Savepoint Analytics
I believe in the 80/20 rule — right up until the last 20% is the answer. What Savepoint is, why it exists, and the one idea it is organized around.
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Data scientist · Analytics leader · Vancouver Island
I go down the rabbit hole for certainty — usually redundant, occasionally where the honest answer was.
I've spent a decade turning messy player data into decisions live-service game teams can trust — experimentation, forecasting, economies, and the platforms underneath them. This site is also my notebook for the systems I cannot quite leave alone.
Writing
Working notes on measurement, and where it goes wrong.
Experimentation, demand estimation, game economies — written the way I'd want to read them, with the method and the failure modes left in. Where a piece has runnable code or an interactive report, it's attached.
The Election-Year Install Tax
US install prices rise in the fourth quarter of an election year because presidential campaigns are bidding for the same impressions. It is real, it is specific to one market, and it is the one piece of seasonality you cannot fit.
Cohort LTV Forecasting
Spend to CPI to installs to actives to spenders to revenue per user to LTV. Seven links, each with its own estimator, and a chain whose weakest point is not where anyone expects it.
A/B Testing in Games
A decade of running experiments on live games, reduced to seventeen rules anyone can apply, a worked case study for each way an experiment lies to you, and the module I built so the pipeline refuses to make the same mistakes twice.
Why Simulate Demand
Bias requires truth. A simulator lets you set the truth, hand an estimator only the observable view, and measure how far off it lands — the one measurement real market data can never give you.
What I do
Measurement tied to a real decision — not dashboards for their own sake.
Game & product analytics
Retention, monetization, economy systems, LiveOps, and player lifetime value — the metrics that move a roadmap, defined so a team can actually trust them.
Experimentation & causal inference
A/B and switchback design, matched cohorts, difference-in-differences, uplift, and guardrails — for when a clean test is possible, and for when it isn't.
Data platform & analytics engineering
Telemetry design, event contracts, dbt and warehouse modeling, orchestration, and marts that hold up when someone finally checks the math.
Forecasting, ML & decision support
Revenue and LTV forecasting, churn and conversion models, recommenders, and anomaly detection — with readouts an executive can act on before lunch.
Where I'm at
Open to select full-time roles.
Ten years in has taught me the difference between the teams where I thrive and the ones where I merely cope. I'm at my best learning something new, inside a group building something I care about — ideally in games, though I'm realistic that the right seat might sit just beside the industry rather than inside it.
Alongside that, I run Savepoint Analytics, a game-analytics platform I've built from scratch. It's how I stay hands-on between roles — a foothold in a field I love, not an exit plan. For the right full-time role, it takes a back seat.
Selected results
- Caught an eight-figure forecast error in an M&A assessment before the deal closed.
- Measured true feature lift with matched cohorts when a clean A/B test wasn't possible.
- Built an alliance recommender and churn diagnostics that lifted retention for a live-service strategy game.
- Cut a studio's 14-hour daily refresh to under an hour by modernizing the stack.
Figures generalized or relative; no former employer's absolute revenue or player counts are reconstructed here.
Off the clock
I chase questions that have nothing to do with the day job.
Sabermetrics, U.S. election analysis, and a bit of economics — the training that taught me to read games as systems of incentives in the first place. A lot of what I build for fun ends up sharpening what I do for work.