Moneyball
2011 · Film · 134 min
Directed by Bennett Miller
The story of Oakland Athletics general manager Billy Beane's successful attempt to put together a baseball team on a budget, by employing computer-generated analysis to draft his players.
29 positive48 neutralfrom 77 commenters
Where to watch
No streaming data for this title.
79 comments on Hacker News
Had some fun combining the movie Moneyball and the Wii game Mario Super Sluggers together.
Just released Sluggers FYI, a comprehensive team builder with 117 inputs and counting. Learn about all the hidden mechanics of the game and pick the perfect team for your play style.
Currently, the default weights choose a Birdo team as the best. Try the site yourself to see if you come up with a better team.
The whole point of the moneyball movie and system is to find players who are undervalued relative to their ability to produce runs and critically continue paying them low relative to their game utility to get a good, competitive team cheap.
There's a market distortion from the rest/majority of teams incorrectly evaluating the players. Knowing that and taking advantage of it is underpaying if you believe in paying people their real worth instead of just what you can get away with paying them.
I pretty much agree with everything you said, but trying to think of a counter example I'm reminded of the movie Moneyball. Reviewing the detailed stats of each baseball player vs. the cost of hiring them seems pretty close to measuring "time doing useful things on the field". I'm not sure how common this practice is in general in current professional sports.
These are not fair analogies to wealth inequality unless you think all people who have more than you got it by cheating people. A better analogy would be the movie Moneyball, about baseball's inequality.
Effectively the problem is that wealth is an inherent feedback loop that naturally creates Pareto distributions instead of normal distributions. People don't have to be 'unfair' for this phenomenon to occur.
Yeah. Sports science is about evaluating individuals in a team and trying to come up with statistics that make team picking more reliable.
Moneyball is a good movie that dramatizes the dawning of this science.
Fantasy Football is a game version that's become popular for online gamblers.
-------
I've seen a paper or two floating around on the subject. But I've forgotten the names of them by now and I dunno any good references unfortunately.
Hope you find something in your search!
It sounds like you have an aversion to sports because you were forced to participate in them as a child, which is completely understandable. If someone had coerced you into the school's drama program you might find theater revolting as an adult as well.
It might help to think of sports less as "people playing a simple game" and more as "a demonstration of human athleticism at the highest level". The game is just a pretext. When you watch table tennis, you're viewing people with some of the most finely honed fast twitch muscle fibers, reflexes, and hand eye coordination in the world demonstrate their skills, not just people bouncing a ball back and forth across a table.
You might also enjoy the data analysis aspect of sports. Sports are increasingly being dictated by statistics. It's somewhat cliche at this point to recommend it, but if you haven't already, it might be worth watching the movie Moneyball (or reading the book it's based on).
Ohhhhhh shit, that has to be the longest paragraph in all of HN. Have you seen the movie Moneyball?
Just start with the premise that bias isn’t helpful and even less helpful when implicit. Let that determine what to measure and will not be harmed. If such decisions twist you into knots then you are the person qualified to make such decisions.
There are comments similar to this all throughout the thread. It reminds me of the movie Moneyball. Even when the numbers are clear to see, people will still much rather follow their intuition. The Oakland Athletics' insistence on sticking to the numbers anyway is what led to their enormous success, and now the same strategy is copied by sports teams around the world.
It can be hard to convince people with math alone.
> Like, if their practices were so inferior, then any domestic firm that didn’t get taken over by them should’ve had a leg up and could’ve dominated the domestic market.
There aren't infinite firms. Plus in the article, the previous paragraph goes into how the tooling industry was being bought up by conglomerates so the finite firms became even easier to count.
A company fitting your argument is Telsa. The existing firms weren't willing to canabalize their existing product lines so they didn't invest into EVs and now a domestic firm is eating at their market share. However, "Who killed the electric car?" is from 2006, there has been a lot of pent up demand for EVs that no domestic firm was selling to.
However, something also fitting your argument is Moneyball [1][2]. It's not until the 2002 season that teams start to use statistics to determine who to staff their roasters? The League is from 1876; it took 126 years of baseball before a team figured out how to use math!
> Don’t you think the more likely source of “pressure” was the international suppliers who had lower costs?
Well, the article agrees in that it says "By now, tools from Japan and other countries were as good as or better than US tools, not to mention cheaper and more reliable.".
However, R&D was also being cut prior to this so if you don't do any R&D and your products become uncompetitive it's probably because R&D was cut.
> Was the policy to outsource everything, or liberalize markets and let firms and consumers more freely choose where to buy things from?
Well, don't forget there are winners and losers when trading.
In this case the losers are the American Tooling Industry; the winners were everybody that bought from them lol.
--
I do think this growth every quarter is a big problem though. The early observers of business cycle all noticed that it has its ups and downs. Pretending that there won't be a down and fudging the numbers so that you don't have any downs is going to cause future problems. Delivering not-finished lathes counts to me as fudging the numbers.
