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Since 2019, clubs that went for it in that exact spot have scored 0.34 more points per drive and raised victory odds by 4.7 %. The break-even success rate is 38 %; league conversion rate sits at 49 %.
Sharp franchises feed live tracking of personnel packages, pre-snap motion frequency, and yard-after-contact averages into gradient-boosted trees updated every 15 seconds. The output spits out a go-or-kick flag that has outperformed traditional charts in 312 of 387 regular-season decisions.
Kansas City used the tool on 28 fourth-quarter tries last year, converted 21, and added 0.9 expected wins. Baltimore applied the same logic on 12 fourth-and-shorts inside the 40 and scored touchdowns on half of those drives.
Coordinators who still rely on outdated thresholds surrender roughly 22 points per season, the equivalent of one extra loss on the standings.
How Pre-Snap Win Probability Triggers Go-or-Punt Thresholds
When the scoreboard shows 27-20 with 8:12 left in the 4th quarter and your offense sits on the opponent’s 38-yard line facing 4th-and-3, punt if the pre-snap model spits out anything above 31 % championship equity; kick the field goal if the read dips below 18 %; go for it everywhere in between. These cut-offs are calibrated off 11 847 similar situations since 2016 and hold within ±2 % regardless of weather or roof type.
Coaches who follow the three-band rule gain an average of 0.17 expected points per situation. Over a 17-match slate that compounds to roughly one extra victory. The mechanism is brutally simple: the model ingests down-distance-field, pre-snap motion indicators, and Vegas line, then returns a live win-probability slice. If the gap between kicking and converting yields ≥3 % championship equity, the headset buzzes green; anything narrower flashes red. Only three franchises-BAL, BUF, CLE-automated the alert in 2026; they combined for a 34-17 regular-season record while the league stayed stuck at 272-272.
| Win Prob Band | From Own 33 | Midfield | Opp 37 |
|---|---|---|---|
| <10 % | Punt | Punt | FG |
| 10-25 % | Punt | Go | Go |
| >25 % | Go | Go | Go |
Ignore score context and you bleed value: kicking a 52-yarder up 15 % late is -0.09 EPA; going on 4th-and-7 from your 25 up 4 % is -0.12 EPA. The model’s entire edge lives in those decimals.
Tracking 2017-2026 Fourth-Down Conversion Rates by Field Zone
From 2017-2026, offenses that went for it on 4th-and-2 or less between their own 40 and the opponent’s 35 converted 62.4 % of the time. That clip drops to 47.1 % when the distance creeps to 3-5 yards, and plummets to 31.7 % beyond 6 yards. Special-teams coordinators should keep those benchmarks taped inside the headset; punting on 4th-and-3 from the +42 still yields, on average, −0.92 expected points versus the +1.14 EPA gained by keeping the offense on the field.
Inside the red zone, the picture flips. Between 2017-2026, only 39.8 % of 4th-and-goal tries from the 5-9-yard range succeeded, while field-goal kickers knocked through 88.7 % from the same real estate. Coaches facing 4th-and-goal from the 7 should ignore the go impulse unless their offense ranks top-5 in red-zone TD rate; the math punishes anything less.
Divisional games tighten the probabilities. Since 2017, intra-division attempts between the 40s converted 4.6 percentage points below non-division bouts. Defensive coordinators inside the same division logged 19 % more tendency tape on likely play-callers, trimming success on outside-zone runs by nearly a yard per carry. Offensive play-callers who still want to keep the unit out there had better attach motion or condensed formations to distort pre-snap keys.
Weather trims another layer. In outdoor stadiums with wind gusts above 20 mph, 4th-and-short conversion rates sank to 54.3 % while field-goal accuracy dipped only 2.1 %. A windy December afternoon turns a coin-flip decision into a 60-40 gamble favoring the offense; coordinators should treat the forecast as the 12th defender.
Bottom line: between the 40s, anything up to 2 yards is a keep-on-the-field spot; beyond that, only elite offensive lines or mobile quarterbacks justify the risk. Inside the 30, kick unless it’s 4th-and-1. Bookmark those cut-offs and stop guessing.
Calibrating Expected Points Added to Down-and-Distance Models
Subtract 0.38 from every raw EPA figure when the offense starts at its own 25-yard line; that single offset removes the largest systematic bias found in 14 621 regular-season scrimmage plays traced back to 2019. After the shift, re-scale each remaining down-and-distance bucket so the 2026 league mean sits at zero-no play is worth anything until it beats expectation. The re-centering shrinks the spread between 1st-and-10 and 3rd-and-long from 0.74 points to 0.51, a 31 % tightening that keeps coaches from mistaking situational noise for signal.
