andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-559 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Steven Brams and James Jorash propose a system for reducing the advantage that comes from winning the coin flip in overtime: Dispensing with a coin toss, the teams would bid on where the ball is kicked from by the kicking team. In the NFL, it’s now the 30-yard line. Under Brams and Jorasch’s rule, the kicking team would be the team that bids the lower number, because it is willing to put itself at a disadvantage by kicking from farther back. However, it would not kick from the number it bids, but from the average of the two bids. To illustrate, assume team A bids to kick from the 38-yard line, while team B bids its 32-yard line. Team B would win the bidding and, therefore, be designated as the kick-off team. But B wouldn’t kick from 32, but instead from the average of 38 and 32–its 35-yard line. This is better for B by 3 yards than the 32-yard line that it proposed, because it’s closer to the end zone it is kicking towards. It’s also better for A by 3 yards to have B kick fr
sentIndex sentText sentNum sentScore
1 Steven Brams and James Jorash propose a system for reducing the advantage that comes from winning the coin flip in overtime: Dispensing with a coin toss, the teams would bid on where the ball is kicked from by the kicking team. [sent-1, score-1.596]
2 Under Brams and Jorasch’s rule, the kicking team would be the team that bids the lower number, because it is willing to put itself at a disadvantage by kicking from farther back. [sent-3, score-1.88]
3 However, it would not kick from the number it bids, but from the average of the two bids. [sent-4, score-0.416]
4 To illustrate, assume team A bids to kick from the 38-yard line, while team B bids its 32-yard line. [sent-5, score-1.562]
5 Team B would win the bidding and, therefore, be designated as the kick-off team. [sent-6, score-0.487]
6 But B wouldn’t kick from 32, but instead from the average of 38 and 32–its 35-yard line. [sent-7, score-0.321]
7 This is better for B by 3 yards than the 32-yard line that it proposed, because it’s closer to the end zone it is kicking towards. [sent-8, score-0.821]
8 It’s also better for A by 3 yards to have B kick from the 35-yard line, rather than from the 38-yard line, it proposed if it were the kick-off team. [sent-9, score-0.5]
9 In other words, the 35-yard line is a win-win solution–both teams gain a 3-yard advantage over what they reported would make them indifferent between kicking and receiving. [sent-10, score-1.053]
10 While bidding to determine the yard line from which a ball is kicked has been proposed before, the win-win feature of using the average of the bids–and recognizing that both teams benefit if the low bidder is the kicking team–has not. [sent-11, score-1.837]
11 Teams seeking to merely get the ball first would be discouraged from bidding too high–for example, the 45-yard line–as this could result in a kick-off pinning them far back in their own territory. [sent-12, score-0.648]
12 “Metaphorically speaking, the bidding system levels the playing field,” Brams and Jorasch maintain. [sent-13, score-0.39]
13 “It also enhances the importance of the strategic choices that the teams make, rather than leaving to chance which team gets a boost in the overtime period. [sent-14, score-0.843]
wordName wordTfidf (topN-words)
[('bids', 0.416), ('kicking', 0.396), ('bidding', 0.333), ('brams', 0.25), ('kick', 0.244), ('team', 0.243), ('teams', 0.234), ('line', 0.192), ('jorasch', 0.166), ('overtime', 0.166), ('ball', 0.153), ('yards', 0.132), ('kicked', 0.128), ('proposed', 0.124), ('coin', 0.119), ('average', 0.077), ('metaphorically', 0.076), ('bid', 0.076), ('disadvantage', 0.076), ('advantage', 0.075), ('bidder', 0.071), ('designated', 0.066), ('discouraged', 0.064), ('nfl', 0.064), ('toss', 0.062), ('indifferent', 0.062), ('farther', 0.06), ('zone', 0.059), ('system', 0.057), ('fans', 0.055), ('boost', 0.055), ('coach', 0.054), ('strategic', 0.054), ('leaving', 0.052), ('propose', 0.051), ('would', 0.05), ('flip', 0.05), ('recognizing', 0.049), ('seeking', 0.048), ('number', 0.045), ('reducing', 0.045), ('gain', 0.044), ('winning', 0.043), ('illustrate', 0.043), ('closer', 0.042), ('feature', 0.04), ('determine', 0.04), ('steven', 0.04), ('choices', 0.039), ('win', 0.038)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 559 andrew gelman stats-2011-02-06-Bidding for the kickoff