[1]: https://en.wikipedia.org/wiki/Moneyball_(film) [2]: https://en.wikipedia.org/wiki/Sabermetrics
Better as a charity, IMHO.
I hosted a movie meetup to see Moneyball. Afterwards, I asked the group, "How many liked the movie?" Every hand went up.
Then I asked, "How many are baseball fans?" No hands went up.
Good times.
I really like this list. I would only add:
- Indie game: the movie
- Moneyball
Arrival, The Martian, Moneyball, The Big Short, there's something magical about these movies that no matter how many times they are on TV I will just sit and watch them again. I don't know what it is about the stories but it probably has has something to do with that XKCD comic about the scene in apollo 13.
Kinda-sorta reminds me of the movie Moneyball. There were big layoffs, including a number of highly paid people, thus lowering the average prevailing wage. Now even if firms are bringing in foreign workers at the average prevailing wage, that wage is still lower than before the layoffs. An effective play on the system, replacing US workers with cheaper foreign workers.
Red Plenty, a novel that is loosely about the invention of linear programming in the Soviet Union (a branch of mathematics, only somewhat related to computer programming). Good description of what the process of scientific invention feels like in the first part of the book. (if you want more depth on the mathematics of linear programming, Understanding and Using Linear Programming is a very readable (and short) textbook that only requires basic linear algebra knowledge).
Moneyball, people have been recommending this to me for years but I never read it because I find baseball so boring. I'm glad I finally read it. It's not really a book about baseball it's about finding and exploiting unappreciated edges.
Who We Are and How We Got Here, makes a lot of the recent genetic research on human origins understandable. Especially relevant this year due to Svante Paabo's nobel prize.
Once Upon a Time in Hollywood, possibly better than the movie (which I love). Reads a bit like a script. The dialog is very good and contains many "scenes" that aren't in the movie.
$12 Million Dollar Stuffed Shark, if you are interested in modern art or how things that look like something anyone could do can sell for absurd amounts of money this book is worth reading.
A Sailor of Austria: In Which Without Really Intending to, Otto Prohaska Becomes Official War Hero No. 27 of the Hapsburg Empire, one of the reviews I read before starting this described it as something like "a techno-thriller set on an austrian submarine in world war one". If that sounds appealing to you, you'll probably like this book. If not don't bother starting it.
Moneyball and The Big Short were about major events that Americans noticed and cared about. Flash Boys wasn't. Most Americans never even heard of HFT.
I still think it would be a great movie, but it never had the same relevance.
Strangely, I just finished watching Moneyball about a minute ago, open HN, and see this quote from the film! That was an unbelievable coincidence!
Just a correction. The fat guy wasn't from his team, he was from some other team. And he didn't fall, he jumped down onto the first base because that's what he was only barely able to do, he knew that in the best case scenario he only have like 50% chance for the first base because he run so slow.
So basically his tactic was: if I run as fast as I can I have 40% to reach first base. If I run as fast as I can and jump I have 50% to reach first base. He didn't even consider going to second base, ever.
Moneyball is great movie, go watch it.
Well, as a big fan of Michael Lewis, I never would have thought Moneyball and The Big Short would make good movies, but here we are. Good scriptwriters are wizards.
There should be a movie like moneyball for this guy.
There are a few (all are based on true story):
- Moneyball
- Good Night, and Good Luck
- Kinky Boots
- Telstar: The Joe Meek Story
- Ek Doctor Ki Maut (a hindi-language movie based on actual events, chronicling the sad state of Indian research scientists in government institutions; the movie is based on the life of an Indian doctor from Kolkata who developed a vaccine for leprosy. The doctor committed suicide, but the movie has changed that)
Not a book, but I really like the "Moneyball" movie (2013) for that reason. They design an algorithm and use it to manage a team.
> It’s striking that so many stories about the triumph of metrics over intuition involve games. I’m convinced that you should put statisticians in charge if your goal is to win a game.
I enjoy playing around with baseball data. About a year before the pandemic, I went out and presented some research at a Sabermetrics conference hosted by the Boston Red Sox. 90% of the pro teams had their data science teams present, and I spoke with many of them (FYI - working in baseball is like working in video games: long hours and low pay).
Anyway... The Red Sox (at that time, at least) divide their analytics department into 3 subject matters, and I spoke to the lead of one of those 3. I asked how different their analysis is compared to all the old baseball knowledge developed over the last 150 years.
He said that it's not different! All the intuition about the game built up by the old-timers spending decades in the industry still holds! The analytics is all about tweaking things and making slight improvement in the odds. If they can improved the odds of a beneficial outcome by 2-3%, they consider it a success, and if they can do that consistently over the course of a season they will win 5-10 extra games. That's it! (SPOILER ALERT) Remember that in the movie Moneyball, the Oakland A's didn't win the World Series. They didn't even make it to the World Series. They were beaten in the first round of the playoffs by another small market team (Minnesota) with a similarly small payroll that wasn't doing the moneyball thing ;)
Moneyball (book 2003, movie 2011) describes how Oakland Athletes build their baseball team in 2002 based on statistical analysis of player performance.
