Evidence Soup
How to find, use, and explain evidence.

18 posts categorized "sports"

Tuesday, 11 October 2016

When nudging fails, defensive baseball stats, and cognitive bias cheat sheet.

What works reading list


1. When nudging fails, what else can be done?
Bravo to @CassSunstein, co-author of the popular book Nudge, for a journal abstract that is understandable and clearly identifies recommended actions. This from his upcoming article Nudges that Fail:

"Why are some nudges ineffective, or at least less effective than choice architects hope and expect? Focusing primarily on default rules, this essay emphasizes two reasons. The first involves strong antecedent preferences on the part of choosers. The second involves successful “counternudges,” which persuade people to choose in a way that confounds the efforts of choice architects. Nudges might also be ineffective, and less effective than expected, for five other reasons. (1) Some nudges produce confusion on the part of the target audience. (2) Some nudges have only short-term effects. (3) Some nudges produce “reactance” (though this appears to be rare) (4) Some nudges are based on an inaccurate (though initially plausible) understanding on the part of choice architects of what kinds of choice architecture will move people in particular contexts. (5) Some nudges produce compensating behavior, resulting in no net effect. When a nudge turns out to be insufficiently effective, choice architects have three potential responses: (1) Do nothing; (2) nudge better (or different); and (3) fortify the effects of the nudge, perhaps through counter-counternudges, perhaps through incentives, mandates, or bans."

This work will appear in a promising new journal, behavioral science & policy, "an international, peer-reviewed journal that features short, accessible articles describing actionable policy applications of behavioral scientific research that serves the public interest. articles submitted to bsp undergo a dual-review process. leading scholars from specific disciplinary areas review articles to assess their scientific rigor; at the same time, experts in relevant policy areas evaluate them for relevance and feasibility of implementation.... bsp is a publication of the behavioral science & policy association and the brookings institution press."

Slice of the week @ PepperSlice.

Author: Cass Sunstein

Analytical method: Behavioral economics

Relationship: Counter-nudges → interfere with → behavioral public policy initiatives


2. There will be defensive baseball stats!
Highly recommended: Bruce Schoenfeld's writeup about Statcast, and how it will support development of meaningful statistics for baseball fielding. Cool insight into the work done by insiders like Daren Willman (@darenw). Finally, it won't just be about the slash line.


3. Cognitive bias cheat sheet.
Buster Benson (@buster) posted a cognitive bias cheat sheet that's worth a look. (Thanks @brentrt.)


4. CATO says Donald Trump is wrong.
Conservative think tank @CatoInstitute shares evidence that immigrants don’t commit more crimes. "No matter how researchers slice the data, the numbers show that immigrants commit fewer crimes than native-born Americans.... What the anti-immigration crowd needs to understand is not only are immigrants less likely to commit crimes than native-born Americans, but they also protect us from crimes in several ways."


5. The What Works reading list.
Don't miss the #WhatWorks Reading List: Good Reads That Can Help Make Evidence-Based Policy-Making The New Normal. The group @Results4America has assembled a thought-provoking list of "resources from current and former government officials, university professors, economists and other thought-leaders committed to making evidence-based policy-making the new normal in government."


Evidence & Insights Calendar

Oct 18, online: How Nonprofits Can Attract Corporate Funding: What Goes On Behind Closed Doors. Presented by the Stanford Social Innovation Review (@SSIReview).

Nov 25, Oxford: Intro to Evidence-Based Medicine presented by CEBM. Note: In 2017 CEBM will offer a course on teaching evidence-based medicine.

Dec 13, San Francisco: The all-new Systems We Love, inspired by the excellent Papers We Love meetup series. Background here.

October 19-22, Copenhagen. ISOQOL 23rd annual conference on quality of life research. Pro tip: The Wall Street Journal says Copenhagen is hot.

November 9-10, Philadelphia: Real-World Evidence & Market Access Summit 2016. "No more scandals! Access for Patients. Value for Pharma."

Tuesday, 09 August 2016

Health innovation, foster teens, NBA, Gwyneth Paltrow.

Foster_care_youth

1. Behavioral economics → Healthcare innovation.
Jaan Sidorov (@DisMgtCareBlog) writes on the @Health_Affairs blog about roadblocks to healthcare innovation. Behavioral economics can help us truly understand resistance to change, including unconscious bias, so valuable improvements will gain more traction. Sidoro offers concise explanations of hyperbolic discounting, experience weighting, social utility, predictive value, and other relevant economic concepts. He also recommends specific tactics when presenting a technology-based innovation to the C-Suite.

