Apple's 'Sports' app shows what sports analytics products keep getting wrong

Slate's technology section published an appreciation of the app in June, timed to the World Cup, and its argument is narrower and more useful than the headline suggests. The writer's complaint is not that sports apps lack data. It is that they have buried the scoreline under it.

Apple's app is called simply "Sports." It carries no news headlines and no feed designed to hold you. The only streaming guide is a channel listing you see if you tap a game. According to Slate, betting odds appear on the scoreboard on first download, but a toggle switches them off permanently. What remains is team names, logos, records, kick-off times and scores, plus a standings page.

Where the money is going

That restraint runs against the direction of the sports data market as it is being sold to teams, leagues and broadcasters. Recent coverage of the sector describes a market measured in billions and growing at double-digit compound rates. Vendors pitch AI-driven fan engagement, ticketing intelligence and performance analytics as the next layer of value. Those numbers come from market research firms and press releases, not from audited operating results, and the headline figures vary widely depending on which report you read. The strategic bet behind them is consistent, though: more data, more personalisation, more reasons to open the app and stay in it.

Slate's author, a self-described heavy sports-app user, argues that bet has produced worse products. Major League Baseball's official app, once a favourite, now surrounds scores and video with what the piece calls window dressing, and it feels heavier for it. ESPN's app, the writer says, prioritises its live TV offering and a vertical video scroll called "Verts." The score section sits in "a vast sea of other buttons."

This is not the world's biggest problem, but it has created a lot more friction when I've tried to use my top sports apps to, well, check sports scores.

That is a consumer complaint, and it should be read as one. It is not evidence that analytics investments fail. But it points at something the analytics vendors rarely measure: the cost of the interface that delivers the analysis.

The same argument, in a different market

A post published in August by Cassis, a data analytics vendor, makes a structurally similar point about enterprise data work. Its claim is that AI agents do not remove the human cost of making data useful. They redistribute it. The post borrows a framework it credits to Hrishi Olickel: schema-on-write versus schema-on-read, or putting clothes straight into a sorted wardrobe versus sorting through a pile when you need something. The choice, Cassis argues, decides whether you pay upfront or at retrieval.

It then splits the cost into three parts. Indexing cost covers upfront work such as designing schemas, writing metric definitions, building dashboards and getting four teams to agree on what "active customer" means. Querying cost is the effort at the moment of retrieval: browsing a wiki, writing SQL, crafting a prompt, or messaging the data team. Reliability is whether the number that comes back is semantically right, not merely technically correct.

The worked example is a CFO asking how many active enterprise customers renewed last quarter. Before anyone can answer, someone has to decide which table holds customer data, what "active" means, what counts as "enterprise," and what date range "last quarter" covers in the company's fiscal calendar. Every system in the stack handles that question differently. The question is who does the thinking, and when.

Cassis describes a weekly rotation at one company where a single person is dedicated to ad-hoc questions, consuming about 20% of a team of five or six people, permanently. The cost is real, the post argues, but it persists because it is the path of least political resistance: no project to fund, no stakeholder alignment to win, no process to design.

More data, more decisions about where to put the work

Read together, the two pieces describe the same trade-off at different scales. A sports app that pushes live odds, vertical video and personalised alerts is making an indexing bet: invest upfront in features that will, in theory, keep users engaged. A bare scoreboard is making a querying bet: assume the user knows what they want, and get out of the way.

Apple's app is not analytics-free. It draws on league and competition data across soccer, tennis, basketball and more. Slate notes the writer had to disable automatic Pittsburgh Penguins notifications during the Stanley Cup playoffs, because the app was reporting developments before the streaming broadcast did. That is a data pipeline working well enough to be annoying.

The difference is where the product spends its complexity budget. Slate's writer says coverage extends to World Cup matches, MLS, the NWSL, roughly 30 other soccer leagues, Ecuadorian Serie A, second-division Bundesliga, men's and women's tennis and the LPGA Tour, with basic box scores, leaderboards and rosters behind a tap. None of it is presented as a feed.

Slate quotes an Apple executive from the app's winter 2024 announcement: "We created Apple Sports to give sports fans what they want, an app that delivers incredibly fast access to scores and stats." The piece contrasts that with the language typical of recent AI product launches, and the contrast is the point.

What this does not prove

One columnist's preference is not a market verdict, and Slate's piece does not claim to be one. Sports organisations sell tickets, subscriptions and advertising, and the analytics vendors selling into them are answering a genuine commercial question: how do you convert a passive score-checker into a paying fan? The Cassis post, meanwhile, is a vendor arguing that cheaper query engines do not make analytics cheap, which happens to support its own positioning. Both sources have an interest in the story they are telling.

Still, the shape of the complaint is worth noting for anyone building in this space. The failure mode described is not missing data or bad models. It is a product that has decided its own engagement metrics matter more than the user's task, and has paid for that with friction at the exact moment the user wanted one number.

Slate's writer ends by calling the app one of two or three favourite consumer tech products of the past two years, alongside a piece of hardware whose only job is to stop a phone working in distracting ways. The pairing is deliberate: both are products that say they will do one thing and then do it. For teams and leagues now being sold dashboards, alerting layers and AI summaries, that is the uncomfortable part. The most-praised sports product of the past two years did not ship a single new metric. It just stopped hiding the ones that already existed.