Then around 2008-2009, Houston Rockets, and Golden State Warriors (Oakland) started to apply similar analysis to basketball, and started to find the value of having quickly moving players who throw precisely, to score 3 point shots, even if these players are not as tall. Now it's called the 3-point revolution.
https://www.thestar.com/sports/basketball/raptors/2008/03/13/moreys_moneyball_approach_paying_off.html
https://en.wikipedia.org/wiki/Daryl_Morey#Houston_Rockets
https://ageofrevolutions.com/2019/02/25/data-science-and-the-3-point-revolution-in-the-nba/
It hindsight, it feels like a long delay from the success of Moneyball, until others applies similar thinking to other sports.
But ... Amazon for example redesigns i are ouse picking to make employees fungible. I am pretty sure project management is becoming totally fungible - the reporting and chasing and so forth, can be replaced by automation and is becoming so despite best efforts of millions of "mqnagers"
Something intrigues me about this idea.
The film / book "Moneyball" showed how to re-create great players "in the aggregate".
I think that we will soon enter a phase of "MOOP" - measuring our daily activities on a deep basis, phone calls to colleagues and clients, tonality, agreements and actions etc.
And at this point redesigning an organisation to recreate CFOs or salespeople "in the aggregate" becomes ... possible?
And if you can do that you get close to the ability to create that fungible option - and close to valuing the job.
If you are a guy then Social skills are about one thing only:
Accepting failures and low success rates
People who love machine learning belong to the camp of the serial optimisers.
There is hardly something which is more at the opposite end of the spectrum
Also given that we are taking about machine learning let me quote the movie Moneyball
Moneyball of course isn’t about machine learning , but it’s actually about something even more important: how clean are the data being used
In any event at some point Billy Beane portrayed by Brad Pitt says :
“I hate losing…in fact I hate losing more than I love winning”
Well in order to be “good with social skills “ you gotta be the opposite …you gotta love winning much more than you hate losing , so that the few victories will get you excited and propel your spirit through all the rejections .
I know what you mean about The Social Network and Silicon Valley. Not tech specifically, but definitely motivating and inspiring for me are:
Moneyball Margin Call Limitless The Big Short Wolf of Wall Street That Fyre documentary and at Christmas - Trading Places
What a sensational title, it's so tech-focused and the article fetishizes smartness in a bizarre way.
There are talent curators in every single field, for instance from baseball, this reminds me of those old advisors in the movie Moneyball, who look at how people seem to be rather than using actual evidence of prior performance. Where is the quantification of TC's magical power? It's narrative, not numbers. The article doesn't address his failure rate or the bias of these selected stars getting a huge boost from a rich established individual.
Skepticism aside, I think we'd do well as a society to pull 18 year olds with a lot of promise out of college and directly into ventures for a year or two. They can always go back if they need to, but I question the wisdom of putting our best youth into a college system where their efforts are entirely hoop-jumping, self-promotional classes that contribute to a GPA but to no one else. We're effectively squandering their apprenticeship on grade values, not on projects or useful deliverables.
software engineering is decades behind other engineering disciplines; it's in the phase of elixirs, etc; that's why it's "so hard" because the industry is optimized around various insular self-reinforcing patterns; like recruiting practices in the movie Moneyball;
Moneyball is one of my favorite movies!
I try to make math fun in my house. A couple of things I’ve found that work:
1. Anytime there’s a “guess how many are in the jar” contest, I get my kids to use the appropriate formula for volume to see if they can guess the right answer. They usually get really close.
2. Show them how math helps them win at games. Monopoly is great for this where you can calculate the ROI for different properties on the board, how many houses are ideal, etc. you can go further with likelihood of landing on certain properties too.
It works. The hardest thing about math motivation as a kid is “where will I use this?”
Moneyball (movie) is a good one too.
That's probably fair. Soccer is a great example that I wasn't really considering as an American.
That being said, if you haven't seen the movie Moneyball, I'd recommend it. Baseball was truly transformed by computerized statistics, and because baseball is basically decided as a series of batter-pitcher matchups, it lends itself to being predictable in a way that other sports can't be. Baseball is the monte carlo simulation of sports.
Moneyball's point was subtly different than this, and I was hoping the article would actually touch on it. The article suggests hiring the best candidates that have skills that mesh well on the team; Moneyball explicitly searches for people whose flaws can be overlooked. This way, you get a team member whose performance outpaces the open-market salary.
In other words, you don't hire candidates whose strengths match your weaknesses, you hire candidates whose weaknesses you care about less than the competition does. Like, the best example from the movie was the pitcher with an effective but highly unconventional delivery mechanism.
Did you see the movie MoneyBall?
That's the point.
Different interviewers look for different things. Just watched moneyball, one thing the scouts looked at was the attractiveness of the candidates girlfriend. Low attractiveness equals low confidence and was a mark against the player. If the movie is to be believed, such inane metrics were used be people whose entire job was scouting candidates. I would not be shocked if there are similar dumb metrics used by a hiring manager when hiring candidates may just be one of many roles they perform.
