WAR, Pythagoras, Poisson and Skellam

Getting into some issues only makes you wish that you hadn’t, when you realize how messed up they are, at a fundamental level.

Here’s a great example involving statistical analysis, as applied to win/loss (“WL”) records of sports teams, the base concept of which is that it’s possible to estimate what a team’s WL record “should” have been, based on the number of goals/runs/points that it scored, and allowed, over a defined number of games (typically, a full season or more). This blog post by Bill James partially motivates my thoughts here.

Just where and when this basic idea originated I’m not 100 percent sure, but it appears to have been James, three to four decades ago, under the name “Pythagorean Expectation” (PE). Bill James, if you don’t know, is the originator, and/or popularizer, of a number of statistical methods or approaches applied to baseball data, which launched the so-called “SABR-metric” baseball analysis movement (SABR = Society for American Baseball Research). He is basically that movement’s founder.

In the linked post above, James uses the recent American League MVP votes for Jose Altuve and Aaron Judge, to make some great points regarding the merit of WAR (Wins Above Replacement), arguably the most popular of the many SABR-metric variables. The legitimacy of WAR is an involved topic on which much virtual ink has been spilled, but is not my focus here; in brief, it tries to estimate the contribution each player makes to his team’s WL record. In the article, James takes pointed exception to how WAR is used (by some, who argue based upon it, that the two players were basically about equally valuable in 2017). In the actual MVP vote, Altuve won by a landslide, and James agrees with the voters’ judgement (pun intended): WAR is flawed in evaluating true player worth in this context. Note that numerous problems have been identified with WAR, but James is bringing a new and serious one, and from a position of authority.

One of James’ main arguments involves inappropriate use of the PE, specifically that the “expected” number of wins by a team is quite irrelevant–it’s the *actual* number that matters when assessing any given player’s contribution to it. For the 2017 season, the PE estimates that Judge’s team, the New York Yankers, “should” have gone 101-61, instead of their actual 91-71, and thus in turn, every Yanker player is getting some additional proportion of those ten extra, imaginary wins, added to his seasonal WAR estimate. For Altuve’s team, the Houston Astros, that’s not an issue because their actual and PE WL records were identical (both 101-61). The WAR-mongers, and most self identified SABR-metricians for that matter, automatically then conclude that a team like this year’s Yanks were “unlucky”: they should have won 101 games, but doggone lady luck was against ’em in distributing their runs scored (and allowed) across their 162 games…such that they only won 91 instead. Other league teams balance the overall ledger by being luck beneficiaries–if not outright pretenders. There are major problems with this whole mode of thought, some of which James rips in his essay, correctly IMO.

But one additional major problem here is that James started the PE craze to begin with, and neither he, nor anybody else who have subsequently either modified or used it, seems to understand the problems inherent in that metric. James instead addresses issues in the application of the PE as input to the metric (WAR) that he takes issue with, not the legitimacy of the PE itself. Well, there are in fact several issues with the PE, ones that collectively illustrate important issues in statistical philosophy and practice. If you’re going to criticize, start at the root, not the branches.

The issue is one of statistical methodology, and the name of the metric is itself a big clue–it was chosen because the PE formula is similar to the Pythagorean theorem of geometry: A^2 + B^2 = C^2, where A, B and C are the three sides of a right triangle. The original (James) PE equation was: W = S^2 / (S^2 + A^2), where W = winning percentage, S = total runs scored and A = total runs allowed, summed over all the teams in a league, over one or more seasons. That is, it supposedly mimicked the ratio of squared lengths between one side, and the hypotenuse, of a right triangle. Just how James came to this structural form, and parameter values, I don’t know and likely very few besides James himself do; presumably the details are in one of his annual Baseball Abstracts from 1977 to 1988, since he doesn’t discuss the issue that I can see, in either of his “Historical Baseball Abstract” books. Perhaps he thought that runs scored and allowed were fully independent of each other, orthogonal, like the two sides of a right triangle. I don’t know.

It seems to me very likely that James derived his equation via fitting various curves to some empirical data set, although it is possible he was operating from some (unknown) theoretical basis. Others who followed him, and supposedly “improved” the metric’s accuracy definitely fitted curves to data, since all parameters (exponents) were lowered to values (e.g. 1.81) for which no theoretical basis is even possible to conceive of: show me the theoretical basis for anything that scales up/down according to the ratio of a sum of parts, and one component thereof, by the power of 1.81. The current PE incarnation (claimed as the definitive word on the matter by some) has the exponents themselves as variables, dependent on the so-called “run environment”, the total number of runs scored and allowed, per game. Thus, the exponents for any given season are estimated by R^0.285, where R is the average number of runs scored per game (both teams) over all games of a season.

