Below, you will find a running list of the analytical measures I have used so far this year. I will try to identify how they are used and what they can tell us. Each is an attempt to take a Counting Stat (TDs, INTs, sacks, total yards) and give further context to highlight a team or player strength/deficiency that might be invisible in a newspaper recap. I tried to indicate what position or unit the measure is best for and group them accordingly!
Team Stats
Defense-adjusted Value Over Average: DVOA is one of the earliest-adopted analytical measures that focuses on how well a team performs as compared to the average across the league. Using all teams, DVOA sets a baseline for basically any situation: 3rd and 5, for instance, has an average amount of yards gained by all teams, and if that average was 3 yards, a certain bit of positive DVOA would be given if you exceeded that 3 yards. An extra bump would be given if your exceeded number was 6 yards, because in that situation, you have gained something extra (a first down). The measurers of DVOA assign “success points” to a given a play, most commonly 1 point for a successful play (that 3rd-and-6 successful run, for instance) and -1 for a negative play (if you get stuffed for no gain on 1st-and-10, you’re well below the league average mark for running on 1st down, for instance). This kind of system, mapping efficiency across the league, has gotten further developed over the years as the measurers added more penalties for turnovers and got better at pinpointing where hidden situations cost teams (and gained teams) points on the scoreboard. It’s all about assigning more points to the 3rd-and-6 conversion at the 35 yard line than the 1-yard TD run later in the drive: though the TD run got the points, it was ultimately an easy and expected chance to score. It was, instead, the QB and WR who kept the drive going who deserve more credit for the score. DVOA was particularly innovative in tracking defenses, and also weighting how well you did against how strong the defense is in a given situation. Converting 3rd-and-12 against the Texans is a whole lot easier than doing it against the 49ers, and DVOA accounts for this. The situation matters, too: converting that score while down by 25 points is less important than converting in a one-score game. Over the past 15 years, DVOA has changed before each year to match league trends (in 2020, for example, they changed scrambles from run plays to pass plays to better match situations and actually weight guys like Josh Allen and Lamar Jackson as they deserved as passers), and they’re re-examined to see whether the weighting truly predicted the best teams in the league. For a defense, you want your DVOA to be negative and vice versa for the offense: the best DVOA offense last year was Kansas City (27.5% above average) while the best defense was New England (-13.9% below the average for defenses, a good thing!)
Expected Points Added: EPA is very similar to DVOA: it also measures efficiency based on a play-by-play scoring system. The biggest difference is what it’s measuring. While DVOA measures how well a team does against other teams (do the Steelers run more or less efficiently in a given situation than the Commanders against the 49ers defense?), EPA measures how many points a given play contributes. Typically, EPA measures how many points a team is expected to get on a drive (if they’re on their own 1-yard-line, that might be a number below 1, whereas that number might be between 5 and 6 if they’re at the other team’s 1-yard-line) and then, when the play occurs, looks at the situation to see how many points they are now expected to get. The numbers are based on probabilities, again assigned by league averages, and then updated every play. Say the Chiefs are at 3rd and 3 from the 40 yard line. If Mahomes completes a 40 yard dime for a TD, that’s a HUGE swing in expected points: the team was hovering less than 3 and got 6. Kaboom! But if Mahomes takes a sack? Then that number plummets because Reid and co are definitely sending the punt team. EPA can be assigned to players (Mahomes in this example) or to teams. Like DVOA, the positive EPAs are good for offenses, but the inverse is true for defenses: the Chiefs had the top EPA per play, .196 every snap. Two pictures illustrate how those points are assigned, courtesy of Nfelo:
You may see this same idea described as “Win Probability” or “Win Shares”, which similarly understands that each play increases or decreases a team’s chance of getting points and winning a game. Instead of putting the value as points, these models understand each situation (time, score, down and distance) as possessing a certain percentage win chance for a team and the difference between that play and the next play can be isolated for teams and players.
