How we build
our predictions.
Victory Analytics and Research doesn't guess. We simulate. Our proprietary prediction engine runs thousands of simulations per game, per matchup, per season, converging on probability distributions that reflect the true complexity of professional sports.
Eight rules. Every published claim has to clear all of them.
The Honest Validation Protocol is the audit-defensible spec VAR validates every win-rate, ROI claim, and live-tier promotion against. Walk-forward across multiple test seasons. Beta-Binomial credible intervals with the lower bound cited. Pre-registered ship gates before looking at test data. Production-code-path verification end-to-end.
Read the protocol →The forward test, signed before kickoff.
Before NFL Week 1 2026, we locked the markets, the sample-size targets, the success criteria, and the explicit failure conditions for VAR's 2026-27 forward test. Signed and dated. Append-only changelog. Pre-registration is what separates analytics from astrology.
Read the pre-registration →Monte Carlo
Simulation.
Most prediction models produce a single number. We produce a distribution.
By running tens of thousands of simulated outcomes for every event we model, VAR captures what single-point forecasts miss: variance, tail risk, and the full range of what can actually happen on a given night or Sunday afternoon.
The output isn't just a winner. It's a probability map, giving our clients a clear picture of edge, uncertainty, and expected value.
Team & Player Performance
Historical efficiency metrics, recent form, and matchup-specific tendencies across every position and unit.
Environmental Factors
Venue, rest days, travel distance, weather conditions where applicable. Every variable that shapes the game before it starts.
Market Signals
Line movement and implied probabilities from sharp books. The market is a data source, not an oracle.
Contextual Variables
Injuries, lineup changes, coaching adjustments, and schedule strength. The factors that shift distributions in real time.
Not a pick.
A probability map.
Six verticals.
Four live, two in calibration.
Game outcomes, spread, totals, and team-level season projections
Game-level simulation with efficiency-based player modeling
Tournament and regular season game prediction
Bout-level simulation incorporating fighter style, reach, and historical finishing rates
Game-level basketball simulation, in calibration
Women's college game prediction, in calibration
Out-of-sample.
Every time.
We hold ourselves to the same standard we'd demand from any model: out-of-sample performance, with the 95% credible-interval lower bound cited.
Our publicly pre-registered NFL PRIME spread tier was validated at 62.83% accuracy with a 95% Beta-Binomial credible-interval lower bound of 56.36% on n=226 across three walk-forward test seasons (2023, 2024, 2025). The lower bound is what we plan and size against, well above the 52.4% break-even threshold for standard -110 markets. The pre-registration is signed and dated at /methodology/2026-27-predictions.
Every model is evaluated continuously against closing lines, the sharpest market signal available. Live forward-test tracking publishes every PRIME-tier pick before kickoff and every result within 24 hours; see /performance.
Infrastructure,
not picks.
API-First Integration
Simulation outputs available via structured data feeds. Plug directly into your existing analytics infrastructure.
Custom Modeling
Bespoke simulation parameters tuned for your specific use case (franchise, media, or operator).
Ongoing Calibration
Models updated in real time as new data becomes available. Continuous improvement, not static snapshots.
Questions,
answered.
What is Monte Carlo simulation in sports analytics?
Monte Carlo simulation runs thousands of randomized possible outcomes for a single game using a probabilistic model of every relevant variable: team efficiency, matchup tendencies, environmental factors, and more. Instead of producing a single point estimate, it produces a full probability distribution showing the range of plausible results and how likely each one is.
How is VAR different from traditional sports prediction models?
Most prediction models output a single number: a winner, a spread, a total. VAR outputs a distribution. By simulating tens of thousands of game outcomes per matchup, we capture variance, tail risk, and expected value rather than collapsing everything into a point estimate that hides how confident the model actually is.
What does "out-of-sample" validation mean?
Out-of-sample means every accuracy figure we publish is measured on games the model never saw during training. Numbers measured on training data ('in-sample') are nearly meaningless because they reflect memorization rather than predictive skill. VAR's publicly pre-registered NFL PRIME spread tier is 62.83% accuracy with a 95% credible interval lower bound of 56.36% on n=226 across three walk-forward test seasons (2023, 2024, 2025); the contract is signed at /methodology/2026-27-predictions.
What is the break-even threshold for ATS betting at -110?
At standard -110 odds, a bettor needs to win 52.4% of their bets to break even after the bookmaker's vig. The figure to plan and size against is the lower bound of a 95% Beta-Binomial credible interval, not the point estimate. VAR's pre-registered NFL PRIME spread CI lower bound of 56.36% (on n=226 across three walk-forward seasons) sits roughly 4 points above break-even, which is the meaningful margin under real-money sizing discipline.
How does VAR account for injuries and lineup changes?
Injuries, lineup decisions, and coaching adjustments are treated as contextual variables that shift probability distributions in real time. Each simulation pass re-weights outcomes based on the most recent personnel and contextual data, rather than treating rosters as static for the entire game.
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full picture?
We share more detailed methodology documentation with qualified partners under NDA.
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