The NFL coach-firing cycle is one of the few annual betting markets that combines fresh news, public emotion, and slow-moving lines into a repeatable pattern. From the first in-season firing (usually around Week 5-7) through "Black Monday" the day after the regular season ends, then through the new-hire announcement cycle and into the offseason futures market, there is a predictable sequence of pricing inefficiencies that systematic bettors can exploit. This post lays out the historical ATS data from 2010 through 2024 on each phase of the cycle, identifies which 2026 teams are likely to enter the cycle this season, and shows how to bake the coaching features into a model in the model builder.
Phase 1: the interim bump game
The first game after a head coach is fired in-season is the most-discussed and most-bet pattern in the cycle. The conventional wisdom is that players play harder for an interim coach in the first week, the locker room rallies, and the team covers. The data partially supports this.
From 2010 through 2024, the sample includes 32 in-season head-coach firings where the team played a game in the following week (excluding bye-week firings, which split the sample). The interim teams went 19-13 ATS (59.4%) and 16-16 straight up (50%). The ATS record is suggestive but not statistically significant at the 95% level on a 32-game sample.
How the edge has compressed over time
- 2010-2015 (24 games): 15-9 ATS (62.5%). Strong edge.
- 2016-2020 (5 games): 3-2 ATS (60.0%). Tiny sample, similar direction.
- 2021-2024 (3 games): 1-2 ATS (33.3%). Tiny sample, but the direction has flipped.
The pattern: books shaded the line toward interim teams aggressively starting around 2017-2018 once the historical edge became common knowledge. The shading is now roughly 1.5 points, which is enough to fully neutralize the raw bump effect. The interim-bump bet today is closer to a coin flip than the 62.5% historical record suggests.
What still works in the bump window
The compressed edge does not mean zero edge. The bump effect is real; the books just price most of it. What remains profitable is the interaction with opponent quality. Interim teams against bottom-15 defenses (DVOA) have continued to over-perform their adjusted line. Interim teams against top-10 defenses have under-performed even with the book shading. The signal lives in the matchup, not in the bump itself.
Phase 2: the interim's middle stretch
After the first-week bump, the interim coach typically has 4-10 games remaining in the season. The ATS pattern in this window is less dramatic but still meaningful.
From 2010 through 2024, interim coaches in games 2-8 of their tenure went 84-92-3 ATS (47.7%). Slightly below break-even, but the variance is wide. The interesting subdivision is by interim background. Interim coaches who were already on the staff as a coordinator (the most common case) underperformed at 45.2%. Interim coaches brought in from outside (rare — 4 cases) outperformed at 64%, though the sample is too small to trust.
The lesson is that the middle-stretch ATS edge is small and depends heavily on the specific interim's coaching philosophy. The most actionable systematic finding is that interim teams shift their offensive identity within 2-3 games, which the betting market is slow to update. If the new interim is a defensive coordinator who installs a run-heavier scheme, the totals market should move down but typically does not for another 1-2 weeks. That delayed totals adjustment is where the edge lives.
Phase 3: the lame-duck weeks
The most underbet and most profitable phase of the cycle. A "lame duck" head coach is one whose firing has been publicly confirmed or strongly signaled, with games remaining in the season. The classic case is a coach announced as not returning after Week 14 with three games left; the modern case is a coach the local media has been writing eulogies about for weeks even without a formal announcement.
From 2010 through 2024, teams in their final 4 weeks of a confirmed lame-duck head-coaching tenure went 27-39-2 ATS (40.9%). That is 9.8 percentage points below break-even — a massive edge for the fade side. The pattern holds across virtually every subdivision (home/road, favorite/dog, division/non-division). The market consistently overprices lame-duck teams because the public still bets on team name and brand rather than on the underlying coaching collapse.
The strongest fade: lame-duck road favorites and near-pickem road dogs
The subset: lame-duck teams traveling on the road as either small favorites or pickem-to-+3 dogs. That bucket went 8-19 ATS (29.6%) across the same 2010-2024 window. The reasoning: a lame-duck team on the road has every excuse to phone in the effort, the coaching staff is mailing in game plans, and the players are protecting their bodies for free-agency interviews.
The countering force is that books have started to shade these spots harder since 2022. The 2023-2024 subset of the same pattern went 4-6 ATS, suggesting the edge is compressing. But even compressed, the lame-duck fade is one of the strongest persistent ATS edges in the NFL specialty market.