2. Laptops → Foster teen success.
Nobody should have to type their high school essays on their phone. A coalition including Silicon Valley leaders and public sector agencies will ensure all California foster teens can own a laptop computer. Foster Care Counts reports evidence that "providing laptop computers to transition age youth shows measurable improvement in self-esteem and academic performance". KQED's California Report ran a fine story.

For a year, researchers at USC's School of Social Work surveyed 730 foster youth who received laptops, finding that "not only do grades and class attendance improve, but self-esteem and life satisfaction increase, while depression drops precipitously."

3. Analytical meritocracy → Better NBA outcomes.
The Innovation Enterprise Sports Channel explain how the NBA draft is becoming an analytical meritocracy. Predictive models help teams evaluate potential picks, including some they might have overlooked. Example: Andre Roberson, who played very little college ball, was drafted successfully by Oklahoma City based on analytics. It's tricky combining projections for active NBA teams with prospects who may never take the court. One decision aid is ESPN’s Draft Projection model, using Statistical Plus/Minus to predict how someone would perform through season five of a hypothetical NBA career. ESPN designates each player as a Superstar, Starter, Role Player, or Bust, to facilitate risk-reward assessments.

4. Celebrity culture → Clash with scientific evidence.
Health law and policy professor Timothy Caulfield (@CaulfieldTim) examines the impact of celebrity culture on people's choices of diet and healthcare. His new book asks Is Gwyneth Paltrow Wrong About Everything?: How the Famous Sell Us Elixirs of Health, Beauty & Happiness. Caulfield cites many, many peer-reviewed sources of evidence.

Evidence & Insights Calendar:

September 13-14; Palo Alto, California. Nonprofit Management Institute: The Power of Network Leadership to Drive Social Change, hosted by Stanford Social Innovation Review.

September 19-23; Melbourne, Australia. International School on Research Impact Assessment. Founded in 2013 by the Agency of Health Quality and Assessment (AQuAS), RAND Europe, and Alberta Innovates.

February 22-23; London UK. Evidence Europe 2017. How pharma, payers, and patients use real-world evidence to understand and demonstrate drug value and improve care.

Photo credit: Foster Care Counts.

Tuesday, 05 July 2016

Brain training isn't smart, physician peer pressure, and #AskforEvidence.

Brain-Training

1. Spending $ on brain training isn't so smart.
It seems impossible to listen to NPR without hearing from their sponsor, Lumosity, the brain-training company. The target demo is spot on: NPR will be the first to tell you its listeners are the "nation's best and brightest". And bright people don't want to slow down. Alas, spending hard-earned money on brain training isn't looking like a smart investment. New evidence seems to confirm suspicions that this $1 billion industry is built on hope, sampling bias, and placebo effect. Arstechnica says researchers have concluded that earlier, mildly positive "findings suggest that recruitment methods used in past studies created a self-selected groups of participants who believed the training would improve cognition and thus were susceptible to the placebo effect." The study, Placebo Effects in Cognitive Training, was published in the Proceedings of the National Academy of Sciences.

It's not a new theme: In 2014, 70 cognitive scientists signed a statement saying "The strong consensus of this group is that the scientific literature does not support claims that the use of software-based 'brain games' alters neural functioning in ways that improve general cognitive performance in everyday life, or prevent cognitive slowing and brain disease."


Journal.pmed.1002049.t001

2. Ioannidis speaks out on usefulness of research.
After famously claiming that most published research findings are false, John Ioannidis now tells us Why Most Clinical Research Is Not Useful (PLOS Medicine). So, what are the key features of 'useful' research? The problem needs to be important enough to fix. Prior evidence must be evaluated to place the problem into context. Plus, we should expect pragmatism, patient-centeredness, monetary value, and transparency.


Antibiotic_use

3. To nudge physicians, compare them to peers.
Doctors are overwhelmed with alerts and guidance. So how do you intervene when a physician prescribes antibiotics for a virus, despite boatloads of evidence showing they're ineffective? Comparing a doc's records to peers is one promising strategy. Laura Landro recaps research by Jeffrey Linder (Brigham and Women's, Harvard): "Peer comparison helped reduce prescriptions that weren’t warranted from 20% to 4% as doctors got monthly individual feedback about their own prescribing habits for 18 months.

