Fantasy Football season is right around the corner, so it is time to do a post-mortem on the 2022 NFL season. What lessons can we take away from the 2022 season, and how can we apply them moving forward? Each season we are looking for high-ceiling players that will be consistent performers on the fantasy football gridiron.
How can you measure a “consistent fantasy football player,” though? That’s where the SGPN Consistency Metric comes into play. Looking at players on a fantasy points-per-game average and factoring in the number of relevant players in a given season, we have some answers about who to target.
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SGPN Consistency Metric: Most Consistent Fantasy Football Players
Consistency Metric Methodology
This is going to be some math. I lied. It’s going to be a lot of math. If you want to skip to where we break down the numbers and analysis, go ahead to the next section.
The big thing to know is what standard deviation is. Loosely, the standard deviation is how spread out the data is. The important thing to remember is that 68% of a data set will fall within one standard deviation above and below the average. 95% of a data set will fall within two standard deviations above and below the average.
For those really interested in seeing the data on an in-depth level or interested in tweaking the calculations, check out the full spreadsheet below. Use the data to build your own metric for identifying consistent fantasy players.
DOWNLOAD THE SGPN CONSISTENCY METRIC EXCEL
Big Picture View
The first step in this process was to analyze the larger trends in fantasy scoring and delineate what constitutes an “Elite Performer” at each position. To do this, we took an average sample size of 427 flex players from each of the last five seasons.
All calculations are based on point-per-reception scoring. However, the concept and logic hold across most scoring formats.
We then determined the average fantasy points for Flexes as a whole, as well as running backs, wide receivers, and tight ends. Additionally, we calculated the standard deviation for the same samples. Using these metrics, we created tiers of players at each position.
| Deep League | Players who registered some kind of stats. |
| Relevant Players | Players scoring greater than the average. |
| Starters | Players scoring greater than the average plus one standard deviation. |
| Top Players | Players scoring greater than the average plus two standard deviations. |
| Elite Players | Players scoring greater than the average plus 2.5 standard deviations. |
Points Per Game
Given this background, we dove into the points-per-game numbers for 2022. To create the metric, we looked at multiple factors. First, we established each position’s average points per game and standard deviation.
| Position | Total Players | Average | Standard Dev. |
| Running Backs | 89 | 9.35 | 8.06 |
| Wide Receivers | 135 | 9.12 | 7.88 |
| Tight End | 61 | 6.83 | 6.30 |
| Quarterback | 79 | 14.53 | 8.65 |
After that, we developed the following classifications for labeling and categorizing games for each position. We developed the following classifications for each player’s weekly performance.
| Bad Game | Games Scoring Below the Average |
| Above Average Game | Games Scoring Above the Average |
| Good Game | Games Scoring Above a Half Standard Deviation Above Average |
| Elite Game | Games Scoring Above a Standard Deviation Above Average |
When looking at the averages, players who did not participate in a game are not calculated for average or standard deviation. This gives us a stronger data set for the calculations.
Consistency Metric Formula
There are several numbers that go into the consistency metric. There are the constant ratio, points ratio, availability score, and bad game score.
The constant ratio divides the constant score by the average constant score plus half a standard deviation.
The constant score is complex. It is effectively how the percentage of above-average games (weighted at 1.00), good games (weighted at 1.50), elite games (weighted at 3.0), and bad games (weighted at 2.00) the player is above or below a “good” player. The thinking behind this is an above-average performance is okay, but we are trying to find players who consistently score at an elite level and avoid dud games.
The points ratio takes a player’s points per game average and divides it by the average points per game at that position.
The availability score takes the number of games the player played divided by the average number of games played at their position.
Lastly, the bad game score is the percentage of bad games a player scored, so games below average are divided by the total games played. I hate bad games. Nothing is worse than the running back who torpedoes your late-season push with a dud game. Looking at you, Dalvin Cook.
We then arrive at four levels of scores for players.
| Average Players | Players who score above the average plus half a standard deviation |
| Above Average Players | Players who score above the average plus one standard deviation |
| Good Players | Players who score above the average plus 1.5 standard deviations. |
| Top Players | Players who score above the average plus two standard deviations. |
| Elite Players | Players who score above the average plus 2.5 standard deviations. |
Statistical Trends Since 2018
Welcome back, those of you who didn’t want a math lesson!
