Oregon HS Tennis Power Rankings

Four years of statewide results, four rating models I designed, a playoff simulator, and a set of live tournament tools, all generated from raw match data and served as one static site.

These are independent rankings I built for myself, like the computer rankings in college football and basketball, but for high school team tennis.

I worked on this in the open. Back in an April 2025 writeup I shared an early version of the formula and the thinking behind it, the proof of concept that the live system grew out of.

The problem it models

A tennis dual meet is eight concurrent matches, four singles and four doubles, and the team that wins five flights wins the meet. That structure breaks ordinary ranking. A 5-3 win at the bottom of the lineup looks identical to a 5-3 win at the top, win-loss records skip over who you played, and lineups can be “stacked” to game any naive metric.

Existing ratings handled none of this. I had to define the measurement before building the rating models, simulation, and the public tools on top of it. It also has to absorb forfeits, ties, six-flight meets, classification changes, and messy results.

The models

APR: Adjusted Power Rating

RPI-style strength of schedule: your wins matter, your opponents’ more, theirs too. “How good are you, given who you played?”

FWS: Flight-Weighted Score

Scores all eight flights on a weighted curve (1st singles / doubles = 1.00 down to 4th doubles = 0.10, max 3.95). The top of the lineup drives the number, but depth still counts: a 7-1 sweep outscores a 5-3 win with the same top flights, which makes stacking pointless. Normalized for flights actually contested, so a six-flight meet still produces a clean number.

FWS+

FWS made legible, indexed like baseball’s ERA+. 100 is exactly average for the classification; 115 is 15% above. It recalibrates as the season fills in, so a team’s standing reads in real time and across history.

Power Index

The blend: (APR × 0.50) + (FWS × 0.50). It combines opponent quality and flight dominance in one number.

TOSS

A later model folding in game-share margin. A/B-tested against the Power Index with results reported.

H2H engine

When teams are close, head-to-head breaks the tie, but only when it avoids a rock-paper-scissors loop (A beats B beats C beats A). Split series defer to the metrics.

On the site

The site grew from an All-State archive into a public suite of tools.

• Rankings dashboard. Every team rated by all four models, filterable by year, classification, and league.
• Playoff simulator. What-if brackets, with a regional mode that optimizes travel against competitive integrity.
• Comparison view. Head-to-head and model-vs-model, plus rating-vs-actual results, showing how well dual-meet strength predicts the individual tournament.
• Methodology. The Power Index explained in plain language, so the rankings are auditable.
• Weekly in-season rankings. Snapshots, week by week, live.
• All-State archive. Teams from 2022 to 2025 in one place.
• Bracket tools. A permutation explorer and a fair-bracket generator that stress-test how different seedings actually play out.
• SD1 draw suite. A live seeding board, draw tool, and printable / live brackets built to run an actual special-district tournament in the room, on the day, with cloud-saved state.
• 2027 format tools. A mixed-doubles standby-list tool and a dual team finals format.
• Changelog and after-action reports. What changed, what was tested, what was learned.

Stack & method

Generation

A single Python program computes every model and emits one self-contained index.html with the data embedded.

Geocoding

Nominatim / OpenStreetMap, geocoded once and cached so fresh clones skip the network. Powers the simulator’s travel optimization.

Frontend

Vanilla JS + Bootstrap + DataTables over embedded JSON. React / TypeScript / Vite in the original prototype.

Live tooling

Netlify Functions (v2) + Netlify Blobs for the cloud-saved SD1 draw.

Hosting

Netlify. Static, zero-infrastructure, offline-capable.

Design decisions

Static HTML with embedded JSON serves coaches and athletic directors, who need something fast, free, and saveable. A dataset this size belongs in the page. The model exposes raw, intuitive scores (0 to 3.95 FWS, ERA+-style FWS+) up front and keeps the normalized math underneath.

The design work went into the model: a rating design that makes stacking pointless, tiebreaker logic that refuses circular results, travel optimization that protects top seeds while cutting miles for the rest, and a methodology published in full so the numbers carry their own evidence.

Oregon HS Tennis Power Rankings