"Doctors with the lower rates were told they were top performers, while the rest were pointedly told they weren’t, in an email that included the number and proportion of antibiotic prescriptions they wrote compared with the top performers." Linder says “You can imagine a bunch of doctors at Harvard being told ‘You aren’t a top performer.’ We expected and got a lot of pushback, but it was the most effective intervention.” Perhaps this same approach would work outside the medical field.

4. Sports analytics taxonomy.
INFORMS is a professional society focused on Operations Research and Management Science. The June issue of their ORMS Today magazine presents v1.0 of a sports analytics taxonomy (page 40). This work, by Gary Cokins et al., demonstrates how classification techniques can be applied to better understand sports analytics. Naturally this includes analytics for players and managers in the major leagues. But it also includes individual sports, amateur sports, franchise management, and venue management.

5. Who writes the Internet, anyway? #AskforEvidence
Ask for Evidence is a public campaign that helps people request for themselves the evidence behind news stories, marketing claims, and policies. Sponsored by @senseaboutsci, the campaign has new animations on YouTube, Twitter, and Facebook. Definitely worth a like or a retweet.

Calendar:
September 13-14; Palo Alto, California. Nonprofit Management Institute: The Power of Network Leadership to Drive Social Change, hosted by Stanford Social Innovation Review.

September 19-23; Melbourne, Australia. International School on Research Impact Assessment. Founded in 2013 by the Agency of Health Quality and Assessment (AQuAS), RAND Europe, and Alberta Innovates.

Tuesday, 14 June 2016

Mistakes we make, Evidence Index, and Naturals vs Strivers.

Putin_pianist

1. Mistakes we make when sharing insights.
We've all done this: Hurried to share valuable, new information and neglected to frame it meaningfully, thus slowing the impact and possibly alienating our audience. Michael Shrage describes a perfect example, taken from The Only Rule Is It Has to Work, a fantastic book about analytics innovation.

The cool thing about the book is that it's a Moneyball for the rest of us. Ben Lindbergh and Sam Miller had the rare opportunity to experiment and apply statistics to improve the performance of the Sonoma Stompers, a minor league baseball team in California wine country. But they had to do it with few resources, and learn leadership skills along the way.

The biggest lesson they learned was the importance of making their findings easy to understand. As Shrage points out in his excellent Harvard Business Review piece, the authors were frustrated at the lack of uptake: They didn't know how to make the information meaningful and accessible to managers and coaches. Some people were threatened, others merely annoyed: "Predictive analytics create organizational winners and losers, not just insights."

2. Naturals vs. Strivers: Why we lie about our efforts.
Since I live in Oakland, I'd be remiss without a Steph Curry story this week. But there's lots more to it: Lebron James is a natural basketball player, and Steph is a striver ; they're both enormously popular, of course. But Ben Cohen explains that people tend to prefer naturals, whether we recognize it or not: We favor those who just show up and do things really well. So strivers lie about their efforts.

Overachievers launch into bad behavior, such as claiming to sleep only four hours a night. Competitive pianists practice in secret. Social psychology research has found that we like people described as naturals, even when we're being fooled.

3. How do government agencies apply evidence?
Results for America has evaluated how U.S. agencies apply evidence to decisions, and developed an index synthesizing their findings. It's not easily done. @Results4America studied factors such as "Did the agency use evidence of effectiveness when allocating funds from its five largest competitive grant programs in FY16?" The Departments of Housing and Labor scored fairly high. See the details behind the index [pdf here].

Photo credit: Putin classical pianist on Flickr.

 

Wednesday, 08 June 2016

Grit isn't the answer, plus Scrabble and golf analytics.

Scrabble

1. Poor kids already have grit: Educational Controversy, 2016 edition.
All too often, we run with a sophisticated, research-based idea, oversimplify it, and run it into the ground. 2016 seems to be the year for grit. Jean Rhodes, who heads up the Chronicle of Evidence-Based Mentoring (@UMBmentoring) explains that grit is not a panacea for the problems facing disadvantaged youth. "Grit: The power and passion of perseverance, Professor Angela Duckworth’s new bestseller, on the topic has fueled both enthusiasm for such efforts as well as debate among those of us who worry that it locates the problem (a lack of grit) and solution (training) in the child. Further, by focusing on exemplars of tenacity and success, the book romanticizes difficult circumstances. The forces of inequality that, for the most part, undermine children’s success are cast as contexts for developing grit. Moreover, when applied to low-income students, such self-regulation may privilege conformity over creative expression and leadership. Thus, it was a pleasure to come across a piece by Stanford doctoral student, Ethan Ris, on the history and application of the concept." Ris first published his critique in the Journal of Educational Controversy and recently wrote a piece for the Washington Post, The problem with teaching ‘grit’ to poor kids? They already have it.

