Coaching Use Cases
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
Scenario 2: My team has been struggling when all five starters are not on the floor. How do I improve these rotations and at least minimize the negative impact?
Pivot Analysis can help this team identify specifically what happens when all five starters are not on the floor and how to improve their substitution rotations. Pivot’s team tools enable coaches to view performance data by lineup, by custom player OnOff participation, by two player combinations, or by single player OnOff. Additionally, because we ensure that the coach has the ability to not only view offensive and defensive rating in each of these visualizations, but also the specific details of offensive and defensive performance. For example, we could identify that the problem is primarily driven by the inclusion of the backup SF in the lineup. When that player plays, the team’s rebounding falls off a cliff. The other rotations are not as problematic, but the backup SF is the 1st player off the bench and so all the follow lineups are affected. One way to mitigate this would be to test this player’s combinations with each of the other starters as well as some of the other backups and identify those that maintain solid rebounding averages. This way the negative impacts can be mitigated and presumably the value this player provides can be maintained. Reviewing lineups and player combinations at the plus minus level or even at the offensive and defensive points per possession level can provide data that is not indicative of future performance. For example, in this scenario, if the data is based a on 2-3 game sample size. It is very plausible that the opposing teams had a bit of shooting luck and the data would be skewed. It is important that the data be reviewed at the Four Factors (shooting efficiency, turnovers, free throw attempts, rebounding) level at least if not further.
Tools Used - Lineup Analysis, OnOff Metrics, Two Player Combination
Scenario 3: I am facing my top rival and I lost to them that 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). When looking at historical games, using only the Four Factors and Pace, we can generate with 99% accuracy what the actual final score was. 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. In the above scenario, we can identify points gained or lost based on each of the Four Factors. 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. 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. 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 team’s matchup against each other is also crucial for the video scouting process. Being to identify before hand 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