First and foremost - it's not about you. Millions of people are getting laid off daily now due to the exceptional circumstances.
It's ok to feel down for some time, but it's important to understand the broad picture and not take this personally - regardless of what you were told.
Now, regarding leadership resources, I would recommend to start with the following (in this order):
Moneyball (movie), books: The Goal, The Phoenix Project, The Mythical Man-Month, The Hard Thing About Hard Things, The Culture Code and in-between those - various Simon Sinek videos.
If you read this far, you should be able to continue finding materials on your own from here ;)
Best of luck!
I'm reminded of a clip from the movie Moneyball: https://www.youtube.com/watch?v=fTjhHrcyiQI
I really like your suggestion that someone check in with the person in a couple days. That helps address the charitable desire without compromising the need for performance at work.
"Moneyball: the art of winning an unfair game." by Michael Lewis.
Pretty good. Makes you think! More in-depth than the movie (as you'd expect)
Sports isn't an equation
Tell that to Billy Beane, general manager of the Oakland A's, who used statistical analysis and team interaction models (i.e. "equations") to turn propel the team into the top ranks with salary expenses of only 1/3th of the other top teams. The story was described in the book Moneyball and dramatized in the film of the same name.
Bit strange to see a piece like this not refer explicitly to Moneyball (2011), or the book by Michael Lewis on which it's based. The film's fantastic, and shows how Oakland Athletics pivoted their player selection process from the talent scout gut-feel approach to a sole focus on statistics, and the dramatic turnaround it brought.
Then again, the piece focuses on the human aspects of removing the mystery behind the game, perhaps it doesn't need to speak to why/how that came about.
Moneyball IMDB: "Oakland A's general manager Billy Beane's successful attempt to assemble a baseball team on a lean budget by employing computer-generated analysis to acquire new players."
https://www.imdb.com/title/tt1210166/
Michael Lewis (Moneyball, Big Short) interviewed Alex Kogan:
https://atrpodcast.com/episodes/the-alex-kogan-experience-s1!d20f3
After listening to this, it sounds like Cambridge Analytica was overblown, in the sense that the information wasn’t as useful as claimed.
My manager had us all watch The Moneyball movie with Brad Pitt, and informed us that the company would rely on Data Science and metrics to make decisions. I’ve seen some good come from that strategy, and some not-so-good.
I think the hard part is in measuring the data we collect, we don’t always see how data is related to what we’re trying to achieve. The data doesn’t lie, but if you make a mistake interpreting it, it can seem like the universe is conspiring against you.
>Figuring out the most likely winner of a game of baseball is a little harder; there's a whole movie about a guy who was basically doing that in 2002.
Nate was literally one of the core people working on Sabermetrics around the time that Moneyball was written.
"Moneyball" is the name of both the movie and the book it was based on.
> It's because film is a visual medium,
It happened with books too: See "Liar’s Poker", by Michael Lewis. He says that he wrote it "so that fewer idealistic college kids would dream of working on Wall Street. ... Somehow that message failed to come across ... They'd read my book as a how-to manual."
http://www.gatsby.ucl.ac.uk/~pel/misc/The-End-of-Wall-Streets-Boom.html https://danwang.co/liars-poker-by-michael-lewis/
Michael Lewis also wrote "Moneyball" and "The Big Short" which were made into films.
Well I'm commenting on the internet Verdex_3. I'm not going to take the time to write out a bunch of interesting statistical examples for you. I wasn't trying to say it's easy to make statistics interesting. Was only trying to refute what I thought that you were asserting; statistics and probability are not interesting to teach. Designing enjoyable, interesting, challenging and fair curriculum is a really hard task to do for any subject...
If you don't believe math can be made interesting I would checkout websites like FiveThirtyEight or youtube channels like numberphile. It's definitely possible to describe statistics or math problems in interesting ways. It's hard to make any class interesting, but any teacher can do it if they work at it hard enough.
Also your moneyball comment makes no sense. All teams operate like the 2002 Oakland A's now, if anything the the movie shows the triump of statistics over bad heuristics.
Like, I want to believe you that such a thing is easy. But I'm not really convinced by the assertion that it is followed by a non-descriptive blurb. I mean I get what you're saying, "Find what they care about and try to apply statistics to it." However, I don't believe that this process is easy.
Sports is a good example of why I don't think this is easy. What exactly is the point of statistics in sports? Predicting what teams are going to win or what strategies are superior. Most children in school are not interested in sports because they're into strategy or because they're predicting who's going to win. They're interested in it for social reasons. Which team do their parents or friends want to win. Telling everyone at thanksgiving that the family team is going to lose the game will probably not go well for them. And as far as strategy goes ... I thought the statistical analysis of the extra point kick in football indicates that you should never do it. Teams rarely make use of this at the professional level. Also didn't they make a movie about how nobody pays attention to the math in baseball (Moneyball?). Anyway, if adults who have money and fame on the line can't be bothered to care about statistics in sports, then I don't see how children are going to be much different.
