The complete guide to football analytics for sports betting, fantasy football, and data science. Learn EPA, win probability, and data-driven decision making.
Kickoff is September 9. The season hub, the frozen prediction ledger, and the opening slate — every number derived, graded in public.
Every 2026 page in one place: the schedule analysis, all eight division previews, and a tracker page for each of the 32 teams.
Open the season hubA frozen win probability for every 2026 game, published before kickoff and graded after. Week 1 is locked; misses stay on the page.
See the predictionsSixteen openers across four days — a Wednesday Super Bowl rematch, Melbourne, and every pairing's head-to-head ledger since 1999.
Read the slateFree interactive calculators for NFL analytics, betting, and fantasy football
Calculate Expected Points for any down, distance, and field position situation.
Use ToolShould you go for it, punt, or kick? Analyze the optimal fourth-down decision.
Use ToolEstimate win probability from the score margin, time remaining, and which side has the ball.
Use ToolFind +EV bets by comparing your probability estimates to betting odds.
Use Tool106 plain-English studies of the advanced numbers — each one the formula, a chart built from the bundled game log, and the caveat that keeps you from over-reading it. Latest September 14, 2026
The library runs from the metrics a broadcast quotes — expected points, DVOA, success rate, CPOE — through the structural effects that move games (home field, rest, weather), to the market studies that test what the betting line already knows (spread accuracy, road favorites, key numbers). The 12 newest are below; the rest are indexed on the explainers page.
On Friday a page here priced Sunday's thirteen picks as one distribution and published what each outcome would mean. The model went nine of thirteen: the second most likely count, and exactly the break-even that puts the season's miss budget back under its opening number. Its Brier score, .2174, was better than its own probabilities expected. The market's favourites went ten of thirteen and beat the model by about the usual margin, all of it in the four games named in advance. Seattle stayed first and seven top-ten seats changed hands, the top of the published range, but the same thirteen winners at seven points each would have moved three. The margins did the rest, and the Chargers' loss to Arizona is the largest rating move of 2026 so far.
Fifteen of the sixteen week-1 games produced 50.0 points a game, more than any full opening week in the nflverse file, and 4.77 a game over their closing totals; one game, Chicago 59, Carolina 37, supplied 68% of that. Across 27 seasons a loud or quiet week 1 has carried about a sixth of its surprise into weeks 2 through 18 (0.151 points per point, SE 0.086), and the closing totals for those weeks have moved by the same sixth (0.150), leaving 0.001 for anyone betting the over. Week-2 totals move 0.179 per point straight away. Written down in advance for 2026: weeks 2–18 near 46.85 points a game, their totals near 46.07, and no lean to the overs.
Of 227 teams that lost their first two games since 1999, 25 made the playoffs (11.0%), against 42.9% at 1-1 and 61.8% at 2-0. The easy explanation is that 0-2 teams were bad to begin with. Holding this site's opening-day rating fixed, each of the first two results is still worth 21.2 points of playoff probability (25.0 without it), so who the team was explains about 15% of the gap; a top-third team at 0-2 got in 20% of the time, a bottom-third team at 1-1 30%. When two 0-1 teams meet in week 2, the winners have made it 37.5% of the time and the losers 12.5%. Week 2 of 2026 has three such games, and the fourteen games without Denver or Kansas City expect 6.91 0-2 teams by the model and 6.98 by the market.
Buy the team that got embarrassed in week 1, sell the one that looked unbeatable: across 422 week-2 games since 1999 it has not worked. Teams that lost their opener by 21 or more covered 25 of 58 decided bets the next week, and fading teams that won by 21 or more went 30–28, under the break-even at standard prices. The line does move, about 0.065 points for every point a team beat or missed its week-1 number, so a 20-point surprise is worth 1.3 points in week 2, and the week-2 result then leans with the surprise by +0.007 points per point (95% interval −0.069 to +0.083). The market reacts the same way after every week of the season. This site's rating engine moves about three-quarters as far, and after the biggest surprises that cost it.
San Francisco 27, Rams 7 moved two ratings 34.4 points, and this page carries the move through both seasons. From the ledger's own ratings the 49ers' expected record went from 9.50 wins to 10.84 and the Rams' from 10.60 to 9.28, and their chances of ten or more wins crossed, 50% to 76% and 72% to 46%. The site's own 20,000-season simulation, re-run after reproducing its published table, moves the 49ers' playoff odds from 55.5% to 80.7% and the Rams' from 76.6% to 51.5%. Is one game worth that much? Across 766 team-seasons since 2002, the remaining season moved 1.01 times as far as week 1 re-priced it (SE 0.28). Blowouts ran a little further than priced, not significantly, and a pooled base rate is still not a forecast for the Rams.
The ledger is one for two, and by Monday people will read a week into the season. Across the sixteen seasons this model has graded, a season's week-1 record against its own stated confidence correlates .13 with the rest of that season (95% interval −.39 to .59), no other week does better, and a season's odd games do not predict its even ones (.023). The reason is that the model has no detectable good or bad years: its seasons scatter 2.40 points around their stated confidence where coin flips alone would scatter 2.88, chi-square 10.55 on 15 degrees of freedom. At the most real spread the data allow, 1.92 points, a sixteen-game week is worth 2.55% of its surprise, and the 1–1 start shades the rest of 2026 by a tenth of a game.
