Decision Science News

DOES IT MATTER WHERE IT IS KICKED? ANALYSIS OF OVER 10,000 ATTEMPTS

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In our third of not one, not two, but three posts on kicking a football in the NFL, we take on a reader question of whether the stadium / home team matters for making a field goal. We pulled up the data on every field goal since 2002 (over 10,000) of them and plotted the probability of scoring as a function of the stadium in which the field goal was kicked. The results are above. Bars are +/- 1 standard error.

Is it a statistically significant effect? Apparently so:

Pearson's Chi-squared test
X-squared = 49.9556, df = 31, p-value = 0.01693

ADDENDUM 1

We had a request to see the broken down by home team or visiting team kicking. Here you go:

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ADDENDUM 2:
Owing to the generosity of the great reader / Scottish economist Adam Smith (see comments), we now have the stadiums broken down by door (indoor or outdoor). The gray stadiums are either convertible (Houston) or have a small hole in the roof (Dallas).

Correlation is not causation, but it sure does seem plausible that having an indoor stadium helps the kicker.

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APPENDIX
To decode the team names, use this list:

ARI: Arizona Cardinals
ATL: Atlanta Falcons
BAL: Baltimore Ravens
BUF: Buffalo Bills
CAR: Carolina Panthers
CHI: Chicago Bears
CIN: Cincinnati Bengals
CLE: Cleveland Browns
DAL: Dallas Cowboys
DEN: Denver Broncos
DET: Detroit Lions
GB: Green Bay Packers
HOU: Houston Texans
IND: Indianapolis Colts
JAX: Jacksonville Jaguars
KC: Kansas City Chiefs
MIA: Miami Dolphins
MIN: Minnesota Vikings
NE: New England Patriots
NO: New Orleans Saints
NYG: New York Giants
NYJ: New York Jets
OAK: Oakland Raiders
PHI: Philadelphia Eagles
PIT: Pittsburgh Steelers
SD: San Diego Chargers
SEA: Seattle Seahawks
SF: San Francisco 49ers
STL: Saint Louis Rams
TB: Tampa Bay Buccaneers
TEN Tennessee Titans
WAS: Washington Redskins

Figure 1 Data:

         
Stadium   Miss Hit
ARI         67 292
ATL         45 257
BAL         45 322
BUF         61 253
CAR         51 269
CHI         64 295
CIN         53 293
CLE         56 266
DAL         53 281
DEN         50 303
DET         34 285
GB          72 274
HOU         53 271
IND         55 308
JAC         64 252
KC          58 285
MIA         58 286
MIN         35 267
NE          63 282
NO          48 285
NYG         59 271
NYJ         50 260
OAK         72 297
PHI         51 300
PIT         65 259
SD          48 258
SEA         55 285
SF          51 289
STL         50 284
TB          58 256
TEN         63 288
WAS         70 254

Figure 2 Data:

