Backcaster

Backcaster is a Python CLI that produces Marcel-style MLB projections from the prior three seasons of data using local Lahman CSV files and pandas.

It supports:

• Single player projections (player)
• Single team projections (team)
• League-wide batch projections (batch)
• Output as terminal table, CSV, or HTML

Installation

Requirements

• Python 3.13+
• An extracted Lahman CSV database covering the three prior seasons
• Internet access for package installation only; projections run offline

Update an installed package

Upgrade to the latest release available on PyPI:

python -m pip install --upgrade backcaster

To install a specific release, such as 0.3.4:

python -m pip install --upgrade backcaster==0.3.4

Run the command using the same Python environment where you run Backcaster. If you use a virtual environment, activate it first. Check the installed version with:

python -m pip show backcaster

Install from source (recommended for contributors)

# from repo root
uv sync

This installs runtime dependencies and the backcaster CLI entrypoint defined in pyproject.toml.

Install in editable/dev mode

uv sync --extra dev

This adds test/lint/type-check/build tooling (pytest, pytest-cov, ruff, mypy, pandas-stubs, build).

Build/install as a package

python -m build
pip install dist/backcaster-*.whl

Quickstart CLI usage

All commands require:

• --year target projection year
• --kind one of batting or pitching

Subcommand-specific required options:

• player requires --name
• team requires --team
• batch requires no additional required option

backcaster <player|team|batch> [subcommand-options] --year <YEAR> --kind <batting|pitching> [--format cli|csv|html] [--out PATH]

1) Player projection

backcaster player --name "Mookie Betts" --year 2026 --kind batting

Offline projections with Lahman CSVs

All projections use local Lahman CSVs. On Windows, the default directory is C:\Baseball\lahman; on other systems it is ~/Baseball/lahman. Set the LAHMAN_DATA_DIR environment variable to change the default, or pass --data-dir to override it for one command. No network requests or online fallbacks are made. The directory must contain People.csv, Teams.csv, and Batting.csv or Pitching.csv for the selected kind. Batter projections also require Appearances.csv and a valid birthYear in People.csv. Filenames are matched without regard to case. The database is not bundled with Backcaster.

For the default Windows directory, no additional flag is needed:

backcaster player --name "Mookie Betts" --year 2024 --kind batting

See Lahman database notes for the supplied 1871–2025 edition's coverage, attribution, license, and historical-data caveats, with its original README.

backcaster player --name "Mookie Betts" --year 2022 --kind batting --data-dir "C:\Baseball\lahman"
backcaster team --team LAN --year 2022 --kind batting --data-dir "C:\Baseball\lahman"
backcaster batch --year 2022 --kind pitching --data-dir "C:\Baseball\lahman" --format csv --out pitchers.csv

Use Lahman's teamID values, such as LAN for the Dodgers (rather than the FanGraphs abbreviation LAD). A projection year needs the preceding three seasons: 2022 requires 2019–2021. Missing seasons produce an error explaining which year is unavailable. If the directory or required files are missing, the command reports the missing file and directory; it never attempts an online fetch.

Offline qualification uses a consistent modern rule across historical seasons: at least 3.1 PA per game for hitters and one inning per game for pitchers. The season's game count is the maximum G in that year's Teams.csv records, so shortened seasons have lower thresholds. The PA threshold is rounded to the nearest integer (502 PA for 162 games, 186 PA for 60 games). This is an analytical filter, not a reconstruction of historical award eligibility or batting-title exceptions. Relievers generally do not qualify under this pitching rule.

Player stints are summed before qualification. Plate appearances are calculated as AB + BB + HBP + SF + SH; missing/blank HBP, SF, and SH values count as zero. Blank numeric counting-stat cells are also treated as zero; use a dataset with complete records for the seasons you intend to project. Pitching IPouts is divided by three for decimal innings, and BFP supplies batters faced. Lahman player IDs keep different players with the same name separate; ambiguous name lookups report the matching IDs.

