Structured, example-driven sports learning

Key Terms

• Slate — a set of games used in a lesson scenario; lets us compare choices on the same day.
• Roster — a hypothetical lineup we build for learning (no entries, no submissions).
• Ceiling / Floor — illustrative high/low outcomes for a player in a scenario.
• Variance — how spread out outcomes are vs. a projection.
• Correlation — when player outcomes tend to move together (positive/negative).
• Ownership (conceptual) — class discussion proxy for “popularity,” not real data.

Why “Formats” in Class?

We use familiar fantasy contest ideas to explain trade-offs (stability vs. upside) and to structure exercises. They are analogies, not invitations to play.

• Compare choices under different risk preferences.
• Practice normalizing and pacing statistics.
• Discuss roster roles and correlations using safe, static examples.

Do / Don’t

• ✅ Do treat all examples as classroom exercises.
• ✅ Do ask “what changes my evaluation?” (pace, roles, weights).
• ❌ Don’t seek real-money links; we don’t provide any.
• ❌ Don’t read examples as advice to enter paid contests.

We discuss stability: prefer players with tighter floors and less variance for the lesson’s objective.

• • Emphasize consistent roles (openers/key starters, high-minutes players).
• • Limit volatile, all-or-nothing profiles.
• • Example focus: “reduce avoidable downside.”

Teach balance: combine dependable cores with limited, reasoned upside swings.

• • Stabilize with roles; add 1–2 controlled variance pieces.
• • Practice normalization to compare similar candidates.
• • Example focus: “don’t overreact to one hot performance.”

Explore variance & correlation: how stacking roles influences ceiling scenarios in class discussions.

• • Identify correlated roles (e.g., top-order + supporting roles, passer + receiver).
• • Accept more variance for educational what-ifs.
• • Example focus: “when correlation helps or hurts.”

Mini Exercise: Choose a Core

You have 3 hypothetical players. Which two form a more stable “core” for an H2H lens?

• Player A: steady role, modest ceiling, almost no penalties
• Player B: occasional spikes, medium role security
• Player C: huge ceiling, frequent penalties

Teaching Notes

• • Normalize stats before making close comparisons.
• • Consider pace/tempo adjustments if a scenario implies it.
• • Roles can be sticky or fluid — state your assumptions clearly.
• • Correlation helps explain why certain pairs rise/fall together.

Normalization Basics

Normalize data to a common baseline before comparing players:

• • Per-minute / per-over rates (remove playing-time bias).
• • Z-scores (how many standard deviations above/below mean).
• • Min-max scaling (rescale to 0–100 for visual teaching).
• • Outlier handling (cap extremes to reduce noise).

Pace Adjustments

Accounting for tempo (overs, possessions, balls faced) is crucial for fairness:

• • League/team tempo affects counting stats.
• • Regression to mean for extreme paces.
• • Scenario testing across different tempos.
• • Use pace-adjusted stats to teach “environment effects.”

Mini Exercise: Pace Context

Two hypothetical players: Player X has 15 points in a fast-paced environment (100 events), Player Y has 12 points in a slow environment (60 events). After pace adjustment, who is more efficient?

Define an Objective Function

Decide what you’re maximizing or minimizing in your class exercise:

• • Total projected points (sum of normalized scores).
• • Ceiling potential under a given scenario.
• • Stability index (variance minimization).

Write it as a simple formula in a spreadsheet so students can see it update live.

Mini Exercise: Shift the Constraint

Using the table above, what happens if you lower the “budget cap” from 25 to 20? Which player drops out?