Disclosure up front: this is my project, so I’m tagging it as self-promotion per the subreddit rules.
A few weeks ago I was trying to build a historical closing line value dataset and realized I couldn’t find one that combined de-vigged fair probabilities, closing values, and game outcomes across multiple sports. Everything I found was either limited to one sport, only included raw odds, or couldn’t be redistributed because of licensing.
So I started building my own derived dataset.
Each row contains things like:
- Sport
- Market
- Selection
- Event time
- Opening fair probability
- Closing fair probability
- De-vig method
- Whether the fair line came from a sharp reference (Pinnacle) or a consensus fallback
- Edge at entry
- Closing line value (CLV)
- Beat close (yes/no)
- Final result
A couple notes:
- This does not include raw sportsbook odds or sportsbook names since my data provider doesn’t allow redistributing that data.
- The dataset only contains derived metrics like fair probabilities and CLV.
- “Beat the close” is meant as a research metric, not proof that a bet was good or profitable.
One thing I think is useful is the anchor field. You can separate observations that were generated from a sharp market reference from ones that used a consensus fallback instead of treating them as the same thing.
I’m planning to keep expanding this over time as more sports and markets are added.
Project: https://edgedesksports.com
I’d really appreciate feedback from people who work with betting or forecasting datasets.
- Are there any columns you’d want added?
- Is there another derived metric that would make this more useful for research?
- Has anyone found a comparable open dataset that covers multiple sports?
submitted by /u/Clean_Reference_9927
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