What GMs Should Expect From an Analytics Consultant

# What GMs Should Expect From an Analytics Consultant This guide is written for General Managers and sports directors evaluating hockey analytics consultants. It explains what “good” looks like in terms of decision impact, workflows, technology choices, communication and honesty about limitations, as well as red flags to avoid. Use it as a checklist when you decide whether to hire or keep working with an external analytics partner. Hockey General Managers live in a world of tight timelines, incomplete information and public scrutiny. When you bring in an analytics consultant, the goal is simple: **better decisions, made with more confidence, in less time.** This article is written from our perspective at The Hockey Brain after working with sports organizations that range from junior clubs to pro teams. It outlines what you, as a GM or sports director, should expect from an analytics partner—and what to be careful about. --- ## 1. Clear link to your real decisions The first question for any analytics consultant should be: > **“Which concrete decisions will this work help us with?”** Good answers sound like: - Roster construction and contract decisions. - Draft and recruitment prioritization. - Evaluating trade targets or free agents. - Tactical planning for special teams or playoff series. - Player development planning and exit meetings. Vague answers like **“we’ll build a dashboard and see what it tells us”** are a red flag. Your consultant should proactively map their work to the decisions you and your staff actually make during the season. **Expectation**: Within the first 2–4 weeks you should have a short list of 2–3 priority decisions where analytics will be used this season—not just in theory, but on your real calendar. --- ## 2. A structured workflow, not heroic one‑off projects Many clubs have had some experience with analytics that looked like this: - Someone builds a big model or report once. - It looks impressive in a presentation. - Six months later nobody is using it. To avoid that, your consultant should push for a **repeatable workflow**, not just impressive one‑offs. That usually includes: - Regular check‑ins (weekly or biweekly) with a clear agenda. - Standard formats for reports before key milestones (draft, transfer windows, playoffs). - Agreement on who can request analysis, and how priorities are set. - A simple way for coaches and scouts to give feedback on what they actually use. **Expectation**: You know *when* you will see analytics outputs, in which *format*, and how last week’s work connects to this week’s decisions. --- ## 3. Technology that fits your reality Elite‑level analytics does not require NHL‑level infrastructure, but it does require realistic alignment with your club’s reality. Your consultant should ask questions like: - What data do you already collect today? - Which video and scouting tools do you use? - How comfortable are staff with spreadsheets, dashboards, or code‑based tools? - What is your budget and internal IT support level? From there, the tech stack should be chosen to **fit your constraints**, not to showcase the consultant’s favorite tools. Some healthy signs: - Preference for open, standard tools (Python, SQL, standard databases, mainstream BI tools). - Clear plan for how you keep access to code and data if the partnership ever ends. - Documentation that someone else could pick up in the future. **Red flag**: You are asked to lock into a completely proprietary system where you don’t fully control your own data or models. --- ## 4. Outputs that staff can actually use Analytics only creates value if it changes behaviour in the organization. Practical things to look for: - **Simple, repeatable formats**: dashboards, PDFs, one‑page player cards, short memos. - **Role‑specific views**: coaches, scouts and management shouldn’t all see the same thing. - **Language that fits hockey**: no need for complicated math terminology in a pre‑game meeting. - **Clarity about uncertainty**: what the numbers can and cannot tell you. A good consultant will sit down with your staff and ask: - “When in your week do you actually have time to look at this?” - “Which decisions are you making in this meeting?” - “What format would be easiest to use on the bench, in video, or in your scouting meetings?” **Expectation**: People in the room can explain the key metrics back in their own words—and they keep asking for the reports because they find them useful. --- ## 5. Honest discussion of limitations Every model has blind spots. Every dataset has gaps. You should expect your consultant to be explicit about this. Good partners will: - Show where the data is strong and where it is weak (leagues, seasons, specific stats). - Talk about sample size issues and noise. - Explain which outputs are more “directional” and which are robust. - Be willing to say “we don’t know yet” instead of forcing a number on every question. This matters because it builds **trust**. If staff see that the analytics side is honest about uncertainty, they will take the strong signals more seriously. --- ## 6. Integration with your hockey people Analytics works best when there is **respect in both directions**: - Analysts respect the experience and intuition of coaches and scouts. - Hockey staff are open to challenging their own assumptions with data. Consultants should help create this bridge by: - Sitting in on hockey meetings when appropriate, not just sending emails. - Encouraging questions and disagreements instead of defending the model at all costs. - Translating between “data language” and “hockey language”. In practice, that might look like: - Player cards that combine subjective scouting grades with objective metrics. - Reports that highlight “things the numbers like that scouts should re‑watch on video”. - Pre‑meeting notes that prepare coaches for which questions the analysis can answer. **Expectation**: Over time, your staff starts using analytics language naturally—because it has become part of how you talk about the game. --- ## 7. Appropriate seniority and availability Analytics is not just a technical function; it is a strategic one. You should know: - Who is actually doing the work day‑to‑day? - Who makes judgment calls when trade‑offs appear? - How quickly can you reach someone when a decision is time‑sensitive? Sometimes the right setup is: - A senior lead who designs the approach and talks with you as GM. - One or more analysts who do the heavy lifting. But even then, you should have **direct access** to the senior person when needed—especially around deadlines like trade windows or the draft. --- ## 8. Red flags to watch for Based on our experience, GMs should be cautious if they see: - Over‑promising around what models can do (“we will predict every breakout star perfectly”). - Heavy focus on public social‑media‑friendly metrics, light focus on your actual questions. - Lack of transparency about data sources and methods. - No documentation or handover plan. - Resistance to integrating with your existing staff and workflows. If you feel like analytics is happening **to** your organization instead of **with** it, something is off. --- ## 9. How to get the most value as a GM There are a few simple things you can do to make the partnership more effective: - **Be explicit about priorities.** Tell your consultant what is most important this month or quarter. - **Share context.** Explain your internal constraints, politics, and decision processes. - **Give fast feedback.** Say what was useful and what wasn’t after each major deliverable. - **Protect time.** Make sure key staff actually have time to look at and discuss analytics outputs. In return, your consultant should: - Surface insights you would not have found otherwise. - Help you see patterns across seasons, not just in single games. - Make you more confident when you go into high‑stakes meetings (ownership, media, agents). --- ## 10. Summary: What “good” looks like When an analytics consulting relationship works well, you should notice: - Fewer discussions that are purely opinion vs opinion. - More decisions backed by a mix of data, video and live scouting. - Clearer language around roles, playing styles and risk. - A feeling that the club is learning faster from its own decisions. If you are considering working with The Hockey Brain or another analytics partner, use this article as a checklist. The right relationship will not remove all uncertainty from your job—but it should **give you more clarity, structure and confidence** when you make the big calls. To explore what that could look like for your organization, you can always [book a consultation](/contact) or review [our services](/services) and [workflow](/insights/end-to-end-analytics-workflow).