Of course that's where you come in. You say it's easy. Personally, I would love to see why you think this because it looks hard to me. I look forward to a more in depth response from you.
I also like to direct people to the following https://www.theguardian.com/technology/2017/may/07/the-great-british-brexit-robbery-hijacked-democracy Also "no way a rag-tag team beats.." is straight from the plot of the movie Moneyball which is what we're talking about here. Also no-one is talking about GDPR which could stop this (please see below).
It will be hiring like in Moneyball movie.
> Having read MB the book (and seen the movie, for what that's worth) I wonder if Billy Beane is missing the potential for deeper insights that only human brains can put together. It is the thing that we are good at, after all ...
The human brain is also very good at misleading itself. Statistics is how you avoid that.
Do remember that Moneyball happened in an era where statistics was just becoming more ubiquitous. Bill James was collecting and collating statistics from a bunch of volunteers who used to snail mail him the scoring sheets from the games. He only quit doing that in 1988 because suddenly there was a flood of statistics.
So, from weak data to Moneyball in less than 10 years is actually fairly impressive.
I think we need to be critical of the Moneyball narrative - and not just because Oakland hasn't actually performed that well under the MB regime ...
The understood narrative is that dummies were choosing players based on broken heuristics and that proper, sober heuristics would uncover hidden value.
Put another way: If you remove human decision-making and replace it with algorithms you will achieve better results.
I am skeptical of that conclusion in every other realm and I don't think baseball (or hiring, as is the case in this article) should be exempt from that skepticism.
Having read MB the book (and seen the movie, for what that's worth) I wonder if Billy Beane is missing the potential for deeper insights that only human brains can put together. It is the thing that we are good at, after all ...
I think diverse hiring practices is a good thing, even if we ignore the social aspects of this discussion and just talk about the money side of things.
Think about it like the movie Moneyball. By rejecting the flawed collective wisdom of baseball insiders, they were able to create a winning team on a budget by using overlooked metrics. Likewise, job interviews are notoriously unreliable and inconsistent at finding good employees. The uneven demographics in tech jobs is likely partly the result of flawed wisdom in the hiring process. If you sample more heavily from an irrationally underrepresented group, you would get on average higher value candidates than if you sampled evenly from the whole pool.
Selecting solely based on merit is only effective if your selection process works properly. In practice, we see that both conscious and unconscious biases pervade the selection process, so I think this method is worthwhile.
Maybe the DOD should go watch the movie Moneyball.
re Moneyball - the big takeaway (clearer in the movie) being that most people who experience tremendous success don't actually realize it, particularly, at the time.
Moneyball. Will affect the way you (strive to) make decisions. Worthwhile even if you've seen the movie and/or don't like baseball.
The End of Faith. Definitely don't agree with all of this, but if you're at all on the fence about religion this book has the potential to be very influential.
Catchy story that solicits moneyball storyline to NBA draft and takes it to the next level... Wait for the movie
This sounds like a large reason we have employment problems in the US, and frankly it stinks of the "old scouts" in the book+movie "Moneyball". Engineers aren't known for their social networking and sales prowess. IMHO, engineers are among the most likely population to undervalue themselves in their early career.
It's difficult and time-consuming to on-board and train a programmer / developer / software engineer, but every programmer needs to start somewhere. I consider myself lucky in that I worked my way through college as a web developer for companies in Silicon Valley. If I didn't have my social network of other local engineering students, I would probably have started my career around $70k-$80k. I suspect that programmers in extremely rural parts of the US (like Alabama, West Virginia, Wyoming) would be lucky to find $70k/year.
There is also the TV-series "Halt and Catch Fire".
Slightly related movie suggestions, based on their interruptive theme: "Field of Dreams", "Moneyball", "The Wolfs of Wall Street"
The article does seem to have a good description of how Silicon Valley venture capital thinks and works.
Of course, again, on average the venture firms have poor ROI:
http://www.kauffman.org/newsroom/2012/07/institutional-limited-partners-must-accept-blame-for-poor-longterm-returns-from-venture-capital-says-new-kauffman-report
http://www.avc.com/a_vc/2013/02/venture-capital-returns.html#disqus_thread
Yes, apparently the Silicon Valley information technology (IT) venture firms are trying to evaluate everything that comes in the door having to do with IT. There's a problem doing this: By analogy, it's like picking dart throwers. Only a tiny fraction of the dart throwers are actually experts, and the rest are amateurs throwing wildly. But, there are so few experts and so many more amateurs that, on average, nearly all the darts in the center of the target come from lucky amateurs and not real experts. So, net, the Silicon Valley is trying to evaluate the huge fraction of amateurs that mostly need just luck.
Much better would be to concentrate on the few real experts who need much less in luck. "Few"? Right, but so is the number of desired successes, and the number of real experts should be sufficient.
Then, with the article, we come to a significant point: The Silicon Valley venture capital (VC) firms just do not know how to plan, do, and evaluate projects from real experts.
Some people do know: The shortest description of project planning is:
Step 1: Pick a good problem.
