One of the two main algorithms that drive our insights is our efficiency algorithm, which helps us understand which players and teams are performing the best. This algorithm is derived straight from the game data, which we analyze to create our efficiency ratings, typically labeled nERD.
Our formula for deriving these ratings produces a truly accurate picture of performance. Unlike most systems for evaluating players, we don’t depend on simplified statistics like passing yards or batting average, which are prone to biases. Instead, we use a complex mathematical formula combined with an advanced algorithm for factoring in situational variables to get a much clearer and more accurate sense of performance.
Expected Points/Net Expected Points (NEP)
Every single situation on the football field has an expected point value; that is, how many points an average team would be expected to score in that situation (given down, distance-to-go, and yard line). For example, the Chiefs may be facing the Pittsburgh Steelers, with a third-and-two on the 50-yard line. That's a ton of variables, but luckily, numberFire has data from the past dozen years of every single play, so most situations have come up at least once. According to our data, an average team may be "expected" to score 1.23 (estimated number) points on that drive. However, Jamaal Charles reels off a 32-yard run to bring the Chiefs into the red zone, increasing the "expected" point value of the next play to 4.23 (still an estimated number) points. Jamaal Charles then gets credit for the difference, in this case 2.96 points, as his NEP total. That's Net Expected Points.
Since passing is often more efficient than running the ball, you'll usually see running backs with negative NEP per play scores, meaning that they are losing their team expected points every time they touch the ball. Receivers and tight ends, meanwhile, will usually have high, positive NEP per play scores, since receivers don't touch the ball unless it's a high-yardage completion. Quarterbacks can be in the middle, either positive or negative: completions typically help their score, while incompletions lower it. So when you're looking at NEP, it's important to look at the numbers based on position. Expected points do not take score and timeleft into account like win probability, and as a result, are a better measure of pure efficiency (since teams will alter their game plan significantly based on score and time).
Probability of a team winning the game at any point during the game.
This is the number of successful plays. Success occurs when NEP > 0. This means that the player performed above expectation and increased his team’s chances of scoring.
The percentage of total plays that ended in success.
The percentage of targets for which the player registered a reception.
Value Over Replacement Player (VORP)
VORP is a truer measure of fantasy value than fantasy points by adjusting players’ rankings and fantasy point production based on their position eligibility and position scarcity.
nF Score or FireFactor™
our internal method for ranking players, this combines both the players overall production and value above replacement.
nERD/nF Score (Pitchers)
Pitcher nERD represents the number of runs prevented by comparison to a league-average pitcher per game.
nERD/nF Score (Batters)
Batter nERD represents the number of runs contributed over a league-average per game (~27 plate appearances).
nERD/nF Score (Teams)
Team nERD represents the runs scored above or below a league-average team per game; in other words, it represents expected run differential per game.
Weighted On-Base Average (wOBA)
wOBA is a pure measure of a player’s offensive contribution by assigning weighted statistical values to every possible outcome of an at bat.
Slugging Percentage (SLUG)
Total Bases/At Bats – This measures a player’s power ability.
On-Base Percentage (OBP)
OBP measures how often a batter reaches base.
On base percentage plus slugging percentage.
Percentage of plate appearances the pitcher surrendered a home run.
Percentage of plate appearances the pitcher allowed a walk.
Percentage of plate appearances the pitcher achieved a strike out.
Walks plus hits per inning pitched.
The numberFire fantasy score takes a player’s statistics (or projected statistics) and adjusts for the player’s ability to score across categories by weighting all categories evenly. Then, once we have this score it is adjusted based on position and position scarcity.
nERD/nF Score (Player)
The player ranking measures the total contribution of a player throughout the course of a season, based on their efficiency. League average is 0. Comparable to win shares, this ranking gives an estimate of how many games above or below .500 a league-average team would win with that player as one of their starters. For example, LeBron James posted an 18.3 rating in the 2010-11 season. If he played on a team with four league-average players, you would expect that team to finish 18 games over .500 (50-32). By summing the nF NERD for all players on one team, we can see how many games above .500 that team is expected to win. The rating will represent a player's total contribution over an 82-game season, not just the games played so far this year.
nERD/nF Score (Team)
The team ranking is on a scale from 0-100, with 50 as the league average. This ranking is predictive of the team's ultimate winning percentage.
nF Efficiency (Player) – The numberFire efficiency metric measures a player's raw efficiency. More specifically, it is an estimate for the point differential that a league-average team would have with that player as one of the five starters. It combines both offensive and defensive production on a per possession basis. The main factors in determining a players' efficiency are usage rate (how many possessions that player uses out of the total team possessions - players like Kobe Bryant will have high usage rates, while the Shane Battier's of the world will have low usage rates), offensive rating (this combines basic statistics to estimate a players' offensive contribution per 100 possessions), and defensive rating (an estimate for the number of points an individual player would allow per 100 possessions).
Developed by Dean Oliver, offensive and defensive ratings accurately measure team efficiency. They reflect the number of points a team would score (or prevent) in 100 possessions.
This estimates the number of possessions per game for a team. It reflects the pace at which the team plays.