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The NBA Pace and Efficiency Screener – A Primer

Our NBA Pace & Efficiency Screener is an app that allows the user to research prior in-season game results in order to simulate possible pace and efficiency models for a given day’s NBA DFS slate. The goal? Identify potential breakout plays for that slate of games based on factors you determine.

In NBA daily and seasonal contests, it is critical to maximize opportunity and efficiency for player selections. The simple maxim “volume is king,” leads us down the path of finding the most possessions possible for our players. Volume comes directly from pace. A higher pace normally equates to more shot attempts, more points, more rebounds, more assists, etc.

Pace, however, isn’t always king. Higher efficiency may also drive above-average scoring. In this case, a game may play at a slower pace, but players can score more than normal due to improved efficiency allowed by the opposing team.

Why is the Screener useful?

Use this tool to view betting information (opening and current spread, game total, and implied team total), Expected Tempo (Pace) and Expected Offensive/Defensive Efficiency for every matchup on the day’s NBA slate. Expected Tempo and Expected Efficiency are derived from Adjusted Tempo, Adjusted Offensive Efficiency, and Adjusted Defensive Efficiency. These adjusted statistics are calculated from game results that you specify, as well as league-wide adjusted averages. This app might be useful in the following ways:1

  • Find offenses with a favorable matchup compared to their previous N games.
  • Find offenses with an unfavorable matchup against an improving defense.
  • Research how an offense or defense performed in the last N occurrences of a particular point spread
  • Find how an offense’s or defense’s Tempo and Efficiency are affected by a player’s availability.
  • Compare N-game windows at different points in the season.

The input under the “Global Filters” section will adjust the window of games used for Expected and Adjusted Tempo/Efficiency calculations. The default filter settings include all venues (home and away), all margins of victory, and screen the last ten games with those two conditions.


What can I learn from the Screener?

The Game Date tab will display the adjusted metrics table (example above), with data for every offense and defense on a given day’s slate. Further, every game on the NBA schedule for that day will have its own tab, called a Matchup Tab. This data will be recalculated per the filtered search that you are currently performing.

As an example, let’s say you want to look at the most recent ten-game window for the Golden State Warriors, who are a 10.5-point favorite over the visiting Utah Jazz. If we assume that the point spread is accurate, we can examine what their Adjusted & Expected Tempo/Efficiency has been in the last N contests with those constraints. We can also compare it with what their opponent concedes defensively.

Above, you see two Matchup Tabs. The first Matchup Tab (left) displays the relevant game data according to default Screener settings. The second Matchup Tab (right) displays the same data, but assumes an error around the point spread of approximately five points. I’ve set the Margin of Victory slider to encompass games Golden State has won by as few as six points and as many as 16 points. I’m also only interested in games where Golden State was the home team, so I’ve unchecked the “Away” box. Since we’re just past the quarter-pole of the season, I’m happy to leave the Game Range at the default (last ten games meeting the filter conditions) to generate the sample. In other words, we’re looking at the last 10 games Golden State played, where they were at home, and won by 6 to 16 points.

In this case, my goal might be to determine whether any of the Warriors are worth the high price on a day’s slate. I can also use a search like this to determine if any Jazz players might enjoy an uncharacteristic boost in their numbers due to the matchup and projected outcome.

Each Matchup Tab will have an image representing the metrics for that game, suitable to publish elsewhere via copy or save. Here you will clearly see the Expected Tempo (Pace), respective adjusted metrics for both teams in the contest, and the adjusted league averages for comparison. The lowest row shows Adjusted Defensive Efficiency for the opponent, which you may find helpful when looking for players that might perform outside of their norm due to game script.

A few notes on usage:

  • Matchup images are regenerated automatically each time the filter is adjusted.
  • After 10PM Central Time, the app will roll over to the next day’s slate of games.
  • Spread & Total data actively updates within a 16-hour window (2AM to 6PM Central Time). Outside of these hours, Spread and Total information may generate N/As.
  • Game Number is tabulated backward from the most recent games that fit within the screen parameters.
  • The Screener can generate N/As if a team within a contest has not played in any games that meet the screen criteria. In cases like these, you may need to expand your screen criteria to find an appropriate sample size.

  1. And in other ways as well…this is just an example of useful possibilities.  (back)
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