athletevalue

Open models that estimate, separately, how much a college basketball player improves his team, what that improvement is worth to his program, and what the roster market would pay him.

Rating sites publish one "NIL value" per athlete from a model nobody outside can inspect. That single number mixes on-court impact, school revenue-sharing budgets, collective payments and endorsement income. athletevalue keeps those apart, attaches an interval and an evidence status to each, and cites every constant it uses. The comparison between value and price is the point:

In 2025-26 the model prices Michigan's Yaxel Lendeborg at $1.55M and credits his wins with $912k of program revenue. It prices Gonzaga's Graham Ike at $394k and credits him with $569k. Both medians carry wide intervals.

Version 0.1 covers NCAA Division I men's basketball.

Install

pip install athletevalue          # or: uv add athletevalue

Python 3.12 or later. The first valuation downloads about 400 MB of public data into a local cache and parses 15 years of EADA spreadsheets, which takes several minutes. Later runs read the cache and finish in seconds. athletevalue cache-path prints the cache location.

Quick start

athletevalue value "Yaxel Lendeborg" --season 2026

Yaxel Lendeborg · Michigan (Big Ten) · 2025-26 · starter

Athletic impact           +14.6 /100   80%: +11.3 to +17.9          ▲ estimated
  offense / defense      +8.2 / +6.4
Wins above replacement           6.8   80%: 5.3 to 8.6              ▲ estimated
Program value                  $912k   80%: $494k to $1.40M         ○ scenario
  win revenue                  $889k   80%: $479k to $1.37M         ▲ estimated
  bid revenue                   $12k   80%: $2k to $52k             ▲ estimated
  tournament units               $3k   80%: $814 to $9k             ○ scenario
Roster market value           $1.55M   80%: $1.19M to $2.01M        ○ scenario
Surplus                       -$656k   80%: -$1.25M to -$42k        ○ scenario
Value vs. price            fair star

Drivers
+ on-court impact ranks 1 of 4030 rated players
~ box-score prior +10.8 per 100; his possessions moved the rating +3.8
+ on the floor for 78% of team possessions
~ team went 37-3, net rating +42.8
+ program reports $21.02M basketball revenue (D1 median $3.04M)
+ Big Ten roster budget tier: power

Price basis: allocation. Status: ● reported ◆ derived ▲ estimated ○ scenario
As of 2026-09-16 · games through 2026-04-06 · model mbb-v0.2.0

from athletevalue import api

valuation = api.value_player("Yaxel Lendeborg", season=2026)
print(valuation.summary())
valuation.war.value, valuation.war.lower, valuation.war.upper

table = api.value_team("Gonzaga", season=2026)  # polars DataFrame, one row per player

Other commands:

Seasons are keyed by ending year: 2026 is the 2025-26 season.

What each number means

Every estimate carries an 80% interval and a status:

• ● reported: stated by a primary source.
• ◆ derived: computed from reported data by a fixed procedure.
• ▲ estimated: depends on a statistical fit or a published third-party figure.
• ○ scenario: depends on an assumption a user should question, such as how a conference splits tournament money.

A result takes the status of its weakest input. Market value is always a scenario in v0.1 because no public dataset records individual college basketball pay.

Validation

Each season fit is checked against the SportsDataverse league-wide RAPM, which is fit on the same possessions, and against Bart Torvik's team ratings, which are not.

Two checks outside the gates support the box-score prior:

The 2025 prior in the first check was trained on 2021-2024, so nothing from 2026 reaches it. Run athletevalue validate --season 2026 to reproduce the gates.

Data and licensing

Code is MIT. Curated data in src/athletevalue/data (the team crosswalk and the deal registry) is CC BY 4.0; see LICENSE-DATA. Data downloaded at runtime stays under its own terms and is never redistributed.

KenPom and the Knight-Newhouse College Athletics Database restrict redistribution. The package has a reader for a user's own KenPom export, for local comparison, and an import-linter contract that stops fitting code from importing it.

Limitations

• Revenue is what schools attribute to men's basketball in EADA. Conference media money, donations credited to the athletic department and brand effects are not in it, so program value is a lower bound on a player's value to the university. 61% of D1 rows report revenue equal to expense and are excluded from the fit.
• Market value is an allocation, not a prediction. It spreads reported average roster budgets across a roster using reported pay ratios by role. It is only available for 2025-26, the season those figures describe.
• College ratings are noisy. A starter's net rating has a posterior SD near 2.6 points per 100 with the box-score prior and near 4 without it. The prior's own coefficient uncertainty is not propagated, so intervals are slightly too narrow.
• Players are rated within a season. Transfers are not linked across seasons.
• Tournament flags before 2023 come from matching ESPN games by date and team name. A few First Four games each season go unmatched.

METHODOLOGY.md gives the equations, estimates and caveats for each layer.

Roadmap

• v0.2 (this release): box-score prior for RAPM with a team adjustment.
• v0.3: a fitted roster-market model that learns from disclosed deals and replaces the allocation once enough labeled player-seasons exist.
• Later: multi-season ratings with recency weights, CollegeBasketballData for in-season updates, optional private KenPom calibration, then football.

Contributing

See CONTRIBUTING.md. Sourced deals for the registry are the most useful contribution; the rules are in src/athletevalue/data/deal_registry/README.md.

Citation

See CITATION.cff. Estimates are research outputs; read DISCLAIMER.md before using them for decisions.