Data for Sports brings game logs, trend charts, and predictive models into one place, then explains what the numbers mean in plain language. Built for fans, players, and analysts who make decisions with sports data.
Over the last 10 games, Player A is averaging 27.4 PTS against a 24.1 season average. Minutes are up to 35.5 and shot attempts to 20.8 per game, so the jump comes from a bigger role, not just hot shooting.
A serious fantasy player pieces together projections from one site, advanced stats from another, rankings from a third, and usage trends from a fourth. The deepest analysis is paywalled per site, and the numbers still have to be interpreted by hand.
One player view with game logs, trend charts, plain-language summaries, and explainable forecasts.
Data for Sports is analytics software for sports data. It assembles each player's games, profile, and seasons into a single record, then puts four tools on top of it: charts to see a trend, summaries to explain it, models to project it, and benchmarks to test it.
Most sports research starts by gathering: a box score from one site, a season split from another, a chart built by hand. Data for Sports starts after that step. Every supported league is loaded into the same structure, so a player in the NHL and a player in the NBA are read the same way.
That shared record matters more than any single feature. Because the chart, the summary, and the forecast are computed from identical game data, they can't contradict each other. When one of them changes, it's because the games changed.
Any stat in the game log can be drawn as a line, area, bar, or scatter chart, over all games or a recent stretch. Two reference lines are added automatically: the period average, and a moving average that smooths out single-game noise so the direction of a trend is easier to read.
The game log is the foundation: one row per game with the full box score, the opponent, and whether it was played at home or away. It can be narrowed to a single season and, where the league has one, to the regular season or playoffs. Every other tool recalculates when the season selection changes.
Each player view opens with a short written summary comparing the last five games to the season, so the main takeaway is readable before any chart is opened. Questions can also be asked in plain English instead of set up through filters.
Over the last 5 games, Player A is averaging 36.8 minutes and 22.2 shot attempts, compared with 35.5 and 20.8 across the 10-game sample.
The forecast model fits a least-squares trend line to the selected games and extends it one game forward. Rather than a single number, it reports a 95% prediction interval: the range the next result is expected to fall in. The interval widens when the games are inconsistent and narrows when they aren't.
Every forecast is published with its error measures, so its reliability can be judged directly. R² describes how much of the game-to-game variation the trend explains; mean absolute error and RMSE describe how far, on average, the fitted line sits from what actually happened.
| Measure | Value | What it means |
|---|---|---|
| Next-game projection | 38.0 | Where the trend line lands one game ahead |
| 95% prediction interval | 33.5–42.5 | The expected range for the next result |
| Trend | +0.45 per game | Improving, strong relative to game-to-game noise |
| R² | 0.32 | Share of the variation the trend explains |
| Mean absolute error | 1.45 | Average miss of the fitted line, in minutes |
| RMSE | 1.88 | Typical miss, weighting large misses more heavily |
A number on its own doesn't help you decide. Each projection comes with a likely range and the factors behind it, so you can judge it for yourself.
A benchmark is a fixed value a stat is measured against: the season average, recent form, the model's projection, or any number entered by hand. Each game is marked as clearing it or falling short, which turns a vague sense of consistency into a count.
The tools are designed to be read together. Take a common question: is Player A's role growing, and should that change how they're valued? Answering it takes four steps, each one checking the last.
The result is a conclusion with its evidence attached: the role is growing, the forecast supports it, and one stat is still worth watching. Data for Sports is built to produce that kind of answer. It provides analysis, not picks or odds.
Go past the box score and back up every take with real trends.
Clear, shareable charts and summaries for your audience.
Structured sports data without building your own pipeline.
Search any player in a supported league to open their unified view.
Game logs and charts compare recent form with season benchmarks.
See the projection, its likely range, and the reasoning behind it.
We surface the numbers that matter for a decision, and explain them in plain language.
Forecasts come with ranges and the factors behind them. No black boxes.
No picks, locks, or odds. We build software for understanding the game.
We're building a public record of every prediction and how it turned out.
