Market Intelligence Feed

heo

what happen when a team score in first minutes of the match how odds moves

Just finished analyzing the xG data from this weekend's Premier League fixtures. A key observation: teams overperforming their xG by more than 0.8 per game over a 5-game stretch tend to see market correction within the next 3 matches.

This is a classic case of regression to the mean — the market often lags behind the underlying data. The question isn't if the correction happens, but when the odds adjust.

What metrics do you use to spot overperformance before the market catches up?

I've been studying which statistics correlate best with long-term market efficiency. Curious what the community thinks — which metric do you find most reliable for predicting market movement?

I'm working through the Probability Fundamentals course and I have a question about implied probability vs. true probability.

I understand that implied probability = 1 / decimal odds. But how do you estimate true probability independently? Is it purely from historical data, xG models, or a combination of factors?

Any resources or frameworks the community recommends for a beginner?

Today's lesson from the Academy really clicked: Closing Line Value (CLV) is the single most important metric for evaluating your decision-making process.

If your closing odds consistently beat the market close, you're making good decisions — regardless of individual outcomes. Variance can mask good process in the short term.

Key takeaway: track your CLV over a minimum of 100 decisions before drawing any conclusions about your edge. Anything less is just noise.

Has anyone here been tracking their CLV systematically? What tools are you using?