Success Rate: Different outlets have different measures for success rate, but typically it is a team stat that requires a number of individual players (it can be used for individuals, but for RBs, the OL is essential, and it’s nearly impossible to separate QBs and WRs). A successful play is defined by down and distance as the yardage needed for a new set of downs (or a TD if you’re in a goal line situation): Warren Sharp defines it as 40% of the yards needed on 1st down, 70% on 2nd down, and 100% on 3rd down. If a back gains 5 on his first run, then a pass play goes for 4 on 2nd down, and the offense QB sneaks for the 1st down, then a team has just run 3 successful plays. Likewise, if the QB takes a sack on 1st down, then gains 8 on a pass on 2nd and 16 (50% back, not bad but not successful), and then the team gets 7 yards to bring up 4th and 1, then that team has had 3 unsuccessful plays in a row. The obvious drawback of this stat is that the 1st and 10 sack was the real problem, which is why it is frequently seen right next to EPA, as EPA would have been far more favorable to the unsuccessful series I described. The success rate is measured by dividing successful plays by total plays run: Warren Sharp did not measure success rate last year for the public, so the most recent data I have is from 2021, where the Chiefs offense led the league with 55% success rate.
PFF Grades: PFF is an organization that watches each game and gives relatively subjective grades on a scale of 0 to 100. PFF analysts grade each play from -2 (worst execution you could imagine on an intended play) to +2 (elite caliber play that reflects an achieved intention and then some, like David Tyree’s helmet catch). The grades have evolved over the years to understand the intents and rules of RPOs, better understanding of intention on the OL and DL, and weighing different positions and skills differently (you need to do 5 different things as a LB, but you only need to do 1 thing as a corner in man coverage). PFF grades are not the be-all, end-all, but they represent an understanding of which players popped (positively and negatively) on tape and, after every play of a season is graded, which players were consistent difference-makers in the large aggregate of data. It’s imperfect, but it is the leading system of analytical data that is derived entirely from grinding the tape.
Explosive Plays: This is another one where analysts themselves set the definitional bar, but it is typically defined as a play that goes for 20 yards through the air or 10 yards on the ground. Explosive plays are also called chunk plays by different analysts because the offense has gained chunks of yards and the defense has lost chunks of yards. Mike McDaniel is a big believer in the need to find explosives (he will frequently use that exact verbiage), which is why he prioritizes speed. The Patriots made a dynasty on keying in on and disrupting explosive players, and Vic Fangio structured his scheme by making explosives next to impossible if players tackle well. The Chiefs had the most explosive passes in the league last year (73, the Dolphins and Eagles were the only other team to even get 60) while the Ravens led the league in running explosives (58). This can also be expressed as a percentage by dividing the explosive passes by total pass plays (the Eagles actually led this, with 11.75% of their pass plays going for an explosive play). Defenses measure it in the inverse: the Saints allowed 34 explosive passes while the 49ers allowed 15 explosive runs, both best in their respective categories (the Titans and Texans were the worst in those categories, with 63 and 62).
Points per Drive and Drive Scoring Percentage: A measure for both offenses and defenses, this measure zooms out from many of the play-by-play metrics above to measure consistency within a team’s ability to maintain drives. Great teams can withstand penalties and third downs to ensure they maximize the opportunity to score points. Points per drive divides total points by the amount of drives that a team had. If you score 24 points on 8 drives, you achieved a points-per-drive number of 3, essentially a FG every time you touched the ball. This stat is similar to points per game (a macro statistic), but adds the additional element of efficiency. Last year, teams averaged about 10 drives per game, with Kansas City averaging the most points per game with 29.2. Elite offenses will nearly hit that 3.0 mark while the average NFL offense scored 21.87 points (so closer to 2.0), while elite defenses want that number well below 2.0. Drive Scoring Percentage instead measures how many drives end in actual points. If you score 24 points on 4 drives (3 TDs and 1 FG) on your average 10 drives, that number would be 40%, a relatively strong outing. Good defenses try to drive that below 30%. TD Drive Scoring Percentage simply eliminates FGs, so the above example would be 30%. The goal is to be a team high in both, of course, and together they tell the story.
Blitz Rate: A percentage measure of the amount of plays in which a team blitzes, defined by sending five or more rushers. Definitions of this stat vary based on whether you count fire blitzes, in which a defensive lineman steps back into zone. Teams with high blitz rates are trying to force quicker and shorter passes, or hurry a QB’s process when throwing into man coverage, if they aren’t getting home. The prevalent strategy in the NFL features less blitzes, relying on deep coverage to accomplish the same thing with more tacklers.
