Hey, baby, it’s better than a lay up!

Thanks, Dickie V!

While Midnight Madness has already come and gone on many college campuses, today– October 15th– is the traditional start of college basketball and time for me to return to the blogosphere.

Here’s a short list of the topics I’d like to cover in the months ahead. Several appeared on my “to do list” a while back, but I never got around to them; others are additions. In all cases, I hope you’ll find them representative of the tag line for this site, “challenging basketball’s conventional wisdom.”

  • We hear a lot about the importance of “balanced scoring” where everyone shares the ball and no one player dominates. But are balanced teams in which scoring is shared somewhat equally among its starters actually more efficient than those where it is not? What happens when the superstar on an unbalanced team has a bad night? Does this impact his team to a greater or lesser degree than when the top performer on a balanced team has an off night? Is the whole notion of “balance” actually more complex than what the typical fan envisions?
  • Basketball is fundamentally a game of transition, a contest in which the competitors seesaw back and forth between offense and defense with few stops. In such a game played “on the fly,” coaches and players need a set of navigation tools to help them “see” and make the right choices quickly. What are the principles by which you read or see the game and its myriad of choices? How does one plot the game’s “latitudes and longitudes” accurately? How does one develop “court sense”? How does an effective offense scheme clarify the choices, helping the attackers improvise? Conversely, how does good defense use court geography to muddy the offense’s options?
  • Bill James, the legendary amateur baseball statistician and father of “sabermetrics,” defined his work as “the search for objective knowledge about baseball.” The analytic revolution he inspired has now found a place in virtually every sport including basketball. Coaches and roundball aficionados are uncovering fascinating trends and patterns through data mining and analytics. But what are the limits? At what point does the sheer volume of data dwarf its practical application? How do you “see through” or “around” the data points to the actual game? Unintentionally, does an emphasis on data mask the simplicity of the game and complicate the strategic coaching choices? Teams and particular styles of play may emerge as statistically “efficient” but are they necessarily “effective”?
  • Is blocking out necessary for effective rebounding? Traditionalists insist on its importance, but John Wooden didn’t think so and neither does Bill Bradley. Who’s right?
  • Is the mid-range jump shot really dead and buried or waiting to be re-discovered? What conclusions emerge when we map the effectiveness of shooting across the full spectrum of distinct spots between the 3-point line and the rim instead of collapsing all of those spots into one statistical area loosely defined as the “midrange”? What happens when we apply Sandy Weil’s optical tracking analysis of shooting, measuring not only wherea particular shot takes place buthowit occurs? What might happen if players were re-schooled in the traditional mid and short-range jumper, the pull-up, and the bank shot, and if offensive schemes catered to creating such shots? Can we create a whole new generation of shooters – able to shoot from a variety of spots, especially in traffic? Would pace and scoring actually climb and the quality of the game improve? 
  • Has the big-time college game ever really been about “amateurs”? What lies ahead when we consider the impact of the evolving FBI investigation of agents, shoe companies, and recruitment? Where will the Rice Commission’s recommendations lead us? Will the end of one-and-done create a better game for both fans and players? Is the NCAA inherently ineffective and corrupt?

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