Step 2: Use IT to get a good solution.
So, sure, in Step 1 want to solve a suitably important problem, one where the first good or a much better solution will be a must have and not just a nice to have for enough people so that the number of people times the average earnings per person is enough to make a real unicorn.
Or, in crude, simple terms, maybe want a must have solution for a big ass problem in a big ass market.
Sure, the ideal situation, although likely not in IT, would be a safe, effective, inexpensive one pill taken once cure for any cancer. Such a project would just "cut right through" (borrowing from the movie Moneyball) all the nonsense about body language, vision to change the world, how to manage 100, 1000, 10,000 people, hard scrabble backgrounds, tone of voice, assertive manner, clothes selections, etc.
And, sure, in Step 2 want to use IT to get the "first good or much better solution", defensible, with barriers to entry, etc.
So, from the article, apparently Silicon Valley VCs don't know how to evaluate projects that follow the two steps above.
Does anyone know how to do such evaluations? Sure:
For Step 1, that the problem being solved is suitably important should, like the example of the cancer pill, be obvious. Right, here get to rule out considering projects such as Fab, PopSugar, Zulily, SnapChat, AirBnb.
So, what's left is Step 2, and evaluating that is mostly just technical.
While apparently Silicon Valley is next to hopeless at evaluating new, powerful technology, our better research universities, NSF, NIH, DARPA, etc. are quite good, thank you.
So, an example of a project with really good technology? Sure:
http://iliketowastemytime.com/sites/default/files/sr71_blackbird_leaking_fuel_cell19.jpg
Sorry, Marc: On my BoD I want -- it's crucial and I insist -- people who can understand my business, including the crucial core, unique IT, and for that, Marc, I want to know what you did in your STEM field Ph.D. program and have done in research since. E.g., I want people like the dissertation advisors in STEM fields in top research universities and problem sponsors at NSF, NIH, DARPA. How many Silicon Valley VCs do you know who qualify? I want people like those who looked at Kelly Johnson's engineering drawings and approved the SR-71; Marc, their batting average is much better than yours or nearly any VC (Chris Sacca may be on the way to being the only exception), and they fund just off pieces of paper.
Marc, from the article your evaluations look like you would have asked Kelly Johnson if his SR-71 could also fly to Mars and refuse to fund him unless he enthusiastically said "Mars? Yes, and to infinity and beyond!" or some such nonsense. Look, Marc, it was Mach 3+, 80,000+ feet, 2000+ miles without refueling, never shot down -- as needed, as planned, as accomplished.
Marc, in simple terms, in IT, you just do not know very well what you are doing; on my BoD, can't use you; your wildly irrational flights of nonsense could sink my company.
There's a quote in the article from an assistant DA claiming it is a 'moneyball' approach but seems more like a hash of somewhat data-oriented approaches.
It's been a long time since I've read the Moneyball book, and never saw the movie, but it seems to me there are two levels of 'moneyball' approach:
1. Use data to make allocate resources differently than gut instinct/received wisdom (of baseball scouts). This level can be applied very widely. Maybe they (Manhattan DA office) are doing this level to decide who to put resources into prosecuting. But clearly they're not doing it comprehensively, given massive prosecutions for marijuana.
2. Do (1) to make a bet in a competitive environment that goes a different way than most competitors. I don't see this level applying to what the DA is doing at all, though I imagine it could be done in the hiring of law enforcement/prosecution, which seems to be competitive, at least at the executive level. But there's probably a lack of data to make such bets. (I haven't looked, but an idle question I have is whether there's data to support shifting police work to a female-dominated profession; if there is maybe that's a bet that could be made.)
I say this in part because I think there are non-sports fields in which both levels could be very directly translated, eg http://gondwanaland.com/mlog/2012/03/04/altmetrics-moneyball/
Believe it or not there are companies out there looking for people exactly like you: individuals that can come in, question the status quo, and push things forward. Many big organizations are realizing that in order to compete they actually have to operate like much more nimble enterprises. You'd be the only one who knows how to do that :-)
I draw a lot of parallels between this and the movie Moneyball. You will have lots of fun with your work - because you bring entrepreneurial passion, creativity, and grit. But there will be many that don't like you coming in, asking obvious questions, and then delivering better stuff. Find someone in executive management that has your back politically and you'll be fine. :-)
Shoot me a note at @thegrif if you want to talk more. Depending on where you are in the world I might be able to facilitate some introductions.
I'm reminded of the movie MoneyBall. Jonah Hill and Brad Pitt are employed with what outsiders would consider horrible jobs, but each stick with it because they enjoy it.
As an outsider you may think Penny Arcade's offer is bad, but someone, somewhere would love nothing more to work with the people behind that legendary comic and expo, no matter how rough it is.
Edit - I would also like to make an analogy with MMO guilds, particularly World of Warcraft. There are players who spend 4+ hours a night with their guild hardcore raiding (especially after the release of a content patch). These hardcore guilds have very strict enlistments. Unless you're as hardcore as them you're not in. An outsider would think they're insane, but there is no shortage of people applying to these guilds because they enjoy the experience of hardcore raiding. Some of these guilds have a very family-like bond toward each other, so you have to consider community/culture fit.