Average Drive Time: The amount of game clock consumed by each drive, measured (similarly to the above) by dividing total time of possession by the drives in the game. The Bills currently lead the league offensively, with 3:36 per drive. A good measure may mean that you’re strong at avoiding 3 and outs or that you’re struggling to find quick explosive plays. Defensively, the measure is the same. This stat says somewhat less about success and more about the flow of the game and strategy.
QB Analytics
Turnover-Worthy Plays: A Turnover-Worthy play is relatively easy to understand! Some INTs and fumbles are a player’s fault (terrible throw into coverage, or holding the ball way out off your body) while others are not (drop by the WR gets tipped into the air, or a huge hit comes from the backside). Turnover-Worthy Plays are a subjective stat tracked by PFF for QBs: did the QB hold onto the ball too long, which led to a fumble? That’s a Turnover-Worthy Play. Did a QB throw directly to a LB who dropped the ball, thus meaning the QB did not throw an interception? That’s a Turnover-Worthy Play. Josh Allen was the worst in the league with 33 last year, while Justin Herbert was the best of qualifying QBs, throwing just 14.
Big-Time Throws: Another subjective metric, Big Time Throws are PFF’s measure of passes, defined as “excellent ball location and timing, generally thrown further down the field or into a tighter window.” As accurate QBs like Tua rarely get credited with Big Time Throws, it is safe to assume the bar includes powerful throws. A tight-window throw for 8 yards is far less likely to be termed a Big Time Throw than a 30-yard fade route. Allen and Herbert again lead the league on either side, this time with Allen being the best (52 BTTs), while Herbert had 24 and was the worst in the league. Big Time Throws require aggressiveness, and it is likely that, if you see a QB with a high rate of both, they can be classified as a gunslinger while the inverse (low BTTR and low TWP rate) would be a caretaker. A QB with a lot of Big Time Throws but few Turnover-Worthy Plays is probably extremely high-functioning, and likely having an MVP season. A QB with few Big Time Throws but a lot of Turnover Worthy Plays is probably struggling big time, or being propped up by good play calling.
Completion Percentage Over Expectation: Completion percentage is an old metric designed to find how accurate a QB is, dividing the completed passes by total targets. If a QB is 8/10, they have an 80% completion percentage. What that stat does NOT tell you is how that 8/10 came about, and even looking at their total yards isn’t terribly helpful. Is the QB simply checking down and getting Henne-style easy completions for a few yards? Is the QB playing a terrible defense and hitting wide open receivers at every level of the defense? CPOE, as it is called, finds an Expected Completion Percentage for every QB’s game by measuring a number of different factors. The 33rd Team broke down Ben Baldwin’s model, which is well regarded by folks in the industry. Here were the factors he used in getting an expected percentage:
- Field position
- Down
- Yards to go
- Air yards
- Distance to sticks (air yards – yards to go)
- Whether possession team is at home
- Whether the game is played indoors
- Era, broken down into 2006-2013, 2014-2017, 2018 and beyond
- Pass location (binary: middle or not middle)
- Whether quarterback was hit on the play
So a throw inside the red zone, for instance, might be less likely to hit than one at the 20. A deeper throw might be harder to hit than a short one. A 3rd and 13 completion might be more difficult than a 1st and 10 one, especially if that pass is 15 yards down the field. You might weight a throw where the QB got demolished in 2 seconds less than a throw with a clean pocket. Once you have that expected completion percentage, then you factor in the actual one: if a QB, by their targets, should have completed 60% of their passes but finished the game with 70%, then you have +10% CPOE. If that same QB completed 53%, then they’d have -7% CPOE. Among starters, NFELO’s model had Geno Smith at +5.6 CPOE for the best and Zach Wilson at -5.6% as the worst.
Adjusted Completion Percentage: A substantially simplified version of CPOE above, adjusted completion percentage is the percentage of passes completed by a QB when you remove throwaway balls, spikes, and drops. The idea is that, if a QB either should have or was not attempting to complete a pass, he should not be penalized. Completion percentage is still used around the league for some reason, but should be retired: adjusted completion percentage, albeit with the caveat that drops and throwaways may be subjective, is far more indicative of accuracy that completion percentage and far less mathy than CPOE.
