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 professional athletes will be willing to share their personal biometric data in real time.

This episode of the Counterpoints podcast looks at whether cohesion analytics can quantify team chemistry — and if so, how to use that data to boost a team’s performance.

Current thinking is that the 7-foot center who used to dominate the NBA is a thing of the past. But a new analytics focus on defense shows that might not be the case. The Philadelphia 76ers’ data scientist, Ivana Saric, explains why.

Because of its sharp focus on measurable outcomes, the study of sports analytics brings many of the most critical issues in management into high relief. Through the lens of sports, there is a great deal to learn about leadership, performance management, decision-making, innovation, and, most of all, managing with data. MIT SMR’s sports analytics podcast, Counterpoints, is a great entry point to the playing field of data-driven management practice.

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.

Companies can turn AI hype into operational hay by developing their capacity for enterprise cognitive computing. This capacity entails five capabilities: data science competence, business domain proficiency, enterprise architecture expertise, an operational IT backbone, and digital inquisitiveness. The capabilities shape and are shaped by four practices: identifying use cases, managing application learning, cocreating applications, and thinking “cognitive.”

Though the QB gets most of the spotlight, a new analysis from ESPN shows that every NFL team lives and dies by the skills and teamwork of players at the tackle, guard, and center positions in the offensive line. Counterpoints looks at the burgeoning field of O-Line analytics with ESPN’s Seth Walder.

One of the newest and most important players in the sports world isn’t a soccer phenom or an NBA all-star — it’s a multiplayer video gaming platform known as eSports. The universe of eSports relies heavily on data analytics, but the surprising twist is the role of social science analytics in this world of virtual contests between far-flung players. Counterpoints talks to eSports analytics guru Tim Sevenhuysen.

When NBA superstars are given a planned night off, the impact of their absence isn’t just felt on the basketball floor — it’s a financial issue as well.

With customized and continuous data-driven feedback becoming a new normal, managers are revisiting the role they should play in delivering, facilitating, and curating face-to-face employee feedback. Does direct managerial involvement complement or compete with data-determined performance reviews?

Elite soccer is a multibillion-dollar business, and top clubs are constantly looking for the next promising young player that they can develop into a superstar. Youth academies are one way for clubs to do this, but they have to find the players with potential before they can work with them. Counterpoints talks with Chelsea Football Club’s head of research and innovation, Ben Smith, who is on an analytics-driven hunt for star material.

What happens when a free-agent player with a hot hand and great stats gets signed to a big, long-term contract, only to perform at a mediocre level? Counterpoints examines the problem of “shirking” in professional sports by looking at the data with Richard Paulsen, who presented his paper, “New Evidence in the Study of Shirking in Major League Baseball,” at the Sloan Sports Analytics Conference.

When teams perform consistently, it’s easy to ascribe their winning (or losing) ways to the coaching staff. But the data sometimes tells a different story. So just how much credit — or blame — for team performance should coaches get? Counterpoints looks at the evidence.

In this episode of Counterpoints, the subject is baseball — specifically, the analytics-centered strategy for pitchers called out-getting, which focuses on pitchers’ efficiency rather than on when, how often, and how much they pitch. Will this practice transform baseball in the classic Moneyball tradition — or will it simply be an interesting tactic that teams sometimes use to gain a temporary advantage?

Now that companies have replaced rigid hierarchies with flatter, more fluid structures to promote agile ways of working, they have also made it harder for employees to chart a path for growth and advancement. This challenge is also a concern for employers, who must — for the sake of engagement and retention — show high performers how they can progress within the organization. Analytics can help highlight opportunities for getting ahead.

ESPN’s Bryan Burke previews the 2019 Sloan Sports Analytics session on deep learning and quarterback decision-making. The ultimate goal: to understand how a quarterback processes information and to track the patterns their minds tend to follow.

The disadvantages of asking people to rate themselves are obvious. For instance: You could fake your way to a higher score, or you might lack self-awareness. But self-report surveys have advantages, too. They make data collection efficient, and nobody but you has 24-7 access to your thoughts, feelings, and behavior. And here’s another benefit many people don’t consider: The act of answering the questions can promote greater self-awareness, which opens the door to self-development.

A good psychometric test can easily outperform a résumé scan and interview at predicting job performance and retention. Yet personality testing and other ways of analyzing potential present some significant challenges: For instance, not all assessments pass the sniff test, and people’s personalities vary from moment to moment, often depending on the challenge at hand. We need a finer-grained understanding of human potential.

Findings don’t have to be earth-shattering to be useful. In fact, obvious insights can help you overcome three barriers to change in your organization: resistance to new data (“But that’s not what my experience has shown”), resistance to change itself (“But that’s the way we’ve always done it”), and organizational uniqueness bias (“That will never work here”). You can also gain trust by confirming what people already believe.