ARI     Home_Team_Kicks         32 152
        Visiting_Team_Kicks     35 140
ATL     Home_Team_Kicks         24 142
        Visiting_Team_Kicks     21 115
BAL     Home_Team_Kicks         23 187
        Visiting_Team_Kicks     22 135
BUF     Home_Team_Kicks         36 129
        Visiting_Team_Kicks     25 124
CAR     Home_Team_Kicks         23 125
        Visiting_Team_Kicks     28 144
CHI     Home_Team_Kicks         32 150
        Visiting_Team_Kicks     32 145
CIN     Home_Team_Kicks         28 162
        Visiting_Team_Kicks     25 131
CLE     Home_Team_Kicks         20 130
        Visiting_Team_Kicks     36 136
DAL     Home_Team_Kicks         30 138
        Visiting_Team_Kicks     23 143
DEN     Home_Team_Kicks         23 154
        Visiting_Team_Kicks     27 149
DET     Home_Team_Kicks         17 152
        Visiting_Team_Kicks     17 133
GB      Home_Team_Kicks         39 144
        Visiting_Team_Kicks     33 130
HOU     Home_Team_Kicks         32 131
        Visiting_Team_Kicks     21 140
IND     Home_Team_Kicks         19 166
        Visiting_Team_Kicks     36 142
JAC     Home_Team_Kicks         36 131
        Visiting_Team_Kicks     28 121
KC      Home_Team_Kicks         29 127
        Visiting_Team_Kicks     29 158
MIA     Home_Team_Kicks         32 141
        Visiting_Team_Kicks     26 145
MIN     Home_Team_Kicks         11 138
        Visiting_Team_Kicks     24 129
NE      Home_Team_Kicks         30 164
        Visiting_Team_Kicks     33 118
NO      Home_Team_Kicks         22 129
        Visiting_Team_Kicks     26 156
NYG     Home_Team_Kicks         27 138
        Visiting_Team_Kicks     32 133
NYJ     Home_Team_Kicks         26 138
        Visiting_Team_Kicks     24 122
OAK     Home_Team_Kicks         29 150
        Visiting_Team_Kicks     43 147
PHI     Home_Team_Kicks         25 176
        Visiting_Team_Kicks     26 124
PIT     Home_Team_Kicks         34 149
        Visiting_Team_Kicks     31 110
SD      Home_Team_Kicks         23 134
        Visiting_Team_Kicks     25 124
SEA     Home_Team_Kicks         19 145
        Visiting_Team_Kicks     36 140
SF      Home_Team_Kicks         25 156
        Visiting_Team_Kicks     26 133
STL     Home_Team_Kicks         22 142
        Visiting_Team_Kicks     28 142
TB      Home_Team_Kicks         30 136
        Visiting_Team_Kicks     28 120
TEN     Home_Team_Kicks         28 155
        Visiting_Team_Kicks     35 133
WAS     Home_Team_Kicks         39 124
        Visiting_Team_Kicks     31 130

Figure 3 Data: See Figure 1 data plus the comments

Graphs were made in R using Hadley Wickham’s ggplot2 package. Pointer to the data can be found at our previous post.

CHANGE THE WORLD IN A GOOD WAY

We received a letter from the Financial Services Authority in the UK. Sounds like some great job opportunities for readers of this website:

We have just posted job openings for behavioural economists and a behavioural psychologist, working in the economics department here at the FSA. The deadline for applications is Wednesday 13th February.

These job opportunities may be of interest to your readers or your students.

Behavioural economics role: http://www.i-grasp.com/fsa01/?newms=jj&id=39970&newlang=1

Behavioural psychology role: http://www.i-grasp.com/fsa01/?newms=jj&id=39972&newlang=1

Not familiar with the FSA you say? Learn all about here

And while we have your attention about behavioral econ jobs in the UK, you may be interested in the Behavioral Insights Team (aka Nudge Unit). Right now they are offering research fellowships for late-stage PhD students and Postdocs.

EVERY NFL FIELD GOAL SINCE 2002

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ADDENDUM: This Decision Science News post was covered by Bloomberg TV!

We looked at NFL punts before on Decision Science News. That’s old news. Field goals are the new hotness and Super Bowl Sunday is coming up, so let’s look at a kicker’s chances.

We’ve taken the same database and looked at the probability of getting the ball through the uprights depending on the yard line from which the kick starts. The result is above.

O data, you are so beautiful sometimes.

Those in need of a handy formula for the sidelines might want to use the following approximation. Let D be the yard line you are on:

• If D>50, you will miss
• Otherwise, your probability of making a field goal is about 1-.0004*D^2

We tried to fit a logit model, but it wasn’t pretty.

Speaking of beauty, one big data lesson is that data are more beautiful when binned. Here’s the more raw version:

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It seems coaches are aware of the maximum distance at which they have a prayer of making a field goal, and don’t even try otherwise:

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Graphs were made in R using Hadley Wickham’s ggplot2 package. Pointer to the data can be found at our previous post.

