Open, 16 days to goSports · Data science · Predict + report · Part of Season 1: Four problems for the planet

Call the game

Predict the home team's winning margin in an NBA game from the two teams' form and ratings before tip-off.

Scored by: MAE; Lower is better
Score to beat: 10.619; Home court +3 (0 on a neutral court)
Entrants: 5; 5 uploads a day
Closes: 29 Nov; 23:59 UTC

Cash prizes for the top three. Amounts are announced on Monday 2 November 2026 and paid after the audit. Prizes need a beaten baseline, a filed solution report and a reproducible pipeline, see the rules and terms.

Overview

The problem. Before tip-off, how many points will the home team win by? Every rating system, every broadcaster's graphic and every fair betting line is an answer to this question. FiveThirtyEight's Elo dataset gives you every NBA and ABA game since 1946 with both teams' ratings before the game. Your job: for the 3,944 games of the 2012-13, 2013-14 and 2014-15 seasons, estimate the home margin.

The data. Train: every game from 1946-47 to 2011-12, 59,213 rows. Test: the three seasons after. Each row has the season, league, playoff and neutral-court flags, day of season, opaque team and franchise codes, both teams' pre-game Elo, FiveThirtyEight's pre-game home win probability, each team's game number in the season and days of rest. The target is home points minus away points. Names, dates and scores are withheld.

Scoring. Mean absolute error in points, lower is better. Games are public (30%) or private (70%) by a hash of the game; your standing is the private split.

Baselines (private split). Home court +3 (0 on a neutral court): 10.62. A default gradient-boosted tree on the shipped columns: 9.47.

Why it is here. Sport is the on-ramp: the skills are identical to the other three problems, the data is the cleanest public sports data there is, and the margin of a game is a number everybody already argues about.

Marking rubric

Exactly how your points are worked out. Mark your own work against it before you file.

70 metric

30 report

Out of 100 final points, 70 come from your score on the hidden test rows and 30 from your solution report, marked against the rubric below.

The metric

70 points

Scored with MAE (lower is better) on the private 70% of the test rows, which nobody sees until results. Your rank there becomes your points: first place earns the full 70, last place earns 0, and everyone between is spaced evenly by rank.

A score that does not beat the home court +3 (0 on a neutral court) earns 0, whatever its rank. Ranks, not raw scores, so one runaway entry cannot squash the rest of the field.

points = 70 × (1 − (rank − 1) ÷ (entrants − 1))For example, 3rd of 21: 70 × (1 − 2 ÷ 20) = 63 points.

The report

30 points, 6 per criterion

Five criteria, equal weight. Each is marked at one of four levels, and every level is defined below, so you can mark your own draft before you file it. Three judges mark blind and independently; each criterion takes the middle mark of the three.

Problem framing and impact
6 points
Who uses this estimate, what decision changes if it is better, and what an error costs them.

0 · Absent0 pts
No statement of who uses the estimate or why.

1 · Developing2 pts
Names the use but not the decision it changes.

2 · Solid4 pts
States the decision, the user and what a given error costs them.

3 · Exemplary6 pts
As 2, and quantifies the value of the improvement over the baseline in the user’s terms.
Data understanding and validation
6 points
A validation design that mirrors the test split, with leakage risks named and handled.

0 · Absent0 pts
No validation design, or validates on the public leaderboard.

1 · Developing2 pts
Holds out data but the split can leak: random rows in a time series, or the same entity on both sides.

2 · Solid4 pts
A split that mirrors the test split; leakage risks named and handled.

3 · Exemplary6 pts
As 2, with evidence the local validation tracks the public score, and a data finding that changed the approach.
Method and rigour
6 points
Choices justified, compared against both pinned baselines, failures reported honestly.

0 · Absent0 pts
No description, or a method with no justification.

1 · Developing2 pts
A reasonable method described, no comparison.

