Counterpoints: The Sports Analytics Podcast from MIT Sloan Management Review

In today's NBA, are analytics just a regular season tool? Analytics have a major place in today’s basketball world. At some level, every team relies on data and analytics for roster construction, salary negotiation, and in-game strategy. But the playoffs? They're a different story. In the NBA's second season, is it time to ignore the numbers and let talent, tenacity, and those hated "intangibles" rule the court? It's a question perplexing GMs and coaches alike. We'll search answers with Los Angeles Lakers reporter, Mike Trudell. 

The explosion of streaming media offers fans unlimited access to sports and entertainment. So how can teams entice their audience to the events happening here and now? Sports Innovation cofounder and CEO Angela Ruggiero says success starts with understanding just how fans’ behavior has changed with the advent of digital technology — meaning, executives of sports companies and media outlets alike must be willing to completely rethink how they approach their marketing.

Recent years have seen an explosion in the NFL passing game and emerging analytics demonstrate that teams that throw the ball more win the game more. So does this mean the run no longer matters? That’s not entirely clear. We talked with The Athletic’s Ted Nguyen who has gone deep on analyzing the value of the run in today’s NFL.

Counterpoints takes on two pressing questions in the sports analytics field: the issue of information overload and whether there is such a thing as too much data, and a very different — but related — issue: Biometrics. We’ll go to the mat over whether or not professional athletes will be willing to share their personal biometric data in real time.

Stop us if you’ve heard this one before: The rag-tag group of underdogs overcomes the more skilled favorite thanks to nothing more than their belief in each other. That popular sports movie cliché may feel unrealistic at times, but when it comes to building a team in real life, the value of cohesiveness and chemistry is increasingly measurable and provable. Whether it’s the NBA’s 2004 Detroit Pistons, the 2016 Leicester City Foxes soccer club, or the miracle 2003 Penrith Panthers of Australian rugby, there are many examples of the right players in the right system doing something seemingly impossible. But is it actually possible to quantify team chemistry — and if so, can such assessments really make a difference on the field? We speak with Simon Strachan of Gain Line Analytics to find out.

In the NBA’s modern era of pace-and-space, small ball, and chucking away from 3, it feels like there’s no more place for the lumbering 7-foot center who used to be the backbone of the league. But the burgeoning field of defensive analytics shows that this "dinosaur" might not be going extinct just yet. Ben speaks with Ivana Saric, data scientist for the Philadelphia 76ers, about how defensive analytics are changing pro basketball and the roles of the people who play it.

A herd mentality and a lack of good data have led teams to make some poor decisions about trading draft picks. Is there a better way?

Which teams make it to the college football playoffs isn’t as random as it sometimes seems, says University of Wisconsin-Madison professor Laura Albert. In this week’s Counterpoints podcast, we look at how Albert uses analytics to predict the brackets and how the football playoff selections compare to that other big college tournament, March Madness.

Though lacking in glitz and glamour, the tackle, guard, and center positions make up the backbone of every NFL offense. Without skilled players in those roles — and players who can work as a unit — a team’s entire strategy can fall apart. In the past few years, teams like the Rams, Chiefs, and Saints have used a punishing offensive line to ignite high-powered offenses, while the Patriots have revolutionized O-Line versatility. Even while these once anonymous units are finally getting their due, new analytics measuring offensive line performance just might prove that we’re *still* underrating these guys. In this week’s interview, ESPN's Seth Walder discusses the growing field of O-Line analytics, and just how much winning the battle of the trenches correlates with winning the battle of the scoreboard.

eSports has arrived as a major player in the sports world. Games like DOTA 2 and League of Legends have hundreds of millions of players, and the best gamers have fan-bases and endorsement deals right up there with the stars of "real" sports. As eSports grows, so do the analytics surrounding it.  But while the nature of eSports means that the amount of quantitative data for every game is staggering, the volatile nature of team-building and managing in the sport only increases the importance of *people* analytics, and how it leads to success. We explore the role of social science analytics in esports with the head of Shadow and one of the leading voices in eSports analytics, Tim Sevenhuysen.

