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Sports teams and leagues are building their own scoring systems for fans and sponsors

Sports teams and leagues are increasingly creating their own scoring systems for fan engagement and sponsorship opportunities. These systems utilize first-party voting and ticketing data to enhance the value for fans and sponsors. The trend is exemplified by initiatives such as Heisman’s 'Hopefuls' hub and the Timberwolves’ Jump rollout.

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By MarketScale Newsroom · Next LeagueHeisman TrophyCollege FootballFan Engagement
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Sports teams and leagues are building their own scoring systems for fans and sponsors

Key takeaways

01

First-party voting and ticketing data are becoming valuable assets for sports teams and leagues.

02

Heisman’s 'Hopefuls' hub and the Timberwolves’ Jump are examples of customized fan engagement platforms.

03

Sports organizations are leveraging data-driven insights to enhance sponsorship and fan interaction.

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The Heisman Trophy Trust wants to stay in the weekly college football conversation rather than be defined by a single awards night. The Minnesota Timberwolves want a dollar value tied to each step in their ticketing workflows. And the NFL, even with record-high audiences, is still testing the TV and streaming measurement currency that supports its ad market. These are separate stories, but each shows a rights holder focusing on how it tracks and manages fan voting, ticketing, or engagement data while broader ratings approaches are still changing.

Heisman’s new “Hopefuls” hub turns a fan vote into a data product

Sports Business Journal reported that the Heisman Trophy Trust’s new “Heisman Hopefuls” platform is the first launch under its new relationship with Next League, which is serving as the Trust’s AI and technology services provider. The fan vote connected to the hub opens Sept. 9, and the experience is designed to run throughout the season rather than peak only around the December trophy presentation. SBJ’s Ethan Joyce reported that “Heisman Hopefuls” ingests official NCAA data from Genius Sports, EA Sports player ratings from its college football franchise, and a voting system developed with the Carnegie Mellon Sports Analytics Center. SBJ also reported that Yangaroo is assisting with administering the ballot process.

That mix is notable because it supports an ongoing scoring and engagement framework that can be updated over time. Next League’s director of strategy and solutions, Matt Reinhart, told SBJ the effort was built in a couple of months and should add more analytics and interactive elements as it develops. For digital, content and sponsorship groups, the structure is clear: a fan vote supported by added data context. It creates a standing destination for the season, prompts fans to come back week to week, and makes it possible to capture first-party signals that can be bundled with media.

When the industry is debating ratings currency, owned engagement metrics can look like the most dependable numbers available.

Jump’s Timberwolves case study puts a dollar figure on ticketing workflow changes

The most concrete figure across the three reports came from ticketing. In SBJ’s Aug. 26 newsletter, Joyce reported that the Timberwolves’ first season using Jump for ticket services produced an estimated $5 million-plus increase in new revenue streams. SBJ credited the gain to several factors, including open distribution work involving the Timberwolves, Jump and inventory distributor Automatiq. The aim was to list tickets across Jump and secondary marketplaces while keeping clearer control of the buyer relationship. SBJ reported that this open distribution effort generated a seven-figure increase.

SBJ’s reporting also cited two operating benchmarks relevant to revenue ops teams considering a platform switch. First, SBJ’s summary of Jump’s case study said the Timberwolves generated 90% of additions to the ticketing database this season with “zero investment.” Second, SBJ reported that team surveying found 95% of fans preferred the Jump-built app to the prior version. SBJ also reported that Jump introduced “agentic” AI tools during the season. Regular AI usage at the Timberwolves grew from three staffers early in the season to nine later on, and SBJ reported that AI automated more than 200 tasks, with most activity occurring in the second half of the season and the playoffs. Joyce’s examples included per-game dynamic pricing setup and bulk seat releases for playoff inventory.

These are presented as specific operational outputs, not general AI marketing. SBJ’s examples focused on work that shows up in day-to-day execution, including dynamic pricing setup and bulk seating releases for playoff inventory. SBJ also reported a campaign metric that can help set expectations: a win-back activation recorded a 41% click-through rate, and 11% of recipients bought within 24 hours.

The NFL’s Nielsen questions are a reminder that external measurement still moves budgets

Expanding first-party measurement does not remove the importance of external currency. It can, however, affect how negotiations are approached. Sportico’s Anthony Crupi reported Aug. 21 that the NFL is still not fully satisfied with Nielsen’s measurement efforts, even after the ratings service’s fall 2025 “currency upgrades” meant to better capture in-home and out-of-home viewing. Sportico reported that Paul Ballew, the NFL’s senior VP of data and analytics, said the league was still reviewing the downstream effects of the recent fixes and did not yet have enough impact data to judge how the revised approach will appear this fall.

Crupi also reported that the NFL is coming off a 36-year audience high, with last season averaging 18.7 million viewers per game. He also noted a preseason data point for anyone selling shoulder programming or exhibition inventory: NBC/Peacock’s Pro Football Hall of Fame Game drew just under 7 million viewers, a five-year high for that game, according to Sportico. Operationally, the near-term issue is planning and procurement. Sponsorship valuation models, ad guarantees, and partner reporting templates will still call for cross-channel measurement that incorporates panel-based ratings, streaming, and owned-platform engagement. If methodology changes affect those metrics, partners will ask for clarification, and internal teams will need to explain what changed.

The sports org with the cleanest data pipeline can spend faster, sell faster, and justify pricing faster.

Where this lands for digital, ticketing, and sponsorship ops teams this fall

In SBJ’s reporting on Heisman’s “Hopefuls” hub and the Timberwolves’ Jump rollout, and in Sportico’s reporting on the NFL’s ongoing questions about Nielsen, each case centers on how measurement is being built, maintained, or evaluated. SBJ described systems that capture fan voting and ticketing activity with defined data inputs and processes, while Sportico described the league continuing to assess how Nielsen’s updates affect reported viewing. For teams working across ticketing, content, and sponsorship, the common task is reconciling owned data with whatever measurement currency the market uses in a given season.

Questions to take into your next platform and partner reviews

  • If a vendor promises “open distribution,” ask for the operating details: which marketplaces, what inventory rules apply, and how buyer identity is passed back into your CRM. SBJ’s Timberwolves example shows this can be a seven-figure lever when executed with a distributor like Automatiq.
  • For any fan vote or interactive hub, set the weighting and audit trail early. SBJ reported Heisman is using a Carnegie Mellon Sports Analytics Center-created voting system and Yangaroo to run ballots, indicating an emphasis on governance rather than a simple popularity vote.
  • Pressure-test partner reporting for measurement volatility. Sportico’s reporting on the NFL’s continued Nielsen questions is a cue to note methodology changes in dashboards and QBR materials so stakeholders can separate behavior shifts from measurement shifts.
  • Treat AI automation claims as an operations exercise, not a branding point. SBJ’s “200+ tasks automated” benchmark is actionable only if teams specify which tasks were automated, what staff time changed, and which approvals still require humans (dynamic pricing, playoff releases, upgrade offers).

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