Interview Template: Questions to Ask a Sports Modeler Before You Quote Them

Start here: one-sentence rule for creators

If you plan to quote a sports modeler, don’t quote the headline — quote the method. The fastest way to protect your credibility is to ask a short, non-technical set of questions that reveal where a model’s outputs came from and how confident the modeler really is.

Why this matters in 2026

Sports models are more influential — and more opaque — than ever. Since late 2024 and through 2025, mainstream outlets and betting platforms moved to promote automated picks and multi-thousand-run simulations as attention drivers. By 2026, newsroom readers, podcast audiences, and social followers expect crisp model-based claims, but they also expect accountability. Leagues and data vendors expanded licensing of microtracking and wearable feeds in late 2025, and AI-driven feature engineering has accelerated. That combination increases predictive power — and the potential for overconfidence or misuse when creators repeat claims without verification.

How to use this template

Below is a practical, non-technical interview template you can copy into your outreach emails, DM threads, or on-the-record interviews. Questions are grouped so you can pick what matters for your story type (short social post, feature, betting advisory, or investigative piece). Every question has a short rationale so you know what the answer should sound like.

Top 12 non-technical questions (fast vetting)

1. What data sources feed your model?

   Rationale: Ask whether the model uses public box scores, licensed tracking data, betting-market prices, or proprietary inputs. A model that claims “player tracking” should name the vendor or type (optical, wearable). If the modeler won’t say whether inputs are proprietary or licensed, treat that as a transparency red flag.

2. How do you handle injured or questionable players?

   Rationale: Responses should describe whether the model removes, downgrades, or probabilistically accounts for absent players. Look for mention of replacement-level assumptions or scenario runs (e.g., “with/without player X”).

3. Do you release past predictions and how they performed?

   Rationale: A credible modeler can point to a validation dataset, a public predictions archive, or at least a summary hit rate. If all you get is “trust me,” ask for at least three recent examples the model predicted and how they actually turned out.

4. How sensitive is the outcome to one player missing?

   Rationale: Some models collapse if a star player is out; others smoothly redistribute impact. A clear answer should explain whether you ran “what-if” scenarios and how large the swing is.

5. Do you include home-field or altitude effects?

   Rationale: Home advantage matters differently across sports — and altitude (e.g., Mile High) or schedule quirks matter for teams that travel a lot. Ask whether these effects are fixed, season-specific, or game-specific.

6. When was the model last updated or re-trained?

   Rationale: Models can drift as seasons progress and rosters change. Recent updates indicate active maintenance; stale models imply lower reliability during rapidly shifting periods (trades, injuries, playoff push).

7. How do you represent uncertainty when you share odds or probabilities?

   Rationale: Good modelers avoid absolute language. They should explain whether they provide ranges, scenario outputs, or confidence bands instead of single deterministic predictions.

8. Do you benchmark against market odds or other public models?

   Rationale: Knowing whether a model is designed to beat the public betting market, mirror it, or produce independent probabilities helps you understand incentives and biases.

9. Are there manual adjustments? If so, why?

   Rationale: Ask whether human judgment alters outputs and on what basis (injury news, insider info). Manual overrides are okay when disclosed; undisclosed tweaks are risky for journalists.

10. Can you share one example where your model failed and what you learned?

    Rationale: Experience matters. A modeler who can name a miss and the fix demonstrates maturity and accountability.

11. Do you or your employer have commercial relationships with sportsbooks, teams, or data vendors?

    Rationale: Disclose conflicts of interest up front. Paid partnerships can shape model goals and should be transparent to audiences.

12. Do you permit me to quote your answer exactly, and how should I attribute it?

    Rationale: Clarifies on-the-record status and avoids legal or ethical back-and-forth after publication.

Expanded categories and follow-ups

If you have time for a slightly longer conversation, use these follow-ups to dig deeper without getting technical.

Data sources and freshness

• Ask whether data are live, delayed, or batched daily.
• Ask how the model treats data corrections (box-score fixes, stat corrections).
• Ask whether player-tracking or wearables are used and whether that data is season-restricted or game-restricted.

Injury weighting and availability

• Ask if questionable tags (Q, probable) are treated as binary or probabilistic.
• Ask whether the model incorporates minutes/usage changes when a starter is out.
• Ask for a simple example: "If Player A misses tomorrow, how does win probability move?"