Check out these threads:
https://news.ycombinator.com/item?id=450702, https://news.ycombinator.com/item?id=4060887
Also my personal list of favorites that entrepreneurs should watch (although not necessarily about technology):
Glengarry Glen Ross - http://www.imdb.com/title/tt0104348/
Moneyball - http://www.imdb.com/title/tt1210166/
The Pursuit of Happyness - http://www.imdb.com/title/tt0454921/
Jerry Maguire - http://www.imdb.com/title/tt0116695/
Catch Me If You Can - http://www.imdb.com/title/tt0264464/
Back in early 2011 I ran a BTC miner for awhile on a spare system and generated around 18 BTC. I bartered them later in 2011 for a pair of movie tickets to take my wife out to see Moneyball (ironically).
It's both surprising and alarming to me how many VCs cite "emotion" (rather than logic) as a main if not primary factor in their decision to fund a startup or not. I'd venture (no pun intended) to say that this is the root cause of much of the "politics" and cronyism described by the OP, and I don't think YC is immune to this either.
Sure, we can talk all day about "human nature" and how "those are the rules of the game", but there's gotta be a better way for investors to make funding decisions than "trusting your gut" or "I have a good feeling about this team."
Yes, VC has changed and evolved over the years thanks in part to new institutions like YC, but the overall VC landscape still reminds me way too much of that scene from the based-on-the-book film "Moneyball" where a bunch of old baseball scouts are sitting around a table discussing who to recruit for next season, and they keep recommending players based on fluff and BS like how good a guy looks swinging the bat (vs. actual hitting stats), how confident he looks, how good he he looks with his shirt off, etc.
This all leads me to believe that, while things are getting better, there's still a long ways to go before Silicon Valley and michaelochurch's so-called "VC-istan" can be considered anything close to a true meritocracy.
Somewhere in this comment, and in the first principles vs. analogy dichotomy, is the dichotomy showcased in the book/film "Moneyball".
Something has always bothered me about it. Something like ... is it better for a human to make a wrong decision than it is for a machine to make a right one ? Perhaps "it depends" ? If so, what is the threshold ? How wrong does a human decision have to be to be inferior to a machine decision ?
The same thing is showing up in Talebs _Antifragile_ ... he argues quite clearly for analogy, and skewers decision making from first principles.
It troubles me somehow...
If you've seen the movie, I'd highly recommend "Moneyball" the book too. It goes into a lot of great detail that the movie doesn't deal with.
Another vote for moneyball. Very good movie on the subject
It's definitely a market opportunity (see: the movie Moneyball), but I wouldn't say programmers should just go into sports unless they actually love sports (and programming). While this type of person is rare, they exist, and it's their market to own, basically.
> Sports are the exact opposite, they are almost completely unpredictable.
No they are not. Sports are very predictable, but they are a stochastic process. You simply have to use a different way to reason about them.
A lot or programmers deal with stochastic processes as well and we have plenty of tools at our disposal that make us able of reasoning about even the most random seeming stuff. Case in point: software that predicts how many cash registers a store might need to never have a single person waiting for more than 3 minutes, while keeping costs at an optimum.
Good movie on this subject: Moneyball
>Scientists, statisticians, economists, and engineers shouldn't be worshiped as sources of incontrovertible truth - they're just humans trying to do the best objective, accurate job that they can!
True, but they're a big step in the direction of progressively less wrong [1] from the traditional alternative punditry and 'gut' analysis [2].
If I were a movie producer I'd buy the rights to Moneyball 2, script it about this election, and recast the gossiping scouting staff as all the blowhards [3] and bs artists [4].
Nate Silver, Andrew Tannenbaum [5], Drew Linzer [6], Sam Wang [7] and the other modelers deserve a lot of credit for helping the public cut through the bullshit.
[1]: http://serendip.brynmawr.edu/sci_cult/lesswrong/lesswrong/
[2]: http://blogs.wsj.com/peggynoonan/2012/11/05/monday-morning/
[3]: http://www.theatlanticwire.com/politics/2012/10/people-who-cant-do-math-are-so-mad-nate-silver/58460/
[4]: http://drudgereport.com
[5]: http://electoral-vote.com
[6]: http://votamatic.org
[7]: http://election.princeton.edu/
the baseball / moneyball metaphor was important.
i know that some folks who don't know baseball won't get it, but the references because of the book & movie were somewhat essential.
It seems that we need to have a conversation about truth, art, and the space between the two. Just because it happened doesn't mean it is worth telling. Just because it happened doesn't mean that it will sell. Just because it happened doesn't mean that it is art.