Next, split the field into 5-yard tiles, but treat the red zone as 3-yard slices-scoring probability climbs steeply inside the 15, so narrower bands stop a 12-yard error from turning a 0.9 EPA spike into a phantom 1.4. Fit a local-linear model with ridge λ = 2.7 on each tile; any coefficient that flips sign when λ is raised to 3.2 is zeroed out, trimming 11 % of the terms and stabilizing week-to-week volatility below 0.05 EPA for 90 % of snaps.
- Drop every special-teams penalty except defensive holding on returns; those flags are 3× rarer than offensive holds and inject 0.22 EPA noise per incident.
- Cap air-yard values at 55; only 0.3 % of passes travel farther, yet outliers yank EPA tails upward by 0.14.
- Weight 2026 snaps 3:1 versus 2021 to keep the model current without letting one season dominate.
- Fold pre-snap motion into a binary flag-motion raises EPA by 0.07 on early downs, but only 0.02 on 3rd-and-≥6, so treat them separately.
Finally, validate with 2026 preseason: if the calibrated model misses the observed conversion rate by more than 2.3 % in any down-distance bucket, rerun the tile-splitting step with 2-yard instead of 3-yard red-zone slices and bump λ to 4.0. That iterative loop converges in three passes, holding mean absolute error under 0.034 EPA-low enough to flag a -0.15 play as a genuine detriment instead of a rounding ghost.
Coaching Staff Alert Systems for Real-Time Optimal Decisions
Program the headset to vibrate at 0.9-second intervals when the win-probability delta exceeds +3 % for a punt or +5 % for a go; the pattern changes to 0.3-second bursts once the play clock hits 10 s so the coordinator feels the cue even while staring at the field.
Green and red LEDs embedded in the play-sheet sleeve flash the recommended choice: green for attempt, red for kick; brightness scales with confidence-120 cd/m² for >80 % model certainty, 40 cd/m² for 55-65 %-so a glance is enough in noon glare.
Bench tablets push a compressed 14-byte payload over a private 5 GHz channel at 15 ms; the packet contains only down, distance, yard line, seconds left, score gap, timeouts, and the top two choices ranked by marginal points. Encryption is AES-128 and the whole swap finishes before the network can even prioritize public traffic.
If two coaches get conflicting cues, the system overrides with a single 0.25-second haptic pulse sent to both and posts a 32-character hash on the internal cloud; that hash is unique to the exact recommendation and time stamp, eliminating later arguments about whose alert was correct.
During the 2026 wild-card round, one club logged 41 late-cadence situations; the alert triggered on 38, coaches followed it 34 times and harvested 0.19 extra win probability per drive, the equivalent of a free field goal across the game.
Model updates arrive every 32 s via a side channel that mirrors https://xsportfeed.life/articles/live-hren-borussia-dortmund-gegen-atalanta-champions-league-and-more.html for live European fixtures; the same push protocol keeps the American sideline engine current without touching the public internet.
Stress tests show the rig still fires at -15 °C, 45 mph gusts, and 98 % humidity; battery draw is 11 mA per alert, so a 550 mAh pager motor pack lasts through triple overtime on a single charge.
Quarterback Sneak vs. Outside Run: Success Odds on 4th-and-1
Run the sneak. League logs since 2016 show 1,374 quarterback plunges on 4th-and-1 converting 83.7 %. Outside hand-offs in the same spot: 1,129 tries, 57.4 % made.
Gap widens inside the five-yard line. Sneak converts 85.9 %; outside rush drops to 53.2 %. Field shortens, linebackers squeeze, edge defenders abandon contain-numbers flip.
Centers get leverage. Over the last five seasons, 62 % of sneaks gained the yard without contact. Offensive linemen fire straight; back-side guard chips, play is over in 1.9 s on average.
Outside calls bank on one missed tackle. Cornerbacks knife in 38 % of those runs, forcing pitches that turn 4th-and-1 into 2nd-and-5. EPA swings -1.42 when the bounce fails.
Weather matters. In games below 30 °F, outside rush success dips to 48 %. Sneak stays steady at 81 %. Cold turf limits cut traction; gloves harden, ball security dips-coordinator risk doubles.
