It’s About Policing Numbers, Not Number of Police

The Police, by Luca Venturi
The Police.  Courtesy of Luca Venturi.

Can big data reduce crime?  Yes it can.  This is a great TED Talk by Anne Milgram about using analytics to improve the criminal justice system.  The talk from October 2013 describes how Milgram successfully attempted to “moneyball” policing and the work of judges in her role as attorney general of New Jersey.  Hers is a great story, and has many features in common with the Moneyball book and movie.

The speaker describes how she built a team, created raw data, analyzed it, and produced simple and meaningful tools.  Her most impressive outcome is a risk assessment tool that helps judges identify the likelihood a defendant will re-offend, not show up in court, or commit a violent act.  She and her team have successfully reduced crime.

Baseball players and police officers alike have a culture of bravado and confidence which may be critical when handling conflict, intimidation, and credibility.  Yet what police officers and baseball players often need is a safe space to question their assumptions, assess whether they could do better, and decide that they will do better.  These types of vulnerable moments don’t play out well when a player is at bat, or when an officer is handling complaints from the perpetrators.

In Milgram’s talk, where others see cool math tricks, I see a change in mindset and demeanor.  The speaker expresses curiosity about the information, enthusiasm for unexpected findings, modesty about baseline effectiveness, a lack of blame, and a can-do attitude about trying to do more and do better.

It’s a great metaphor for business.  In those workplaces where managers fiercely claw their way to the top, there may be a reduced willingness to talk about shortcomings in a manner that requires trust and collaboration.  Yet making exceptional decisions require that leaders choose an entirely different mood and posture while they explore an uncharted area, allow information to out-rank instinct, and aspire to a more subtle kind of greatness.  Put posture aside, and just do good work.  The way things are changing, those are the only kinds of people who will stay on top.

First Do Your Homework, Then You Can Play Ball

Shane Battier. Courtesy of Keith Allison.
Shane Battier. Courtesy of Keith Allison.

What impact does analytics have on teamwork?  Plenty, it turns out.

I happened upon an interesting blog post by Thomas Marsden of Saberr, a team-development firm.  Marsden was fascinated by a session with Shane Battier at the Wharton People Analytics Conference.  Battier is a basketball player with many distinctions.

One distinction is that Battier won a team-player award because he served as his team’s data translator.  Players on his basketball team were handed massive statistics packs about the opposing team’s behavior.  He actually read them, a behavior that was rare.  I often wonder what happens when I produce a ream of analysis and send it off to a client.  Sometimes (but not always) someone comes back to me with tough questions, follow-up inquiries, and demands for deeper dives.  In those cases I have struck upon an expert consumer.  They are like wine experts, indie rock snobs, or film buffs.  They don’t produce the product; they just really know how to consume it.

Battier is an expert consumer.  He did his homework and made interpretations in order to play better.  Other players had not done this, so he would help teammates and “drip feed information at the right moment through the game.”  He was acting as the intermediary between the statistical analysts and the front-line players.  This is a key bridging link between two cliques.  In network theory the person who causes information to jump from one crowd to the next becomes a go-to person for both cliques.

For some people, they see the shots being made.  For me, one of the greatest games on earth is watching the information pass from one person to the next.  There is a bounce, a spin, a clever move, a change of play.  I watch big people, breaking a sweat, moving my data across the court.  And when they score, it’s fist-pumps and high-fives.  Good game.

The Math Behind Basketball

Kobe Bryant, Photo by Alexandra Walt (public domain).jpg
Kobe Bryant.  Photo by Alexandra Walt.

In this TED talk by Rajiv Maheswaran, the speaker describes the translation of basketball moves into a series of moving dots, looking at games played by professional basketball players.  Using machine learning they were able to identify the difference between the baseline probability that a shot would be successful, and an individual player’s personal odds that they would make a shot. This distinction allows us to tell the difference between those who have more opportunities (or create more opportunities), as opposed to those who perform well based on what is in front of them.

The speaker invites us to consider other applications for the analysis of moving dots.  In my opinion, this means we can think of activity levels, workplace layout, and injury statistics alongside other workplace wellbeing indicators.  It might be that physical movements have a substantial impact on non-physical workplace performance.