Even assuming that James did in fact try to base his PE on theory somehow, he didn’t do it right, and that’s a big problem, because there is in fact a very definite theoretical basis for exactly this type of problem…but one never followed, and apparently never even recognized, by SABR-metricians. At least I’ve seen no discussion of it anywhere, and I’ve read my share of baseball analytics essays. Instead, it’s an example of the curve-fitting mentality that is utterly ubiquitous among them. (I have seen some theoretically driven analytics in baseball, but mostly as applied to ball velocity and trajectory off the bat, as predicted from e.g., bat and ball elasticity, temperature, launch angle, and etc, and also the analysis of bat breakage, a big problem a few years back. And these were by Alan Nathan, an actual physicist).

Much of science, especially non-experimental science, involves estimating relationships from empirical data. And there’s good reason for that–most natural systems are complex, and often, one simply does not know, quantitatively and apriori, the fundamental operating relationships upon which to build a theory, much less how those interact with each other in complex ways at the time and space scales of interest. Therefore one tries instead to estimate those relationships by fitting models to empirical data–often some type of regression model, but not necessarily. It goes without saying that since the system is complex, you can only hope to detect some part of the full signal from the noise, often just one component of it. It’s an inverse, or inferential, approach to understanding a system, as opposed to forward modeling driven by theory; these are the two opposing approaches to understanding a system.

On those (rare) occasions when you do have a system amenable to theoretical analysis…well you dang well better do so. Geneticists know this: they don’t ignore binomial/multinomial models, in favor of curve fitting, to estimate likely nuclear transmission genetic processes in diploid population genetics and inheritance. That would be entirely stupid, given that we know for sure that diploid chromosomes conform to a binomial process during meiosis the vast majority of the time. We understand the underlying driving process–it’s simple and ubiquitous.

The binomial must be about the simplest possible stochastic model…but the Poisson isn’t too far behind. The Poisson predicts the expected distribution of the occurrence of discrete events in a set of sample units, given knowledge of the average occurrence rate determined over the full set thereof. It is in fact exactly the appropriate model for predicting the per-game distribution of runs/goals scored (and allowed), in sports such as baseball, hockey, golf, soccer, lacrosse, etc. (i.e. sports in which scoring is integer-valued and all scoring events are positive and of equal value).

To start with, the Poisson model can test a wider variety of hypotheses. The PE can only predict a team’s WL record, whereas the Poisson can test whether or not a team’s actual runs scored (and allowed) distribution, follows expectation. To the extent that they do follow is corresponding evidence of true randomness generating the variance in scores across games. This in turn means that the run scoring (or allowing) process is stationary, i.e., it is governed by an unchanging set of drivers. Conversely, if the observed distributions differ significantly from expectation, that’s corresponding evidence that those drivers are not stationary, meaning that teams’ inherent ability to score (and/or allow) runs is dynamic–they change over time (i.e. between games). That’s an important piece of knowledge in and of itself.

But the primary question of interest here involves the WL record and its relationship to runs scored and allowed. If a team’s runs scored and allowed both closely follow Poisson expectations–then prediction of the WL record follows from theory. Specifically, the distribution of differences in two Poisson distributions follows the Skellam distribution, described by the British statistician J.G. Skellam in the 1950s, as part of his extensive work on point processes. That is, the Skellam directly predicts the WL record whenever the Poisson assumptions are satisfied. However, even if a team’s run distribution deviates significantly from Poisson expectation, it is still possible to accurately estimate the expected WL record, by simply resampling–drawing randomly several thousand times from the observed distributions–allowing computers to do what they’re really good at. [Note that in low scoring sports like hockey and baseball, many ties will be predicted, and sports differ greatly in how they break ties at the end of regulation play. The National Hockey League and Major League Baseball vary greatly in this respect, especially now that NHL ties can be decided by shoot-out, which is a completely different process than regulation play. In either case, it’s necessary to identify games that are tied at the end of regulation.]

