During 2020 and 2021, Pivot Analysis worked to add new functionality and stats to our basketball coaching toolbox. We have identified some specific scenarios and how our tools can be of use.
Scenario 1: My team run a defense that drops its bigs on defense, likes to play in transition, and shoots a lot of threes and I need to add PF or C to replace a leaving big.
Pivot Analysis can help this team find suitable candidates by combining traditional counting stats, advanced counting stats and on court metrics. In this scenario, this team can identify players with the following analytical defensive parameters: force a low amount of shots at the rim when on the floor, force a low FG% on shots at the rim, force a high turnover rate, and force a low offensive rebound rate. They can also compare potential incoming players with their current roster using traditional stats (blocks, steals, rebounds) not only at a per game basis, but also per minute, per possession, or on court share (% of stat X of that team consumed by that player when on the floor). These comparisons can ensure that players can be compared across teams using pace adjusted data (per possession) or scheme adjusted data (on court share) as well as the standard rate comparisons.
On offense, they can apply the same methodology and identify players that, when on the floor, increase the team’s three point attempt rate, lower the mid range attempt rate, and increase the pace of play. Additionally, they can use the comparisons in individual stats to make sure that three point attempt rates are properly understood. This multi-faceted approach also is perfect primer for the video scouting part of the process due to the fact that it ensures a wholistic understanding of how the team performed with that player on the floor, what affects the player had on the team, and specifically how they are generating their offensive and defensive metrics.
Tools Used - Player Visualization and Comparison