Stable/Unstable Metrics: Stable metrics are statistical categories that stay consistent year over year. In some situations or positions, there are far too many variables to ever properly predict how your numbers will look by the end. Coverage numbers, for instance, might depend on how good the QBs and WRs are on your opponent’s team. Numbers against the blitz may depend, for a QB, on how well the OL picks up the blitz: those numbers will naturally go up if you have an OL that communicates effectively and go down if the blitz is always getting home. These are unstable metrics. Meanwhile, if you are a QB playing out of a clean pocket, you’re on a fairly even playing field with all other QBs in a clean pocket. Thus, the good ones should separate themselves from the bad, and the numbers prove this by staying relatively consistent in each year of a QB’s career. Stable metrics for QBs include play from a clean pocket, standard 3-5 step drops, passing on 1st and 2nd down, throwing without play action, throwing beyond the 1st down marker, and avoiding negative plays. Some may be counterintuitive, but decades of research indicate these stats are replicative for most QBs. Unstable metrics include play under pressure, throwing from outside the pocket, 3rd and 4th down performance, play action, and positively graded plays. It makes sense: each of the unstable numbers add more variables and/or happen less often so trends are harder to see. If a QB has great stable metrics, but bad unstable metrics, you can be encouraged that the QB just needs a better team, coaching, or luck to reach a higher potential. If a QB rocks the unstable metrics but struggles in the stable, you might be seeing an outlier season that will come back to Earth in the next year.
Heat Maps: QB heat maps are charts that almost look like topography, lighting up blue or red depending on a QB’s performance in a certain area. They can be used in two ways, both measuring routes (if the NFL average is 37 routes down the deep seams, then a team who sends 15 up the seams would be blue and a team with 55 would be red) and targets (if the average NFL QB targets the middle at 12 yards 6 times a game, a QB who does it 3 times would be blue while one who does it 15 times would be red). This can be measured both against league averages or against the totality of a QB’s throws. There are separate understandings to be gleaned: by measuring against league averages, you can see where the QB trends as opposed to his peers whereas against all of his own throws, you can see areas of the field that are simply his bread and butter. Tua, for example, has a dark red heat map against his own throws in the middle of the field from 10-20 yards and ice cold blue in the middle from 0-10 yards. This tells us that Tua wants to, and is, targeting his receivers much further downfield, illuminating how the gameplan is functioning. Every week, teams monitor tendencies like this to try and measure their own depth, like when the 49ers beat Tua by launching their own talented MLB Fred Warner back rapidly upon the snap.
Rushing Analytics
Yards After Contact per Attempt: YCO/A is a measure of how many yards per play a rusher gains after they have come into contact with a tackler. If an RB falls forward through a tackle, they might reasonably pick up about a yard. If they stiff arm a dude and run for 50, they’ll pick up those 50 yards. Divide the total yards after contact by attempt and you get an idea of the runners who will pick up yards through contact. Among qualifying runners, Breece Hall of the Jets led the league in this stat before his injury, with a staggering 4.13 YCO/A, and the rest of the top 10 is filled with the guys you associate with “power back” (Derrick Henry, Rhamondre Stevenson, Nick Chubb, Tyler Allgeier). When a speedy and/or smaller RB makes this list, it is typically because the runner is able to make one man miss en route to an explosive carry (Tony Pollard, Khalil Herbert, and Raheem Mostert all made the top 10 last year).
Breakaway Percentage: A PFF stat, BAY% measures how many of a runner’s total yards came on explosive plays (PFF defines this as more than 15 yards on a run). This is typically a great way to identify the players who are more boom/bust on their carries. If you had 1 carry and took it 45 yards in 2022, you would have a 100% BAY%. Meanwhile, if you had 1 carry for 5 yards, then took your next carry for 45 yards, you would have a 90% BAY%. The best backs in the league actually hover between 25% and 35% in this metric, which makes sense: you want a back who is gaining you consistent yards, one who can pop off a quick 10-yard carry with some frequency, AND can take a ball 70 yards to the house. If a player is gaining next to nothing until they hit a home run, that high BAY% can indicate that player’s performance is uneven. Last year, the top 5 BAY% list included James Cook, Kenneth Walker, Breece Hall, Tony Pollard, and Kenyan Drake. Each runner on the list is young (except for Drake, who we already knew as Dolphins fans either hit home runs or got stuffed) and has the athletic ability to break off the big one without the experience. Pollard and Hall were all-around stars while Walker and Cook were solid contributors (Drake was Drake). There isn’t a good or bad in this stat, but rather an increased understanding of how rushing yards were gained.