If instead you take an empirical data set and fit some equation to those data–any equation, no matter how good the fit–you run the risk of committing a very big error indeed, one of the biggest you can in fact make. Specifically, if the data do in fact deviate from Poisson expectation, i.e. non-stationary processes are operating, you will mistake your data-fitted model for the true expectation–the baseline reference point from which to assess random variation. Show me a bigger error that you can make then that one–it will affect every conclusion you subsequently come to. So, if you want to assess how “lucky” a team was with its WL record, relative to runs scored and allowed, don’t do that. And don’t get me started on use of the term “luck” in SABR-metrics, when what they really mean is chance, or stochastic, variation. The conflation of such terms in sports that very clearly involve heavy doses of both skill and chance, is a fairly flagrant violation of the whole point of language. James is quite right in pointing this out.

I was originally hoping to get into some data analysis to demonstrate the above points but that will have to wait–the underlying statistical concepts needed to be discussed first and that’s all I have time for right now. Rest assured that it’s not hard to analyze the relevant data in R (but it can be a time-consuming pain to obtain and properly format it).

I would also like to remind everyone to try to lay off high fastballs, keep your stick on the ice, and stay tuned to this channel for further fascinating discussions of all kinds.  Remember that Tuesdays are dollar dog night, but also that we discontinued 10 cent beer night 40 years ago, given the results.

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Aesculus glabra!

Ezekiel Elliott, Ohio State

Ezekiel Elliott, Ohio State, breaks through the line in Ohio State’s NCAA football championship game victory over Oregon Monday night, capping an improbable run to the title in the first year of the college football playoff. Photo by Kirby Lee, USA TODAY sports

Awesome Buckeyes, just plain awesome.
Enough said.

Scenes at the local library

This post briefly interrupts the TCS prediction posts with a library-related theme, given the overwhelming popularity of these in the past.

For various reasons, I sometimes work at the local public library. It’s a small place, but still the largest one in the county, and a nice place overall. The magazine section looks like your typical sort of situation:
Library3

I decided to check it out a little more extensively today, prompted by a recurring failure to find any true scientific journal in most public libraries in small to medium sized towns, and in some cases even in large cities. There are 137 magazine publications total; I tried to break down the topics represented by the current issue’s front cover, into some thematic categories, neither thorough, systematic or mutually exclusive. It came out as:

Humans: 65 (Women: 39; Men: 22; Kids: 4)
Animals/Plants: 10 (Animals: 8; Plants: 2; Deer: 4; Birds: 3; Snails: 1)
Holidays: 13 (Thanksgiving: 2; Christmas: 11)
Food/Cooking: 12
Crafts: 11 (Mostly quilting and sewing)
Health/Nutrition: 9 (e.g. Bicycling, Eat Well, Vegetarian, Yoga)
Homes/Decor: 8
Sports: 9 (Football: 3; Hunting: 3; Golf: 2; Cycling: 1)
Cars: 5
Ships/Sailing: 3
Trains: 2
Gardening: 2
Puzzles: 2
Lighthouses: 1

There were a number of uncategorized others also, like one devoted to autism, one to retirees, one to coin collecting and etc. Three of the four deer-related were pictures of bucks on hunting magazines, while the fourth was a pair of deer on a quilt in a quilting magazine. Two of the three birds were male cardinals, and the third was of a pair of great blue herons.

On what I might term the quasi-academic front, it breaks out this way:
Science- or engineering-related: 10 (Scientific American, Popular Science, Popular Mechanics; Natural History, Audubon, Science News; Sky and Telescope; Smithsonian, Discover, Journal of Inland Seas)
History-related: 6 (World War II, Civil War Times, Timeline, Discover, J of Inland Seas)
Literature: 2 (Ohioana Quarterly; New York Times Book Review; Analog (SciFi))
Journal Format: 3 (J Inland Seas, Ohioana Quarterly, Timeline, with only the first thereof being published by a truly research-oriented group (The Great Lakes Historical Society))

The politically oriented stuff is there too of course, although it’s a small proportion comparatively, and nothing really radical. Whether there was any intention in placing the three on this shelf the way they are is an open question: Library2

For the adult human covers, the (admittedly subjective) interpretation of “overall suggestiveness” fell out as follows. For the 37 with women on the cover, 19 were focused on some aspect of personal appearance, and 8 of those implied sexuality. The typical suspects were involved here (Esquire, Self, etc), including the partially pornographic e.g.: Library1 For the 21 with men on the cover, that breakdown was 2 appearance-oriented and either 0 or 1 sexually suggestive, respectively.

The famous are there but not in huge numbers, and other than Abe Lincoln, Bob Dylan, Oprah Winfrey and JJ Watt, I don’t recognize them. Movie and TV stars probably. JJ Watt and Taylor Swift (?) are on 2 covers each. And no I’m not going to Google to find out who she is, I’m really not.