2. Does Scrabble have its own Billy Beane?
It had to happen: Analytics for Scrabble. But it might not be what you expected. WSJ explains why For World’s Newest Scrabble Stars, SHORT Tops SHORTER.

Wellington Jighere and other players from Nigeria are shaking up the game, using analytics to support a winning strategy favoring five-letter words. Most champions follow a “long word” strategy, making as many seven- and eight-letter plays as possible. But analytics have brought that "sacred Scrabble shibboleth into question, exposing the hidden risks of big words."

Jighere has been called the Rachmaninoff of rack management, often saving good letters for a future play rather than scoring an available bingo. (For a pre-Jighere take on the world of Scrabble, see Word Wars.)

3. Golf may have a Billy Beane, too.
This also had to happen. Mark Broadie (@MarkBroadie) is disrupting golf analytics with his 'strokes gained' system. In his 2014 book, Every Shot Counts, Broadie rips apart assumptions long regarded as sacrosanct - maxims like 'drive for show, putt for dough'. "The long game explains about two-thirds of scoring differences and the short game and putting about one-third. This is true for amateurs as well as pros." To capture and analyze data, Broadie developed a GolfMetrics program. He is the Carson Family Professor of Business at Columbia Business School, and has a PhD in operations research from Stanford. He has presented at the Sloan Sports Analytics Conference.

Pros have begun benefiting from golf analytics, including Danny Willett, winner of this year's Masters. He has thanked @15thClub, a new analytics firm, for helping him prep better course strategy. 15th Club provided insight for Augusta’s par-5 holes. As WSJ explained, the numbers show that when players lay up, leaving their ball short of the green to avoid a water hazard, they fare better when doing so as close to the green as possible, rather than the more distant spots where players typically take their third shots.

4. Evidence-based government on the rise.
In the US, "The still small, but growing, field of pay for success made significant strides this week, with Congress readying pay for success legislation and the Obama administration announcing a second round of grants through the Social Innovation Fund (@SIFund)."

5. Man + Machine = Success.
Only Humans Need Apply is a new book by Tom Davenport (@tdav) and @JuliaKirby. Cognitive computing combined with human decision making is what will succeed in the future. @DeloitteBA led a recent Twitter chat: Man-machine: The dichotomy blurs, which included @RajeevRonanki, the lead for their cognitive consulting practice.

Tuesday, 26 April 2016

Baseball decisions, actuaries, and streaming analytics.

Cutters from Breaking Away movie

1. SPOTLIGHT: How are innovations in baseball analytics like data science?
Last week, I spoke at Nerd Nite SF about recent developments in baseball analytics. Highlights from my talk:

- Data science and baseball analytics are following similar trajectories. There's more and more data, but people struggle to find predictive value. Oftentimes, executives are less familiar with technical details, so analysts must communicate findings and recommendations so they're palatable to decision makers. The role of analysts, and  challenges they face, are described beautifully by Adam Guttridge and David Ogren of NEIFI.

- 'Inside baseball' is full of outsiders with fresh ideas. Bill James is the obvious/glorious example - and Billy Beane (Moneyball) applied great outsider thinking. Analytics experts joining front offices today are also outsiders, but valued because they understand prediction;  the same goes for anyone seeking to transform a corporate culture to evidence-based decision making.

Tracy Altman @ Nerd Nite SF
- Defensive shifts may number 30,000 this season, up from 2,300 five years ago (John Dewan prediction). On-the-spot decisions are powered by popup iPad spray charts with shift recommendations for each opposing batter. And defensive stats are finally becoming a reality.

- Statcast creates fantastic descriptive stats for TV viewers; potential value for team management is TBD. Fielder fly-ball stats are new to baseball and sort of irresistible, especially the 'route efficiency' calculation.

- Graph databases, relatively new to the field, lend themselves well to analyzing relationships - and supplement what's available from a conventional row/column database. Learn more at FanGraphs.com. And topological maps (Ayasdi and Baseball Prospectus) are a powerful way to understand player similarity. Highly dimensional data are grouped into nodes, which are connected when they share a common data point - this produces a topo map grouping players with high similarity.

