year (me being one of them).”
  • Finally, students have lots of personal problems and sometimes you might need to help.  Remember what it was like being 20?  It’s not easy.  Girl problems, boy problems, family problems, money problems, school problems, life problems.  When you are 18/19/20/21/22, you’ve got these problems.  And sometimes you might be the person that a student feels comfortable talking to about it.  Or, because a student either misses class or assignments, you may end up  hearing about their troubles.  I was not prepared for this, and I’m still not exactly sure how to deal with it.  I guess my best advice is to just sit and listen.  Sometimes that’s what people need.  Just someone to listen.  Other times students tell me about problems that they are going through, and I went through the exact same thing.  Without going into details here, I think it’s really helpful to know that someone went or is going through what you are going through.  I know it made me feel better when I was in college, and I hope that I can do that for someone else.


Regression or Reversion? It’s likely the latter

Originally posted on StatsbyLopez:

With interest in statistical applications to sports creeping from the blogosphere to the mainstream, more writers than ever are interested in metrics that can more accurately summarize or predict player and team skill.

This is, by and large, a good thing. Smarter writing is better writing.  A downside, however, is that writers without a formal training in statistics are forced to discuss concepts that can take more than a semester’s work of undergraduate or graduate training to flesh out. That’s difficult, if not impossible and unfair.

One such topic that comes up across sports is the concept of regression toward the mean. Here are a few examples of headlines:

Regression to the mean can be a bitch! (soccer)

Clutch NFL teams regress to the mean (football)

Beware the regression to the mean (basketball)

30 MLB players due for regression to the mean (baseball)


View original 1,294 more words


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