On the newspaper front, there are the several papers from the county seats of the surrounding counties, but if you want state, national and international news, it’s either the Toledo Blade, Cleveland Plain Dealer, New York Times or Wall Street Journal. All good papers fortunately.

Caught stealing

Nope, not a post about the Kansas City Royals, or baseball at all for that matter, though I do hope to get there at some point.

Rather, it’s about getting away with thievery at the local library yesterday. I did. Twenty-five, count ’em, 25 cents each for the two books pictured below. Now, I have no idea how many person-hours went into these, nor any idea how to go about estimating same, but I do know that each is over 800 pages and jam-packed with mucho useful information that could remind me of the 99.9% of the French I’ve forgotten, or get me across the outback some day, should I consult them. And that each required a great deal of work to obtain, organize and print the information contained in them. And that if they sold them for 25 cents new, all the contributing writers would’ve long ago died of starvation. Hell, I’d gladly pay a quarter just for that Australian hotel picture. I win.
Books
Can you beat that with a stick? Tell me about your best book bargains from used book stores, yard sales, dumpsters, your Aunt Maybelle’s attic corner, whatever.

Bring it book thieves. Bring it or I’ll inflict a baseball post or two on you, I’m warning you now.

Break

I won’t be writing much for a while; need to take care of more important things. I hope to come back to it at some point, if I feel there’s something worth saying, but we’ll see. I’ll leave the old stuff up though, at least for now.

Thanks to the readers who’ve added to the site with their typically very good comments, especially Matt, Harold, Clem and Dave. I don’t have a lot of commenters, but the ones I do have have been great–and I would not have it any other way.
Jim

Note: Just after writing this post, I’d decided to inactivate the blog, and the only way to do that without deleting it, is to make it private, requiring a password. Somehow, right at that time there came an inexplicable flood of views of my posts related to both tree rings and ebola spread–far more than is normal. This stuff is months to years old now, but whatever. So I’m leaving all posts visible. Furthermore, there are probably some pretty important things I still need to express in writing, but we’ll see what time and energy allows on that.

Ebola epidemiology data scraper

Note: The following post is current as of WHO report of 09-18-14, which includes data to 09-14-14. [I’ve altered this code a number of times because of the nearly constantly changing format and location of the WHO data.]
#######

I wanted to find certain statistics for the West African Ebolavirus (EBV) outbreak from its inception, e.g. recent case and death rates. By “recent” I mean at WHO GAR reporting intervals, typically a few days. But the sites where I’d expect to find such (WHO, CDC etc) didn’t have them, at least in a synthesized form. So, I wrote an R script to scrape, compile, rearrange, and plot the data, for any country or all together, as taken from two sources. Starting with the WHO July 1 GAR report, tables of new and cumulative cases and deaths, categorized by degree of certainty, for each of the three countries in the outbreak, are given. Wikipedia has a less detailed table for all dates from March 25 on. I used it to obtain total cases and deaths up to July 1.

Below are graphs of the recent, per-day (1) case rates, and (2) death rates, from March 22. Each shows the raw data (thinner line), and a loess-fitted trend (thicker line). Note that reporting issues are partly, perhaps largely, responsible for extreme fluctuations in the raw data.

Ebola case recent rates 2q
Ebola death recent rates 2q

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What do you want to see?

I need peoples’ considered opinion on how to make this blog maximally worthwhile.

As most of us are aware, there’s an enormous and growing body of things to read on the internet. Because time is always at a premium, you really have to be careful in choosing what to read. [This point is magnified when it comes to deciding what to write, because writing takes longer than reading does.] Aside from the desire to be entertained, I think it’s fair to say that most of us want to read things that inform us about some topic we are interested in, which are short and clear, which are trustworthy, and preferably on which we also have something to contribute to the conversation, however small, even if only questions.

What I need feedback on is the question of what kind of article/information is most helpful to you, or conversely, the kind of thing that is not, for whatever reason. This can include things like the topics covered, the way they’re covered (depth vs breadth, quantitative vs verbal, etc), sites you already favor/disfavor for whatever reason, the types of things that typically confuse or throw you–anything at all really. My personal view on this is that posts that help people to understand scientific concepts/practices/methods for themselves are the most useful, because people trust conclusions only when they first understand how they were reached, and there is often a tremendous amount of murk and confusion in scientific discourse that prevents this. Lecturing on things as some kind of authority is unlikely to be helpful in promoting real understanding–what we need is helpful contributions/perspectives from as many people as possible.

