Cher and copyrights

This has nothing to do with statistics, but it’s too good to not post.
My friend posted a bunch of prints on Society6.com, and attempted to caption one of them “Quabbin Reservoir, Belchertown, Massachusetts”, but it was red flagged for copyright infringement. So they wrote them to see what the claimed copyright was, and this was the response:
Hi *****,
We truly do appreciate your comments and questions.
Unfortunately, in an effort to respect the rights of intellectual property owners, we are not able to support the inclusion of certain words, names, phrases, or combination thereof in artist submissions. In this particular case the term “Cher” was used in “BelCHERtown” and we are not able to support the inclusion. Please replace this word to your description accordingly. All words in your listing must be accurate and refer only to the item for sale.
We understand that this particular exclusion may be overbroad as applied to your submission, and we appreciate your patience as we continue to improve our policy and process for the benefit of the overall marketplace.
We apologize for any inconvenience.
Sincerely,
********
So obviously she couldn’t just let that go, because it’s too ridiculous. So she replied:
Hi *********,
Thank you for your reply. This is, of course, not a huge inconvenience, and I understand and empathize with the company’s preference for avoiding the creation of liabilities. I also acknowledge that there is a slippery slope argument to be made here — if you allow BelCHERtown, what’s to stop the next interloper from using words like “teaCHER,” “CHERry,” “bleaCHERs,” or worse, “debauCHERy”?
However, I would point out that the names of municipalities are generally not subject to claims for copyright infringement, and certainly not in the present context. Interestingly, Belchertown was named for former governor Jonathan Belcher, who was born in the Massachusetts Bay Colony in the early 1680s. While it is true that the town was called Cold Spring in the earliest years of its existence, the name “Belchertown” was well-established by 1946 when the former Cheryl Sarkisian was born. Even assuming that the blessed event put the world on notice that the moniker “Cher” was forever protected from infringement, these protections would have no application to Governor Belcher’s namesake town. (Home of the Orioles!)
In fact, as a direct descendant of his excellency, the admittedly unfortunately named Governor Belcher, I am keenly interested in protecting his memory and estate from those who would appropriate his good name to their own use, particularly when that use involves ass-less leather chaps. Unfortunately, the relevant statute of limitations has no doubt run, and I am left without recourse in my attempts to halt Cher’s wrongful and tasteless assumption of the name. Indeed, there is truth to the artist’s assertion that one cannot turn back time.
Again, thank you for your reply.

Sincerely,
********
Cheers

NFL Predictions – Superbowl XLIX

Total (weeks 1-17) – SU: 170-85-1 ATS: 126-124-6 O/U: 135-118-3 

Playoffs – SU: 8-2, ATS: 6-4, O/U: 8-2

Week 1 – SU: 9-7-0 ATS: 8-8-0 O/U: 13-3-0

Week 2 – SU: 10-6-0 ATS: 10-6-0 O/U: 10-6-0

Week 3 – SU: 12-4-0 ATS: 9-6-1  O/U: 8-8-0

Week 4 – SU: 7-6-0 ATS: 5-7-1  O/U: 5-8-0

Week 5 – SU: 14-2-0 ATS: 6-9-0  O/U: 9-6-0

Week 6 – SU: 11-3-1 ATS: 8-7-0  O/U: 6-9-1

Week 7 – SU: 11-4-0 ATS: 7-8-0  O/U: 8-7-0

Week 8 – SU: 11-3-0 ATS: 8-7-0 O/U: 8-7-0

Week 9 – SU: 9-4-0 ATS: 8-5-0 O/U: 4-8-1

Week 10 – SU: 9-4-0 ATS: 4-9-0 O/U: 6-7-0

Week 11 – SU: 9-5-0 ATS: 8-6-0 O/U: 7-7-0

Week 12 – SU: 10-5-0 ATS: 7-8-0 O/U: 8-7-0

Week 13 – SU: 11-5-0 ATS: 8-8-0 O/U: 7-9-0

Week 14 – SU: 7-9-0 ATS: 9-6-1 O/U: 11-5-0

Week 15 – SU: 11-5-0 ATS: 6-8-2 O/U: 10-6-0

Week 16 – SU: 8-8-0 ATS: 10-6-0 O/U: 9-7-0

Week 17 – SU: 12-4-0 ATS: 5-10-1 O/U: 6-9-1

Week 18 – SU: 3-1-0 ATS: 2-2-0 O/U: 3-1-0

Week 19 – SU: 3-1-0 ATS: 3-1-0 O/U: 4-0-0

Week 20 – SU: 2-0-0 ATS: 1-1-0 O/U: 1-1-0

New England vs Seattle

Prediction: Seahawks 25-24

Pick: Seahawks +1.5

Total: Over 49

Bob Smizik wrote something mind bogglingly stupid

This article contains one of the dumbest paragraphs I have ever seen a sports writer write [emphasis added]:

Those who love to blame everything that happens in the NFL on Roger Goodell are yelping about the fact investigators have yet to interview Brady. Trust me, he will be interviewed. In depth. The NFL has called upon excellent people to handle this investigation. It knows it must get this right after it so badly mishandled the Ray Rice investigation.

