Methods & sources
Every number on this site comes from a model you can read about here or from a named outside source. We show where they agree and where they don't, and we keep score on how our own forecasts have done.
Our track record
Every completed game re-forecast using only what was known before kickoff. Accuracy is how often the team we favored won. The Brier score measures how well-calibrated the probabilities were (0 is perfect; always saying 50% scores 0.25).9
Our forecasts
Football forecast (NFL and college)
Blends two ratings built only from games already played: Elo,1 which moves after every result (more for bigger wins), and schedule-adjusted scoring margin,2 which counts last season early on and fades it out by mid-season. The weights were fitted on 2018 to 2022 and checked on 2023 to 2025, seasons the fitting never saw: 65% of NFL winners and 71% of top-division college winners, up from 63% and 68% for our earlier model. Injuries and news are added on top.
Sportsbook lines lead when there is one. Before kickoff we use the sportsbook line (DraftKings, via ESPN), converted to a win chance with the bookmaker's cut removed.5 On 2023 to 2025 games it called 67.6% of NFL winners and 72.2% of top-division college winners, more than our ratings (65.2% and 70.5%), and adding our ratings on top didn't improve it. Lines already move with injury news, so we don't add our own news adjustment to them. Our ratings are always shown separately as a check. We show lines only as win chances, never as odds.
Projected NFL scores also account for kickoff weather (wind, rain or snow, cold, indoors). Our study of 1,718 games found weather changes scoring but not who wins. See our accuracy report.
Main model (other sports)
In the NBA, WNBA, MLB, NHL, men's college basketball and the Premier League, La Liga, Serie A, Bundesliga and Ligue 1, the sportsbook line leads when there is one, just like football. On samples of last season's games it called more winners than the model below: NBA 71% vs. 68%, WNBA 69% vs. 66%, MLB 58% vs. 54%, NHL 58% vs. 52%, men's college basketball 75% vs. 75% (with better-calibrated chances), and in soccer, where the exact result (win, draw or loss) has to be right, 47% to 58% vs. 45% to 54% depending on the league. In soccer the line also gives a draw chance directly. The model is still shown next to it as a check, and it's the forecast for women's college basketball (where the line didn't do better in our test), college hockey and baseball, MLS, Liga MX, the Champions League and any game without a line.
Starts from each team's record, pulled toward .500 early in the season so a few lucky games don't mislead it (regression to the mean).4 Half of the rating comes from scoring margin (points scored vs. allowed, using the "Pythagorean expectation"), which predicts future results better than wins alone.3 Home teams get a sport-specific edge, kept modest because research shows home advantage has shrunk in recent years.7
On game pages it then adjusts for injured statistical leaders (a starting quarterback counts most) and for recent form. During games, it updates with the score and time remaining, treating the final margin as roughly normal around the expected margin (about 13.5 points wide in the NFL), the standard approach since Hal Stern's 1991 work.6
Elo ratings NFL & college football
A second, independent model. Every team carries a rating that moves after each game, by more when the result is a surprise and by more for bigger wins. Ratings carry over between seasons, pulled part of the way back to average. This is the same family of method FiveThirtyEight used for its football forecasts.1
SRS NFL & college football
The Simple Rating System: each team's average scoring margin, adjusted for how strong its opponents were.2 This season counts fully and last season at a third. Blowouts are capped so one lopsided game can't distort a rating.
Player outlooks
"Likely to perform" ranks each player by their own season averages (their record follows them through trades and transfers), weights recent games when they're recent, and leaves out players listed as out.
"Why we favor" on every game
Each game page lists the signals behind the forecast and which team each one points to, so you can see the case for both sides instead of just a number.
The signals: the sportsbook line, our own ratings, the average of outside forecasts, Elo ratings (football), season record, scoring margin per game (once teams have played 3 games), home and road records, home field, recent form (last five games), key players out, and head-to-head results this season.
The dots show how much each signal counts: ●●● the sportsbook line, the most accurate single source in our tests; ●● our ratings, outside forecasts, big gaps in record or scoring; ● smaller edges. Signals are shown when the gap between the teams is big enough to matter. What could flip it lists the signals on the underdog's side, because upsets happen: a 70% favorite still loses about 3 games in 10.
Outside sources
We show these next to our numbers so you can compare. We never send anyone to place a bet; prediction market prices appear only as data.
ESPN's team ratings, built from efficiency on offense, defense and special teams and simulated through the season. Used for game forecasts and Super Bowl / national title chances. espn.com/nfl/fpi
Prices on game winners and championships reflect what many traders collectively expect; research finds market forecasts are usually accurate and beat most simple benchmarks.8 We convert prices to probabilities and remove the house margin so each list adds to 100%.5
Another large prediction market with its own traders; when it and Kalshi agree, that's a strong signal. Placeholder entries for undecided slots are removed.