When NBA superstars like Steph Curry, Joel Embiid, or Kawhi Leonard are given a planned night off, the impact of their absence isn’t just felt on the floor - it’s a financial issue as well. Whether it’s fans not getting what they believe they paid for, prices on the secondary ticket markets crumbling, or teams dealing with empty seats and depressed TV viewership, the consequences of a planned absence of a major star reverberate across the sport. But just how much is everyone losing when stars sit out - and which stars are creating the biggest holes in NBA pockets? To get to the bottom of one of the biggest financial questions surrounding the NBA, Paul spoke to Scott Kaplan, who presented his paper detailing the economic impact of NBA Superstars at the 2019 Sloan Sports Analytics Conference.

Even as soccer has become a multibillion-dollar business, with superstar transfer fees exceeding nine digits figures in euros, the best clubs can still gain a competitive advantage through their youth academies. Ajax, FC Barcelona, and Manchester United are just a few examples of teams that have consistently developed diamonds in the rough into championship contributors. But to develop those players, first you have to find them — and find them before anyone else does. At Chelsea Football Club, home to an academy that has recently produced burgeoning talents like Callum Hudson-Odoi and Ruben Loftus-Cheek, some of that responsibility falls on the head of research and innovation Ben Smith.

Almost every team’s fans can point to a signature free agent signing that fell flat. Sometimes the reasons for the players’ underperformance are clear -- injuries for example. But often, it’s harder to pinpoint the cause. In those cases we tend to point to lack of effort as the culprit. This is what researchers call “shirking.” According to Richard Paulsen, who presented his paper “New Evidence in the Study of Shirking in Major League Baseball,” at the Sloan Sports Analytics Conference, “shirking occurs when an employee exerts effort... that is suboptimal in the eyes of the employer.” Richard speaks with us about his research on shirking and his belief that the Phillies are going to regret at least one free-agent signing from this off-season.

Eddie Robinson at Grambling. Mike Krzyzewski at Duke. Gregg Popovich with the Spurs. It’s hard to underestimate the impact these coaches have had on their organizations. But are coaches always an X factor? Consider the Golden State Warriors. Dominating as they have been under Steve Kerr’s steady guiding hand, they have been every bit as successful during his two extended absences. So, how much do coaches actually matter? Two researchers from the University of Chicago just might have the answer.    

Most Counterpoints listeners are well familiar with the sweeping changes that Moneyball brought to offensive strategy in baseball. Are we now on the cusp of a similarly profound transformation of pitching? Some believe the practice of "out-getting" is just that. Out-getting focuses getting outs as efficiently as possible, irrelevant of who gets them, when, and how. It upends traditional convention about how long or frequently a pitcher should pitch, in which role, or in which circumstances.  Will out-getting  prove to be a Moneyball-level transformation that ushers in fundamental change or just an occasional strategy deployed as much by desperation as by tactical brilliance? Ben and Paul disagree.

It is incredibly difficult to judge individual talent in the NFL, because so much of a player's ability to succeed is based on context: his teammates and the system in which he operates. But the need to isolate performance is huge. Those who gain a reliable method will have a huge advantage. So where do you start? You start with the most valuable position on the field, the quarterback. Then, you isolate further on what appears to be the greatest differentiator between elite level quarterbacks: their minds. The ultimate goal: to understand how a quarterback processes information and to track the patterns their minds tend to follow. Or said another way: to map Tom Brady's brain. This is the quest of ESPN's Bryan Burke, who will be presenting this research on deep learning and quarterback decision-making at the 2019 Sloan Sports Analytics conference next week. He joined us for a preview of his work.

Supporters of the shift believe it's a solid baseball strategy. By overloading defenders to the side of the field where a hitter routinely drives the ball, teams should get outs more easily. When to deploy the shift is a game of numbers. It depends on the likelihood of the batter to hit the ball one direction or the other, given his tendencies and the circumstances on the field. It's analytical thinking in all its glory. And yet there is a growing legion of shift haters. The drumbeat of those who blame the shift not just for reducing hits, but for increasing strikeouts, robbing the game of excitement, and any number of other sins grows louder by the day.   So what would happen if the shift was banned? That's the question legendary baseball writer Jayson Stark set out to investigate. He joined us to share his findings.