2. Will AI replace insurance actuaries?
10+ years ago, a friend of Ugly Research joined a startup offering technology to assist actuaries making insurance policy decisions. It didn't go all that well - those were early days, and it was difficult for people to trust an 'assistant' who was essentially a black box model. Skip ahead to today, when #fintech competes in a world ready to accept AI solutions, whether they augment or replace highly paid human beings. In Could #InsurTech AI machines replace Insurance Actuaries?, the excellent @DailyFintech blog handicaps several tech startups leading this effort, including Atidot, Quantemplate, Analyze Re, FitSense, and Wunelli.

3. The blind leading the blind in risk communication.
On the BMJ blog, Glyn Elwyn contemplates the difficulty of shared health decision-making, given people's inadequacy at understanding and communicating risk. Thanks to BMJ_ClinicalEvidence (@BMJ_CE).

4. You may know more than you think.
Maybe it's okay to hear voices. Evidence suggests the crowd in your head can improve your decisions. Thanks to Andrew Munro (@AndrewPMunro).

5. 'True' streaming analytics apps.
Mike Gualtieri of Forrester (@mgualtieri) put together a nice list of apps that stream real-time analytics. Thanks to Mark van Rijmenam (@VanRijmenam).

Tuesday, 12 April 2016

Better evidence for patients, and geeking out on baseball.

Health tech wearables

1. SPOTLIGHT: Redefining how patients get health evidence.

How can people truly understand evidence and the tradeoffs associated with health treatments? How can the medical community lead them through decision-making that's shared - but also evidence-based?

Hoping for cures, patients and their families anxiously Google medical research. Meanwhile, the quantified selves are gathering data at breakneck speed. These won't solve the problem. However, this month's entire Health Affairs issue (April 2016) focuses on consumer uses of evidence and highlights promising ideas.

  • Translating medical evidence. Lots of synthesis and many guidelines are targeted at healthcare professionals, not civilians. Knowledge translation has become an essential piece, although it doesn't always involve patients at early stages. The Boot Camp Translation process is changing that. The method enables leaders to engage patients and develop healthcare language that is accessible and understandable. Topics include colon cancer, asthma, and blood pressure management.
  • Truly patient-centered medicine. Patient engagement is a buzzword, but capturing patient-reported outcomes in the clinical environment is a real thing that might make a big difference. Danielle Lavallee led an investigation into how patients and providers can find more common ground for communicating.
  • Meaningful insight from wearables. These are early days, so it's probably not fair to take shots at the gizmos out there. It will be a beautiful thing when sensors and other devices can deliver more than alerts and reports - and make valuable recommendations in a consumable way. And of course these wearables can play a role in routine collection of patient-reported outcomes.


Statcast

2. Roll your own analytics for fantasy baseball.
For some of us, it's that special time of year when we come to the realization that our favorite baseball team is likely going home early again this season. There's always fantasy baseball, and it's getting easier to geek out with analytics to improve your results.

3. AI engine emerges after 30 years.
No one ever said machine learning was easy. Cyc is an AI engine that reflects 30 years of building a knowledge base. Now its creator, Doug Lenat, says it's ready for prime time. Lucid is commercializing the technology. Personal assistants and healthcare applications are in the works.

Photo credit: fitbit one by Tatsuo Yamashita on Flickr.

Tuesday, 08 March 2016

NBA heat maps, FICO vs Facebook, and peer review.

Curry-heatmap

Curry-heatmap2016

1. Resistance is futile. You must watch Steph Curry.
The Golden State Warriors grow more irresistible every year, in large part because of Curry’s shooting. With sports data analytics from Basketball-Reference.com, these heat maps illustrate his shift to 3-pointers (and leave no doubt why Curry was called the Babyfaced Assassin; now of course he’s simply MVP).

2. Facebook vs FICO. Conventional (FICO) vs. creepy (Facebook): What's the future of consumer lending decisions?

Conventional (FICO) vs. creepy (Facebook): What's the future of consumer lending decisions?

Fintech startups are rethinking how people access their money, and borrow money from individuals or institutions. Peer-to-peer lending (Lending Club,Prosper, others) is one of the innovative business models being explored.NerdWallet is adding speed and transparency to choosing financial products.

What signals a reasonable credit risk? Another big idea is replacing traditional credit scores with rankings derived from social media profiles and other data. Just 3 months ago, Affirm and others were touted in Fortune’s piece Why Facebook Profiles are Replacing Credit Scores

Not so fast. But now the Wall Street Journal says those decisions are falling out of favor, in Facebook Isn’t So Good at Judging Your Credit After All. Turns out, regulatory restrictions and limited data-sharing policies are interfering. Plus, executives with startups like ZestFinance find social-media lending “creepy”. 