ADDENDUM:

Reader Anders made a chart that looks at the count of good and bad field goals by distance (yard line + 17):

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ADDENDUM
We changed the model we had originally posted for one with just 1 free parameter. Here’s the fit:

Click to enlarge

ADDENDUM

Some folks have asked if some of the longer attempts are noise. It appears so. Here are the descriptions of the attempts from behind the 50 yard line. On a regular attempt, the distance of the field goal is the yard line plus 17, so the case where the yard line is 86 and the field goal distance is suppose to be 68 yards, for example, seems to be a typo.

24TH SUBJECTIVE PROBABILITY, UTILITY, AND DECISION-MAKING CONFERENCE

The European Association for Decision Making invites you to attend its next biannual
24th Subjective Probability, Utility, and Decision Making Conference (SPUDM
24), which will be held at IESE Business School – University of Navarra in Barcelona,
Spain, on August 18-22, 2013.

Submissions of paper abstracts, poster abstracts, and proposals for workshops are
invited on any topic in basic and applied judgment and decision making research.

Deadline for all submissions is March 8, 2013.

The organizing committee is pleased to announce that the conference will feature the
following invited speakers:
• Timothy D. Wilson, University of Virginia, USA
• Colin F. Camerer, California Institute of Technology, USA
• Robin Hogarth, Universitat Pompeu Fabra, Spain
• Ralph Hertwig, Max Planck Institute for Human Development, Germany

The call for papers is available at:

http://www.iese.edu/en/events/OtrosEventos/SPUDM24/Home/Home.asp

Attending this meeting will also be an opportunity to discover Barcelona, one of the
most unique and architecturally distinctive cities of the world. Barcelona is the capital
of Spain’s Catalan region, which has produced a number of the world’s most prominent
artists including Pablo Picasso and Salvador Dalí. The architect Antoni Gaudí also left
his indelible mark on the city through a number of remarkable buildings such as La
Sagrada Familia, La Pedrera, and La Casa Batlló.

We look forward to seeing and welcoming you in Barcelona!

The local organizing committee:

Elena Reustkaja (IESE, Spain)
Mario Capizzani (IESE, Spain)
Franz Heukamp (IESE, Spain)
Robin Hogarth (UPF, Spain)

NEW DECISION JOURNAL FOCUSES ON THEORETICAL ISSUES

Good news for Judgment and Decision Making researchers: a new APA journal entitled Decision is getting ready to launch. Be on the lookout for its web site in the next weeks. Here are some details.

Decision
A journal for research on judgment and decision making

Decision is a multidisciplinary research journal focused on a theoretical understanding of neural, cognitive, social, and economic aspects of judgment and decision-making behavior. Decision will publish articles on all areas related to judgment and decision- making research including probabilistic inference, prediction, evaluation, choice, decisions under risk or uncertainty, and economic games. The journal will publish articles that present new theory or new empirical research addressing theoretical issues or both. To achieve this goal, Decision will publish three types of articles: long articles that make major theoretical contributions, shorter articles that make major empirical contributions attacking important theoretical issues, and brief review articles that target rapidly rising theoretical trends or new theoretical topics in decision making.

Editorial Board

Colin Camerer
Nick Chater
Peter Dayan
Gerd Gigerenzer
Reid Hastie
Mark Machina
Amnon Rapoport
Roger Ratcliff
Valerie Reyna
Jim Sherman
Richard Shiffrin
Josh Tenenbaum
Joyce Wang
Thomas Wallsten
Angela Yu

ANALYSIS OF THE PUNTING DECISION BASED ON 11 YEARS OF PLAY-BY-PLAY DATA

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The site reddit told us about data on every single NFL (U.S. National Football League) play since 2002. We read it in and did an analysis of punting. The results are beautiful.

North Americans metaphorically say they’re “going to punt” and people not familiar with American and Canadian Football can be unclear about what they mean. In the UK, “punt” means many things (such as pushing a boat with a stick or visiting a prostitute), but not the North American metaphorical sense.

