On April 24th, 1963, the Boston Celtics took the floor at the Los Angeles Sports Arena holding onto a 3-2 advantage in the NBA Finals against the Los Angeles Lakers. For the only time in the history of the league, a Finals team trotted out only (later to be named) Hall of Fame players: Bob…
In a recent post, we took a brief glimpse at offensive rebounding and discussed some pro’s and con’s about crashing for rebounds and provided an illustration about where rebounds go after missed attempts. In one such instance, you would have seen this plot: For the sake of argument, let’s suppose that every single red circle…
In the era of possession-based statistics, we often look at items such as per-possession or per-100 possessions. This type of parsing makes sense as a possession is deemed to be the period of time a team “controls” the basketball. The technical definition of a possession defines the end of the “control” period as the point of…
In the Regularized Adjusted Plus-Minus (RAPM) model, one of the perceived challenges is understanding the error associated with the resulting posterior RAPM value a player receives. In a previous post, we noted that RAPM is a Bayesian model in which we assume that “player contribution” can be estimated through weighted offensive ratings conditioned on the…
Synergy: Breaking Down Field Goal Types With the creation of Synergy, the basketball world gained valuable access to previously hard-to-obtain data on all field goal events in the league. One of the biggest introductions was the “primary defender” tag on field goal events. With play-by-play data, when a player drives to the basket or attempts…
Warning: This is a lecture from one of previous statistics courses taught over the years. It will be theoretically heavy, but offers insight on some of the research process required for developing analytics. (End Disclaimer) Thought Exercise: Perimeter Defense Whenever we develop an analytic to help describe the game, we typically have to ask three things.…
In Dean Oliver’s Four Factors, we are interested in effective field goal percentage, offensive rebounding percentage, free-throw rate, and turnover percentage. If a team cannot dominate a couple of these categories, then it will be unlikely for that team to win. For instance, let’s consider effective field goal percentage. The Golden State Warriors have posted a .558 eFG% while limiting their…
Fresh off a Boston Celtics sweep of the Indiana Pacers, across Twitter phrases of “Remember that the Boston starters were -3.5 Net Rating for the series” piped up. The implication was that the Boston starters should have lost the series, and the most common rumblings came about the depth of Indiana’s bench costing them the…
Whenever we talk about scoring in the NBA, typically the the conversation points towards heroic scoring feats such as Wilt Chamberlain’s 100 point game or Kobe Bryant’s 81 point game. Or Wilt Chamberlain’s 78 point game… Or Wilt Chamberlain’s pair of 73 point games… or Wilt Chamberlain’s 72 point game. Of those other spurious 70+…
The end goal for all basketball analytics is to gain wins. In order to gain wins, a team must score more points than their opponent. It may seem like a completely obvious and yet, vague statement; but this is the reality: how does this “newfound” intelligence gain wins? In sports, if this intelligence is proprietary,…
Squared Statistics: Understanding Basketball Analytics 