Over the past five seasons, the averages for players in these categories have been fairly consistent. There has been an average of 11 elite flex players each year (scoring more than 279.5 points). That number bottomed out in 2019, with only seven players clearing the Elite mark that year (279.3). In 2018, for comparison, there were 14 players that cleared the Elite mark that year (283.9).
All of this means that you need to figure out who the elite players are at a given position. For running backs, there are three guys each year. Only four wide receivers and only three tight ends. If you can land multiple of these guys, you are set!
None of this is news to anyone. Find elite and consistent fantasy players, and win your league. It’s a tale as old as time.
However, we play a weekly game. This gets less intimidating when you expand those numbers just a little (one standard deviation from the “Elite” level) on a per-game basis.
There are 12 running backs who will score 14+ points per game. There are 20 wide receivers who will score 14+ points, and seven tight ends will score 10+ points per game.
This is where you can win your league. Knowing how to identify the players who will score just below that “Elite” level and will consistently score there is how you can win your league. And so, now we get into the application of the SGPN Consistency Metric (SCM)!
Running Backs
The big thing to know with running backs is that they miss games. The average running back played 13.7 games. That’s not too bad, considering there are only 16 or 17 fantasy-relevant weeks to the season. When considering which running backs to target, be sure to bake in some missed games, even among the most consistent fantasy players.
As mentioned earlier, the average running back score in 2022 was 9.35 points per game. The score needed to have a good game on a weekly basis averaged around 17.4 points, and there were 10+ players hitting that mark in 15 weeks last season.
Running Back Rankings
| Metric | Player | Metric | Player |
| 7.45 | Austin Ekeler | 1.34 | Alvin Kamara |
| 7.00 | Christian McCaffrey | 1.31 | Cordarrelle Patterson |
| 5.34 | Saquon Barkley | 1.14 | Travis Etienne Jr. |
| 5.10 | Derrick Henry | 1.08 | Miles Sanders |
| 4.88 | Josh Jacobs | 0.81 | Jonathan Taylor |
| 4.31 | Nick Chubb | 0.55 | Latavius Murray |
| 3.33 | Rhamondre Stevenson | 0.36 | David Montgomery |
| 3.10 | Joe Mixon | 0.32 | Jeff Wilson Jr. |
| 3.03 | Tony Pollard | 0.10 | Raheem Mostert |
| 2.87 | Aaron Jones | 0.03 | Jerick McKinnon |
| 2.81 | James Conner | -0.02 | Devin Singletary |
| 2.66 | Leonard Fournette | -0.14 | Tyler Allgeier |
| 2.40 | Dalvin Cook | -0.22 | Antonio Gibson |
| 2.31 | Najee Harris | -0.28 | AJ Dillon |
| 2.21 | Jamaal Williams | -0.31 | Javonte Williams |
| 1.91 | Breece Hall | -0.34 | Clyde Edwards-Helaire |
| 1.90 | Kenneth Walker III | -0.38 | J.K. Dobbins |
| 1.83 | Ezekiel Elliott | -0.60 | Brian Robinson Jr. |
| 1.43 | Dameon Pierce | -0.63 | Elijah Mitchell |
| 1.38 | D’Andre Swift | -0.72 | Cam Akers |
With running backs and consistency, the big takeaway is that there are six backs who are “Good” (SMC of 3.36 or greater, Rhamondre Stevenson can get lumped in as he is .03 behind that mark). Stevenson is currently undervalued at his ADP of 29.8 (Sleeper) (RB12). He scores as RB07 in terms of SCM and was dragged down by five bad performances. Move him up your rankings and take him earlier.
Right next to Stevenson in ADP is Travis Etienne at RB13 (ADP 31.6). Etienne is miles away from Stevenson in terms of SCM (Stevenson 3.33, Etienne 1.14). He posted seven bad performances in 2022 and only four elite finishes.
If Breece Hall had kept the pace he was on, he would have finished the season with zero bad performances. Jonathan Taylor was below the SCM cutoff for relevancy with five bad performances across 12 games. His current ADP is 8.1 (RB03). Aaron Jones has an ADP of 40.1 (RB16) but has the 10th-best consistency metric.
Wide Receivers
Unlike running back, where there is a big gap between the top-tier players and the next steps, there is a steady step down across the board at wide receiver. There are 23 receivers who are above average, but the biggest swing is there are several receivers with high constant scores but were dropped down by low availability scores.