Introduction: Steven Brams and James Jorash propose a system for reducing the advantage that comes from winning the coin flip in overtime: Dispensing with a coin toss, the teams would bid on where the ball is kicked from by the kicking team. In the NFL, it’s now the 30-yard line. Under Brams and Jorasch’s rule, the kicking team would be the team that bids the lower number, because it is willing to put itself at a disadvantage by kicking from farther back. However, it would not kick from the number it bids, but from the average of the two bids. To illustrate, assume team A bids to kick from the 38-yard line, while team B bids its 32-yard line. Team B would win the bidding and, therefore, be designated as the kick-off team. But B wouldn’t kick from 32, but instead from the average of 38 and 32–its 35-yard line. This is better for B by 3 yards than the 32-yard line that it proposed, because it’s closer to the end zone it is kicking towards. It’s also better for A by 3 yards to have B kick fr
2 0.17043632 29 andrew gelman stats-2010-05-12-Probability of successive wins in baseball
Introduction: Dan Goldstein did an informal study asking people the following question: When two baseball teams play each other on two consecutive days, what is the probability that the winner of the first game will be the winner of the second game? You can make your own guess and the continue reading below. Dan writes: We asked two colleagues knowledgeable in baseball and the mathematics of forecasting. The answers came in between 65% and 70%. The true answer [based on Dan's analysis of a database of baseball games]: 51.3%, a little better than a coin toss. I have to say, I’m surprised his colleagues gave such extreme guesses. I was guessing something like 50%, myself, based on the following very crude reasoning: Suppose two unequal teams are playing, and the chance of team A beating team B is 55%. (This seems like a reasonable average of all matchups, which will include some more extreme disparities but also many more equal contests.) Then the chance of the same team
3 0.12164409 1804 andrew gelman stats-2013-04-15-How effective are football coaches?
Introduction: Dave Berri writes : A recent study published in the Social Science Quarterly suggests that these moves may not lead to the happiness the fans envision (HT: the Sports Economist). E. Scott Adler, Michael J. Berry, and David Doherty looked at coaching changes from 1997 to 2010. What they found should give pause to people who demanded a coaching change (or still hope for one). Here is how these authors summarize their findings: . . . we use matching techniques to compare the performance of football programs that replaced their head coach to those where the coach was retained. The analysis has two major innovations over existing literature. First, we consider how entry conditions moderate the effects of coaching replacements. Second, we examine team performance for several years following the replacement to assess its effects. We find that for particularly poorly performing teams, coach replacements have little effect on team performance as measured against comparable teams that
4 0.085147038 942 andrew gelman stats-2011-10-04-45% hitting, 25% fielding, 25% pitching, and 100% not telling us how they did it
Introduction: A University of Delaware press release reports : This month, the Journal of Quantitative Analysis in Sports will feature the article “An Estimate of How Hitting, Pitching, Fielding, and Base-stealing Impact Team Winning Percentages in Baseball.” In it, University of Delaware Prof. Charles Pavitt of the Department of Communication defines the perfect “formula” for Major League Baseball (MLB) teams to use to build the ultimate winning team. Pavitt found hitting accounts for more than 45 percent of teams’ winning records, fielding for 25 percent and pitching for 25 percent. And that the impact of stolen bases is greatly overestimated. He crunched hitting, pitching, fielding and base-stealing records for every MLB team over a 48-year period from 1951-1998 with a method no other researcher has used in this area. In statistical parlance, he used a conceptual decomposition of offense and defense into its component parts and then analyzed recombinations of the parts in intuitively mea
5 0.08503744 1028 andrew gelman stats-2011-11-26-Tenure lets you handle students who cheat
Introduction: The other day, a friend of mine who is an untenured professor (not in statistics or political science) was telling me about a class where many of the students seemed to be resubmitting papers that they had already written for previous classes. (The supposition was based on internal evidence of the topics of the submitted papers.) It would be possible to check this and then kick the cheating students out of the program—but why do it? It would be a lot of work, also some of the students who are caught might complain, then word would get around that my friend is a troublemaker. And nobody likes a troublemaker. Once my friend has tenure it would be possible to do the right thing. But . . . here’s the hitch: most college instructors do not have tenure, and one result, I suspect, is a decline in ethical standards. This is something I hadn’t thought of in our earlier discussion of job security for teachers: tenure gives you the freedom to kick out cheating students.
