Prepared: June 2026 · Basis: Public sources only · Type: Independent company profile for investment screening

Overview: ShotQuality is a sports-analytics company that uses computer vision and machine learning to turn broadcast video into structured spatial data, generating “expected value” signals for sporting events independent of actual outcomes. Originating as a basketball shot-quality model for coaching staffs, the company has expanded into a B2B real-time sports-intelligence provider serving sportsbooks, trading syndicates, and prediction markets, alongside a consumer-facing betting-analytics product (ShotQuality Bets). Its core thesis is that evaluating the underlying quality of a play — rather than its result — produces a predictive edge before conventional statistics catch up.

1. Snapshot

Field	Detail	Confidence
Legal name	ShotQ LLC (also referenced as ShotQuality Inc.)	Med-High
Founded	2020	High
HQ	Northeastern US (founder and investors concentrated in NY / Illinois)	Med
Founder & CEO	Simon Gerszberg	High
Co-founders	Muhammad Yasoob Ullah Khalid; Kyle Wildenberg (per public databases)	Med
Headcount	Reported in the 11–50 band; estimated under $5M revenue scale	Low-Med
Stage	Seed	High
Total raised	Reported between ~$3.2M and ~$4.87M depending on source (see Capital)	Med
Named investors	KB Partners, TIA Ventures, CTC Venture Capital, Colgate University	Med-High
Estimated value	No reliable valuation disclosed in public sources	n/a
Core products	ShotQuality data/signals (B2B); Trader Tools; ShotQuality Bets (B2C)	High

2. Business & Product

What it does. ShotQuality transforms broadcast video into structured spatial data — reconstructing player positioning, defender proximity, and shot or play context — and computes an expected-value signal for each event. In basketball, its original domain, it evaluates the quality of every shot attempt independent of whether it was made, using a model the company describes as drawing on 90+ variables and 50M+ data points. The stated value proposition is identifying when teams or players are over- or under-performing their underlying process, allowing “regression” to be detected before it shows up in conventional box-score statistics.

Customer segments. The company serves three distinct bases:

Teams / coaching (origin market). Predictive analytics for player and lineup selection. Public materials reference a substantial base of NCAA Division-I programs as subscribers.
Gaming / trading (growth market). Real-time signals for sportsbooks, trading syndicates, and prediction markets — spread/total adjustment, in-game regression detection, and live-pricing inputs. This includes a “Trader Tools” suite and an injury-detection capability built from live video.
Consumer (ShotQuality Bets). A subscription product bringing the same data to individual bettors for prop and game-line research across NBA, WNBA, and NCAA men’s basketball.

Product expansion. Originally basketball-only, the company has publicly described extending its approach to additional sports — football (referenced as “DriveQuality”), baseball, and tennis — and to 12+ international basketball leagues, signaling a multi-sport real-time-signal roadmap aimed especially at the trading/sportsbook segment.

Delivery. Signals are described as available through multiple continuously updating delivery channels (feeds/APIs and tools), suited to live in-game decision-making.

3. Financials & Traction

Financial disclosure is limited, and the available figures come from third-party databases and company statements rather than audited accounts. They should be treated accordingly.

Revenue scale: Third-party business databases place the company in an under-$5M revenue band. No precise revenue figure is publicly confirmed.
Growth claim (company-stated): In recruiting materials the company has stated that revenue grew more than 700% from its first year to its second, with expectations of another large increase in year three. This is a self-reported figure off an early, small base and is not independently verified.
Model performance (company-stated): The company reports backtested results for certain betting models — for example, a basketball game-totals model described as showing a ~2.5% average ROI across multiple randomized train/test splits, with the model described as in beta. As with all backtested figures, these warrant independent validation and are not a guarantee of live performance.
Commercial footprint: Public materials and partner commentary reference adoption by NCAA programs (coaching side) and use by trading desks (gaming side), along with a media partnership (RotoGrinders) distributing NBA projections. Specific contract values and customer counts are not disclosed.

4. Team

Person	Role	Background (per public sources)
Simon Gerszberg	Founder & CEO	Founder and public face of the company since its 2020 launch; leads strategy and commercial direction across coaching and gaming segments.
Muhammad Yasoob Ullah Khalid	Co-founder	Listed as a co-founder in company databases.
Kyle Wildenberg	Co-founder	Listed as a co-founder in company databases.
Ryan Keur	Former – Chief Revenue Officer	Joined to lead revenue growth; described by the company as an experienced leader in gaming and sports analytics.

The company also references computer-vision and data-science specialists on its technical team, consistent with its video-to-data product. Public information on the full leadership team is partial; additional detail on engineering depth and tenure would support a fuller assessment.