On a weekly basis, the goal again is to target players scoring double digits. On any given week, there are 42 receivers who meet that requirement. Additionally, there are 16 players each week who score 16.5 points or more per week. With a wider group of receivers, it is possible to find strong consistency at the position. There are several players who are consistent fantasy options at wide receiver.
Wide Receiver Rankings
| Metric | Player | Metric | Player | Metric | Player |
| 6.32 | Tyreek Hill | 2.17 | Michael Pittman Jr. | 0.46 | Rondale Moore |
| 6.25 | Justin Jefferson | 2.11 | Jerry Jeudy | 0.41 | Brandin Cooks |
| 5.61 | Davante Adams | 2.04 | Brandon Aiyuk | 0.40 | Mecole Hardman Jr. |
| 5.49 | Stefon Diggs | 1.90 | Jakobi Meyers | 0.35 | Sterling Shepard |
| 5.35 | CeeDee Lamb | 1.86 | Mike Evans | 0.34 | Joshua Palmer |
| 4.93 | A.J. Brown | 1.84 | Garrett Wilson | 0.30 | Gabe Davis |
| 4.16 | Ja’Marr Chase | 1.77 | Chris Olave | 0.22 | Diontae Johnson |
| 4.06 | Amon-Ra St. Brown | 1.70 | Allen Lazard | 0.15 | Donovan Peoples-Jones |
| 4.02 | Cooper Kupp | 1.62 | Mike Williams | 0.14 | Darius Slayton |
| 3.52 | DeVonta Smith | 1.35 | Deebo Samuel | 0.09 | George Pickens |
| 3.48 | Tyler Lockett | 1.28 | DJ Moore | 0.08 | Drake London |
| 3.26 | Chris Godwin | 1.11 | Michael Thomas | -0.16 | Tyler Boyd |
| 3.03 | Jaylen Waddle | 1.05 | Marquise Brown | -0.21 | Nico Collins |
| 3.03 | Tee Higgins | 0.93 | Jahan Dotson | -0.30 | Isaiah Hodgins |
| 2.75 | Christian Kirk | 0.88 | Adam Thielen | -0.34 | Darnell Mooney |
| 2.75 | DeAndre Hopkins | 0.61 | Zay Jones | -0.47 | Richie James Jr. |
| 2.69 | DK Metcalf | 0.59 | JuJu Smith-Schuster | -0.49 | Wan’Dale Robinson |
| 2.67 | Terry McLaurin | 0.57 | Curtis Samuel | -0.56 | DJ Chark Jr. |
| 2.62 | Keenan Allen | 0.55 | Christian Watson | -0.76 | DeVante Parker |
| 2.59 | Amari Cooper | 0.54 | Courtland Sutton | -0.76 | Rashod Bateman |
Currently, Ja’Marr Chase is currently going off the boards at an ADP of 3.0 (WR02), but he has a suppressed SCM, 4.16 (WR07), but that is due to missing four games last season. Even with that, he has the 8th-best constant score. Chase is really good and deserves to be highly drafted, but players like Stefon Diggs, CeeDee Lamb, Davante Adams, and AJ Brown all have better SCM scores. Oh, and Chase’s one bad game was the canceled Week 17 game.
Despite eight bad games in 2022, DJ Moore still finished as the WR31 by SCM. He also had the 16th most Elite (more than 17 points) level games. If Moore can eliminate the bad games, he could be an absolutely transcendent talent in Chicago. His current ADP is 539 (WR22) despite having the same number of Elite games as Garrett Wilson and Amari Cooper and more than Mike Evans and Jerry Jeudy.
Cooper Kupp was tied for the best constant score and only had one bad game out of nine played and, as a result, finished as WR09 in SCM. Kupp tied with Michael Thomas for the best constant score. Thomas only played in three games but had an elite level of production. DeAndre Hopkins and Kennan Allen both had top 12 constant scores but sub-par availability scores. Both are going outside the Top 16 wide receivers.
Tight End
It’s Travis Kelce’s world, and we are all just living in it. He creates the biggest gap between the next tier of players. So big that he removes the ability for any other players to finish in the Top or Elite category. Below him are three players in the “Good” tier, five if you consider Dallas Goedert and Zach Ertz being within 0.1 of that tier.