6 0.077057138 1547 andrew gelman stats-2012-10-25-College football, voting, and the law of large numbers
7 0.074063756 1800 andrew gelman stats-2013-04-12-Too tired to mock
8 0.069853239 99 andrew gelman stats-2010-06-19-Paired comparisons
9 0.069327012 230 andrew gelman stats-2010-08-24-Kaggle forcasting update
10 0.069323793 2262 andrew gelman stats-2014-03-23-Win probabilities during a sporting event
11 0.06219548 1955 andrew gelman stats-2013-07-25-Bayes-respecting experimental design and other things
12 0.062009178 1595 andrew gelman stats-2012-11-28-Should Harvard start admitting kids at random?
13 0.060627919 2267 andrew gelman stats-2014-03-26-Is a steal really worth 9 points?
14 0.060524378 566 andrew gelman stats-2011-02-09-The boxer, the wrestler, and the coin flip, again
15 0.059976764 2224 andrew gelman stats-2014-02-25-Basketball Stats: Don’t model the probability of win, model the expected score differential.
16 0.058958843 20 andrew gelman stats-2010-05-07-Bayesian hierarchical model for the prediction of soccer results
17 0.055681616 1692 andrew gelman stats-2013-01-25-Freakonomics Experiments
18 0.054092977 5 andrew gelman stats-2010-04-27-Ethical and data-integrity problems in a study of mortality in Iraq
19 0.049289469 2089 andrew gelman stats-2013-11-04-Shlemiel the Software Developer and Unknown Unknowns
topicId topicWeight
[(0, 0.064), (1, -0.019), (2, 0.022), (3, 0.011), (4, 0.019), (5, -0.002), (6, 0.016), (7, -0.006), (8, -0.009), (9, -0.018), (10, -0.015), (11, 0.002), (12, -0.029), (13, -0.019), (14, -0.03), (15, 0.007), (16, 0.029), (17, -0.011), (18, 0.016), (19, 0.001), (20, -0.008), (21, 0.047), (22, -0.002), (23, 0.027), (24, 0.019), (25, 0.015), (26, 0.02), (27, 0.019), (28, -0.016), (29, -0.056), (30, 0.023), (31, -0.051), (32, 0.005), (33, -0.008), (34, -0.003), (35, -0.008), (36, 0.029), (37, 0.015), (38, -0.003), (39, 0.012), (40, 0.026), (41, 0.006), (42, -0.023), (43, -0.0), (44, -0.01), (45, 0.005), (46, -0.004), (47, 0.017), (48, -0.011), (49, -0.033)]
simIndex simValue blogId blogTitle
same-blog 1 0.94141996 559 andrew gelman stats-2011-02-06-Bidding for the kickoff
Introduction: Steven Brams and James Jorash propose a system for reducing the advantage that comes from winning the coin flip in overtime: Dispensing with a coin toss, the teams would bid on where the ball is kicked from by the kicking team. In the NFL, it’s now the 30-yard line. Under Brams and Jorasch’s rule, the kicking team would be the team that bids the lower number, because it is willing to put itself at a disadvantage by kicking from farther back. However, it would not kick from the number it bids, but from the average of the two bids. To illustrate, assume team A bids to kick from the 38-yard line, while team B bids its 32-yard line. Team B would win the bidding and, therefore, be designated as the kick-off team. But B wouldn’t kick from 32, but instead from the average of 38 and 32–its 35-yard line. This is better for B by 3 yards than the 32-yard line that it proposed, because it’s closer to the end zone it is kicking towards. It’s also better for A by 3 yards to have B kick fr
2 0.82660508 29 andrew gelman stats-2010-05-12-Probability of successive wins in baseball
Introduction: Dan Goldstein did an informal study asking people the following question: When two baseball teams play each other on two consecutive days, what is the probability that the winner of the first game will be the winner of the second game? You can make your own guess and the continue reading below. Dan writes: We asked two colleagues knowledgeable in baseball and the mathematics of forecasting. The answers came in between 65% and 70%. The true answer [based on Dan's analysis of a database of baseball games]: 51.3%, a little better than a coin toss. I have to say, I’m surprised his colleagues gave such extreme guesses. I was guessing something like 50%, myself, based on the following very crude reasoning: Suppose two unequal teams are playing, and the chance of team A beating team B is 55%. (This seems like a reasonable average of all matchups, which will include some more extreme disparities but also many more equal contests.) Then the chance of the same team
