Global Soccer Odds Movement Analysis

I have a large XLS dataset that tracks how bookmakers adjust their soccer odds during the four-hour window leading up to kick-off. The file covers 98 different leagues worldwide, the majority of them second-tier competitions, and every row shows timestamped price snapshots from several providers.

My main objective is to understand pure price fluctuations in that pre-match window: how fast the market reacts, whether margins tighten or widen, and which providers tend to lead or lag in moving a line. Volume, external-factor modelling, and other behavioural angles are out of scope for now—this phase is solely about the odds themselves.

Here is what I need from you:
• Clean the raw XLS so each match has a coherent four-hour timeline for every provider.
• Produce summary metrics (mean change, max swing, time of first significant move, etc.) and clear visualisations that reveal provider-by-provider patterns.
• Highlight any leagues or regions where movements deviate markedly from the global average.
• Deliver an annotated workbook (or a Python/R notebook plus a refreshed XLS) that I can rerun when fresh data drops.

Please use whichever analytical stack suits you—Excel-only is fine, but if Python (pandas, matplotlib), R (tidyverse), or Power BI will speed things up, go ahead. Just make sure the final artefacts remain easy for me to follow without specialised software.

If this first round goes smoothly, I will commission a second stage on correlating those movements with bet volume and external signals.