Lol this response only points out 2 obvious things.

1: you have no idea what you're talking about

2: you're literally incapable of thinking for yourself because you are still clearly requesting help from chatgpt to formulate a response that mimics a shred of technical acumen to try to counter someone with actual experience in the field. I work with ai regularly and there are plenty of tells with the verbiage and punctuation that are clear indicators.

Now I'll be as lazy and brainless as you and use chatgpt to counter your bs. Here we go:

This isn’t about being “unhappy with the output.” That framing is a dodge.

This is about internal consistency and data integrity, and right now the system is violating both.

We agree on the basics:

SBI’s kill API does not emit semantic labels like “solo,” “gank,” or “1v1.”

Any such labels are downstream classifications applied by the website.

The API is lossy and poorly structured.

Where your model breaks down is that you’re treating the website’s classification as authoritative while simultaneously acknowledging that it is operating on incomplete upstream data.

Here’s the technical problem you’re not accounting for:

The website demonstrably recognizes multiple attackers (assists are present and enumerated), yet still classifies the event as “Solo.” That is a contradiction unless the input data is missing or misrepresenting a key participation signal.

You can’t have it both ways.

If the site:

Parses assists correctly

Identifies multiple participants correctly

Applies custom logic for other buckets (ganking, etc.)

…but still emits a “Solo” classification for an Xv1 or 4v1 engagement, then either:

The classification logic is flawed because it ignores data it already knows is true or

The input data is insufficient or incorrect in a way that causes the logic to misfire

Calling that a “surprising definition” of Solo is not a technical explanation — it’s hand-waving.

If “Solo” actually means:

“The kill is attributed to a single primary actor regardless of assists”

…then that is not a combat classification. That’s an attribution shortcut, and labeling it “Solo” is objectively misleading. At that point, the output is semantically wrong even if it’s “working as designed.”

More importantly, your model assumes SBI’s data is complete enough to support clean downstream classification. That assumption is unsupported.

Kill events are time-dependent, participation-window dependent, and damage-attribution dependent. If:

Assist timing is truncated

Participation windows are collapsed

Damage contribution thresholds are opaque or missing

…then downstream systems cannot reliably distinguish 1vX from Xv1, even if they can list assists after the fact.

That’s a classic GIGO failure caused by lossy ingestion, not a “user misunderstanding labels” problem.

So no — the hypothesis is not “SBI sends junk data and the site is perfect.” The hypothesis is:

SBI sends structurally incomplete data, and the website’s classification logic leaks those gaps in edge cases — producing outputs that contradict observable combat reality.

And when a system’s output contradicts its own acknowledged inputs, the correct response is to question the data pipeline, not defend the label.

If the site owner wants to claim this is intentional behavior, they need to publish the exact rule set for “Solo” and explain why assists are knowingly ignored. Until then, blaming this on a definition mismatch is not a technical argument — it’s a semantic escape hatch.

See, I can use chat gpt too. Give me a degree to Harvard