Every offseason, the running back market sets itself up the same way. A back posts a monster year — 1,400 yards, 14 touchdowns, RB3 finish — and his ADP rockets into the first round. A year later, half of those backs have drifted back into the RB2 range. The other half got hurt. This is not bad luck. It is regression to the mean, and the Sharksnip projection model is built around expecting it.
What "regression" actually means
Regression to the mean is not a prediction that a good player will become bad. It is the observation that extreme outcomes contain noise, and noise does not repeat. A running back who scored 14 touchdowns is partly a great player and partly the lucky recipient of a season's worth of coin flips inside the 5-yard line. Strip the luck out, and his "true" rate was probably more like 9 or 10 — which means his fantasy ceiling next year is lower than his last finish suggests.
Our model breaks every RB stat line into two components: opportunity (carries, targets, red-zone touches, snap share) and efficiency (yards per carry, yards per route, TD rate per touch). Opportunity is sticky. Efficiency, especially TD rate, is not.
Why TD rate is the biggest regression flag
Touchdown rate per carry has the lowest year-over-year correlation of any major RB stat. League average is roughly one TD per 60 carries. A back who scored one TD per 30 carries last year almost certainly will not repeat that. The reason is structural — touchdowns are scarce events in a small window of the field, so a few extra coin-flip outcomes inflate or deflate them dramatically.
Yards per carry behaves similarly. Backs who post 5.5+ YPC seasons regress to roughly 4.4 the following year about 70% of the time, which is enough to drop a top-5 fantasy finish into the RB10–15 range without anything "going wrong" in a narrative sense.
The three regression archetypes
Our model flags RBs as regression candidates using a feature called opportunity_minus_efficiency_share, which compares a back's volume percentile to his per-touch percentile. When the per-touch number is much higher than the volume number, regression is likely. We see three flavors of this every year.
1. The TD-vulture beneficiary
A back who got 6 of his 12 TDs from the 1-yard line behind a great offensive line. Volume was fine; TDs did the heavy lifting. Strip out half those TDs and you are looking at an RB2, not an RB1. If the offensive line lost a starter or the team's red-zone trips are projected to fall, you have a clean fade.
2. The big-play-driven YPC outlier
A back whose 5.6 YPC was juiced by three 60+ yard runs. The model strips explosives out and looks at median rush yards. Median rushing yards is much stickier than mean, and it is the better predictor of next-year efficiency.
3. The high-leverage receiver
A back who caught 70 passes but averaged 12+ yards per reception on a tiny target diet of designed downfield concepts. PPR scoring is sticky on volume; it is not sticky on yards-per-target. When the target depth was driven by 4 or 5 broken plays, the next-year median catch radius collapses, and the PPR floor with it.
What does NOT regress
Just as important as the regression flags is knowing what holds. The model treats these features as sticky year-over-year:
- Snap share. Backs over 70% snap share retain that workload roughly 75% of the time when healthy.
- Route participation rate. If the back is on the field on third-and-7, he was last year too, and that is one of the strongest signals for PPR floor.
- Target share among RBs on his team. Coaching staffs treat backs the same way two years in a row much more often than not.
- Red-zone carries (not red-zone TDs). Volume in the red zone is sticky; conversion rate is not.
The cleanest fade is a back whose opportunity metrics all dropped slightly while his efficiency metrics all spiked. Those backs hit RB1 finishes in their follow-up year less than 25% of the time, even at top-12 ADPs.
How to use this in a draft
Pull up our fantasy rankings and sort RBs by the "regression score" column. Anything in the red — meaning efficiency outpaced opportunity by more than one standard deviation — is a candidate to fade relative to ADP. Anything in green is a candidate to draft early. The model is not asking you to never draft a hot back; it is asking you to discount the ADP based on how much of last year's finish came from sticky inputs.
Pair this with the ADP value tiers piece for a structural view of where consensus systematically overpays at the position.
The veteran-back trap
One specific case worth calling out: 30+ year old RBs coming off a top-10 finish. Historically, that profile has hit a top-15 finish again about 30% of the time. Consensus drafts them in the top-10. That is a 65-cent dollar at every pick, year after year, and it is the single most consistent regression edge in fantasy football.
Bottom line
RB regression is the most predictable phenomenon in fantasy. Touchdowns and yards-per-carry are noisy; snap share and route participation are not. When a back's last season was driven by the noisy stats, his ADP is a trap, even if the name on the jersey is great. When it was driven by usage, you can pay up. The math is the same every year — drafters keep paying for last season's TD luck and getting bitten by it.
Use the Sharksnip draft kit to see every RB's regression score next to their ADP and pick your spots accordingly.