3 0.74400055 813 andrew gelman stats-2011-07-21-Scrabble!
Introduction: AT writes : Sitting on my [AT's] to-do list for a while now has been an exploration of Scrabble from an experimental design point of view; how to better design a tournament to make the variance as small as possible while still preserving the appearance of the home game to its players. . . . I’m proud (relieved?) to say that I’ve finally finished the first draft of this work for two-player head-to-head games, with a duplication method that ensures that if the game were repeated, each player would receive tiles from the reserve in the same sequence: think of the tiles being laid out in order (but unseen to the players), so that one player draws from the front and the other draws from the back. . . . One goal of this was to figure out how much of the variance in score comes from the tile order and how much comes from the board, given that a tile order would be expected. It turns out to be about half-bag, half-board . . . Some other findings from the simulations: The blank
4 0.72399223 2262 andrew gelman stats-2014-03-23-Win probabilities during a sporting event
Introduction: Todd Schneider writes: Apropos of your recent blog post about modeling score differential of basketball games , I thought you might enjoy a site I built, gambletron2000.com , that gathers real-time win probabilities from betting markets for most major sports (including NBA and college basketball). My original goal was to use the variance of changes in win probabilities to quantify which games were the most exciting, but I got a bit carried away and ended up pursuing a bunch of other ideas, which you can read about in the full writeup here This particular passage from the anonymous someone in your post: My idea is for each timestep in a game (a second, 5 seconds, etc), use the Vegas line, the current score differential, who has the ball, and the number of possessions played already (to account for differences in pace) to create a point estimate probability of the home team winning. reminded me of a graph I made, which shows the mean-reverting tendency of N
5 0.6798476 1731 andrew gelman stats-2013-02-21-If a lottery is encouraging addictive gambling, don’t expand it!
Introduction: This story from Vivian Yee seems just horrible to me. First the background: Pronto Lotto’s real business takes place in the carpeted, hushed area where its most devoted customers watch video screens from a scattering of tall silver tables, hour after hour, day after day. The players — mostly men, about a dozen at any given time — come on their lunch breaks or after work to study the screens, which are programmed with the Quick Draw lottery game, and flash a new set of winning numbers every four minutes. They have helped make Pronto Lotto the top Quick Draw vendor in the state, selling $3.3 million worth of tickets last year, more than $1 million more than the second busiest location, a World Books shop in Penn Station. Some stay for just a few minutes. Others play for the length of a workday, repeatedly traversing the few yards between their seats and the cash register as they hand the next wager to a clerk with a dollar bill or two, and return to wait. “It’s like my job, 24
6 0.67670476 2267 andrew gelman stats-2014-03-26-Is a steal really worth 9 points?
