How the APEX model works
A team's APEX rating estimates how many points they are expected to contribute above an average team, adjusted for opponent strength, alliance partners, event difficulty, and recent performance.
Experimental, AI-assisted analytics
predictFRC uses AI-assisted experimental analytics. Predictions, rankings, ratings, and insights are not guaranteed to be accurate and should not be treated as official results or professional advice. Data may be incomplete, delayed, miscategorized, or incorrect. predictFRC is a new project under active development, and accuracy will improve over time.
The core idea, in one sentence
Take everything a team has done this season, figure out how hard it was given who they played with and against, weight recent and high-stakes matches more heavily, and express the result as a single number: expected points contributed above average.
What goes in
The model blends many signals. None on its own tells the whole story — the rating comes from how they fit together.
Component ratings
Offensive rating
Points a team adds through scoring, above an average robot.
Defensive rating
Points a team prevents opponents from scoring.
Auto rating
Expected contribution during the autonomous period.
Endgame rating
Expected contribution from endgame and climb tasks.
Every metric, explained
What it means · how it's calculated · what feeds it · when to trust it.
The adjustments that matter
Partner & opponent adjustment
Your numbers are corrected for the strength of who you played with and against, so easy schedules don't inflate ratings.
Event difficulty
A strong performance at a tough event counts for more than the same result against a weak field.
Recency weighting
Recent matches carry more weight, so the rating reflects current form, not the early-season version of a team.
Playoff weighting
Elimination matches are weighted up because they test teams under real pressure.
Handling missing & messy data
- Missing breakdowns: if phase-level data is absent, the metric falls back to a league-average prior and is tagged Data incomplete.
- Tiny samples: ratings are pulled toward the mean until enough matches exist, preventing wild week-1 swings.
- Surrogates & backups: excluded or down-weighted so stats aren't misattributed.
- Outliers: a single lucky or unlucky match is damped, never allowed to dominate a rating.
- Delayed data: stale values show an older "last updated" timestamp and reduced confidence.
How confidence scores work
Confidence (0–100) combines four factors: sample size (more matches → higher), data completeness (fewer missing fields → higher), recency (fresher data → higher), and matchup closeness (coin-flip games are inherently less certain). Low-confidence outputs are labeled, not hidden.
How predictions are tested
Every completed match feeds an audit comparing predicted vs actual winner and score. We track winner accuracy, average score error, and confidence calibration — whether a stated 70% really wins ~70% of the time. See the full Accuracy Report →
Avoiding overreaction
The model uses recency-weighted averaging with regression to the mean and outlier damping. One blowout win or a broken-down match nudges a rating slightly rather than rewriting it, so APEX tracks genuine improvement without chasing noise.
2026 REBUILT game intelligence
A game-specific config layer the model reasons over.
Autonomous
First seconds: robots run pre-programmed routines with no driver input.
- Auto Leave3 pts
- Auto Score (Low)4 pts
- Auto Score (High)6 pts
Teleop
Drivers control robots to acquire and score game pieces.
- Teleop Score (Low)2 pts
- Teleop Score (High)5 pts
- Fast Cycle Bonus1 pts
Endgame
Final seconds: high-value parking / climbing objectives.
- Endgame Park4 pts
- Endgame Climb12 pts
Ranking point objectives (estimated)
- 2 ranking pointsMatch Win — Alliance wins the qualification match.
- 1 ranking pointsMatch Tie — Qualification match ends in a tie.
- 1 ranking pointsScoring Threshold RP — Alliance exceeds an estimated objective scoring threshold.
- 1 ranking pointsEndgame RP — Alliance meets an estimated endgame objective.
- 1 ranking pointsCoopertition Bonus — Both alliances cooperate to meet a shared objective (lowers a separate threshold).AI-assisted
REBUILT scouting metrics
Real vs estimated vs AI-inferred.
| Game Piece Throughput | Average game pieces scored per match. | Estimate |
| Auto Reliability | How often the autonomous routine executes as intended. | AI-assisted |
| Cycle Time | Average time for one acquire-and-score cycle. | Estimate |
| Scoring Accuracy | Successful scores divided by attempts. | AI-assisted |
| Preferred Scoring Location | Where the robot scores most often. | AI-assisted |
| Driver Efficiency | Path efficiency and decision quality under pressure. | AI-assisted |
| Defense Resistance | Ability to keep scoring while defended. | Estimate |
| Endgame Reliability | How often the endgame objective is completed. | Estimate |
| Ranking Point Contribution | Estimated RP this team helps its alliance earn. | AI-assisted |
| Penalty Risk | Likelihood of incurring fouls (lower is better). | Estimate |
| Alliance Role Fit | Best complementary role on an alliance. | AI-assisted |
The data pipeline
How real FRC data will flow once connected.
- 1
Fetch
Pull raw events, teams, matches and score breakdowns from public APIs.
- 2
Validate
Reject malformed payloads, flag missing fields and duplicate keys.
- 3
Normalize
Map every source into a single internal schema for teams, events and matches.
- 4
Categorize
Split score breakdowns into game-specific phases (auto / teleop / endgame).
- 5
Compute Metrics
Derive APEX contributions, consistency, momentum, schedule difficulty, etc.
- 6
Assign Confidence
Score each output by sample size, data completeness and recency.
- 7
Store
Cache computed outputs so pages render instantly without recomputation.
- 8
Display
Render with provenance labels, freshness timestamps and uncertainty.
FRC knowledge the model assumes
Qualification matches
Randomized round-robin matches that set the ranking order.
Ranking points
Bonus points for wins, ties, and meeting game objectives — separate from match score.
Alliance selection
Top-seeded teams draft partners to form playoff alliances.
Playoff brackets
Double-elimination bracket that decides the event winner.
District vs regional
Districts use a cumulative points system across events; regionals advance winners directly.
District points
Points earned at district events that determine district championship qualification.
Championship divisions
Large championships split teams into divisions that feed a final playoff.
Strength of schedule
Some teams simply draw harder partners and opponents — APEX corrects for it.
Coopertition
Game-specific incentives that reward both alliances for cooperating.
Auto / teleop / endgame
The three scoring phases, each modeled separately.
Fouls & penalties
Rule violations award points to the opponent and add noise to raw scores.
Surrogate matches
Extra matches that don't count toward a team's ranking.
Backup robots
Substitute robots called in during playoffs — flagged so stats aren't misattributed.
Event advancement
How teams progress from districts to district champs to worlds.
Season improvement
Teams iterate their robots across weeks; recency weighting captures it.
Raw score vs true contribution
A high score can come from strong partners — APEX isolates a team's own contribution.
A note on honesty
APEX is an original model built for predictFRC. It is not affiliated with, nor a copy of, any other analytics provider. Ratings are estimates — they describe expected performance, not certainties, and this build uses simulated data.