Yes, everyone who was upset with the NFL for botching the Ray Rice investigation will realize all of their anger at the NFL over the Ray Rice scandal will not have been in vain if the NFL gets the Ballghazi investigation right.  #sarcasm

Even speaking about Ballghazi and the Ray Rice situation in the same breath is insulting and ignorant.  Sports writers are the worst.

Cheers.

Here’s what @statsbylopez did on the first day of statistics class

Originally posted on StatsbyLopez:

Ample literature has gone into what teachers should do on the first day of class. Should they do an ice-breaker? Dive right into notes? Review a few example questions to motivate the course?

I don’t really have control groups to use as a comparison, but I think these two activities were helpful and engaging, and I figured it was worth passing along.

Introduction to Statistics (Intro level, undergrad)

I stole this one from Gelman and Glickman‘s “Demonstrations for Introductory Probabiity and Statistics.”

When the students come in, I split the course (appx 25 students) into eight groups. Each group was given a sheet of paper with a picture on it, and the groups were tasked with identifying the age of the subject in question. I had some fun coming up with the pictures – I went back to the 90’s with T-boz from TLC and Javy Lopez of the Atlanta Braves…

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A better place kicking measure

I was watching the football games last weekend and one of the announcers said something like “This kicker is 16/17 on the season.”  I absolutely hate this.  16/17 means nothing if you don’t factor in how long the field goals are (I’ve talked about this before here.)  So I spent a little bit of time thinking about what would be a better metric and I’ve come up with my first iteration of an improved kicking metric.  So let me introduce you to the Booting Individual Rating Objective Numeric Accuracy Statistic (B.I.R.O.N.A.S.) #awesome.

The deets

Let X_i = the random variable representing the number of points scored on the i-th field goal attempt, x_i the actual observed number of points scored on the i-th field goal, d_i = the distance of the i-th field goal attempt, and n is the total number of field goals attempted.  Then:

BIRONAS = \sum\limits_{i=1}^n x_i / \sum\limits_{i=1}^n  E[X_i|d_i]

So the only detail left to fill in is how to estimate E[X_i|d_i].  Using logistic regression and all field goal attempts from 2000-2014 the probability of making a field goal is approximately expit(5.5-.1 yards).  This is a nice formula and implies that a 20 yard field goal will be made over 97% of the time and a 30 yard field goal will be converted 92.4%.  40 and 50 yard field goals are expected to be made about 81.8% and 62.2%, respectively.  Under this model, at 55 yards, a field goal is exactly a coin flip and a 60 yard field goal has about a 37.8% chance to be made.  By multiplying these probabilities by 3 (i.e. the value of a field goal), we can get the expected value of a an attempt.  Below is a graph of distance of field goal versus the expected points of the attempt.

expPts

What does this mean?

Using these expected points we can calculate BIRONAS, which is the ratio of the total points scored on field goals to the total number of expected points scored.  Thus, BIRONAS could be interpreted as the percentage of excess points that a kicker provided to his team above an average NFL kicker.  So if a kicker has a BIRONAS of 1 it means that the kicker scored exactly the same number of points that was expected based on average kicking.  A BIRONAS of 1.25 means that a kicker score 25% more points than expected compared to an average kicker.  Likewise a BIRONAS of .75 means a kicker scores 25% fewer points than expected.  So who had a good year according to BIRONAS?

2014 BIRONAS

 
Rank Name Bironas FGpct AvgYardage n
1 GarrettHartley2014 1.19 1.00 37 3
2 SebastianJanikowski2014 1.18 0.86 44 22
3 MattBryant2014 1.17 0.91 39 32
4 AdamVinatieri2014 1.16 0.97 35 35
5 StephenGostkowski2014 1.13 0.95 36 37
6 DanCarpenter2014 1.13 0.89 38 38
7 JoshBrown2014 1.11 0.92 36 26
8 ConnorBarth2014 1.11 0.94 34 16
9 DanBailey2014 1.11 0.84 41 31
10 JustinTucker2014 1.11 0.87 38 38
11 ShaunSuisham2014 1.10 0.91 35 35
12 PatrickMurray2014 1.09 0.83 41 24
13 ChandlerCatanzaro2014 1.08 0.88 38 33
14 CodyParkey2014 1.06 0.89 35 36
15 RandyBullock2014 1.05 0.86 38 35
16 PhilDawson2014 1.05 0.81 40 31
17 RyanSuccop2014 1.04 0.86 36 22
18 KaiForbath2014 1.04 0.89 35 27
19 NickNovak2014 1.03 0.85 37 26
20 StevenHauschka2014 1.03 0.84 37 38
21 MasonCrosby2014 1.02 0.82 38 33
22 MattPrater2014 1.02 0.82 38 28
23 NickFolk2014 1.02 0.82 37 39
24 GrahamGano2014 1.02 0.82 39 39
25 ShayneGraham2014 1.01 0.86 35 22
26 GregZuerlein2014 1.00 0.80 38 30
27 JoshScobee2014 1.00 0.77 41 26
28 MikeNugent2014 1.00 0.79 39 34
29 CairoSantos2014 0.99 0.83 35 30
30 BlairWalsh2014 0.98 0.74 40 35
31 CalebSturgis2014 0.96 0.78 36 37
32 JayFeely2014 0.92 0.75 38 4
33 BillyCundiff2014 0.92 0.76 37 29
34 RobbieGould2014 0.91 0.75 37 12
35 BrandonMcManus2014 0.83 0.69 35 13
36 NateFreese2014 0.53 0.43 38 7
37 AlexHenery2014 0.31 0.20 49 5