Please fire away; write as much as you want. This is quite important and I’m going to be reading the responses carefully.

Warmest since….when?

There’s a new study out in GRL (press release), the third (at least) in the last couple of years using plant remains that have been newly uncovered by receding ice caps in Greenland and Baffin Island to estimate the most recent date at which temperatures must have been about the same as current. I’ve now seen four numerous notices on Twitter about it, and a popular media story on it has just popped up. That story contains the basic storyline statement: “…summer temperatures in the Canadian Arctic are higher today than they’ve been in at least 44,000 years, and are likely the warmest they’ve been in 120,000 years.

Interestingly, the author of that piece says “I’m a journalism professor and science journalist. Miller told me about his results last spring, and I’ve been waiting for his study to be published in a peer-reviewed journal before writing about them.” Well, given that he’s a journalist and had six months, it would have been nice if he’d asked some basic level investigative questions during that time instead of taking stated conclusions on faith.

When I first started reading this I was actually excited about novel evidence that might be relatively decisive regarding how unique current temperatures are in the arctic, relative to the Holocene/Pleistocene. Alas, as I continued reading that hope faded quickly and I now instead marvel at how the authors could state the conclusions they do, given the methods and data presented.

Continue reading

Bad science

I became aware of a couple of interesting opinion pieces in the academic literature this week, both via Twitter.

The first one’s titled Benchmarking Open Access Science Against Good Science (“Commentary”) by Lindenmayer and Likens, published at the Bulletin of the Ecological Society of America, ref. via Sean Ulm. The second is apparently (no author given) an editorial at The Economist, titled “Trouble at the lab” (open access). I consider both to be well worth reading, and more or less right on the money. I’ll summarize the first one briefly here for those without access.

The authors’ principal point is that scientists who use publicly available data sets in their studies need to be very careful with their analyses to avoid coming to wrong conclusions. The basic reason for this: there are often details and subtleties to such data that need to be thoroughly understood, but which are often not. They state:

Our extensive experience from a combined 80 years of collecting empirical data is that large data sets are often nuanced and complex, and appropriate analysis of them requires intimate knowledge of their context and substance to avoid making serious mistakes in interpretation. We therefore suggest that it is essential that those intending to use large, composite open-access data sets must work in close collaboration with those responsible for gathering those data sets.

Then they really unload on a certain class of scientists:

There is also the emerging issue of a generation of what we term here as “parasitic” scientists who will never be motivated to go and gather data because it takes real effort and time and it is simply easier to use data gathered by others. The pressure to publish and extraordinary levels of competition at universities and other institutions (Lindenmayer and Likens 2011) will continue to positively select for such parasitic scientists. This approach to science again has the potential to lead to context-free, junk science. More importantly, it may create massive disincentives for others to spend the considerable time and effort required to collect new data.

It’s not every day you see such harsh things said in academic journals, and they could have avoided use of “parasitic”, but their point is well founded.

Anti-doping research, politics, etc.

Those who saw what I wrote regarding the Lance Armstrong decision last fall will know I’m interested in the issue of athletes using performance enhancing substances/practices (“PEDs”) to gain an unfair advantage. And since I’m an even bigger baseball fan than cycling fan, and Major League Baseball has just gone through it’s second round of problems on this score (and not likely finished either), the topic’s on my mind.

I was pointed to this piece in the New York Times a couple days back, which pertains to use among track and field athletes and the question of how many athletes are actually using these things compared to the number who are being caught. There are two interesting issues here. The first should rather be called disturbing actually. The NYT article reports that the World Anti Doping Agency (WADA) commissioned a research study on PED use among track and field athletes, but now has apparently decided not to allow publication of the results, which apparently show that only a very small percentage of those cheating are being caught. The reason given:

Nick Davies, a spokesman for track’s governing body, the International Association of Athletics Federations, said in an e-mail that the original study “was not complete for publication,” adding that it was “based only on a social science protocol, a kind of vox pop of athletes’ opinions.” Davies indicated blood tests from the world championships this month in Moscow would be combined with the previous research to produce what the I.A.A.F. believed would be a more comprehensive study.

Excuse me, what?