2 · Solid4 pts
Choices justified, compared against both pinned baselines, at least one thing that failed reported honestly.

3 · Exemplary6 pts
As 2, with an ablation or sensitivity check that shows which parts matter.
Error analysis and limitations
6 points
Where the model is wrong and why, and what would go wrong if someone deployed it.

0 · Absent0 pts
No error analysis at all.

1 · Developing2 pts
Says errors exist, no pattern.

2 · Solid4 pts
Shows where the model fails (segments, conditions) and gives a plausible reason.

3 · Exemplary6 pts
As 2, and states the deployment risks: who is harmed if it is over-trusted, what would have to be monitored.
Reproducibility and clarity
6 points
Code, environment and steps a reader could run; prose that is clear and within length.

0 · Absent0 pts
No code, or steps that cannot be followed.

1 · Developing2 pts
Code linked but steps incomplete or environment unstated.

2 · Solid4 pts
Code, environment and steps that a reader could run; prose is clear and within length.

3 · Exemplary6 pts
As 2, with fixed seeds, a one-command run, and results that match the report.

If two judges are more than one level apart on a criterion, a person marks it instead. Judge prompts, model ids and calibration figures are published with the results.

Medals and prizes

Final rank is by total points. Ties go to the better metric rank, then to whoever reached their score first.
With five or more entrants: gold for the top 10%, silver for the next 20%, bronze for the next 30%.
Every medal and prize needs a score that beats the baseline and a filed report.
At least the top ten are audited: we re-run your code from train.csv and the result has to match.

The full rules and terms apply to every entry.

Submission format

Two things to hand in: a predictions file, scored automatically, and a report, marked against the rubric.

Your predictions, as a CSV

Header: id,home_margin. Column order does not matter; extra columns are ignored.
Rows: 3,944 rows, one per id in test.csv. A missing id rejects the file and tells you which.
home_margin: A number. Decimals are fine; no units, no thousands separators.
File: UTF-8, comma separated, up to 10 MB. sample_submission.csv is a valid starting point.
Limits: 5 scored uploads a day (resets 00:00 UTC). A file that fails these checks is rejected with the reason and does not use one. Pick up to 2 for final scoring; pick none and your best public score is used.

submission.csvExample

id,home_margin<first id in test.csv>,12.40<second id>,9.85<third id>,14.10…

Your solution report

Written in the editor on this page, in Markdown, 600 to 1,500 words. Use these five headings, in this order; each one maps to a rubric criterion.

1
## The problem and who it serves
What decision changes if this estimate is better, for whom, and what an error costs them.
2
## Data and validation
What you found in the data, how you built your own validation, what could leak and how you avoided it.
3
## Method
What you built and why, what you tried that did not work, how it compares with the two pinned baselines.
4
## Error analysis and limitations
Where the model is wrong and why; the risks if someone deployed it: fairness, safety, over-trust.
5
## Reproducibility
Link to the code, the environment, the exact steps to regenerate your predictions from train.csv.

Code link, required

A public repository or notebook URL. The audit runs it from train.csv to regenerate your predictions.

AI use, declared

Human: no AI assistance
AI-assisted: I used AI tools and made the decisions
Autonomous: an agent produced the submission

Save a draft as often as you like. The panel marks what is there at close; a report counts as filed once it is submitted or reaches 600 words.

Leaderboard

5 entrants, best public score each. The baseline and our reference model are pinned where their scores fall.

Public leaderboard, best score per entrant on the public 30% of test rows
Rank	Entrant	Uploads	Best score
	Default gradient-boosted tree on the shipped featuresour reference model		9.472
1	MoFarina	3	9.491
2	dhruval	5	9.519
3	bruno	5	9.535
4	AlexCChristou	1	9.546
5	ShinjiNakasaki	1	9.556
	Home court +3 (0 on a neutral court)baseline to beat		10.619

Scored on the public 30% of the test rows while the competition runs. Final standings use the other 70%, so a public lead is not a final one. The two reference rows are scored on those final rows.

