gr-analytics 0.3.2

Scoring and salary calculation for GridRival fantasy F1

gr_analytics

Python package for scoring and salary calculation in GridRival fantasy F1.

Installation

pip install -e .

Quick Start

import pandas as pd
from gr_analytics import score_event, score_my_team, optimal_lineup

# Load your race scenario
scenario = pd.read_csv("my_race.csv")

# Score the event (defaults to the latest round in driver_data)
result = score_event(scenario)

# Or score a specific round
result = score_event(scenario, round=1)

# Score your specific team selection
points, salary_change = score_my_team(
    scenario,
    drivers=["RUS", "ANT", "LEC", "BEA", "LIN"],
    team="MER",
    star_driver="BEA",
    round=1,
)

# Find the optimal lineup (maximise points, £100M budget)
lineup = optimal_lineup(result)

# With locked-in drivers (already under contract, cost nothing)
# and a budget for the remaining open spots
lineup = optimal_lineup(
    result,
    locked_in=["HAM", "LEC"],   # driver_abbr or team code
    optimize_for="points",       # or "salary_change"
    budget=60.0,                 # £M available for non-locked picks
)

Scenario Format

The scenario DataFrame must have one row per driver with these columns:

Column	Type	Description
driver_abbr	str	Driver abbreviation (e.g. "RUS", "VER")
qualifying_position	int	Official qualifying position (1–22)
completed_qualifying	int	1 if driver completed qualifying, 0 if DNQ
finishing_position	int	Race finishing position (1–22)
completed_pct	float or "DNS"	Fraction of race completed (0.0–1.0), or "DNS" if driver did not start

DNQ drivers get 0 qualifying points but their qualifying_position is still used to calculate overtake points.

DNS drivers (completed_pct="DNS") get 0 for all race-related points (race, overtake, improvement, completion, teammate).

DNF drivers (e.g. completed_pct=0.3) receive partial completion bonus points and their finishing position is used normally.

Qualifying and race positions must each form a complete sequence 1..n with no duplicates or gaps.

Output

score_event returns a DataFrame with all drivers and constructors, with scoring columns appended:

Drivers:

• pts_qualifying, pts_race, pts_overtake, pts_improvement, pts_completion, pts_teammate
• points_earned — total fantasy points
• salary_after_event, salary_change

Constructors:

• pts_qualifying, pts_race (sum across both drivers, using constructor-specific tables)
• points_earned, salary_after_event, salary_change

Scoring Rules

All scoring follows GridRival's rules for Grand Prix events (no sprint races).

Drivers

Bonus	Rule
Qualifying	P1=50, P2=48, … P22=8 (step −2)
Race finish	P1=100, P2=97, … P22=37 (step −3)
Overtake	(qualifying pos − finishing pos) × 3, min 0
Improvement	Points for finishing ahead of 8-race average (2 pos=2 pts, 3=4, 4=6, 5=9, 6=12, 7=16, 8=20, 9=25, 10+=30)
Completion	3 pts each at 25%, 50%, 75%, 90% of race distance (max 12)
Teammate	Points for beating teammate by margin: ≥1 pos=2 pts, ≥4=5, ≥8=8, ≥13=12

Constructors

Constructor qualifying and race points use separate tables (P1=30/60, step −1/−2 per driver) summed across both drivers. No overtake, improvement, completion, or teammate bonuses.

Salary Adjustment

After each race, salaries adjust based on the gap between a driver's actual starting salary and the default salary for their points-ranking position:

adjustment = truncate(variation / 4, to nearest £100K)

Capped at ±£2M for drivers, ±£3M for constructors.

Driver Data

Bundled driver data (gr_analytics/data/driver_data.csv) contains starting salaries and 8-race averages by round:

• round=0 — pre-season (before Australia 2026)
• round=1 — post-Australia 2026

Eight-Race Average

GridRival's "8 race average" can be computed instead of entered by hand. GridRival seeds the season with 8 slots holding a hard-coded initial average (the round=0 values in driver_data); each race replaces one slot with the driver's classified finishing position, and the displayed value is the ceiling of the slot mean. Race finishing positions live in gr_analytics/data/race_results.csv.

from gr_analytics import calculate_eight_race_averages, eight_race_average

# All drivers, after the latest round in race_results.csv
calculate_eight_race_averages()

# All drivers, after round 2
calculate_eight_race_averages(through_round=2)

# Single driver from scratch: seed 1, finished P6 then P16
eight_race_average(1, [6, 16])

This reproduces GridRival's displayed values exactly for all rounds so far (verified in tests/test_eight_race_average.py).

Lineup Optimisation

optimal_lineup uses mixed-integer linear programming (via scipy.optimize.milp) to find the best 5-driver + 1-constructor lineup within a salary budget.

lineup = optimal_lineup(
    scored,                  # DataFrame from score_event()
    locked_in=None,          # list of driver_abbr / team codes already on your team
    optimize_for="points",   # "points" or "salary_change"
    budget=100.0,            # £M for non-locked picks
)

• locked_in picks are included free (already under contract) and don't count against the budget.
• optimize_for="points" selects the optimal star driver (who earns double points) across all candidates.
• optimize_for="salary_change" maximises total salary gain for the next race's team valuation.

The returned DataFrame has a star column (1 = starred driver, points mode only).

Running Tests

python -m pytest tests/test_scoring.py -v

Tests include hand-calculated unit tests for all scoring components and a full integration test against real GridRival results from Australia 2026 (22 drivers + 3 constructors).