Benjamin Franklin probably didn't do anything with a key and a kite and a thunderstorm. Did you know? Do you care? It makes a good story so we tell it. We build the mythology of our heroes (and villains) through the best narratives that we can construct, not necessarily the most true. Aaron Sorkin is not a truth-teller; he is a storyteller. His entire purpose is to entertain and he has a knack for doing just that.[0] Hopefully he manages to do so with this film, because there are parts of Steve Jobs' life that would do incredibly well with the Sorkin treatment. Personally, I can't wait. The author of this piece is not so excited. I have some advice for him: don't watch it, particularly since you don't seem to know the difference between biography and story.
[0]: See: The West Wing, Social Network, A Few Good Men, Moneyball.
Just watched Moneyball last night, and the end of this post makes me think of what was said in the movie. I presume this to be true, because it's one of those simple things (for a little longer, FMRI's are starting to read thoughts afterall).
The point was that a scout can spot a player with great talent and abilities, but no one but the player knows what goes on inside his head, and no one knows how he'll do for sure in the big leagues.
Seems like a perfect analogy to founders. YC is spring training where they've identified people with potential and will then try to coach them onward.
The big difference, though, is that you can get 'the majors' (a successful company) without YC, and as stated in a similar post today, the greatest founders will find a way regardless.
Props to all who try. More props to those who try, fall, and try again.
Not surprised that most experts(NBA & DI school scouts) don't embrace this because it is easier and safer to go with the typical archetype as opposed to raw stats. Its "human nature" safe in the sense most people will not question if you go with the established archetype. It also devalues the current process of scouting.
An apt quote from the movie Moneyball: "I know you've taken it in the teeth out there, but the first guy through the wall. It always gets bloody, always. It's the threat and not just the way of doing business, but in their minds it's threatening the game. But really what it's threatening is their livelihoods, it's threatening their jobs, it's threatening the way that they do things. And every time that happens, whether it's the government or a way of doing business or whatever it is, the people are holding the reins, have their hands on the switch. They will bet you're crazy. I mean, anybody who's not building a team right and rebuilding it using your model, they're dinosaurs. They'll be sittin' on their ass on the sofa in October, watch the Boston Red Sox win the world series."
This thinking is exactly what Moneyball was all about in Baseball. Football looking at the wrong things.
For instance, 225# reps, vertical leap, reach, etc (all NFL combine measurements). Great, you are measuring and ranking, but are you doing anything meaningful? Are you actually looking at the right things? Probably not.
If they were, Wes Welker, Tom Brady, Victor Cruz (all in the past superbowl) would have been first round picks, probably top 10. Not undrafted, or late round picks. And JeMarcus Russell and Ryan Leaf wouldn't have been drafted #1 and #2 overall (all the physical tools but not the mental - flame outs).
The point is that we as people tend to think we know what to measure and track, but we likely don't. Frankly, we are probably making it up on the fly and we convince ourselves and others that these are good measures, and until someone figures out the next best thing, they actually are. But, as I said, they are probably not the best, or maybe even good, in the infinite wisdom sense.
But, think about it this way. Both football and programming have ways we can actually see if someone can do what they say that can do: literally, look at the film. Look at game day film on a player. Look at github or other places for programmers. And, just like with football players, give them REAL scenarios that test their ability to think through a problem, in real time. Do the same with a programmer. Put these two together and you get rid of the people who can't think (Russell and Leaf) and you get rid of the "physical specimens" that can't play (Most any Oakland WR drafted under Davis).
I personally think this is a much better way that weeds out the most people. Refine your questions and technique and you can spot the people who can and can't perform pretty quickly. If you are unsure, give them a simulated game (programming problem at home) to see what they can do.
I doubt permission would have to be given - and at least in the case of the book permission was given.
I'd also love to see a good Jobs movie, and looking at Moneyball and The Social Network, it proves it can be done right if they continue the trend.
It would be a daunting task on Sorkin's part, that's for sure.
Paul DePodesta, the subject of the film Moneyball, actually spoke two days ago at the O'Reilly Strata (big data) conference in New York.
http://strataconf.com/summit2011/public/schedule/detail/21651
So you can get his insights, straight from the horse's mouth, in a talk aimed at data geeks. For me, one of his main takeaway points was to question your assumptions. Always ask: "If we were to start again today, would we do things this way?" This exercise is used to tease out entrenched biases that you might not normally examine.
Because it was a keynote address, it should be publicly available on the O'Reilly youtube channel in the next few days, if not sooner: http://www.youtube.com/user/OreillyMedia#g/c/2543D0F253DD85CE
Here's a take on the book and movie from Daryl Morey, GM of the Houston Rockets, who's attributed for the most part in bring sabermetrics to the NBA:
http://www.grantland.com/story/_/id/7001767/moneyball-houston-rockets
Mark Cuban owns the Dallas Maverick and is heavily into data analysis as well: http://www.hardballtimes.com/main/article/state-of-sabermetrics-insights-from-the-2010-sloan-sports-analytics-confere/
It's a lot more interesting (and complex) in basketball than baseball in my opinion because it's more of a fluid sport where the actions of a player will directly affect those of the other nine.
Also I can't get over the fact that a movie about data will be shown this weekend in almost 4,000 theaters and will star one of the biggest movie stars in the world!