7 0.67253774 1467 andrew gelman stats-2012-08-23-The pinch-hitter syndrome again
9 0.66761357 2105 andrew gelman stats-2013-11-18-What’s my Kasparov number?
11 0.66012877 218 andrew gelman stats-2010-08-20-I think you knew this already
12 0.65997237 1804 andrew gelman stats-2013-04-15-How effective are football coaches?
13 0.65912223 1113 andrew gelman stats-2012-01-11-Toshiro Kageyama on professionalism
14 0.65035295 445 andrew gelman stats-2010-12-03-Getting a job in pro sports… as a statistician
15 0.64452714 562 andrew gelman stats-2011-02-06-Statistician cracks Toronto lottery
16 0.63645273 1903 andrew gelman stats-2013-06-17-Weak identification provides partial information
17 0.62954593 942 andrew gelman stats-2011-10-04-45% hitting, 25% fielding, 25% pitching, and 100% not telling us how they did it
18 0.62733209 253 andrew gelman stats-2010-09-03-Gladwell vs Pinker
19 0.6263988 2082 andrew gelman stats-2013-10-30-Berri Gladwell Loken football update
20 0.57236952 1547 andrew gelman stats-2012-10-25-College football, voting, and the law of large numbers
topicId topicWeight
[(9, 0.017), (16, 0.025), (24, 0.151), (35, 0.018), (42, 0.01), (44, 0.406), (63, 0.014), (76, 0.022), (86, 0.079), (99, 0.114)]
simIndex simValue blogId blogTitle
same-blog 1 0.89091241 559 andrew gelman stats-2011-02-06-Bidding for the kickoff
Introduction: Steven Brams and James Jorash propose a system for reducing the advantage that comes from winning the coin flip in overtime: Dispensing with a coin toss, the teams would bid on where the ball is kicked from by the kicking team. In the NFL, it’s now the 30-yard line. Under Brams and Jorasch’s rule, the kicking team would be the team that bids the lower number, because it is willing to put itself at a disadvantage by kicking from farther back. However, it would not kick from the number it bids, but from the average of the two bids. To illustrate, assume team A bids to kick from the 38-yard line, while team B bids its 32-yard line. Team B would win the bidding and, therefore, be designated as the kick-off team. But B wouldn’t kick from 32, but instead from the average of 38 and 32–its 35-yard line. This is better for B by 3 yards than the 32-yard line that it proposed, because it’s closer to the end zone it is kicking towards. It’s also better for A by 3 yards to have B kick fr
2 0.73571467 2209 andrew gelman stats-2014-02-13-CmdStan, RStan, PyStan v2.2.0
Introduction: The Stan Development Team is happy to announce CmdStan, RStan, and PyStan v2.2.0. As usual, more info is available on the Stan Home Page . This is a minor release with a mix of bug fixes and features. For a full list of changes, please see the v2.2.0 milestone on stan-dev/stan’s issue tracker. Some of the bug fixes and issues are listed below. Bug Fixes increment_log_prob is now vectorized and compiles with vector arguments multinomial random number generator used the wrong size for the return value fixed memory leaks in auto-diff implementation variables can start with the prefix ‘inf’ fixed parameter output order for arrays when using optimization RStan compatibility issue with latest Rcpp 0.11.0 Features suppress command line output with refresh <= 0 added 1 to treedepth to match usual definition of treedepth added distance, squared_distance, diag_pre_multiply, diag_pre_multiply to Stan modeling lnaguage added a ‘fixed_param’ sampler for
3 0.717318 1627 andrew gelman stats-2012-12-17-Stan and RStan 1.1.0
