I think the analytics in sports are table stakes; the real value comes from how you use the data and the questions you ask.

This issue can be compared to a basketball team; if most players run towards the basket while some go off to the side, it disrupts the play and makes scoring difficult.

The NBA is in the experience business, not just basketball; fans remember the experiences they have, not just the scores.

A bad team can sometimes be a neutral signal; context matters, as individuals may perform differently under different circumstances, and past experiences with them may not reflect their current capabilities.

Young people's attention spans are shrinking, which correlates with declining viewership in sports like baseball, while sports like basketball are adapting by focusing on clips rather than full games.

Many organizations focus on high-level performance metrics, which can mask undesired behaviors in AI systems, leading to a lack of robustness.

In live game teams, we need individuals who are customer-centric and can analyze data effectively, as these insights play a crucial role in decision-making.

You can't judge decisions solely by their outcomes; instead, you should evaluate them based on their expected value.

A good team makes better decisions as a whole than individuals would alone.