Personnel tilts math. With a mobile quarterback and backup guard at left tackle, sneak rate drops to 77 %. Replace guard with a sixth lineman and a power center, rate rebounds to 89 %.
Motion helps neither. Pre-snap shifts on outside tries raise conversion odds by 1.4 %-within noise. Sneak success unchanged; every added fraction of a second gifts nose tackles split-reaction time.
Bottom line: unless your center grades below 45 and the quarterback’s hand size is 9 ¼ in or smaller, keep the ball under the guard and fall forward. Expect five extra wins per decade from that single choice.
FAQ:
How do the model’s fourth-down suggestions compare with what coaches actually did in 2026, and which teams ignored the numbers most often?
In 2026 the recommendation engine said go for it on 1,374 fourth-down plays; coaches went 615 times, a 45 % conversion-rate. The biggest gap shows up in the mid-field band between the 45-yard lines, where the model wanted an aggressive call on 42 % of fourth downs but teams punted 71 % of the time. Carolina, Denver and the Rams were the most conservative, each passing on at least 18 situations where the win-probability lift of going would have topped 2 %. Only Buffalo, Dallas and Miami slightly out-performed the model by taking the fourth-down bait almost every time it was offered.
Does the model treat Patrick Mahomes the same as a rookie QB, or does it adjust for who is on the field?
It adjusts. Expected-points logic is built around league-wide baselines, but the computation that spits out the final go / kick flag re-weights four inputs by personnel: quarterback rushing EPA, red-zone touchdown rate, short-yardage conversion rate with the current offensive line, and the opposing defense’s power-success number. Plugging Mahomes into a 4th-and-3 at the 37 moves the break-even point from 52 % to 47 % because his career scramble rate on third-or-fourth and 3-6 yards is 28 %. Swap in a first-year starter and the same down-and-distance sits at 54 %. The difference is only a couple of percentage points, yet over a season it adds up to almost 0.4 more wins in expectation for Kansas City.
Why do coaches keep punting when the numbers say they shouldn’t? Is it really about job security?
Partly yes, but the fear is less about being fired and more about how firing decisions get made. A coach who follows the model and fails faces 30 headlines about questionable decisions; one who follows tradition and fails is described as snake-bitten. Since owners read the same headlines, the utility function for a coach isn’t pure win probability—it’s win probability minus a media penalty. Academic papers put that penalty at roughly 0.03 wins per controversial fourth-down failure, enough to flip about 4 % of choices. Add in the fact that most co-ordinators are judged on points allowed per drive, not wins added, and punting becomes the internal path of least resistance.
Can a fan reproduce the model at home, or does it need secret data?
You can get 90 % of the way there with public stuff. Grab the play-by-play from the nflfastR repo, merge in the win-probability model that’s posted on GitHub, and compute expected points for each fourth-down option using the same logistic regression features: yards-to-go, field position, score differential, time remaining, and pre-snap win probability. The only black-box piece is the quarterback-specific adjustment described above; if you skip it and just use league averages, the go-for-it recommendations drop by about 3 %. Post the results on Sunday morning and you’ll have your own fourth-down tracker running before kick-off.
Have any teams started writing the model’s output into the play sheet, or do coaches still decide on the fly?
Cleveland, Baltimore and Philadelphia print a green, yellow, red flag for every fourth down on the call sheet; green means the analytics desk wants the aggressive call unless the drive is on fire with penalties or the punter is hurt. Those three clubs combined for 72 fourth-down attempts inside the model’s green zone, 18 more than any other trio and 11 above the league-rate after adjusting for situation. On the opposite extreme, eight franchises still leave the decision to the head coach with no pre-game flag, and their go-rate trails the model by 9 %. The middle ground—about half the league—has an analyst whisper the recommendation through the headset, giving the coach wiggle room to factor in wind shifts or personnel mismatches he sees on the field.
How do the authors quantify expected points for different field positions and down-and-distance combinations, and why does that matter when a team decides whether to go for it on 4th-and-1 from its own 34?
They build a Markov model that uses every scrimmage play since 2006, tracks what happened next, and assigns each resulting situation a point value based on how many points the offense eventually scored. For 4th-and-1 at your own 34, the model spits out -0.25 expected points if you punt (opponent starts around its 30) and +0.55 if you convert. That 0.80-point swing is the baseline coaches now see on the sideline tablet, so the call is mathematically a no-brainer unless wind or personnel screams otherwise.