6 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You start by reviewing match footage from the previous day — usually 2–4 recent games — and write a short report in Cloud Notebooks or a shared Google doc. Morning work: pull stats with SQL or Shadow.gg APIs (kills, economy, objective timing), then slice clips in GRID for coaches and players.
Afternoons: meet with coaches to explain insights using Tableau dashboards or simple charts from Python (pandas/matplotlib). Before matches you prepare 10–15 short clips of opponent habits and a one-page strategy outline for the team.
Start with Shadow.gg and GRID because they give direct match data and clip-making skills you’ll use daily. Shadow.gg provides match logs and event data; GRID helps you trim and annotate video for players.
Next learn basic SQL to extract specific stats (player economy, ability usage). Then Python for cleaning data and making quick charts; finish with Tableau if you need polished dashboards for staff.
Set a regular routine: after every major patch, spend 2–4 hours reading patch notes and then run a quick SQL query or Shadow.gg report comparing the last 3 days of matches to the previous week (win rate, pick rate, item/ability usage).
Make a one-page notes file in Cloud Notebooks summarizing concrete effects: which champions/items dropped in win rate by at least 5% and why. Share that with coaches so changes become actionable, not just theoretical.
Yes, use AI for summarizing match reports or auto-clipping highlights from GRID, but always verify outputs. AI can mislabel events or miss context (e.g., a fake rotation that looks like a mistake).
Keep raw data (SQL query results, original clips) and note where AI was used. Never share AI-generated scouting outside your org without coach approval, because it can contain hallucinations or reveal sensitive tactics.
Entry-level analysts for semi-pro teams often start around $25,000–$40,000 USD per year; pro academy or regional leagues range $40,000–$70,000; established pro-team analysts can earn $70,000–$120,000 plus bonuses. These ranges come from public hiring posts, esports salary surveys, and reported contracts.
Freelance or part-time analysts may charge $20–$60 per hour for clip work or opponent reports. Pay varies by region, the game’s money pool, and whether you also do coaching or video editing.
An analyst focuses on match footage, data extraction (SQL/Shadow.gg), and creating actionable reports and clips in GRID for coaches and players. You identify opponent tendencies, strengths/weaknesses, and suggest strategy adjustments.
A coach manages player practice, psychology, and in-game calls. A statistician builds large-scale models or dashboards (often in Tableau/Python) but might not produce tailored video clips or sit in team meetings. Analysts sit between both roles.
Show concrete examples: a 1–2 page opponent report, a small SQL query with outputs (CSV), and 5–10 annotated clips exported from GRID or another editor. Use Cloud Notebooks or GitHub to host your work so recruiters can open it.
Mention tools: Shadow.gg for match data, Python for cleaning, Tableau for a simple dashboard. Also show you can explain insights clearly — include a one-page summary that a coach can read in five minutes.