Home-court, travel, and schedule context

• Ask whether rest days, back-to-back games, or cross-country travel are included, and how those effects differ by team.
• Ask about specific environments such as high-altitude stadiums and how they are modeled.

Practical interview scripts you can paste

Use these short scripts for quick outreach. They’re designed to be DM-friendly and on-the-record ready.

Short DM (3 questions)

"Hi — quick on-the-record Qs for a post: (1) What data sources feed your model? (2) How do you account for injuries/questionable players? (3) Do you permit direct quotes?"

Email / longer interview template

"Thanks for agreeing to speak. For context in my story: please state the data sources your model uses; explain in one sentence how you handle injured or unavailable players; describe whether and how home-court/travel effects factor in; give one recent example where the model missed and what changed; and confirm whether I can quote you directly and how to attribute."

What good answers sound like

• Transparent: "We use public box scores plus licensed player-tracking from Vendor X; we rerun projections nightly."
• Accountable: "We publish a public archive of predictions; last season our model overestimated underdogs on short rest and we added a rest penalty."
• Cautious: "We report a probability band (40–52%) rather than a point estimate when key players are questionable."

Red flags and phrases to avoid

• Vague data answers: "We use a bunch of feeds" without naming any categories or vendors.
• Absolute language: "This model guarantees X%" — models can never guarantee.
• Refusal to share past performance or corrections: if they won’t provide examples, treat the claims cautiously.
• Undisclosed manual adjustments — if they confirm the model is hand-tuned but won’t say when or why, insist on disclosure before quoting.

How to use model quotes responsibly in journalism

When you do quote a modeler, include a short parenthetical or sentence that explains the model’s bounds and incentives. Examples:

• "Our model, which uses box scores and licensed tracking data and is updated nightly, gives Team A a 62% win probability," said X — but add the modeler's note if injuries are still being confirmed.
• "Modeler Y hedges: ‘If Player Z doesn’t play, that 62% drops to roughly 45%,’ ” which gives readers context on sensitivity.

Quick verification workflow for creators (60–90 seconds)

1. Ask three rapid questions: data sources, injury handling, permission to quote.
2. Request one recent prediction and its outcome (or link to an archive).
3. Look for one transparency signal: named data vendors, published calibration, or a public record.
4. If any answer is evasive, either add a clear caveat in your copy or don’t use the model as the main evidence for a claim.

2026 trends you should mention when quoting models

• Increasing use of microtracking and wearables: These inputs can improve short-term player impact estimates but are often proprietary and unevenly available across leagues and teams.
• Betting-market co-movement: Models tuned to betting prices can reflect market signals rather than independent predictive insight — ask whether the model uses market odds as an input.
• Explainable AI push: Regulators and some publishers pushed for explainability in late 2025. Expect modelers to be prepared to explain why a number moved, not just that it did.

Example: a short exchange you can copy into an article

"We simulate games 10,000 times nightly using licensed tracking and box-score data," said Jane Doe, lead modeler at Z Labs. "If the starting center is out, our win probability typically moves 8–12 points; if two starters miss, we run scenario outputs. We publish a monthly review of our hits and misses and disclose partnerships with sportsbooks."

That exchange gives readers the key signals: simulation scale, inputs, sensitivity, auditability, and conflicts — all without technical detail.

Checklist before you publish a model-based claim

• On the record? Confirm attribution phrasing and permission to quote.
• Data sources named? If not, add a caveat that the modeler declined to disclose inputs.
• Injury handling explained? If not, don’t report precise probabilities for games impacted by availability.
• Past performance or validation available? Prefer modelers who can show outcomes or calibration summaries.
• Conflicts disclosed? Note partnerships or commercial ties in your copy.

Final takeaways — what to remember

1) Always ask where the numbers come from. Numbers without provenance are hearsay. 2) Watch for undisclosed manual tweaks and conflicts of interest. 3) Prefer modelers who publish or summarize past performance and are willing to discuss uncertainty.

"If a model can't say when it's wrong, it's not ready to be quoted without caveats." — Practical guidance for creators in 2026

Call to action

Use this template in your next outreach. Copy the short DM or the email script into your notes, and save the checklist as a publishing pre-flight. If you want a ready-to-use Google Doc or Airtable checklist version of this interview template, sign up to get the downloadable pack we update weekly as data-vendor and league policies change.

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