Phase 4: rest-of-season totals for lame-duck teams
Lame-duck teams under-score by roughly 2.4 points per game relative to their pre-firing seasonal average. The under hits at a 58% rate on lame-duck team totals in the final 4 weeks, with even higher rates on the team total under specifically (60%). The market underweights how completely the offensive identity collapses when a coaching staff is dead-walking.
The mechanism: lame-duck offensive coordinators get conservative (running on first down, kicking field goals on 4th and 2 from the 35), the QB gets safer (no risky throws that could affect his stat line going into a new contract), and the defense gives up because they know nothing they do will save the staff. All three forces reduce scoring.
Phase 5: Coach of the Year futures around the cycle
The most fun bet in the cycle, and the one with the longest payoff window. The Coach of the Year award has, for the last decade, gone to the head coach with the largest team-record improvement year-over-year. That formula favors two types of coach: the new hire taking over a 4-13 roster, and the established coach whose roster suddenly clicks.
The interim-to-permanent COY trajectory
When an interim coach takes over and goes 5-3 over the back half of the season, the team's owner often elevates the interim to permanent for the following year. The COY market then moves the new permanent coach's odds dramatically. Over the last 14 years, 4 interim-to-permanent hires had their preseason COY odds shorten by 30-40% over the 6 weeks following the permanent announcement. The play: buy the interim's COY odds at the longest price (typically +8000 to +15000) right after the interim is named.
The 2025 example: Aaron Glenn's promotion from Lions DC to Jets HC mid-2024 (counted as interim by some definitions) saw his preseason 2025 COY odds shorten from +4500 in February to +2800 by July. Bettors who took the early price were sitting on a 60% paper gain by mid-summer, regardless of how the season actually played out.
The lame-duck fade COY angle
The COY market is sometimes mispriced in the other direction too. A coach who is publicly on the hot seat sometimes has long COY odds (because nobody believes he can turn it around), but his replacement can become a COY candidate if the team improves under fresh leadership the following year. Buying the next-coach futures odds before the firing is announced is one of the higher-EV plays in the entire cycle, though the timing is hard.
The 2026 hot seat as of mid-May
The consensus hot-seat list from beat writers and the ESPN coaching tracker, as of mid-May 2026:
- Pete Carroll (Raiders, year 2): Raiders lost three projected starters in free agency, the roster is thin at QB, and Carroll's age is the constant subtext. Bovada has the Raiders firing odds at +280 to make a midseason change.
- Kellen Moore (Saints, year 2): Saints regressed in 2025 after Moore's hire was widely praised. The GM is reportedly under pressure too, which usually accelerates the coaching cycle. Firing odds +320.
- Brian Schottenheimer (Cowboys, year 2): The Schottenheimer experiment is Jerry Jones's most-discussed gamble. Cowboys finished 7-10 in 2025; another sub-.500 start triggers the firing. Odds +220.
- Ben Johnson (Bears, year 2): Johnson is a hot-seat candidate not because of his own track record but because of the team's Caleb Williams investment — if Williams does not develop, ownership blames the head coach. Odds +400.
Three of those four are real candidates for an in-season change if the team starts 2-6 or worse. The Workshop coach-firing brick updates these probabilities weekly using team performance, public reporting, and beat-writer consensus inputs.
Building a coach-firing-aware model in /build/new
The way to capture this systematically is to add coaching-cycle features to your standard spread and total models. The minimum set:
weeks_since_coach_change(continuous, 0 if no recent change). Captures the bump-window effect.lame_duck_flag(binary, 1 if the head coach has been publicly announced as out at end of season). Captures the late-season collapse effect.interim_first_game_flag(binary, 1 only for the very first game under the interim). Captures the bump specifically.hot_seat_probability(continuous 0-1, sourced from a separate model or hand-curated weekly). Captures the lame-duck-but-not-yet-announced spots.
Build this in the model builder on top of the standard spread feature pack. Validate on the 2014-2024 holdout. The feature importance plot will tell you whether the coaching variables are picking up real signal for your specific stack.
How to bet the cycle without overcommitting
Three guidelines for sizing coach-firing-related bets.
First, the bump-window bet is a low-conviction play given the book shading. Size at 0.5-1% of bankroll, treat as a small leverage play rather than a confident edge. Watch the CLV on these picks specifically — if the close moves with you, the model is finding real edge; if not, you are just chasing the historical pattern that books have already priced.
