Fantasy basketball: Analytics glossary

We all know who Carmelo Anthony is, but do you know what the analytics term CARMELO means? Isaiah J. Downing-USA TODAY Sports

Analytics are a key component in decision-making for NBA franchises -- and they should be for your fantasy team too. Here is a handy glossary of key statistics that you can use to get the winning edge on your opponents.

eFG%: Effective field goal percentage is just like traditional field goal percentage but properly accounts for the additional value of a 3-point shot. 2016-17 league average: 51.4 eFG%

TS%: Like eFG%, true shooting percentage properly accounts for 3-pointers. In addition, it also considers free throws. 2016-17 league average: 55.2 TS%.

TRB%: Percentage of rebounds available while a player was on the court that he grabbed. Can also be split into offensive and defensive rebound percentage.

AST%: Percentage of teammate field goals while a player was on the court that he assisted on.

BLK%: Percentage of opponent field goal attempts while a player was on the court that he blocked.

Usage rate: The percentage of a player's possessions in which he was directly involved in the attempt at a basket (via a shot attempt, free throw attempt) or a turnover. Can give fantasy players an expectation of volume for certain counting statistics.

Pace: The average number of possessions a team uses per game. Critical for fantasy owners to consider, as pace -- and an opponent's pace -- can alter the number of opportunities a player may have at both ends of the court. 2016-17 league average: 96.4.

Per 100 possessions statistics: Looking at basic box score statistics on a per-possession basis allows fantasy players to understand their player's production independent from the pace at which his offense plays.

Offensive rating: Using field goal, assist, free throw and possession data, this statistic tries to quantify a player's points produced per 100 possessions. 2016-17 league average: 108.8.

Defensive rating: Defensive rating calculates a player's points allowed per possession through a combination of individual box score stats (like steals, blocks and rebounds) and a player's team's defensive performance. 2016-17 league average: 108.8

Team offensive efficiency: The number of points a team scores per 100 possessions. Keep in mind that because this is pace-neutral, differences may be more or less pronounced depending on how fast a team or its opponent plays.

Team defensive efficiency: The number of points a team allows per 100 possessions. Keep in mind that because this is pace-neutral, differences may be more or less pronounced depending on how fast a team or its opponent plays.

PER (player efficiency rating): An attempt at a single-number valuation of a player using an amalgamation of box-score stats -- many of which are fantasy categories -- such as 3-pointers, assists, rebounds, blocks and steals. This statistic will give you an overall sense of how good a player may be, but won't offer insight into exactly how that value is attained. 2016-17 league average: 15.0.

Box plus-minus (BPM): Box plus-minus uses advanced box score statistics -- such as true shooting percentage and rebound percentage -- to try to quantify a player's impact on the game, above or below average, per 100 possessions. 2016-17 league average: 0.0.

Real plus-minus (RPM): A spin-off of the traditional plus-minus statistic that attempts to tease out an individual player's impact, separate from his teammates and factoring in opponents. Measured in net point differential per 100 offensive and defensive possessions. 2016-17 league average: 0.0.

RPM wins: A combination of real plus-minus and possessions played in order to estimate the number of wins the player added to his team during the course of a season.

CARMELO: This model, created by FiveThirtyEight, predicts a player's future performance by identifying his skill set based on some of the statistics listed above, comparing him to similar players of the past, and projecting forward in terms of wins above replacement (WAR), a combination of productivity and playing time. Though CARMELO does not project individual fantasy statistics, it can be useful for fantasy players because it projects forward, rather than simply stating what happened in the past.

Quantified shot quality (qSQ): Created by Second Spectrum, qSQ takes into account the position and movement of the players on the court to determine quality of the shot that a player took, independent of that player's shooting ability. qSQ is on the same scale as eFG%. Fantasy players might use qSQ to compare the quality of looks a player changing teams got on his old team with, for example, the player who former occupied the player's role with his new squad.

Quantified shooter impact (qSI): This metric works by comparing a player's qSQ to his actual output, therefore quantifying a shooter's ability to score points above or below expectation set by the difficulty of his shots. Can be a good way for fantasy players to get a true sense of how skilled of a shooter a player is, independent of scheme or teammates.

Player tracking and play type data: This information, on NBA.com via Synergy Sports, breaks down statistics based on certain individual events, such as drives, pull-up jumpers or touches in the post. Statistics can also be filtered by certain types of plays, such as an isolation, pick and roll (ball handler and roll man), off-ball screens, etc. Can be useful to fantasy owners trying to evaluate how a player's skills might translate to a new scheme or, possibly, against a specific opponent.

Potential assists: The number of assists a player could have if all shots off of his passes resulted in a score.

Contested rebounds: Percentage of a player's rebounds that were contested. Useful for attempting to figure out, for example, how much skill is behind a player's rebound total.