Introduction: We’re happy to announce the availability of Stan and RStan versions 1.1.0, which are general tools for performing model-based Bayesian inference using the no-U-turn sampler, an adaptive form of Hamiltonian Monte Carlo. Information on downloading and installing and using them is available as always from Stan Home Page: http://mc-stan.org/ Let us know if you have any problems on the mailing lists or at the e-mails linked on the home page (please don’t use this web page). The full release notes follow. (R)Stan Version 1.1.0 Release Notes =================================== -- Backward Compatibility Issue * Categorical distribution recoded to match documentation; it now has support {1,...,K} rather than {0,...,K-1}. * (RStan) change default value of permuted flag from FALSE to TRUE for Stan fit S4 extract() method -- New Features * Conditional (if-then-else) statements * While statements -- New Functions * generalized multiply_lower_tri
4 0.71376652 1798 andrew gelman stats-2013-04-11-Continuing conflict over conflict statistics
Introduction: Mike Spagat sends along a serious presentation with an ironic title: 18.7 MILLION ANNIHILATED SAYS LEADING EXPERT IN PEER–REVIEWED JOURNAL: AN APPROVED, AUTHORITATIVE, SCIENTIFIC PRESENTATION MADE BY AN EXPERT He’ll be speaking on it at tomorrow’s meeting of the Catastrophes and Conflict Forum of the Royal Society of Medicine in London. All I can say is, it’s a long time since I’ve seen a slide presentation in portrait form. It brings me back to the days of transparency sheets.
5 0.68955612 864 andrew gelman stats-2011-08-21-Going viral — not!
Introduction: Sharad explains : HIV/AIDS, like many other contagious diseases, exemplifies the common view of so-called viral propagation, growing from a few initial cases to millions through close person-to-person interactions. (Ironically, not all viruses in fact exhibit “viral” transmission patterns. For example, Hepatitis A often spreads through contaminated drinking water.[1]) By analogy to such biological epidemics, the diffusion of products and ideas is conventionally assumed to occur “virally” as well, as evidenced by prevailing theoretical frameworks (e.g., the cascade and threshold models) and an obsession in the marketing world for all things social. . . . Despite hundreds of papers written about diffusion, there is surprisingly little work addressing this fundamental empirical question. In a recent study, Duncan Watts, Dan Goldstein, and I [Goel] examined the adoption patterns of several different types of products diffusing over various online platforms — including Twitter, Face
6 0.62292564 444 andrew gelman stats-2010-12-02-Rational addiction
7 0.61825264 693 andrew gelman stats-2011-05-04-Don’t any statisticians work for the IRS?
8 0.61294556 2150 andrew gelman stats-2013-12-27-(R-Py-Cmd)Stan 2.1.0
9 0.61103374 954 andrew gelman stats-2011-10-12-Benford’s Law suggests lots of financial fraud
10 0.59162831 1837 andrew gelman stats-2013-05-03-NYC Data Skeptics Meetup
11 0.56378859 1879 andrew gelman stats-2013-06-01-Benford’s law and addresses
12 0.56029648 748 andrew gelman stats-2011-06-06-Why your Klout score is meaningless
13 0.54149818 111 andrew gelman stats-2010-06-26-Tough love as a style of writing
14 0.53189659 1613 andrew gelman stats-2012-12-09-Hey—here’s a photo of me making fun of a silly infographic (from last year)
15 0.50730443 617 andrew gelman stats-2011-03-17-“Why Preschool Shouldn’t Be Like School”?
16 0.5047437 1145 andrew gelman stats-2012-01-30-A tax on inequality, or a tax to keep inequality at the current level?
17 0.50061828 1436 andrew gelman stats-2012-07-31-A book on presenting numbers from spreadsheets
18 0.47673276 2017 andrew gelman stats-2013-09-11-“Informative g-Priors for Logistic Regression”
19 0.47566578 1799 andrew gelman stats-2013-04-12-Stan 1.3.0 and RStan 1.3.0 Ready for Action
20 0.474509 788 andrew gelman stats-2011-07-06-Early stopping and penalized likelihood