For a comprehensive breakdown of the metrics that power the leaderboards on the site, checkout my deep dive article on Substack that details the methodology and findings from the production (DRE) & consistency metrics (COV%) I'm displaying here: The 5th Factor: A Basketball Analytics Deep Dive.
DRE is a single-game composite metric that estimates whether a player had a good game, based entirely on their box score. You can think of DRE as Game Score's more advanced cousin, as it derives the box score weights from RAPM (Regularized Adjusted Plus-Minus) to better capture the statistical value of each box-score input (points, rebounds, assists, offensive rebounds, steals, etc.). RAPM assists in building a more accurate snapshot of single-game player performance by calculating plus-minus data from every NBA lineup, then adjusting for opponent and teammate influence to deduce a player's impact per 100 possessions. DRE is much more punitive in comparison to Game Score, dinging players more harshly for turnovers while also crediting players heavily for forcing steals, in addition to appropriately weighting 3-point field goal attempts & rebounding. Those improvements make DRE's upper and lower bounds a good bit tighter than Game Score. Hollinger notes on Basketball-Reference that a 40 Game Score is an outstanding performance, while 10 is an average performance. The best prospect DRE performance in my database was achieved by Ben Simmons on December 12th, 2015, who recorded a 33.4 DRE by putting up 43 points, 14 rebounds, 7 assists & 5 stocks on 80.8 TS% with just 2 turnovers. The WORST performance was achieved by Tyreke Evans on February 26th, 2009, scoring just 8 points on 14 shots (26.9 TS%) with 0 stocks, 4 fouls and nine turnovers, recording a DRE of -12.6. For reference, the average DRE among future NBA players in their collegiate seasons (in my database dating back to 2003) is 5.24. The calculations on this site are based off of Kevin Ferrigan's work in 2017: Updating DRE Tweaks.
A measure of consistency that shows how much a player's performance (quantified by DRE) varies from game to game. Lower COV% indicates more consistent performance, while higher COV% suggests more variability. Calculated as (standard deviation / mean) × 100. By using each player's mean and not the sample, COV% accurately calculates how variable a player's performance is relative to their own baseline, NOT the prospect/league average. This means that a player like Pat Ngongba can be MORE consistent than Cam Boozer, despite Boozer doubling Ngongba's average production. Generally speaking, it is easier for big men and older prospects to remain consistent game to game, which makes consistent guard play/freshmen play that much more notable. Credit to Frank and Sravan for their work quantifying game-to-game consistency on Twitter.
An advanced shooting efficiency metric that accounts for 2-point field goals, 3-point field goals, and free throws. Formula: Points / (2 × (FGA + 0.44 × FTA)). This metric gives a more complete picture of scoring efficiency than traditional FG%.
A shooting metric that adjusts field goal percentage to account for the fact that 3-point shots are worth more than 2-point shots. Formula: (FGM + 0.5 × 3PM) / FGA. This provides a more accurate measure of shooting efficiency than basic FG%.
The combined total of steals and blocks (STL + BLK). This metric represents a player's defensive playmaking ability.
Games played against teams in different NET ranking quadrants. Teams are placed in 1 of 4 buckets based on their NET: Quad 1 (top 30 teams), Quad 2 (31-75), Quad 3 (76-160), and Quad 4 (161+). Opponent quality drops off drastically against Q4 opponents, so it's best practice to filter those games out to get a more accurate depiction of a prospect's performance. These classifications help assess strength of schedule and performance against varying levels of competition and are venue-adjusted to give teams additional credit for winning on the road. You can explore the minute details on the NCAA's website: NCAA NET Rankings.
The number of games played against opponents ranked in the top 100 of the NET rankings. This metric helps evaluate how players perform against high-level competition.
The number of games played against opponents ranked in the top 220 of the NET rankings. This broader measure of quality competition helps to filter the lower third of opponents.