Loosely put, to punt means to play it safe by not trying something risky that could leave you worse off. In football, it’s kicking the ball into a better field position instead of staying where you are and fighting because if you stay and fight you might lose and your opponent will have you in a bad field position. You punt when your back is to the wall, on your last “down” or opportunity to do something before the opponent automatically gets the ball. Higher risk alternatives to punting are passing or running with the ball. When close to the opponent’s goal, an alternative is trying to kick a field goal. [I realize that’s a bad description but maybe we can crowdsource a better one].

We made some graphs of the punting decision and found some beauty in these strategic behavior data.

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Thanks to the reddit post and Advanced NFL Stats, as well as to the open source R language and Hadley Wickham‘s ggplot2.

DEADLINE JANUARY 10, 2013

Via Michael Birnbaum:

In this conference, investigators present topics that might be empirical or theoretical, involving questions that may be basic or applied, and studying theories that may be normative or descriptive. Topics deal with judgment and decision theory, basic and applied, either normative or descriptive, and are NOT limited to Bayes theorem or Bayesian statistics.

Daniel Cavagnaro and Michael Birnbaum will co-host the 51st meetings in Fullerton February 14-16, 2013. There will be a reception on the evening of February 14, with meetings on Friday and Saturday, Feb 15-16. Note that Monday, February 18 is “President’s Day”, which is a holiday in the USA and might permit an extra travel day for many American participants.

We hope you will accept the invitation to attend, which has more information on the conference.

Deadline to submit papers is January 10, 2013. On-Line Paper Submission and Registration for the 2013 Conference.

A Bit of History of these Meetings
The Bayesian Research Conference was hosted for 41 years by Ward Edwards, and for many years it was held each year in mid-February at the Sportsman’s Lodge in Studio City, California. These meetings have been very high in quality, extremely diverse in topic and approach, and under Ward’s leadership since 1961, they established a pattern of extremely fruitful, constructive, and congenial meetings. Starting with the 42nd meeting, the event has been held on the campus of California State University, Fullerton. picture of Ward Edwards and Jim Shanteau

The 44th and succeeding conferences are now the Edwards Bayesian Research Conference, honoring Ward Edwards, a founder of the field of behavioral decision research, who passed away in early 2005. In this picture, Ward and Jim Shanteau converse at an earlier meeting. As Ben Newall (2009) has written, “Ward Edwards is commonly regarded as the Father of behavioral decision making. In two papers, The Theory of Decision Making published in Psychological Bulletin in 1954 and Behavioral Decision Theory published in the Annual Review of Psychology in 1961 he founded and then subsequently gave a name to the field. Then in 1963 he introduced psychologists to Bayesian thinking with his paper Bayesian Statistical Inference for Psychological Research published in Psychological Review. These truly seminal papers have become part of the folklore of the field and their influence can hardly be overstated.” To those accomplishments, one might add his paper in the Psychological Review on Subjective probabilities inferred from decisions in which Edwards introduced the idea of probability weighting functions that depend on the configuration of consequences, an idea that is central to Prospect Theory, which was co-authored by his former student, Amos Tversky, and Daniel Kahneman.

The 2013 meeting will honor the memory of R. Duncan Luce, a leader in the field of behavioral decision making and a regular participant in this conference, who passed away in 2012.

ADDING DECISION-MAKING TRAINING INTO TRADITIONAL ACADEMIC COURSES

Chris Spetzler contributes this article, recently published in PLoS ONE, which finds that putting decision-making training into a a U.S. history course raised competence in both the material of the course, as well as in decision-making.

Title:

Improved Learning in U.S. History and Decision Competence with Decision-Focused Curriculum

Citation:

Jacobson D, Parker A, Spetzler C, Bruine de Bruin W, Hollenbeck K, et al. (2012) Improved Learning in U.S. History and Decision Competence with Decision-Focused Curriculum. PLoS ONE 7(9): e45775. doi:10.1371/journal.pone.0045775

URL:

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0045775

Abstract:

Decision making is rarely taught in high school, even though improved decision skills could benefit young people facing life-shaping decisions. While decision competence has been shown to correlate with better life outcomes, few interventions designed to improve decision skills have been evaluated with rigorous quantitative measures. A randomized study showed that integrating decision making into U.S. history instruction improved students’ history knowledge and decision-making competence, compared to traditional history instruction. Thus, integrating decision training enhanced academic performance and improved an important, general life skill associated with improved life outcomes.