A long-running site that collects predicted margins from independent computer rating systems for every NFL and college game, including Massey, nfelo, FEI, Billingsley, Dunkel, Ashby AccuRatings, Sonny Moore and TeamRankings. We use only its published prediction files and leave out betting lines. thepredictiontracker.com
Overall Rating, Predictor (score-based), Golden Mean and Recent, each with its own home-field value. Updated weekly for the NFL, college football and the NHL. sagarin.com
A forecasting community that uses play money. Included only when at least 20 people have weighed in. manifold.markets
Live scores, standings (for scoring margin), rosters, player statistics, injury reports and ESPN's play-by-play win probability. espn.com
Division rankings, champion status, fighter records, physical measurements and per-minute fight statistics, straight from the UFC. ufc.com/rankings
The latest news note shown on player cards, and the injury and practice updates the news bot reads for each team's key players.
Public headline feeds the news bot reads. We only read feeds these sites allow automated readers to use, and every story links back to the original article.
The news bot
Every game page checks the last week of news about the two teams: league headlines, each team's ESPN news and the latest notes on its key players.
When a story names a player on either roster, the bot sorts it into a type: injury (out or uncertain), returning from injury, suspension, personal matter (such as a death in the family), roster move, coaching change or a recent game recap. Injuries, suspensions and personal matters lower that team's chances by an amount based on how much the player usually matters (a starting quarterback the most). A player returning raises them. Recaps, trades and coaching news are shown for context but don't change the number.
A player already listed on the official injury report isn't counted twice, only the strongest story per player counts, and the total effect per team is capped. The bot works from keywords and names, so it can misread a story. That's why each item is shown with its effect and a link to read it yourself.
Boxing
ESPN doesn't publish boxing data, so we don't have a boxing model of our own yet. Fight forecasts come from Kalshi and Polymarket, averaged when both list a fight.
Fighter records (every professional fight, with result, method, round, date and venue), current world champions (WBA, WBC, IBF, WBO and The Ring) and Zuffa Boxing cards and results come from Wikipedia, whose editors update them after each fight. Wikipedia content is free to reuse with credit (CC BY-SA), and each page links back to its source. We don't use BoxRec, because its terms don't allow automated access.
What fans are saying shows recent public posts from Bluesky, plus Reddit's r/Boxing when Reddit access is set up. Posts with betting promotions or abusive language are left out. Where posts name a winner, we count which way fans lean. That's shown for interest only: it isn't a forecast and doesn't change one. X (Twitter) and TikTok aren't included because neither offers a free public search.
What no model here can see
Coaching decisions, specific matchups, motivation, weather outside NFL score projections, and news the bot hasn't picked up or has misread. Probabilities are estimates with real uncertainty: a 70% favorite should still lose about three games in ten. That's what a well-calibrated forecast means, and it's what the Brier score checks.9
Research we build on
The methods above aren't invented here. These are the published sources behind them, so you can check our reasoning.
- Elo ratings. FiveThirtyEight, "How Our NFL Predictions Work" (archived copy), the best-known Elo model for football, which ours is based on; and the general method: Elo rating system (Wikipedia).
- Simple Rating System. Sports-Reference, "The Simple Rating System": scoring margin adjusted for strength of schedule, the method our SRS uses.
- Pythagorean expectation. Bill James's finding that points scored and allowed predict future winning better than won-lost record: Pythagorean expectation (Wikipedia).
- Regression to the mean. Why early-season records are pulled toward average: Regression toward the mean (Wikipedia).
- Removing the bookmaker's cut. Sportsbook and market prices add up to more than 100% because of the house margin (the "vig"); we scale them back to 100%: Vigorish (Wikipedia).
- Margins and win chances. Hal Stern (1991) showed NFL final margins are roughly normal around the expected margin (about 13.9 points wide then; recent estimates are closer to 13.5). Summarized in "Converting College Football Point Spread Differentials to Probabilities" (arXiv, 2022).
- Home-field advantage. Benz, Bliss and Lopez, "A comprehensive survey of the home advantage in American football" (arXiv, 2024): home advantage has declined in the NFL and top-level college football.
- Prediction markets. Justin Wolfers and Eric Zitzewitz, "Prediction Markets," Journal of Economic Perspectives 18(2), 2004: market forecasts are typically fairly accurate and beat most moderately sophisticated benchmarks.
- Brier score. Glenn W. Brier, "Verification of Forecasts Expressed in Terms of Probability," Monthly Weather Review 78(1), 1950, the standard measure of probability forecasts. Overview: Brier score (Wikipedia).
Our own results, including the 1,718-game NFL weather study and how each model did on 2023 to 2025 seasons it never saw, are on the accuracy page.