Playing an Unfair Game: How Modern Police Departments Can Win with Data Analytics
Posted
August 16, 2025
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The Game Has Changed
Police departments are facing an impossible equation. Officer injuries are up. Liability claims are skyrocketing. The best candidates are choosing other careers. And when something goes wrong, the financial and human costs are devastating.
Steve Brewer, a 25-year veteran of insurance risk analytics, sees what many in law enforcement already know but rarely say out loud: the old playbook isn’t working anymore. In the video outtake below from a recent webinar, Brewer explains, “we’re asking officers to handle mental health crises, substance abuse cases, and community trauma with fewer resources and less experienced teams.” He goes on to say that “meanwhile, society’s tolerance for mistakes has never been lower.”
The Fear No One Talks About
Here’s what keeps risk pool managers and police chiefs up at night: they’re flying blind. While departments have sophisticated systems for tracking crime patterns and response times, they have almost no data on what predicts officer performance or prevents costly incidents.
Most agencies are making million-dollar decisions based on gut instinct and outdated practices. They know something needs to change, but without data, they’re just guessing.
A Different Approach
This is where Benchmark’s story gets interesting. Instead of studying one department at a time like traditional researchers, they’re tapping into something powerful: the collective data of Risk Pools and public entity insurers.
Risk Pools have performance data spanning multiple agencies across multiple years. They see which departments consistently have fewer injuries, lower liability claims, and better retention rates. That’s a goldmine of insights that’s never been adequately analyzed, until now.
The Moneyball Moment
In the video, Brewer draws a parallel to Michael Lewis’s “Moneyball.” When the Oakland A’s couldn’t compete on salary, they competed on intelligence. They stopped evaluating players based on their appearance and started focusing on what won games.
Law enforcement is undergoing the same revolution. Which specific practices reduce officer injuries? What early indicators predict liability events? Which training programs genuinely improve outcomes?
For the first time, there’s real data to answer these questions.
Evidence-Based Policing Is Possible
Here’s the breakthrough: Benchmark isn’t just collecting data. We’re turning it into actionable intelligence that departments can use. This isn’t academic theory—it’s practical, evidence-based guidance drawn from real-world results.
The agencies that embrace this approach won’t just save money on claims and injuries. They’ll create safer communities, healthier officers, and restore trust at a time when it’s desperately needed.
The Bottom Line
Behind every data point is a human story, an officer who goes home safely, a crisis resolved peacefully, a career saved from burnout. That’s what makes this work matter. Law enforcement has always been about protecting and serving. Now, for the first time, there’s a way to use data to do it better.
The game may be unfair, but with the right analytics, it’s still winnable.
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*This blog post was generated with the help of artificial intelligence.
Transcript of Steve Brewer’s Comments.
My name’s Steve Brewer with Benchmark. I’ve been in and around the insurance risk space here for just over 25 years. A large portion of that was in the property world, so property has always got a soft spot in my heart; with CoreLogic and creating all the analytics that we need to understand and address the risk and property.
About five years ago, I met up with the founder of Benchmark. We’ll tell our story in a moment. And really started to learn about the opportunity to apply advanced analytics to exactly the problem space just outlined. And really partner up with those on the call today who are vested in the success of law enforcement, improving outcomes for our communities, and really bring that rich analytic background and experience I have to help solve this problem. It was a no-brainer.
We’re here today to talk about how research and analytics can truly help those that need us in law enforcement command staff. Because in the end, if you think about the key metrics of law enforcement today, we see the front headline news, but the reality of it is the cost of injuries to our officers, the cost of repairing squad car damage, the cost of liability events. The increase in turnover are all trending in the wrong direction. And it’s not a frequency problem from our research. It’s a severity problem. Society’s tolerance for mistakes in law enforcement is as low as it’s ever been, and it’s across the board. Mistakes cost more money than they ever have before. So if we’re gonna solve this problem we need to address frequency and we need to provide tools that command staff can use to fundamentally change those outcomes. So that’s what we’re gonna talk about today.
The reality of it is that there’s a human component to this. When we’re looking at severity of work comp injuries, and we’re looking at liability claim dollars. That’s harm to the community. That’s an officer who was injured, that’s a community member who was injured. And so the human toll and the opportunity to improve these outcomes is just so compelling.
And so our story today starts from the pen of Michael Lewis and really one of the great analogies that most people have heard of. So if you like Michael Lewis, if you like analytics, if you like baseball, or even if you’re a Brad Pitt fan this story’s for you. And so many of us have at least heard of the concept of Moneyball and the subtitle to Moneyball was “The Art of Winning in An Unfair Game.”
Now, that metaphor means a lot because if you think about today’s law enforcement environment, it’s a pretty unfair game. We’re asking agencies, chiefs, sheriffs, their command staff officers on the street. Many of whom have less experience than they had before. Because of all the turnover, agencies are hiring less qualified candidates.
We’re asking them to absorb the trauma of our community, which is not the same as it was 20 years ago. Officers today have to engage those in mental crisis. Those in substance abuse that are not getting the resources that they might otherwise need from the community. And so the bar has gone up, the skillset and the talent and the, and just the raw horsepower.
Instead of having 200 people to pick from, agencies are trying to find candidates. So we know the quality of candidates is going down. This is an unfair game for law enforcement today. And when a mistake gets made, juries have zero tolerance. Those things cost more. So how do we win an unfair game? This Sabermetrics concept, Billy Bean, GM of the A’s in 2002 where the story became a really commonplace was that they were able to outcompete teams in bigger markets with bigger salaries by the use of analytics.
And the premise of the way they thought about analytics was, we’re gonna focus on a, and you look at moving from evaluating a player based on how they looked to their wins above replacement, how they contribute to help that team win. You move from a statistic like a batting average to an on-base percentage.
Not just how often they get a hit, but do they get on base. How slugging can they get multiple bases they hit for power. Those are metrics that are outcomes. They’re helping their team win. Now, if we take that concept and how Moneyball thought about using analytics in baseball, we absolutely can apply the same concepts to law enforcement.
Which agencies are winning in the sense of they’re having fewer injuries. They’re having fewer uses of force that lead to complaints, incidents, or claims, and how do we build those metrics and put those in the hands of risk managers at risk pools. So when they go out to work with their members, they can be promoting evidence-based approaches.
They can be focusing that agency’s limited time and money resource on an outcome or an activity that we know works. And so that’s really, as I mentioned, the founding mission of Benchmark. Our founder and CEO, who’s a former deputy superintendent in Chicago PD, he was responsible for all the people in Chicago, and he knew that Law enforcement needed a better way. They needed the ability to look at people analytics in a similar way that they look at crime analytics or they look at emergency response 911 analytics. And yet even today most agencies in this country do not have the people analytics they need to solve this problem.
And so that’s ultimately where this research came in. Academia knows very little about police officer performance. Most of what’s out there in published papers comes from a single organization comes from a large city police department. They’ve brought in data from public records. But risk pools are so critical to answering this question. Public entity insurers are so critical to answer this question because you have access through many different agencies, many different cities, you have results that span multiple years. And that’s the foundation where we can learn and develop what are now evidence-based practices. So Benchmark was founded to really be a pivotal answer to this question to provide research and analytics.
But more than that is to work with partners to put this research into practice and most importantly to measure, to build measurements of outcomes. Are the outcomes working? Are we seeing a reduction in liability in work comp injuries?
And so that’s really what we’re here today to share a little bit about the methodology, how we bring analytics to our partners in law enforcement in a new way. And how that process is working.
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