Photo credit: http://www.flickr.com/photos/library_of_congress/3253742644/

MACHINE LEARNING, COMPUTATIONAL SOCIAL SCIENCE, ALGORITHMIC ECONOMICS, MARKET DESIGN AND MORE

Last week, we let you know about the Microsoft Research Postdoc in online experimental social science.

This week, we’re announcing four more postdocs! See below.

Microsoft Research NYC [ http://research.microsoft.com/newyork/ ] seeks outstanding applicants for 2-year postdoctoral researcher positions. We welcome applicants with a strong academic record in one of the following areas:

* Computational social science: http://research.microsoft.com/cssnyc
* Online experimental social science: http://research.microsoft.com/oess_nyc
* Algorithmic economics and market design: http://research.microsoft.com/algorithmic-economics/
* Machine learning: http://research.microsoft.com/mlnyc/

We will also consider applicants in other focus areas of the lab, including information retrieval, and behavioral & empirical economics. Additional information about these areas is included below. Please submit all application materials by January 11, 2013.

———-

COMPUTATIONAL SOCIAL SCIENCE
http://research.microsoft.com/cssnyc
With an increasing amount of data on every aspect of our daily activities — from what we buy, to where we travel, to who we know — we are able to measure human behavior with precision largely thought impossible just a decade ago. Lying at the intersection of computer science, statistics and the social sciences, the emerging field of computational social science uses large-scale demographic, behavioral and network data to address longstanding questions in sociology, economics, politics, and beyond. We seek postdoc applicants with a diverse set of skills, including experience with large-scale data, scalable statistical and machine learning methods, and knowledge of a substantive social science field, such as sociology, economics, psychology, political science, or marketing.

ONLINE EXPERIMENTAL SOCIAL SCIENCE
http://research.microsoft.com/oess_nyc
Online experimental social science involves using the web, including crowdsourcing platforms such as Amazon’s Mechanical Turk, to study human behavior in “virtual lab” environments. Among other topics, virtual labs have been used to study the relationship between financial incentives and performance, the honesty of online workers, advertising impact as a function of exposure time, the implicit cost of annoying ads, the testing of graphical user interfaces eliciting probabilistic information and also the relationship between network structure and social dynamics, related to social phenomena such as cooperation, learning, and collective problem solving. We seek postdoc applicants with a diverse mix of skills, including awareness of the theoretical and experimental social science literature, and experience with experimental design, as well as demonstrated statistical modeling and programming expertise. Must know and love R. Specific experience running experiments on Amazon’s Mechanical Turk or related crowdsourcing websites, as well as managing virtual participant pools is also desirable, as is evidence of UI design ability.

ALGORITHMIC ECONOMICS AND MARKET DESIGN
http://research.microsoft.com/algorithmic-economics/
Market design, the engineering arm of economics, benefits from an understanding of computation: complexity, algorithms, engineering practice, and data. Conversely, computer science in a networked world benefits from a solid foundation in economics: incentives and game theory. Scientists with hybrid expertise are crucial as social systems of all types move to electronic platforms, as people increasingly rely on programmatic trading aids, as market designers rely more on equilibrium simulations, and as optimization and machine learning algorithms become part of the inner loop of social and economic mechanisms. We seek applicants who embody a diverse mix of skills, including a background in computer science (e.g., artificial intelligence or theory) or related field, and knowledge of the theoretical and experimental economics literature. Experience building prototype systems, and a comfort level with modern programming paradigms (e.g., web programming and map-reduce) are also desirable.

MACHINE LEARNING
http://research.microsoft.com/mlnyc/
Machine learning is the discipline of designing efficient algorithms for making accurate predictions and optimal decisions in the face of uncertainty. It combines tools and techniques from computer science, signal processing, statistics and optimization. Microsoft offers a unique opportunity to work with extremely diverse data sources, both big and small, while also offering a very stimulating environment for cutting-edge theoretical research. We seek postdoc applicants who have demonstrated ability to do independent research, have a strong publication record at top research venues and thrive in a multidisciplinary environment.

