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: I am facing my top rival and I lost to them the last two times by 5 and 7. How do I attempt to bridge this gap with a sound analytical strategy? Pivot Analysis offers a sound method for comparing two teams and projecting the outcome within our Matchup Machine. This method is based on the Four Factors, which are shooting efficiency (EFG), turnovers (TOV), rebounding (ORR), and free throw shooting (FTR). We also have built a editable version of this tool, our Efficiency Calculator. When looking at historical games, using only the Four Factors and Pace, we can generate, with 99% accuracy, the actual final score. Therefore our focus on the Four Factors is extremely accurate as well as providing a method for understanding, with some level of detail, how a win or loss was generated. In this scenario, we can plug in the expected Four Factors of each team as well as the expected pace. This will generate an expected final score as well as how each of the Four Factors will contribute to that final score. We can also identify points gained or lost based on each of the Four Factors.
Using our Efficiency Calculator, a team can input the results from the Matchup Machine and identify specifically what changes in the game plan would reduce the gap. For example, if the rival is favored by 5 and the two teams are dead even in expected EFG, ORR, and FTR, then that expected 5 points is based on the difference in turnover rate. To offset this, the team can test what happens if they focus on turning the ball over less or focus on getting to the rim and changing their expected EFG or focus on preventing second shots and reducing the opponent’s ORR. There are a number of methods and the calculator will automatically adjust the projected score. Hoping to beat a team by “shooting well” is not a sound strategy as it is not a fully controllable element, however taking the right shots, cleaning the glass, forcing TOVs, avoiding fouls are somewhat more controllable.
Additionally, the team can leverage their or their opponent’s lineup data to force rotations that are more beneficial. For example, in this scenario, maybe the opposing backup PG turns the ball over more and is a less impactful defender. So focusing on maximizing that player’s time on the floor can yield the specific results you are looking for. Modeling and understanding how teams matchup against each other is also crucial for the video scouting process. Being able to identify beforehand how a team wins their games and which players are most crucial in those aspects can be very helpful in framing the scouting process. Tools Used - Lineup Analysis, OnOff Metrics, Two Player Combinations, Matchup Machine, Efficiency Calculator