We’re going to ignore Garrett Hartley who only had 3 attempts in 2014 and award the BIRONAS award to Sebastian Janikowski.  The “Polish Cannon” is a great example of why field goal percentage is terrible.  His field goal percentage in 2014 was around 86% whereas Adam Vinatieri  had a percentage of about 97%.  Looking at that Vinatieri had a better year, but BIRONAS has Janikowski about 2% better than Vinatieri this year.  The difference between the two kickers can clearly be seen when you look at their average yardage for an attempt: Janikowski’s – 44 yards and Vinatieri 35 yards.  Janikowski’s average kick was almost 10 yards further than Vinatieri’s.

Cowboys kicker Dan Bailey, ranked 9th, is another interesting case.  While his field goal percentage was only 84%, his BIRONAS was 1.11, tied with 3 other kickers who had percentages of 87%, 92%, and 94%.  What is holding him up?  His average attempt was from 41 yards and he made 5 of his 7 kicks from over 50 yards.

Cheers.

Going beyond the mean to analyze QB performance

Originally posted on StatsbyLopez:

A few months ago, my friend & writer Noah Davis asked me a question that was bothering him. I’ll paraphrase, but this was roughly what he said:

Does consistency matter for quarterbacks? Like would you rather have an average QB who is never really great, or a good QB who occasionally sucks?

Well, fortunately there are ways to measure performance consistency, and one of them is standard deviation. QB’s with high standard deviations in their game-by-game metrics are the less consistent ones, and visa versa.

But perhaps an even better idea than just measuring each QB’s standard deviation of a certain metric is to compare the overall distribution of performance. This can be done using many tools, and we chose density curves, which are just rough approximations of the smoothed lines that one would fit over a histogram.

The culmination of our project into looking at QB density curves is summarized here on FiveThirtyEight. In addition, I…

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NFL Picks – Conference Championship

Total (weeks 1-17) – SU: 170-85-1 ATS: 126-124-6 O/U: 135-118-3 

Playoffs – SU: 6-2, ATS: 5-3, O/U: 7-1

Week 1 – SU: 9-7-0 ATS: 8-8-0 O/U: 13-3-0

Week 2 – SU: 10-6-0 ATS: 10-6-0 O/U: 10-6-0

Week 3 – SU: 12-4-0 ATS: 9-6-1  O/U: 8-8-0

Week 4 – SU: 7-6-0 ATS: 5-7-1  O/U: 5-8-0

Week 5 – SU: 14-2-0 ATS: 6-9-0  O/U: 9-6-0

Week 6 – SU: 11-3-1 ATS: 8-7-0  O/U: 6-9-1

Week 7 – SU: 11-4-0 ATS: 7-8-0  O/U: 8-7-0

Week 8 – SU: 11-3-0 ATS: 8-7-0 O/U: 8-7-0

Week 9 – SU: 9-4-0 ATS: 8-5-0 O/U: 4-8-1

Week 10 – SU: 9-4-0 ATS: 4-9-0 O/U: 6-7-0

Week 11 – SU: 9-5-0 ATS: 8-6-0 O/U: 7-7-0

Week 12 – SU: 10-5-0 ATS: 7-8-0 O/U: 8-7-0

Week 13 – SU: 11-5-0 ATS: 8-8-0 O/U: 7-9-0

Week 14 – SU: 7-9-0 ATS: 9-6-1 O/U: 11-5-0

Week 15 – SU: 11-5-0 ATS: 6-8-2 O/U: 10-6-0

Week 16 – SU: 8-8-0 ATS: 10-6-0 O/U: 9-7-0

Week 17 – SU: 12-4-0 ATS: 5-10-1 O/U: 6-9-1

Week 18 – SU: 3-1-0 ATS: 2-2-0 O/U: 3-1-0

Week 19 – SU: 3-1-0 ATS: 3-1-0 O/U: 4-0-0

Week 20 – SU: 2-0-0 ATS: 1-1-0 O/U: 1-1-0

New England at Indianapolis 

Prediction: Patriots 29-23 (65.3%)

Pick: Colts +7

Total: Under 54

Green Bay at Seattle

Prediction: Seahawks 24-21 (60.3%)

Pick: Packers +7.5

Total: Under 46.5