Blitzcast

From the booth

How this whole thing works

No black box, no secret sauce. Here's what the model sees, the rule that keeps it honest, and how it holds up against the toughest opponent in football: the market.

The inputs

What the model actually looks at

Every prediction comes out of a trained XGBoost classifier, one number between 0 and 100 percent, built from twenty features that fall into a few plain-English buckets:

  • Team strength. An Elo rating for each team that updates after every game, the same idea chess uses to rank players.
  • Recent form. Offensive and defensive efficiency (EPA per play) over each team's last several games, not season-long averages that miss a hot or cold streak.
  • Rest. Days between games for each side, short weeks and byes included.
  • Injuries. Who's banged up, weighted heavier when it's the quarterback.
  • Weather. Temperature, wind, and precipitation at kickoff, dome games treated as a controlled environment.
  • The market itself. The current spread and the market's own implied win probability are two of the twenty inputs. The model gets to see what Vegas thinks before it makes its own call, and it still doesn't beat the market outright. More on that below.

Each prediction ships with the top factors that moved the number, in plain language and pointed at the team they favor, not a bare percentage you have to take on faith.

The leakage rule

Nothing from the game leaks into the pick

Every feature for a given matchup is built strictly from data available before that game's kickoff. Elo ratings, recent form, rest, injuries, all of it is a snapshot of the week before, never the week of. A prediction never gets to peek at a score, a stat line, or anything else from the game it's predicting.

This sounds obvious until you've seen how easy it is to get wrong by accident, a model that accidentally learns from data it shouldn't have seen looks brilliant in testing and falls apart the moment it has to call a real, unplayed game. It's enforced with a dedicated automated test, not just a coding habit, so it can't quietly regress as the pipeline changes.

The LLM boundary

Claude calls the game, it doesn't call the winner

The XGBoost model produces the win probability. SHAP explains which factors pushed it that direction and by how much. Claude's only job is to turn that probability and those factors into the 2 to 4 sentences of broadcast-style color you read on a matchup page.

Claude never touches the math. It is handed the number, the factors, and a short fact sheet of verified pre-game facts: each team's record, last result, and streak, the betting line written out in words, and for the NFL the official injury report. Every name, percentage, and betting claim it writes is checked against that sheet before it is published. A draft that fails gets another try with the reason attached. If every draft fails, the last preview stays up only if it is still exactly true today, otherwise the page shows a plain, template-written preview built from the same verified facts, and if even those facts cannot be gathered, a one-line summary of the model's percentages. Every upcoming matchup gets a booth section.

The scoreboard

How often the model is wrong, honestly

Every model gets evaluated the same way real forecasters do: walk-forward by season, training only on seasons already played and then grading the next one, compared against the closing betting lines for those same games with the bookmaker's cut removed. That's a demanding bar. Closing lines are one of the hardest baselines to beat using only public data, and this site doesn't beat it.

NFL, walk-forward 2023 to 2025
SeasonGamesModel BrierVegas BrierModel AccVegas Acc
20232850.24150.218660.4%67.7%
20242850.20990.201069.1%70.5%
20252850.21730.210465.6%66.3%
All8550.22290.210065.0%68.2%

College football gets its own model, its own Elo history, and its own backtest, graded against de-vigged closing lines from CFBD (FBS versus FBS games only, since the market barely prices FCS mismatches):

CFB, walk-forward 2023 to 2025
SeasonGamesModel BrierVegas BrierModel AccVegas Acc
20237550.18420.168569.5%73.9%
20247570.18440.178171.7%72.7%
20257630.17540.171973.8%74.8%
All22750.18130.172971.7%73.8%

Read straight: on the full sample, the NFL model calls the winner about 65.0 percent of the time against the market's 68.2 percent, a gap of roughly 3 points. CFB runs 71.7 percent against 73.8 percent, about 2 points back. Brier score, which grades not just whether the pick was right but how confident the model was while making it, tells the same story: the market scores lower (better) than the model in every single season, both sports, no exceptions.

None of that means the model is guessing. It lands in the neighborhood of a real sportsbook line using only public data, which is a genuinely hard thing to do. It just doesn't mean the model has an edge on Vegas, and this page isn't going to pretend otherwise. Every prediction on the site is labeled with a model version and a timestamp, and nothing here is betting advice.