Data

Columns in the data files
Column	What it holds
`id`	Row id. Use it in your submission.
`season`	Season, by the year it ended (1947 = 1946-47).
`league`	NBA or ABA.
`playoffs`	1 if a playoff game.
`neutral`	1 if played on a neutral court; the first-listed team is still called 'home'.
`season_day`	Days since the first game of that season.
`home_team`	Home team code (consistent across files; names withheld).
`away_team`	Away team code.
`home_franchise`	Franchise code, a franchise keeps its code across relocations and renames.
`away_franchise`	Away franchise code.
`home_elo`	Home team's Elo rating before the game (FiveThirtyEight's method).
`away_elo`	Away team's Elo rating before the game.
`home_forecast`	FiveThirtyEight's pre-game probability that the home team wins, from the two Elos and home court.
`home_seasongame`	The home team's game number in that season (1 = opener).
`away_seasongame`	The away team's game number in that season.
`home_rest`	Days since the home team's previous game; -1 if unknown.
`away_rest`	Days since the away team's previous game; -1 if unknown.
`home_margin` predict this	Target. Home points minus away points (train only). Negative when the away team won.

Sources and licences

FiveThirtyEight NBA Elo (nbaallelo.csv), Creative Commons Attribution 4.0 International (CC BY 4.0), retrieved 2026-09-21
Data: FiveThirtyEight, 'The Complete History Of The NBA' (nba-elo), CC BY 4.0

Split. Seasons through 2011-12 are train; 2012-13, 2013-14 and 2014-15 are test. Within the test seasons each game is public (30%) or private (70%) by a hash of its id.

Changes made. Two-sided rows collapsed to one row per game; home/away derived from game_location; rest days and day-of-season computed from the dates; team, franchise and game ids replaced by opaque codes; dates, points and post-game Elo removed from the shipped files.

Withheld. Team and franchise names; Game dates; Points scored; Post-game Elo (elo_n, win_equiv).

Rows: train 59,213, test 3,944 (public 1,185, private 2,759).

Artefact audit passed

No duplicate rows across train and test. 0 of 3944 test rows have an identical feature row in train
1-NN does not beat the honest model. 1-NN mae 13.6775 vs honest 9.5181
No single feature encodes the target. strongest |corr| 0.409 (home_forecast)
Row order is uncorrelated with the target. spearman(row index, target) = -0.023
The id does not encode the target. spearman(id number, target) = -0.005

The listed 'home' side on a neutral court is the first-listed team in the source; the neutral flag tells you which rows those are.

Timeline

OpensMon, 5 Oct 2026(done)
Data released; uploads accepted.
Mid-pointMon, 2 Nov 2026(done)
Prize pool announced.
ClosesSun, 29 Nov 2026
Reports and selections are frozen. The last 48 hours are a quiet period.
ResultsMon, 7 Dec 2026
Private standings, medals and the marking pack. Seven days to appeal.

Rules

One entry per person; your handle is fixed once you join. Entering means you accept the competition rules and terms.
Up to 5 submissions a day (resets 00:00 UTC). Select up to 2 for final scoring; if you select none, your best public score's file is used.
Train on the shipped data only. No external data beyond what the data card lists, and never the withheld targets from any source, looking them up is disqualification.
Your solution report (30% of your points) is written on this page and graded as it stands at close. Medals and prizes need a filed report and a beaten baseline; the top ten are audited by re-running their code from train.csv.
Sharing code or ideas publicly is fine; sharing a submission file is not (identical files are detected). One account per person. Text aimed at the grader scores zero.
The final 48 hours are the quiet period: the public board freezes for viewers while you still see your own scores. Results are published once, after the audit, and are final subject to appeal.

The full competition terms apply to every entry.

Read the brief, then enter.

Free with a student account. The brief and the public leaderboard need no account.

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