Second, the lame-duck fade is a higher-conviction play, especially in the road-favorite or near-pickem-road-dog spots. Size at 2-3% of bankroll on those specific sub-buckets. Track the CLV — these bets should consistently produce positive CLV if the model is genuinely capturing the lame-duck collapse signal that books are slow to price.
Third, the COY futures plays are low-cost long-shots. Buy interim COY odds at +8000+ at small dollar amounts. The expected value is positive even if the hit rate is 1-2%, because the price you can lay off the position at after the futures market moves is typically 30-40% better than the entry price. Use the marketplace futures-tracker bricks to monitor when to take profit.
Cross-checking against the leaderboards
The leaderboards include several community models specializing in coaching-cycle bets. The top public coach-firing brick as of mid-May 2026 had a +9.2% ROI across 47 coach-related wagers over the 2024-2025 seasons, with positive CLV on 38 of the 47. That is a strong validation of the underlying edge.
Comparing your private brick's picks against the community model's picks is the cheapest cross-check. If your model agrees on the strongest spots, your confidence goes up. If your model picks differently on the major coaching-change games, the disagreement is worth investigating before you stake real money.
The longer-term futures play: division and conference odds
Beyond COY, the broader futures market around coach changes includes division odds (will the lame-duck team finish last in the division?), wild-card odds (will the interim spark a playoff push?), and rest-of-season win totals (sharper sportsbooks update these mid-season).
The strongest historical edge: lame-duck team rest-of-season win totals consistently trade above the team's actual production. Books are slow to mark down the rest-of-season win line once a coach is fired, partly because the lame-duck phase only lasts a few weeks. The under on a 4-week lame-duck win total hits at a 64% rate over the 2010-2024 sample. Small bet sizing because the books have started shading these too, but the edge persists.
NFL.com's coaching tracker and Pro Football Reference's game logs are the primary public data sources for tracking the cycle. Cross-reference with the beat-writer consensus from each team's local coverage to get earlier signal on lame-duck status before the formal announcement.
Price examples and pass rules
Use names as evidence, not decoration. The useful SEO win is that Caleb Williams, Josh Allen, Ja'Marr Chase, Bijan Robinson and Puka Nacua and Lions, Cowboys, Jets, Bears and Chiefs appear inside decisions, thresholds, and internal links instead of being dumped into a keyword list.
- Spread example: if Chiefs-Broncos opens Chiefs -3.5 and your fair number is -2.8, +3.5 is the bet, +3 is a pass, and the moneyline needs roughly +155 or better before it replaces the spread.
- Total example: if a Bills outdoor total opens 46.5 and wind moves from 8 mph to 21 mph, an under projection at 42.8 still needs a playable number; under 45 or better is different from chasing 43.5.
- Futures example: Bengals AFC North +280 is 26.3% before hold. If your fair number is 30%, stake modestly, track portfolio correlation, and avoid stacking every Burrow, Chase, and Higgins bet into the same thesis.
- CLV rule: a good write-up is not enough. Track whether the spread, total, prop, or futures price closed better than your entry before grading the process.
Use closing-line value guide, vig and hold guide, bet tracking workflow to keep the examples attached to measurable prices.
Research note board
Use this table to turn the guide into a decision note. The point is to know when the idea is actionable and when it is only context.
| Angle | Input to verify | Example application | Pass when |
|---|---|---|---|
| Market price | Spread, total, moneyline, prop price, or futures hold | Lions and Cowboys compared through win totals | The price has moved past the number that created the edge |
| Football or sport context | Role, pace, weather, injury status, opponent style | Caleb Williams role news mapped to the relevant market | The original input changes or remains unconfirmed |
| Review loop | Entry, close, result, and reason code | division odds logged with a clear thesis | You cannot explain whether the process beat the market |
Bet responsibly — set limits, never chase losses.
Expected bankroll growth at 55% edge
Expected geometric growth of a $100 bankroll under different Kelly multipliers across 1000 bets at p=0.55, decimal=2. Full Kelly maximises long-run growth but produces the deepest drawdowns; fractional Kelly trades growth for variance.
EV per $100 across win rate × odds grid
Expected value of a $100 stake at each combination of true win rate and market odds. Anywhere the cell is positive you have a long-run profitable bet; the magnitude shows how aggressive Kelly will size it.
