aiqbench analysis
This article details a technical workflow using Microsoft Fabric's Aura Graph Analytics to analyze 26 years of professional sumo bout data, transforming 82 specific winning techniques into six style vectors for graph clustering. The primary focus is on the application of graph algorithms and data engineering within the Microsoft ecosystem rather than the sport itself or general software updates.
SumoDB in Neo4j: Microsoft Fabric bridging Decades of Rikishi — Part 4
Nostalgia in sports is common — we often look back on previous generations with gilded lens. Ask any sumo fan whether the sport has changed and you’ll get the same answer: the big men don’t grapple like they used to. The belt… Read more →
Nostalgia in sports is common — we often look back on previous generations with gilded lens. Ask any sumo fan whether the sport has changed and you’ll get the same answer: the big men don’t grapple like they used to. The belt battles that defined the 2000s have given way to shoving matches and quick pull-downs. The first three parts of this series built a graph of professional sumo and used Neo4j to rank wrestlers by more than raw wins. Part 4 puts the claim to the test across 26 years of results, and it does the whole thing in Microsoft Fabric. The bout data lands in a Fabric Lakehouse, the graph algorithms run on Aura Graph Analytics, and the scored results flow straight back to OneLake where any Fabric tool can read them. The data comes from sumo-api.com, a free JSON API with Grand Sumo results back to 1958. We pull every bout from the top two divisions, Makuuchi and Juryo, for January 2000 through the present. That’s 78,710 bouts, roughly 47,000 in the…
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