Yards Before Contact: This stat is super interesting. YBC measures how many yards a runner gained before they made any contact with a defender. This one is really in the eye of a beholder: on one hand, this is an O-line stat, as the big fellas can clear the path so that a runner is never touched until they’re at full speed. On the other hand, this is an RB stat because you must have vision to find the holes that minimize contact. The most yards before contact last year was actually QB Justin Fields, with 856. Fields could run around as a passer before choosing open windows, running until he went out of bounds or slid. The next two backs were Miles Sanders and Nick Chubb, both of whom are good players with special offensive lines. Then comes Josh Jacobs and Saquon Barkley to round out the top 5, who had incredible seasons with top notch volume and inconsistent line play. This is a truly fascinating stat that speaks to team success in game plan execution. Put all of these running stats together and you get a picture: if your back had fantastic yards after contact but poor yards before contact and a high breakaway percentage, that means that you have a back who is behind a terrible line, fighting like hell to beat through defenders and winning big when he does. Likewise, if you have a back with a low breakaway percentage and yards after contact, and high yards before contact, you have a back with poor vision who is only getting what is blocked for him. This illustrates how analytical stats can tell us a story.
Receiving Analytics
Yards per Route Run: This metric is a fantasy player’s best friend, as it divides a player’s total receiving yards and divides it by the amount of times a player ran a route on a pass play. As you might imagine, this one highlights players who are frequently targeted and players who are consistently maximizing their receptions. Tyreek Hill scorched the rest of the league last year, averaging 3.2 yards every time he ran a route on a football field. This stat is often far more useful than receiving yards or TDs because it is about totality of play, and the top 6 list reads fairly close to where you would rank receivers in the league based solely on 2022 (Hill, Justin Jefferson, A.J. Brown, Jaylen Waddle, Stefon Diggs, Davante Adams).
Contested Catch Rate: Measured by PFF, the number is the percentage of catches deemed “contested” by graders, most often measured by physical contact or the tightness of a window. We can all imagine the underthrown jump ball as a contested catch, but throws with immediate hits or a corner draped all over you (potentially garnering a pass interference call that is declined) also count. This is an unstable metric, with only the best maintaining high rates, as so much depends on the situation. Players who have a consistent contested catch rate generally are just bad at the practice, and have more frequent drops as well.
Yards After Catch: YAC, as it is called, is one coaches in the NFL love because it gives value to how receivers create plays. If a receiver catches a ball on a screen pass, but takes it 60 yards to the house, then they’ve done a lot more work than one who caught a 60 yard TD due to a coverage breakdown. YAC is typically displayed by showing the average per play, so Deebo Samuel (who led the league last year) will be shown as averaging 9.6 YAC per reception. Good players vary in this stat based on their game style: Deebo catches a lot of short passes in space and should always have a high number whereas Tee Higgins, another star, catches a bunch of contested passes and cannot be reasonably expected to gain yards after the catch as often.
Average Depth of Target: ADOT, as it is called, takes all targets and measures them by how deep down the field the receiver is when he is targeted. Drops, incompletions, and catches are all factored in. This is useful in identifying receivers who are being used as deep threat and receivers who are thrown to more conservatively. Taken as a large picture between all of the receiving analytics, you can track where a receiver is providing great value. Jaylen Waddle can be a case study: Waddle averaged 17.9 yards per catch in 2022, the best in the league. His yards per route run was 4th in the league and he caught 78 passes on 121 targets, so we know that he was both frequently targeted and gaining a ton of yards. At 12.7 ADOT, we might be surprised to see that Waddle was gaining far more yards per catch than he was being targeted, so we can assume he made a lot of that discrepancy up with his legs. Sure enough, Waddle was 2nd in the league in average yards after catch. So Tua is throwing 12 yards to Waddle on average, and he’s gaining 18 on average when he catches it. You can see his value.
QB Rating When Targeted: Another easy one. QB rating is a measure of the major categories (completion percentage, TDs, INTs, yards, etc.) that are number measures of all QB stats rolled into one. It is, of course, an imperfect measure as many passers rely on schemes and skill players to either drive up or crater their rating. QB Rating When Targeted measures just the QB rating for the QB when targeting one specific WR: if a QB is wildly successful throwing to Stefon Diggs but less so when throwing to a practice squad guy, that further illustrates the gulf between players. This stat is dependent on many factors, and cannot be a total measure of efficacy in a game.
