But it should be reasonably close to the actual total if you’re including at least several games’ worth of data.
2007 NBA PLAY BY PLAY DATA FREE
Of course, this won’t be exactly accurate over small periods of time, simply because the number of possession-ending free throws isn’t exactly 43.6% of the total all the time. So if you’re willing to use this estimator on your free throw totals, the total points per possession (or, more accurately, points per 100 possessions) of any offense should be: However, our analysis of NBA free throw patterns shows that approximately 43.6% of all free throws are possession-ending, a result that makes sense when you consider the number of “and-1” and technical free throws, when the shooting team doesn’t lose the ball. The first three of these things can easily be calculated from a box score, but the last one cannot – just by looking at the amount of free throws taken, you can’t tell how many of these were possession-ending free throws. The ball can only change hands four different ways: (1) your team makes a shot (2) your team misses a shot and doesn’t get an offensive rebound (3) your team turns the ball over and (4) your team goes to the line for two or three shots and either makes the last shot or doesn’t get the rebound of a missed last shot. It’s not hard to determine from a box score how many points your team scored, but it is difficult to determine how many possessions you had. From here, the NBA derived a metric for estimating possessions. The box score of a game yields many basic statistics such as rebounds, steals, assists, points, and so on. This means, possession data must be derived or counted. The super plus-minus requires play by play data. In these cases of analytics, items such as PER and spacing are derived from box scores. This is due to the cross-correlation whitening process that is used to remove correlation between all players. Similarly, the expected number of positive interactions will over-weight starters that are solid contributing role players. Here, it claims that defensive stalwarts are near worthless and tends to overvalue players that are effective due to systems. For instance, PER suffers from players who do important tasks that are not directly found in the box score.
Call this latter metric the “super plus-minus.”Įach have their information gain, as well as glaring holes.
Recently, we have received possesion-type analytics such as spacing metrics to identify the average type of spacing for a team’s particular play or even expected number of positive actions on offense and defense for each player in correlation with other players on the court. Roughly fifteen years ago, the Hollinger metric capitalized on this plus-minus type idea by introducing the Player Efficiency Rating (PER) a “per minute” analysis that normalizes player productivity in accordance to tempo of the team as the league average is normalized to 15.0. This means technical free-throws alone are not possessions.Īs basketball analytics have evolved and started to look more like how players, coaches, and executives think we have seen a significant rise in “per possession” or “per 100 possessions” analytics. In analytics, possession changes occur when one team yields the ball to the opponent for an opportunity to hold, dribble, or pass the ball. There is a little change to that definition even, as a field goal attempt does not really indicate the end of a possession. Team possession ends when the defensive team gains possession or there is a field goal attempt.
A team is in possession when a player is holding, dribbling or passing the ball.
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