The model's .5789 on the Rams lost by twenty in Melbourne, and the ledger reads one for two at a Brier of .2193. This page grades the site's own pre-registrations against the miss: the rule landed the 49ers fifth and the Rams eighth exactly where yesterday's ladders said a nineteen-plus-point 49ers win would, six rows changed inside the published four-to-eight, and the 34.4-point move is a 94th-percentile night against 7,276 historical moves. Then the harder part. A .58 pick losing by twenty is one draw from a distribution that produces one about every nineteen games in the .55–.60 band; separating .5789 from a coin takes 157 of them. The market moved toward the Rams before kickoff, its second move toward a loser in two games. The miss budget is heavier by .58. The miss is owned at full price.
Thirteen of the sixteen week-1 games kick on Sunday, all unplayed, and this page treats them as one object. The exact distribution of how many of the ledger's thirteen frozen picks land has its mode at eight (22.6%), expects 8.08, and gives a losing Sunday 17.9%; nine or more, the count that leaves the season budget under its opening 99.11, is 41.1%. The market, asked the same question about the model's picks from Friday's lines, says 7.86, 21.0% and 36.0%, agrees on ten of thirteen games and sides against the model on three: Miami at Las Vegas (18.9 points apart), the Giants over Dallas, Houston over Buffalo. Between June and September 11 the market moved its opinion of this board by one-twentieth of a game. Five top-ten teams play; between three and seven top-ten seats change hands; Seattle is first in all 8,192 outcomes.
Seattle 13, New England 10, and the model's .6785 is one for one. This page grades the site's own pre-registrations: the move landed on Tuesday's table to the hundredth — +8.42, a 13th-percentile night against 7,276 historical moves — the 24-point crossover never came into play, and no week-2 pick flipped. Then the harder part. Four pre-kickoff numbers all picked Seattle, so the winner separated none of them; the model's .1033 Brier beats the June market's .1262 and a records-only .1861 only because it was the most confident, and the order reverses in the counterfactual. The spread pushed at the close. The miss budget is .32 lighter. One game grades a pick; it cannot grade a probability.
One game graded and the rankings did not change: Seattle rose 8.4 to 1683.0 and is still first, New England fell 8.4 to 1584.4 and is still sixth, and the other thirty numbers are untouched. What moved is the gaps — Seattle's lead over Buffalo from 58.7 to 67.1, New England's hold on sixth from 11.6 to 3.2. Since 2000, 905 of 7,017 games changed no rank at all, and three season openers did the same, 2025 among them. Tonight is different: San Francisco and the Rams in Melbourne are priced at .5789 on a neutral field, any Rams win makes them second, any 49ers win drops the Rams to fourth or fifth and lifts the 49ers three to five places, and the widest result rearranges eight rows. Priced both ways, before kickoff.
Two forecasters that share no inputs post their worst week in the same week. The market's closing spread misses by 10.97 points in week 10 against 10.26 overall; this site's Elo picks winners at .5856 there against .6458. Shuffle the week labels 20,000 times and a random calendar's worst week is at least that bad 65% of the time on the market side and 42% on the model's, and the coincidence itself happens 11% of the time. Week 10 has been worse than its own season in 15 of 27 years, five of which supply the entire effect; strip them and it is 0.31 points better. The peak-bye explanation runs backwards — the bye games are the calm third of week 10 — and a simulated 2026 season of pure .6458 coins has a 6-and-8 week in it.
Seven teams open 2026 under a new head coach and this site's rating cannot see a single one of them. It does not need to. Teams that changed coaches gained 8.12 points of win percentage against a 2.39-point decline for teams that kept theirs — 5.8 standard errors — but they had gone .3529 the year before against .5441, and holding last season's record fixed turns the bounce into −2.80 points (SE 1.46) and −17.7 points of differential. The model is unbiased on exactly the teams it is blind to: +1.17 percentage points on 1,921 team-games, cluster-robust SE 1.22, and .6465 accuracy against .6456. The market is the only party that pays — about a third of a point of spread, three times as much for a coach who has run a team before, and it does not come back.
From foundational concepts to advanced machine learning techniques, this textbook covers everything you need to master NFL analytics.
Browse All ChaptersData infrastructure, nflverse, wrangling, and visualization basics.
5 ChaptersPassing, rushing, EPA analysis, success rate, and efficiency metrics.
7 ChaptersCoverage analysis, pass rush, run defense, and scheme evaluation.
6 ChaptersFourth down decisions, two-point conversions, and win probability.
4 ChaptersMachine learning, Bayesian methods, tracking data, and simulation.
5 ChaptersSpecial teams, personnel, college football, and future trends.
18 ChaptersIn-depth analytics profiles for all 32 NFL teams
100+ code examples in R and Python covering data loading, EPA analysis, visualization, betting models, and machine learning.
# Load NFL play-by-play data with nflfastR
library(nflfastR)
library(tidyverse)
# Get 2024 season data
pbp_2024 <- load_pbp(2024)
# Calculate team EPA per play
team_epa <- pbp_2024 %>%
filter(!is.na(epa)) %>%
group_by(posteam) %>%
summarize(
plays = n(),
total_epa = sum(epa),
epa_per_play = mean(epa),
success_rate = mean(success)
) %>%
arrange(desc(epa_per_play))
# View top offenses
head(team_epa, 10)Data-driven betting strategies
Learn how to use analytics to find +EV bets, manage your bankroll, and make smarter betting decisions.
Data sources, tools & community
Curated collection of the best NFL analytics resources, data sources, APIs, and learning materials.
Start with Chapter 1 and work your way through the full curriculum.