Line Analytics
Pass Block/Rush Win Rate: PBWR or PRWR are both measures of line play for the respective team units. The bar is 2.5 seconds: a player registers a pass rush win by beating their block in that time while the inverse is true for the pass blocking player, who registers a win if it is held for more than 2.5 seconds. This metric is measured for both individuals (Micah Parsons had the best PRWR with 30% while Joe Thuney had the best PBWR with 99%) and teams (the Cowboys and Eagles tied for best with 52% PRWR while the Chiefs had the best PBWR with 75%). For PBWR, team numbers are harder to attain because success requires all of the blocks to be sustained for that 2.5 seconds, and one weak link in your line or a misidentified blitz could count as a loss for the team. Even an elite pass rusher will only win about 25% of the time individually, which means OL on an individual level have a much higher rate than their team as a whole.
Pressures and Pressures Allowed: Quite simply, this statistic adds up hurries, QB hits, and sacks into one number. Pressures are given to the defense while pressures allowed are given to the offense. This is far more illustrative of line play than sacks because sacks are dependent on many factors (a QB holding onto the ball too long, great coverage, a missed sack due to a blown assignment by the corner, etc.). The NFL stats industry has begun to move away from sacks as a measure of success to any level of disruption to a QB. Jason Kelce and Lane Johnson of the Eagles both allowed the fewest pressures of all qualifiers with 11, while Micah Parsons led the NFL in total pressures with 106 (!!). Pressure rate can also be measured as a percentage of total plays in which the team or individual achieved a pressure.
Defensive Analytics
Missed Tackle Rate: The amount of missed tackles divided by total attempts. If you miss one tackle in a game in which you made 4 others, you would have a 20% rate. Like many of the contested stats, much depends on circumstance. If you are the guy assigned one year to meet Derrick Henry in the hole, you might have a larger missed tackle rate than years in which you did not play Derrick Henry. Another stat that is part of a larger picture. Players who have sub-10% seasons with plenty of opportunities are typically having an excellent year in terms of overall success.
Defensive Stops: Stops are plays made by a defender that constitute a failure for the offense. Remember success rate: an offense is successful if it gains a certain amount of yards on a certain down and distance. If a player stops that, stuffing a run for no gain for instance, they are credited with one stop. This is typically a linebacker stat because they are so often needed against both the run and the pass. Foyesade Oluokun led the league with 84 last year, and LBs rounded out the top 10 (though Christian Wilkins was shockingly 11th).
Reception Percentage, Coverage Snaps Between Targets, and NFL Passer Rating Against: These three numbers go together because they measure the elusive art of coverage. A corner is so reliant on a variety of factors (scheme, opponent, teammates) to get wins in coverage. An INT is a team stat, brought on by a good coach, pass rush, and/or a bad decision by a QB as much as the interceptor. Reception percentage measures how many targets are actually caught (a high percentage, such as 98 catches on 109 targets, indicates that a player is not stopping receivers when targeted). The problem with this metric is that great corners should not be targeted much at all: their coverage should be so tight that a QB won’t even throw in their direction. You might be a great corner, but only see 20 targets all year and see 18 of them caught because they only targeted you when they knew you were out of position. The same problem exists with NFL Passer Rating Against: this measures how well QBs did by isolating their QB rating when they threw into your coverage. A low rating means that the QBs got few completions, yards, and/or scores throwing at a player while potentially also throwing INTs (whether that was to the player in coverage, or the player in coverage tipped the ball to someone else). Coverage Snaps per Target (S/TGT) helps balance this flaw in the other two metrics, measuring how many snaps on average that a player is in coverage before a QB will target them. If you blanket your coverage, that number might look somewhere like 10 snaps in coverage between targets (Pat Surtain II led the league with 9.2) and if you are targeted more frequently, it might look like 5 (Kader Kohou had 5.1, poor guy). Coverage metrics remain imperfect, and I am always interested in new ones that are less subjective. Player separation as measured by GPS satellites to see how sticky a defensive player is in coverage is the new frontier, but that data is not public and will never be if the Players’ Union has anything to say. Players don’t want their bodies to be evaluated and financially weighed by their individual footsteps, and for good reason.
Future analytics used in previews will be added to this glossary.
