Team production and performance or, more simply put - winning basketball games - is not an exact science and requires a lot of skill, practice, and sometimes a bit of luck. While it is by no means a stand alone coaching solution, the use of advanced analytics (and the software that computes it) reduces the reliance on luck and can maximize the talents of a team.
Specifically when planning the rotations and lineups for a specific game, the use of historical statistics can prove to be quite valuable, especially when used in conjunction with the intuition and first-hand knowledge that a coach brings to the table. Let's take a look at how the Pivot Analysis analytics tools can be used to understand the situational team performance, analyze weaknesses, track the responsible players, and identify optimal substitutions.
Analyzing the Situational Performance of the Team
The Pivot Analysis Lineup Explorer allows a user to quickly review team performance from several perspectives, using advanced filters on the prebuilt reports. Take the 2018-2019 Duke Blue Devils for example, a coach can easily identify how the team performed in each period, how the team performed during the starters' minutes versus follow on lineups, and whether or not the second half starters differed from the first half.
Duke - Per 80 Possessions
Category: Offense - Defense = Net
1st half Overall: 92.4 - 77.0 = 15.4
2nd half Overall: 97.57 - 78.72 = 18.85
Starters: 98.72 - 76.80 = 21.92
Rotations: 94.24 - 78.08 = 16.16
1st half Starters: 92.76 - 77.75 = 15.01
2nd half Starters: 105.25 - 75.76 = 29.49
1st half Rotations: 92.34 - 76.84 = 15.50
2nd half Rotations: 96.09 - 79.29 = 16.81
When analyzing these statistics, it is important to compare apples to apples. In this tool, you will be able to compare the lineups and metrics using per possessions points scored (Offense) and allowed (Defense), as well as team and opponent shooting percentages, rebounding percentages, turnover rates, and free throw rates. These statistics enable coaches and team personnel to understand which lineups and/or combinations contribute the most to winning as well as what factors enable those contributions (shooting, rebounding, etc.).
Analyzing the Situational Performance of Players and Lineups
In order to optimize performance in the identified under-performing game situations, the specific lineups on the floor during the 1st half would need to be reviewed. This can be accomplished through review of full 5 player lineups as well as player combinations.
Reviewing the specific 5 player lineups is one approach to understanding which players may play better in certain situations. In this case, the normal starting four (Cam, Zion, RJ, Tre) with Marques Bolden performs at a much higher level than those same four plus Javin DeLaurier. The team is actually a net negative during those first starters' minutes with Javin.
The inverse is true when looking at the follow-on lineups. This data can be found in the 5 player lineup data as well as in the player combination data. When Javin comes off the bench in the first half, the team performs much better than when Marques Bolden comes off the bench.
Our goal at Pivot Analysis is to support coaches in getting the most out of their team by generating impactful metrics and delivering them in a seamless and visually intuitive platform. Advanced analytics have become mainstay in professional leagues and Pivot Analysis wants to prove that they can be easily leveraged and mastered by teams at all levels.
You can read more case studies here...