SEEKING MATHEMATICALLY & COMPUTATIONALLY SKILLED APPLICANTS

Microsoft Research NYC seeks outstanding applicants with strong quantitative and programming skills for a postdoctoral researcher position in the area of online experimental social science.

Deadline for Full Consideration: January 11, 2013

Online experimental social science involves using the web, including crowdsourcing platforms such as Amazon’s Mechanical Turk, to study human behavior in “virtual lab” environments. Among other topics, virtual labs have been used to study the relationship between financial incentives and performance, the honesty of online workers, advertising impact as a function of exposure time, the implicit cost of “bad ads”, the testing of graphical user interfaces eliciting probabilistic information and also the relationship between network structure and social dynamics, related to social phenomena such as cooperation, learning, and collective problem solving. Eligible applicants must hold a Ph.D. in Computer Science, Experimental Economics, Experimental Psychology, Statistics, Mathematical Sociology or a related field. The ideal applicant will possess a diverse mix of skills, including awareness of the theoretical and experimental social science literature, and experience with experimental design, as well as demonstrated statistical modeling and programming expertise. Programming knowledge should include server-side and browser-side languages, interaction with databases and third party APIs and facility with the R language for statistical computing. Specific experience running experiments on Amazon’s Mechanical Turk or related crowdsourcing websites, as well as managing virtual participant pools is also desirable, as is evidence of UI design ability. Postdoc researcher positions at Microsoft Research provide emerging scholars (Ph.D.s received in 2012 or to be conferred by July 2013) an opportunity to develop their research career and to interact with some of the top minds in the research community. The position also offers the potential to have research realized in products and services that will be used worldwide. Postdoc researchers are invited to define their own research agenda and demonstrate their ability to drive forward an effective program of research.

Postdoc researchers receive a competitive salary and benefits package, and are eligible for relocation expenses. Postdoc researchers are hired for a two-year term appointment following the academic calendar, starting in July 2013. Applicants must have completed the requirements for a Ph.D., including submission of their dissertation, prior to joining Microsoft Research. We do accept applicants with tenure-track job offers from other institutions so long as they are able to negotiate deferring their start date to accept our position.

About MSR-NYC
Microsoft Research provides a vibrant multidisciplinary research environment with an open publications policy and close links to top academic institutions around the world. Microsoft Research New York City is the most recent MSR lab, comprising 16 full-time researchers and postdocs, working on theoretical and applied aspects of machine learning and information retrieval, computational and online experimental social science, and algorithmic and experimental economics. The lab is highly collaborative and interdisciplinary, and its members also maintain active links both with the local academic and tech communities.

For more information about the lab, visit:

http://research.microsoft.com/en-us/labs/newyork/default.aspx

To apply for a postdoc position at MSR-NYC:

1. Submit an online application at:

https://research.microsoft.com/apps/tools/jobs/fulltime.aspx

* Indicate that your research area of interest is “Online Experimental Social Science” and that your location preference is “New York.” Include the name of a Microsoft Research contact if you have one.

* In addition to the CV and names of three referees (including your dissertation advisor) that the online application will require you to include, upload the following 3 attachments with your online application: a) two conference or journal articles, book chapters, or equivalent writing samples (uploaded as 2 separate attachments); b) an academic research statement (approximately 3-4 pages) that outlines your research achievements and agenda.

2. After you submit your application, send an email to msrrt@microsoft.com (copy the Microsoft Research contacts you identified in step 1, if any) alerting us that you have uploaded your application. If an applicant meets the requirements above, a request for letters will be sent to your list of referees on your behalf. All letters of recommendation must be received by the deadline for full consideration of the application. Please make sure to check back with your referees or us if you have any questions about the status of your requested letters of recommendation. For more information, see:

http://research.microsoft.com/en-us/jobs/fulltime/postdoc.aspx