SchemeSync · Applied Predictive Analytics
Prediction only matters
if you keep score.
An applied predictive-analytics environment where game forecasts meet actual outcomes. Four leagues. A visible feedback loop between prediction, measurement, evaluation, and learning.
Open SchemeSyncFour product experiences
Professional and college football. Professional and college basketball. Distinct product areas connected by the same need to compare an estimate with what actually happens.
Professional football
Professional football matchups, probability estimates, and game analysis. Explore the current product experience for available matchups, forecast views, and evaluation tools.
College football
College football matchups, forecast analysis, and possible game outcomes. Explore the current product experience for available matchups, forecast views, and evaluation tools.
Professional basketball
Professional basketball matchups, probability signals, and game analysis. Explore the current product experience for available matchups, forecast views, and evaluation tools.
College men’s basketball
College men’s basketball matchups, analytical trends, and outcome estimates. Explore the current product experience for available matchups, forecast views, and evaluation tools.
A complete evaluation loop
A forecast becomes useful research when its assumptions and outcomes can be compared. SchemeSync makes sports a concrete setting for that discipline.
Examine teams, game context, projected outcomes, and probability estimates together. Each forecast expresses uncertainty that can be measured against the final result.
Track whether predicted winners matched the outcome. Accuracy answers one question; the strength of the probability estimate and the quality of the comparison set answer others.
Where available in the current dashboard, evaluate recommendations separately from winner picks. Market lines, prices, settlement rules, and timing can change how a result should be interpreted.
Cumulative records put individual results in context. Examine the sample, season, league, and forecast type to understand performance over time.
Longer-horizon questions depend on paths through remaining schedules and competition. Revisit assumptions as the field changes; a game forecast and a championship forecast have different horizons.
Compare estimates with outcomes, check relevant baselines, and study errors. A good evaluation distinguishes a useful pattern from a favorable stretch of results.
Evidence-driven sports analytics
Outcome measurement connects each result to the information, assumptions, and uncertainty behind the forecast. That evidence guides model evaluation, refinement, and learning.
Explore league-specific predictions, cumulative records, recommended-bet performance, and postseason forecasts in the public dashboards. Outcomes provide a continuing record for evaluating and refining the models.
SchemeSync brings applied predictive analytics into sports. PIP advances autonomous predictive-intelligence research through its own forecasting experiments, evidence, and evaluation process.
The wider SchemeSync family
Existing product information, recruiting entry points, and educational resources remain available.
High-school football evaluation and recruiting tools, with the existing product information and trial experience preserved.
Explore SchemeSync HSOpen HS applicationRead the existing high-school football articles or explore the other established product pages.
Read the blog