 Naturally a lot more goes into a consumer lending decision than a simple evaluation of a score, whether it's traditional FICO or an experimental Facebook metric. ZestFinance is doing some interesting work, using machine learning to discover meaningful patterns in people's credit reports. The path to better #fintech will likely be a bumpy ride, but it's fascinating to watch.

3. How to fix science journals.
Harvard Med School’s Jeffrey Flier wrote an excellent op-ed for the Wall Street Journal, How to Keep Bad Science from Getting into Print [paywall]. Key issues: anonymous peer reviewers, and lack of transparent post-publishing dialogue with authors (@PubPeer being a notable exception). Flier says we need a science about how to publish science. Amen to that.

4. Longing for civil, evidence-based discourse?
ProCon.org publishes balanced coverage of controversial issues, presenting side-by-side pros and cons supported by evidence. The nonprofit’s site is ideal for schoolteachers, or anyone wanting a quick glance at important findings.

Tuesday, 10 November 2015

Working with quantitative people, evidence-based management, and NFL ref bias.

This week's 5 links on evidence-based decision making.

1. Understand quantitative people → See what's possible → Succeed with analytics Tom Davenport outlines an excellent list of 5 Essential Principles for Understanding Analytics. He explains in the Harvard Business Review that an essential ingredient for effective data use is managers’ understanding of what is possible. To counter that, it’s really important that they establish a close working relationship with quantitative people.

2. Systematic review → Leverage research → Reduce waste This sounds bad: One study found that published reports of trials cited fewer than 25% of previous similar trials. @PaulGlasziou and @iainchalmersTTi explain on @bmj_latest how systematic reviews can reduce waste in research. Thanks to @CebmOxford.

3. Organizational context → Fit for decision maker → Evidence-based management A British Journal of Management article explores the role of ‘fit’ between the decision-maker and the organizational context in enabling an evidence-based process and develops insights for EBM theory and practice. Evidence-based Management in Practice: Opening up the Decision Process, Decision-maker and Context by April Wright et al. Thanks to @Rob_Briner.

4. Historical data → Statistical model → Prescriptive analytics Prescriptive analytics finally going mainstream for inventories, equipment status, trades. Jose Morey explains on the Experfy blog that the key advance has been the use of statistical models with historical data.

5. Sports data → Study of bias → NFL evidence Are NFL officials biased with their ball placement? Joey Faulkner at Gutterstats got his hands on a spreadsheet containing every NFL play run 2000-2014 (500,000 in all). Thanks to @TreyCausey.

Bonus! In The Scientific Reason Why Bullets Are Bad for Presentations, Leslie Belknap recaps a 2014 study concluding that "Subjects who were exposed to a graphic representation of the strategy paid significantly more attention to, agreed more with, and better recalled the strategy than did subjects who saw a (textually identical) bulleted list version."

Tuesday, 20 October 2015

Evidence handbook for nonprofits, telling a value story, and Twitter makes you better.

This week's 5 links on evidence-based decision making.

1. Useful evidence → Nonprofit impact → Social good
For their upcoming handbook, the UK's Alliance for Useful Evidence (@A4UEvidence) is seeking "case studies of when, why, and how charities have used research evidence and what the impact was for them." Share your stories here.

2. Data story → Value story → Engaged audience
On Evidence Soup, Tracy Altman explains the importance of telling a value story, not a data story - and shares five steps to communicating a powerful message with data.

3. Sports analytics → Baseball preparedness → #Winning
Excellent performance Thursday night by baseball's big data-pitcher: Zach Greinke. (But there's also this: Cubs vs. Mets!)

4. Diverse network → More exposure → New ideas
"New research suggests that employees with a diverse Twitter network — one that exposes them to people and ideas they don’t already know — tend to generate better ideas." Parise et al. describe their analysis of social networks in the MIT Sloan Management magazine. (Thanks to @mluebbecke, who shared this with a reminder that 'correlation is not causation'. Amen.)

5. War on drugs → Less tax revenue → Cost to society
The Democratic debate was a reminder that the U.S. War on Drugs was a very unfortunate waste - and that many prison sentences for nonviolent drug crimes impose unacceptable costs on the convict and society. Consider this evidence from the Cato Institute (@CatoInstitute).