5. Capital & Ownership

Total raised is reported inconsistently across sources, which is itself worth confirming: one database reports ~$3.2M in a single 2022 seed round; another reports ~$3.25M; a third reports ~$4.87M across three seed-stage events (2022–2023). The most likely reconciliation is a seed raise (with possible follow-on/extension tranches) totaling somewhere in the low-single-digit millions, but the exact figure and round structure should be verified directly.
Named investors across sources include KB Partners, TIA Ventures, CTC Venture Capital, and Colgate University (an institutional/endowment-type backer). Investor base skews toward sports-tech and early-stage venture.
No reliable post-money valuation is available in public sources.
Legal entity appears as ShotQ LLC; the LLC structure (versus a C-corp) is a point to confirm, as it can affect how an investment or acquisition is structured.

6. Market & Competitive Position

Category and positioning. ShotQuality sits at the intersection of sports-data/computer-vision and betting-market intelligence. Its current public positioning emphasizes the higher-value B2B trading/sportsbook/prediction-market use case (real-time, decision-grade signals) layered on top of its original coaching-analytics business.
Differentiation. The distinctive asset is proprietary spatial “shot/play quality” data derived from broadcast video, which the company argues is not replicated by outcome-based models. The injury-detection-from-video capability and the multi-sport expansion are presented as further edge-creation for trading clients. The depth of basketball-specific data is a genuine niche strength.
Competitive pressures. The broader space includes large, well-funded sports-data and video-analytics companies (for example Hudl, Veo, and ShotTracker on the team/video side, and major data suppliers on the betting side). Key competitive questions are whether ShotQuality’s basketball-rooted edge transfers convincingly to other sports, whether larger data incumbents could match the spatial-signal approach, and how durable the trading edge remains as more participants adopt similar tools.
Dual-market consideration. Serving both coaching staffs and betting/trading clients with related data can be a strength (shared data asset, two revenue streams) but can also raise integrity and conflict-of-interest questions that buyers in this sector typically diligence carefully.

7. Diligence Considerations & Information Gaps

Category	Publicly known	Open items to confirm
Financial	Under-$5M revenue band; 700% Y1→Y2 growth (self-reported); seed-stage funding	Audited/actual revenue and growth; recurring vs. one-time mix; gross margin; burn and runway; customer concentration
Capital / structure	~$3.2M–$4.87M raised (sources conflict); named seed investors; ShotQ LLC	Exact total raised and round structure; cap table; entity type (LLC vs. corp); any debt
Commercial	NCAA programs (coaching); trading-desk use; media partnership; consumer subscriptions	Paying customer counts per segment; contract values and terms; B2B vs. B2C revenue split; churn
Product / technology	Computer-vision spatial model; multi-sport expansion; injury detection	Independent model validation; data-rights to broadcast video inputs; latency/reliability at scale; defensibility of the data asset
Performance claims	Backtested model ROI figures (beta)	Independent verification; live (not backtested) results; methodology review
Integrity / regulatory	Serves both teams and betting clients	Data-integrity controls; conflict-of-interest and information-handling policies; any sports-data-rights/licensing obligations
Team	Founder-led; CRO hire; CV/data-science staff	Full org chart; engineering depth and retention; advisors and board

8. Summary Perspective

Strengths. A genuinely differentiated, proprietary data asset (spatial shot/play-quality signals from broadcast video); an established niche position in basketball analytics with a real coaching-side customer base; a credible and timely pivot toward the higher-value trading/sportsbook/prediction-market segment; a named, sports-tech-focused investor group; and self-reported high growth off an early base.

Risks and open questions. Financial disclosure is limited and the headline growth and model-performance figures are self-reported or backtested and unverified. Public funding figures are inconsistent across sources. The competitive field includes far larger data and video-analytics companies, and the durability of a trading “edge” is inherently uncertain as adoption spreads. Serving both teams and bettors introduces integrity considerations that warrant careful review. Confirmation that the basketball-rooted edge transfers to newer sports is still developing.

Net perspective. ShotQuality is an early-stage but operating company with a real, differentiated data product and a sensible strategic shift toward monetizing that data with sportsbooks, syndicates, and prediction markets. Its profile is that of a niche data-asset business whose value hinges on the proprietary nature and defensibility of its spatial signals, the verifiable economics behind its growth claims, and the success of its multi-sport expansion.

9. Suggested Next Steps for Evaluators

Reconcile the funding history — confirm the exact total raised, round structure, investors, and current cap table, given the discrepancies across public sources.
Obtain actual financials — verified revenue, growth, B2B/B2C split, margins, customer counts, churn, and runway, under appropriate confidentiality.
Independently validate model performance — review live (not just backtested) results and methodology for the trading signals.
Assess data rights and defensibility — the licensing status of broadcast-video inputs and how protectable the spatial-data asset is.
Review integrity controls — given simultaneous service to teams and betting clients, examine information-handling and conflict policies.
Test the multi-sport thesis — evidence that the basketball edge transfers to football, baseball, tennis, and international leagues.

Sources (public, accessed June 2026): shotquality.com (home and basketball product pages); company LinkedIn; Tracxn; PitchBook; Crunchbase; CB Insights; Wellfound; RotoGrinders; independent tool reviews. Funding totals, revenue, and model-performance figures are estimates, third-party-reported, or company-reported and have not been independently verified. This profile is a preliminary summary compiled from public information and is not investment advice or a recommendation.