In total, 11 tight ends are relevant to roster from a consistency standpoint. Really it is a group of 14 that has a fairly consistent step-down and gradual fall-off. So the good news is that if you do not get Kelce, you are not going to be at a massive disadvantage—just a moderate one. If you miss out on the most consistent fantasy player, Travis Kelce, other players will not have the same pull and edge.
Tight End Rankings
| Metric | Player |
| 10.06 | Travis Kelce |
| 3.99 | T.J. Hockenson |
| 3.26 | George Kittle |
| 3.14 | Mark Andrews |
| 3.08 | Dallas Goedert |
| 3.02 | Zach Ertz |
| 2.51 | Pat Freiermuth |
| 2.04 | Dalton Schultz |
| 2.03 | David Njoku |
| 1.94 | Gerald Everett |
| 1.90 | Evan Engram |
| 1.79 | Tyler Higbee |
| 1.70 | Taysom Hill |
| 1.37 | Dawson Knox |
| 1.08 | Greg Dulcich |
| 0.80 | Darren Waller |
| 0.80 | Juwan Johnson |
| 0.53 | Cole Kmet |
| 0.40 | Tyler Conklin |
| 0.07 | Hayden Hurst |
Dallas Goedert only had one bad game in 2023, only behind Travis Kelce. What kept him from having a higher SCM was only three elite-level games and only playing 12 games. If he can stay healthy, he is highly likely to take over the TE02 mantle from TJ Hockenson.
Speaking of Hockenson, he had three bad games, and they were spread throughout the entire season. He also has a clear advantage over George Kittle and Mark Andrews, TE03 and TE04. Darren Waller is in a new situation with the Giants and scored as TE16 in SCM with only nine games played last season. Look for him to take a big step forward in 2023.
Quarterback
You do not need to draft quarterbacks early to have a relevant or above-average quarterback on the season. According to SCM, 18 QBs finished above average in 2022, and 12 are reliable every-week starters. Unlike other positions, there is a much smaller points-per-game gap separating consistent players in mid-tier for fantasy quarterbacks.
With that being said, there is a clear trio of quarterbacks at the elite level and only one in the next tier. However, much like at receiver, it is a steady decrease in consistency scores from QB04 on the way down to QB15. If you miss on the top three, you can afford to be patient and do not need to rush to draft a quarterback.
Quarterback Rankings
| Metric | Player | Metric | Player |
| 6.48 | Patrick Mahomes II | 1.83 | Jared Goff |
| 5.79 | Josh Allen | 1.72 | Tua Tagovailoa |
| 5.70 | Jalen Hurts | 1.65 | Daniel Jones |
| 3.98 | Joe Burrow | 1.50 | Dak Prescott |
| 3.04 | Justin Fields | 1.48 | Justin Herbert |
| 2.60 | Geno Smith | 1.00 | Marcus Mariota |
| 2.23 | Kirk Cousins | 0.83 | Tom Brady |
| 2.21 | Kyler Murray | 0.78 | Derek Carr |
| 2.12 | Trevor Lawrence | 0.70 | Russell Wilson |
| 2.02 | Lamar Jackson | 0.41 | Jimmy Garoppolo |
Patrick Mahomes is the clear QB01 with an SCM of 6.48, followed closely by Josh Allen at 5.79 and Jalen Hurts at 5.70. Hurts was held back by missing multiple games but still finished with an above-average availability score. Moreso than other positions, the quarterback is defined by their availability. If given a choice between a mid-tier quarterback who will start most of their team’s games and a hit-or-miss player, go with the guaranteed starter.
Currently, Justin Herbert has an ADP of 48.5 (QB07). He played all 17 games for the Chargers in 2022 and finished as QB15 in SCM. Given this, he is currently overvalued. He had one of the worst rates of bad games (41%) of any quarterback in the top 15. This puts him in the company of the aging Aaron Rodgers and Tom Brady.
On the flip side, Geno Smith had a 2022 SCM of 2.60 (QB06) and is currently going off the board at an ADP of 125.2 (QB16). Jared Goff also finished with a higher-end SCM of 1.83 (QB11) and is currently going off the board as QB17. Goff and Smith are excellent late-round values with talented receivers around them. Do not sleep on them as late options.
