PRO FOOTBALL LAB
METHODOLOGY

Built to earn trust.

Pro Football Lab combines actuarial discipline, predictive analytics, football context, and transparent scorekeeping to turn raw NFL data into useful forecasts.

THE STANDARD
Good models predict outcomes. Great processes prepare for uncertainty.
  • Game-script scenario analysis Fantasy rosters reflect coherent game scripts, allowing some players to outperform while others fall short; they do not assume everyone reaches a ceiling at once.
  • Past information only No result is allowed to predict itself.
  • Out-of-sample evaluation Performance is measured on games the model did not train on.
  • Visible scorekeeping Hits, misses, calibration, and ROI remain part of the record.
  • Game-ready updates Injuries, depth charts, weather, and late-week evidence are processed quickly before kickoff.
MODEL DESIGN

Different questions deserve different models.

A single forecast cannot optimally answer every football question. Pro Football Lab separates game outcomes, scoring and margins, player production, and season-long decisions, then combines compatible outputs only when the comparison is mathematically sound.

Win probability

How likely each team is to win, with uncertainty made explicit.

Spread and total

Expected margin and scoring distributions built for market comparison.

Player and fantasy

Opportunity, efficiency, scoring, and salary-aware roster outcomes.

Survivor strategy

Weekly safety combined with future schedule value and contest dynamics.

THE TOOLKIT

Method follows the problem.

Candidate methods range from play-by-play and possession simulations to power ratings, regression, tree ensembles, gradient boosting, and neural networks. More complexity is not automatically better. Models are judged on accuracy, calibration, stability, interpretability, and performance on unseen games—not novelty.

Why use more than one approach?

Different methods see different structure. Agreement can add confidence; disagreement is useful information, not something to hide.

FOOTBALL CONTEXT

There is no one-model-fits-all matchup.

Overall team strength matters, but it does not tell the whole story. Personnel, scheme, pace, line play, venue, weather, rest, and injuries change how two teams interact. Specialist views focus on the evidence most relevant to the game being forecast.

The Lab makes that structure visible. Visitors can compare broad team-strength signals with matchup-specific models and build a custom consensus without being handed a prescribed answer.

Open NFL Betting Lab Open DFS Rosters
BROAD SIGNAL

Current strength

Recent and longer-term evidence adjusted for opponents and game state.

MATCHED SIGNAL

Matchup fit

How personnel, scheme, pace, and positional strengths interact.

KNOWN CONTEXT

Game-week inputs

Only information available at the forecast cutoff enters the prediction.

DATA FIRST

Most of the work happens before a model is trained.

Schedules, play-by-play, rosters, injuries, depth charts, weather, betting markets, and fantasy salaries must be sourced, cleaned, reconciled, organized, and converted into measurements a model can use.

01

Source

Bring together schedules, plays, teams, players, rosters, injuries, weather, markets, and fantasy data.

02

Clean

Standardize team and player identities, dates, games, missing values, and inconsistent records.

03

Organize

Create a stable historical backbone with one clear meaning for every field.

04

Engineer

Convert history into rolling form, efficiency, usage, matchup, and context signals.

05

Validate

Test on unseen games, compare alternatives, and preserve the honest result.

FAIR TESTING

The future stays out of the past.

Football data is chronological, so validation must be chronological too. Historical features are lagged, model choices are made without peeking at the holdout result, and completed games are scored with only the information that would have been available at the time.

1. Train

Fit candidate models using earlier seasons and model-safe historical features.

2. Tune

Compare settings inside the training period without using the final test games.

3. Hold out

Generate forecasts for unseen weeks and lock each prediction before the result.

4. Score

Record accuracy, calibration, betting results, survivor decisions, fantasy performance, and the miss—not just the win.

No forecast is a guarantee.

Football contains injuries, turnovers, weather, officiating, coaching decisions, and randomness. A credible process quantifies uncertainty and keeps the complete record visible.

THE DISCIPLINE BEHIND THE MODELS

Quantitative training with an operator's mindset

Pro Football Lab is built by an actuary with Society of Actuaries predictive-analytics training and an Executive MBA. Actuaries qualify through a demanding professional education and examination system centered on mathematics, statistics, risk, modeling, and professional judgment.

The actuarial foundation brings measurement discipline. Predictive-analytics training brings modern model design. The Executive MBA adds the operating discipline to move from an interesting experiment to a repeatable product: define the question, build the process, validate the result, and ship what survives scrutiny.

BACKGROUND

Professional training and technical foundations

The actuarial pathway Professional credentialing → Predictive Analytics Advanced analytics training → Executive MBA Systems thinking at Villanova → Gradient boosting machines Foundational research →