Composite scoring

Break a complex judgment into atomic scores, combine with weights you control in code.

Oftentimes we want to rank a set of items based on several criteria at once. Composite scoring is an easy way to think about this: break the judgment into independent dimensions, score each one separately, and combine them with weights you control in code.

Example: resume screening

Let's imagine you are processing resumes for engineering roles. You want to rank the candidates based on several criteria, and ultimately select the top X candidates for further review.

%%{init: {"fontFamily": "Inter, sans-serif", "flowchart": {"rankSpacing": 35, "wrappingWidth": 300, "subGraphTitleMargin": {"top": 12, "bottom": 36}}}}%%
flowchart LR
    resume["candidate resume"]

    subgraph req["TypeSafe evaluates questions<br/>in parallel"]
        direction TB
        py["<b>Score:</b> Python depth"]
        lead["<b>Score:</b> team leadership"]
        arch["<b>Score:</b> system design"]
        general["<b>Score:</b> generalist"]
        %% Invisible links stack the questions; they are answered in parallel.
        py ~~~ lead ~~~ arch ~~~ general
    end

    resume -- "one request<br/>resume + 4 questions" --> req
    req -- "one response<br/>4 score answers" --> normalize["<b>normalize scores to 0–1</b><br/>divide each by 4 in your code"]
    normalize --> ic["<b>senior IC weights</b><br/>40% Python + 10% leadership<br/>40% design + 10% generalist"]
    normalize --> em["<b>engineering manager weights</b><br/>15% Python + 40% leadership<br/>20% design + 25% generalist"]
    ic --> rank["rank candidates<br/>for each role"]
    em --> rank

Step 1: score each dimension independently

Example

Interactive example available on the official TypeSafe docs.

Step 2: combine with weights

Each dimension is normalized to 0–1 and weighted. The weights give you an easy way to adjust the relative importance of each dimension, without losing any of the nuance of the individual scores.

scoring.py

py      = response.answers["python_depth"].score / 4
lead    = response.answers["team_leadership"].score / 4
arch    = response.answers["system_design"].score / 4
general = response.answers["generalist"].score / 4

# Senior IC
ic_score = (0.40 * py) + (0.10 * lead) + (0.40 * arch) + (0.10 * general)

# Engineering Manager
em_score = (0.15 * py) + (0.40 * lead) + (0.20 * arch) + (0.25 * general)

This gives you the ability to rank the candidates based on the composite score. But more importantly, it gives you visibility into how exactly the final score is being calculated. If the highest ranking candidates are not matching your expectations, you can adjust the weights to find the right balance.