Since when does an international track and field organization, or any other athletic organization for that matter, no matter how powerful, have the right to tell a group of scientists that (1) their research protocol was faulty, and (2) that they can’t publish their scientific results when they feel they’re ready to? And the rationale given for this delay is to allow more blood tests to be done? That’s the whole point of the study in the first place: the athletes are successfully avoiding positive tests by various means. Heard about Lance Armstrong at all? HELLO??!! Unfortunately, the scientists involved apparently signed a “non-disclosure” agreement that prevents them from publishing until allowed to by either WADA or this IAAF organization. Once again we see scientists being forced to be subservient to political interests. Sure would like to read the text of that non-disclosure agreement.

This denigrated “social science protocol” as he called it, is in fact a questionnaire methodology that’s been around for nearly 50 years, if not longer, known as a randomized response survey method. It’s a way of getting a more accurate estimate of a sensitive practice or belief from a surveyed group than would otherwise be obtained, yet without incriminating anyone, i.e. indvidual anonymity is guaranteed and accuracy of estimate maximized. The basis of the method is pretty interesting. More than one (usually two) questions are presented to each respondent, who picks which of the two to answer based on a simple probability mechanism that he/she controls, like the roll of a die. The interviewer does not know what the result of the roll was, and thus, which question the respondent answered.

For example, if a 1, 2, or 3 comes up you answer question #1, which is innocuous and has a clear right answer, for example, “Does the ocean contain water?”. But if instead a 4 through 6 comes up, you then answer the sensitive question of interest, in this case whether you’ve used PEDs in the last 12 months. Because the researcher knows that 1/2 the time the first question will be answered, a minimum of half the answers are thereby guaranteed to be “yes”. The percentage of respondents answering yes that exceeds 50% response is then 1/2 the percentage of the population that is practicing the sensitive behavior. And that’s a minimum, because some people who answer the sensitive question will still lie. That’s why the stated estimates of 29% use at the World Championships, and 45% at the Pan-Arab games, are both likely to be on the low side, and why the IAAF’s position statement in the article, if correctly quoted, is ludicrous.

Yeah sure, the results could be in error–they are likely under-estimates of PED use in track and field! Are these people really this blatantly deceptive and/or stupid or do they just think the rest of us are?

A good farmer

“A good farmer in our times has to know more about more things than a man in any other profession. He has to be a biologist, a veterinary, a mechanic, a botanist, a horticulturist, and many other things, and he has to have an open mind, eager and ready to absorb new knowledge and new ideas and new ideals.

A good farmer is always one of the most intelligent and best educated men in our society. We have been inclined in our wild industrial development, to forget that agriculture is the base of our whole economy and that in the economic structure of the nation it is always the cornerstone. It has always been so throughout history, and it will continue to be so until there are no more men on this earth. We are apt to forget that the man who owns the land and cherishes it and works it well is the source of our stability as a nation, not only in the economic but the social sense as well. Few great leaders ever came out of city slums or even suburbs…most of the men who have molded the destinies of the nation have come off the land or from small towns. The great majority of leaders, even in the world of industry and finance, have come from here. As a nation, we do not value our farmers enough; indeed I believe that good farmers do not value themselves highly enough. I have known all kinds of people, many of them celebrated in many countries, but for companionship, good conversation, intelligence and the power of stimulating one’s mind, there are none I would place above the farmer.

But there are two other qualities, beyond the realm of the inquiring mind or the weight of education, without which no man could be a good farmer. These I believe are born in him. They are a passionate feeling for the soil he owns and an understanding and sympathy for his animals. I do not believe that these traits can be acquired; they are almost mystical qualities, belonging only to people who are a little “teched” [touched] and very close to Nature itself.”

Louis Bromfield, Pleasant Valley, 1943, pp51-52.

‘Round and about

I’m going to move towards greater emphasis on ecology discussions, where some thoughtful people have some very interesting and well considered things to say online, in rather stark contrast to the acrimony, bias and flat-out confusion that dominates much of the online climate/paleoclimate “discussions”. Here are a few for starters.

Steve Walker notes in his real nice piece on what science is that he’s actually just trying to figure things out instead of win any arguments. Radical. He links to a piece that argues that an underlying sense of honesty is what really matters (and not just in science), rather than purely technical criteria like Popper’s “falsification” ideas.

Brian McGill talks about different types of Bayesianism and isn’t overly impressed by those who self-identify as practicing Bayesians as if that alone means something important.

Jeremy Fox asks if we should try to reproduce others’ findings. I say yes, for sure. Those who’ve followed the online dendroclimatology “discussions” know all about this one.

Lastly, here’s a practical method for navigating academic politics that you can give a try. Wear a helmet just in case.