Batter league regression and rebaselining use all non-pitcher player-seasons; qualification selects the displayed players. Pitching retains its qualified-only baseline. See the batter model for the six-stage calculation, real-age adjustment, PA formula, and explicit rebaselining choices. Single-player projections require qualification in all three preceding seasons. Team and batch projections use each player's available qualified seasons within that window. For team selection, a traded player's full-season totals are assigned to the team in their latest stint for that season; this is not a current-roster forecast.

2) Team projection

backcaster team --team LAN --year 2026 --kind pitching

3) Batch projection (all players returned by source data)

backcaster batch --year 2026 --kind batting

File outputs

backcaster player --name "Mookie Betts" --year 2026 --kind batting --format csv --out ./out/mookie_2026.csv
backcaster team --team LAN --year 2026 --kind pitching --format html --out ./out/lan_pitching_2026.html

--out is required for csv and html formats.

Output examples

CLI table output (--format cli, default)

Example shape (columns vary by batting vs pitching):

   PA    AB     H   2B   3B    HR    BB    SO   HBP    SF    AVG    OBP    SLG    OPS  Reliability          Name  Year
610.2 545.1 162.4 34.1  2.8  32.6  67.9 119.3   5.6   4.7  0.298  0.384  0.531  0.915        0.89  Mookie Betts  2026

CSV output (--format csv)

A single-row CSV for player; multi-row CSV for team and batch.

HTML output (--format html)

A basic HTML table (no custom CSS) suitable for quick sharing or embedding.

Local data updates

Backcaster reads the local CSV files for each command and does not use the old FanGraphs season cache. To refresh the data, replace the CSVs with a complete Lahman distribution from the same edition. Existing online caches are ignored.

Dependency notes

Runtime dependencies are intentionally minimal:

• pandas for tabular data processing and projection output

Development dependencies (--extra dev) include:

• pytest, pytest-cov
• ruff
• mypy, pandas-stubs

Lockfile policy (uv.lock)

uv.lock pins transitive versions for reproducible environments when working from a Git checkout.

Release/source ZIP archives may not include uv.lock. If it is missing in your environment, regenerate it locally:

uv lock

Do not hand-edit uv.lock; regenerate it when dependencies change.

What this projection does and does not claim

What it does

• Implements a Marcel-style weighted/regressed projection over prior seasons.
• Uses configurable reliability/regression/playing-time assumptions in code defaults.
• Produces transparent, table-based outputs for downstream analysis.

What it does not claim

• It is not an official MLB/club forecasting system.
• It does not model injuries, role changes, park effects, coaching strategy, or transaction context in a comprehensive way.
• It does not guarantee predictive superiority versus commercial/public projection systems.
• It should be treated as a baseline projection tool, not a betting or financial decision engine.

Development and quality checks

For release preparation and PyPI Trusted Publishing, see RELEASING.md. Changes for the next release are recorded in CHANGELOG.md.

Run tests

pytest

Coverage + JUnit reports

pytest --junitxml=pytest.xml --cov=src --cov-report=xml:coverage.xml

Lint and types

ruff check .
mypy

SonarScanner

sonar-scanner -Dsonar.projectVersion=$(PYTHONPATH=src python -c "from backcaster import __version__; print(__version__)")

Sonar consumes:

• coverage.xml
• pytest.xml

Acknowledgments

Thank you to Tom Tango for developing and publishing the Marcel system, and to Ben Dilday for marcelR, whose open R implementation provides a valuable methodological reference for our Python work. We document the reviewed formulas, source revision, and implementation differences in our reference notes. The complete marcelR MIT notice is preserved in THIRD_PARTY_NOTICES.md and shipped with our distributions.

Thanks also to SABR, Sean Lahman, and the database contributors. See Lahman attribution and licensing. These acknowledgments do not imply endorsement or exact agreement with another implementation's projections.

Security and responsible use

Please review SECURITY.md for reporting guidance. Backcaster is an analytical utility and should be validated independently before use in production pipelines.