When the Patriots and the Rams take the field on Sunday, February 3, at Mercedes-Benz Stadium in Atlanta for Super Bowl 53, both teams will be there on the backs of several players few expected to become as important as they are — and in spite of several others who failed to meet expectations. Or so Cade Massey would have us believe. Massey is a professor of the practice at the University of Pennsylvania’s Wharton School and the co-host of “Wharton Moneyball” on SiriusXM Business Radio. He argues that it’s [damn near] impossible to judge individual talent in football. We invited Cade to defend this thesis.

On the Formula One circuit, tenths and even hundredths of seconds can be the difference between podium glory and being out of a job. Races are won and lost thanks to the skills of the drivers and the strength of the car, it is true.  But as the sheer volume of data available to F1 teams increases, another group of individuals have become key contributors to a team’s success: data analysts. Analysts run countless simulations, incorporating every possible variable, to inform their drivers’ race strategy on Sunday and achieve maximum performance for the driver and the racecar. But while salaries and sponsorships can push a driver’s annual income into the 8-figure range, the mathematical brains in the background make just a fraction of that. If the person analyzing the numbers and making decisions about race strategy is just as important as the person steering the wheel, shouldn’t they also be reaping the financial windfall? James Allen certainly thinks so. The President of Motorsport Network, James has covered Formula 1 as a journalist for over 30 years and has seen firsthand the sport’s data-driven revolution. We asked James to defend his position.

In this week’s episode, we take on a question that could lead to an existential crisis for this very podcast: How much of a difference do analytics actually make? We’d better hope the answer is that analytics make a big difference. The emergence of sports analytics has spawned quite an industry already, and it promises a great deal of growth still to come. We don’t want to be overly dramatic, but there are jobs on the line. Nevertheless, we have a duty on this podcast to take on the hard questions. Analytics naysayers and doubters abound. Do they know something that true believers have failed to see, either willfully or otherwise? Could Charles Barkley be right? Are analytics simply a baseless conceit created by a bunch of nerds so they could get jobs in sports and, you know, dates? Analytics expert and author Ben Alamar returns to help seek out proof that analytics do lead to improved results.

In pairing right-handed defensemen with left-handed defensemen, many NHL teams believe they are following a proven and, seemingly obvious, formula. If defensemen play on their strong side, they will be more effective at their jobs. Conventional wisdom on what works can codify with surprising speed in sports. But when something becomes an accepted best practice, we tend to stop asking how it became so and, more dangerously, we often become blind to the evidence that the practice may not be best at all. Tyler Dellow, senior analyst at The Athletic NHL, believes this is precisely the case with defensemen pairings in the NHL.

Does everyone benefit equally from the same amount of sleep? And how exactly does one measure its specific impact on performance — athletic or otherwise? After all, if you’re going to convince a young athlete to give up party time or practice time to grab some extra z’s, you’d better have some serious data to make your case. Kristen Holmes-Winn, vice president of Performance Optimization at WHOOP and former head coach of the Princeton field hockey team, joins the show to defend the hypothesis: Sleep is the greatest legal advantage in sports. 

It’s easy to understand why winning teams would draw more fans — a great team features exciting players, entertaining competition, and the expectation of feeling satisfied once the game is over. But are those qualities actually tied to wins and losses? Or can any team with the right combination of potential and promotion draw a large audience, regardless of what happens during the game? Jessica Gelman, CEO of Kraft Analytics Group and cofounder and cochair of the MIT Sloan Sports Analytics Conference, joins the show to defend the hypothesis: Teams can win at selling tickets without winning on the field.

Basketball IQ is one of the game’s holy analytical grails. We know it’s there, or at least we think we know it’s there. But what is it even made of? Intuition? Vision? Knowledge of the game? Or is it simply natural intelligence and brain processing speed applied to a specific context? Former director of sports analytics at ESPN Ben Alamar is one of sports’ leading proponents for and experts in basketball IQ. In this episode, Ben joins the show to defend the hypothesis: NBA teams would make fewer draft mistakes if they measured basketball IQ.

This is a sports analytics show for sports professionals, data junkies, and fans alike. It’s a show for anyone who knows that numbers are about much more than the score. Hosts Ben Shields (MIT Sloan School of Management) and Paul Michelman (MIT Sloan Management Review) engage the world’s premier sports analytics experts in a lively, occasionally controversial, conversation about what’s really happening both on and off the field. New episodes launch November 15.