16 posts categorized "sports"

10 November 2015

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

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."

20 October 2015

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

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).

06 October 2015

Superforecasting, hot hand redux, and junk science.

1. Good judgment → Accurate forecasts → Better decisions Jason Zweig (@jasonzweigwsj) believes Superforecasting: The Art and Science of Prediction is the "most important book on decision-making since Daniel Kahneman's Thinking Fast and Slow." Kahneman is equally enthusiastic, saying "This book shows that under the right conditions regular people are capable of improving their judgment enough to beat the professionals at their own game." The author, Philip Tetlock, leads the Good Judgment Project, where amateurs and experts compete to make forecasts - and the amateurs routinely win. Tetlock notes that particularly good forecasters regard their views as hypotheses to be tested, not treasures to be guarded. The project emphasizes transparency, urging people to explain why they believe what they do. Are you a Superforecaster? Find out by joining the project at GJOpen.com.

2. Better evidence → Better access → Better health CADTH (@CADTH_ACMTS), a non-profit that provides evidence to Canada's healthcare decision makers, is accepting abstract proposals for its 2016 Symposium, Evidence for Everyone.

3. Coin flip study → Surprising results → Hot hand debate The hot hand is making a comeback. After a noteworthy smackdown by Tom Gilovich, some evidence suggests there is such a thing. Ben Cohen explains in The 'Hot Hand' May Actually Be Real - evidently it's got something to do with coin flips. Regardless of how this works out, everyone should read (or reread) Gilovich's fantastic book, How We Know What Isn't So.

4. Less junk science → Better evidence → Better world The American Council on Science and Health has a mission to "provide an evidence-based counterpoint to the wave of anti-science claims". @ACSHorg presents its views with refreshingly snappy writing, covering a wide variety of topics including public policy, vaccination, fracking, chemicals, and nutrition.

5. Difference of differences → Misunderstanding → Bad evidence Ben Goldacre (@bengoldacre) of Bad Science fame writes in The Guardian that the same statistical errors – namely, ignoring the difference in differences – are appearing throughout the most prestigious journals in neuroscience.

29 September 2015

Data blindness, measuring policy impact, and informing healthcare with baseball analytics.


1. Creative statistics → Valuable insights → Reinvented baseball business Exciting baseball geek news: Bill James and Billy Beane appeared together for the first time. Interviewed in the Wall Street Journal at a Netsuite conference on business model disruption, Beane said new opportunities include predicting/avoiding player injuries - so there's an interesting overlap with healthcare analytics. (Good example from Baseball Prospectus: "no one really has any idea whether letting [a pitcher] pitch so much after coming back from Tommy John surgery has any effect on his health going forward.")

2. Crowdsourcing → Machine learning → Micro, macro policy evidence Premise uses a clever combination of machine learning and street-level human intelligence; their economic data helps organizations measure the impact of policy decisions at a micro and macro level. @premisedata recently closed a $50M US funding round.

3. Data blindness → Unfocused analytics → Poor decisions Data blindness prevents us from seeing what the numbers are trying to tell us. In a Read/Write guest post, OnCorps CEO (@OnCorpsHQ) Bob Suh recommends focusing on the decisions that need to be made, rather than on big data and analytics technology. OnCorps offers an intriguing app called Sales Sabermetrics.

4. Purpose and focus → Overcome analytics barriers → Create business value David Meer of PWC's Strategy& (@strategyand) talks about why companies continue to struggle with big data [video].

5. Health analytics → Evidence in the cloud → Collaboration & learning Evidera announces Evalytica, a SaaS platform promising fast, transparent analysis of healthcare data. This cloud-based engine from @evideraglobal supports analyses of real-world evidence sources, including claims, EMR, and registry data.

22 September 2015

Writing skills series, encyclopedia of slide layouts, and fantasy sports decision-making

1. Well-crafted writing → Evidence explained → Uptake of ideas On October 14, our founder, Tracy Allison Altman, will talk about Communicating Messages Clearly with Data. This free presentation will include techniques for writing about data, and telling a simple story about complex science.

2. Use the Slide Chooser → Tell your story → Inspire action The Extreme Presentation method is a step-by-step approach for designing presentations of complex or controversial information. It's based on empirical research, and has been tested with big companies. The companion book is the Encyclopedia of Slide Layouts by Paul Radich and Andrew Abela.

3. Fear of losses → Baseball decisions → Daily fantasty sports results In baseball daily fantasy sports, as in life, we are more motivated to minimize losses than to maximize gains. Dr. Renee Miller explains how cognitive biases expedite decision-making and influence outcomes.

4. Rethink strategy → Lower blood pressure targets → Reduce death risk The U.S. National Institutes of Health released early findings from a big study of blood pressure management in people over 50. The Sprint trial seems to tell us that keeping systolic pressure below 120 can reduce cardiovascular disease and risk of death by as much as one third.

5. Large data sets → LASSO method → Valid predictions Today's large data sets create fantastic opportunities to make useful predictions - but traditional methods of variable selection are unwieldy and unreliable. Daniel Samarov writes on the Experfy blog about LASSO, a modern way to select variables for predictive models. This sparse regression, using Least Absolute Shrinkage and Selection Operator, is becoming a mainstay for analyzing data with lots of variables.

18 August 2014

How to tell if someone really is a unicorn.


This Sunday is Unicorn Backpack giveaway day at the Oakland A's game. Given the current mythology about good data scientists a/k/a unicorns, Billy Beane of baseball analytics fame (G.M. of the Athletics) comes to mind.

Unicorn verification process. I'm not minimizing the difficulty of policy research, analytics, data science, and other efforts to find meaningful patterns in data. But communication skills and business savvy dramatically influence people's ability to succeed. As part of an engagement or hiring process, I suggest asking a potential unicorn these questions:

1) What evidence have you worked with that can potentially improve outcomes? Where might it be applicable? 2) How do you translate a complex analysis into plain English for executive decision makers? 3) What visuals are most effective for connecting findings to important business objectives?

Can you talk the talk and walk the walk? While Mr. Beane brilliantly recognized the value of OBP and other underappreciated baseball stats, that's not what made him a unicorn. His ability to explain his findings and advocate for nonobvious, risky, high-stakes management decisions - and to later demonstrate a payoff from those decisions -  is what made him a unicorn.

A colleague of mine worked at a successful, publicly traded telecom company. As a PhD economist, he managed a group of 25 economists. And he says the reason he led the team, and did most of the interacting with senior executives, was that he could explain their economic modeling in business terms appropriate for the audience. 

Connect to what matters. Accenture’s extensive research of analytics ROI has found that “most organizations measure too many things that don’t matter, and don’t put sufficient focus on those things that do, establishing a large set of metrics, but often lacking a causal mapping of the key drivers of their business.”

It's a common theme: Translate geek to English. SAP’s chief data scientist, David Ginsberg, says a key player on his big-data team is someone “who can translate PhD to English. Those are the hardest people to find”. Kerem Tomak, who manages 35 retail analysts, explained to Information Week that “A common weakness with data analytics candidates is they’re happy with just getting the answer, but don’t communicate it”. "The inability to communicate with business decision-makers is not just a negative, it's a roadblock," says Jeanne Harris, global managing director of IT research at Accenture and author of two books on analytics. 

Will Mr. Beane be wearing a unicorn backpack at the game on Sunday? I sure hope so. 

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