About DataBaller

Sports intelligence on-demand. Ask your NBA, NFL, and MLB questions. Get original analysis of the latest data. Know the game.

DataBaller is a conversational sports analyst for the NFL, NBA, and MLB. Ask a question and get analyst-quality answers grounded in primary sports data, original metrics, and transparent reasoning.

Use DataBaller to evaluate players, diagnose performance, compare matchups, test narratives, and uncover what conventional statistics miss. Whether you are settling a debate, managing a fantasy team, or trying to understand what is happening beneath the headlines, DataBaller delivers sports intelligence on demand.

Why it exists

Sports fans use analysis to get more from the ways they already engage with sports. They use it to build stronger fantasy teams, make better predictions, settle debates, form and defend opinions, understand why teams and players are changing, anticipate what may happen next, and experience games with more context.

The challenge is that doing those things well increasingly requires synthesizing more sports data and context than most fans have the time, tools, or expertise to analyze themselves.

DataBaller performs that analytical work on demand. It isolates the relevant evidence, establishes meaningful baselines, decomposes performance changes, identifies anomalies, and states what the data can and cannot support. The result is evidence and insight that fans can use to compete, debate, anticipate, and deepen their experience of the sports they follow.

What it does

• Assess player value. What a player is actually contributing once role, efficiency, availability, schedule, and sustainability are accounted for.
• Diagnose performance. What is really driving a team's or a player's results, and whether it is a structural change or a soft schedule, a hot streak, or noise.
• Evaluate matchups. The strengths, weaknesses, personnel interactions, and uncertainties that matter when two teams meet.
• Discover hidden signals. The patterns, anomalies, and divergences you did not already know to go looking for.

How an answer gets made

Every substantive answer follows the same five steps, and you can see them in the shape of what comes back.

1. Isolate the window. Pull the records for the exact period you asked about: every box score in the month, every game since the trade.
2. Establish the baseline. Compare it against the same team or player's other stretches and against the rest of the league over the same dates. A number means nothing until you know what it is being measured against.
3. Decompose the change. Work out which underlying statistics moved, and which minutes, usage, roles, or personnel moved with them.
4. Find the anomalies. Surface what deviates materially from that baseline, including the single outlier game that is quietly carrying a whole month's average.
5. State the boundary. Say what the available data cannot explain. "We can't measure that" is a finished answer, not a failure.

What we hold it to

• Reality before narrative. The conclusion should come out of the evidence, rather than the evidence being gathered to support a conclusion that sounded good first.
• Context before conclusion. A claim worth making names its subject, its window, what it is being compared with, how large the effect is, and how confident it is.
• Metrics are proxies. Points per game is not scoring ability, wins are not team quality, and fantasy points are not player value. Every metric stands in for something, and the useful question is when it stops tracking the thing it stands for.
• Contradictions are evidence. When two measures tell different stories, that is worth investigating, not averaging away.
• Uncertainty and confidence are different things. How wide the range of outcomes really is — a genuinely streaky player is unpredictable no matter how much we know about him — is a separate question from how far to trust our own read of him. A short track record makes us unsure; it does not make the player volatile. Collapsing the two into a single "give or take" hides the part you came for.
• Thin evidence is said out loud. A small sample, a short stretch, or missing data is part of the answer, not a caveat dropped off the end of it.

Answers are graded against these regularly, including on questions built to be traps: a false premise, a cherry-picked record, a four-game sample, a streak that is really a soft schedule. Refusing the bait is part of what "good" means here, so it is part of what gets tested.

Any score we compute ourselves is held to something stricter. Before it ships it has to say what it claims will happen and what result would prove it wrong, and then be checked against seasons that have already played out. A claim that does not survive the check gets dropped rather than softened. One already has. A score nobody has tested against what actually happened is decoration.

What it will not do

It won't make the pick. DataBaller does the analysis; the decision stays yours. It will not tell you what to bet, how much to stake, who to start, or what is going to happen. The reason is not squeamishness. A real wagering or roster decision turns on things no stat line contains: what you can afford to lose, your league and its rules, and how much risk you actually want to carry. Those are yours, and an answer that quietly assumed them would be worth less than it looked.

The line is drawn at the recommendation, not at the subject. Betting and fantasy questions get real work: what a number implies, how a market has priced something, where the evidence and the consensus disagree, and how much confidence that reading deserves. Odds are facts like any other number and are treated as facts: quoted with the sportsbook they came from and when they last moved. The only thing that never follows is "so take it."

DataBaller holds betting lines from several sportsbooks: the spread, moneyline and total for each game, player props, and how each line has moved since we began recording it (MLB from August 2026, the NFL from week 1 of the 2026 season, the NBA from its next season). It also holds fantasy projections, draft-board rankings and ADP for the NFL, and DraftKings salaries and slates for all three leagues. Asked for a pick, or for a number it does not hold, DataBaller says so.

Where the numbers come from

Every figure in an answer is looked up or computed. DataBaller queries a statistics database built from licensed providers: six seasons of NBA, NFL and MLB games, with the game logs, schedules, standings, injuries and roster moves that go with them, fantasy projections and draft boards for the NFL, and DraftKings salaries and slates for all three. Alongside it sits a separate knowledge base for what a metric measures, how it is calculated, and when it misleads.

Any metric mentioned in an answer opens in the side panel, which shows what it is built from and which direction is good, so you can check an answer rather than take it.

An answer can hold three kinds of number, and they are not equally solid:

• Recorded. A figure from the box score. It is what happened, and it is as good as the data feed and the query behind it.
• Derived. A score we compute from those records: whether recent form is supported by what is underneath it, or how much a player's role has moved. It is worked out on a schedule and stored alongside the version of the method that produced it, so the same inputs give the same number and an answer can always say where it came from. The five of these DataBaller leans on most are its decision metrics, and they have a page of their own.
• Reasoned. The argument the answer builds on top of both. This part is generated, and generated reasoning can overreach: reading a trend into a small sample, or taking a soft schedule for a real improvement.

Each layer can be wrong in its own way, which is what the side panel is for: it shows what a number is built from, and for a derived one, what went into it.

Data is synced through the season, and box-score figures land once games are final, so a question asked mid-game reflects the last completed one, and every answer is a snapshot of what was known when you asked.

Where this is going

Today DataBaller answers the question you bring it. The larger goal is for it to find the consequential thing you did not know to ask about: the team whose record most overstates how it is playing, the hitter whose last month understates him, the trend that is nowhere near the standings.

The reaction we are building for is: I hadn't noticed that.

Getting in touch

DataBaller is open to anyone. No invite code, no waitlist. Create an account and you are in. A free account gets five analyses a week, and the Professional plan is $24 a month for full access.

Discord is where we talk to the people who use this. Questions, bug reports, an argument about a number, what you want it to do next: start in the feedback forum, discord.gg/pKHJkAZ4. One forum takes all of it, so there is nothing to work out before you post.

Tell us when something is broken or an answer looks wrong. A wrong number is a bug, and we want the question that produced it. Paste it in. Discord is the fastest way to reach us; support@databaller.com gets there too.