Methodology

How Footylab Anytime Try Scorer Analysis Works

This page explains the current Footylab anytime try scorer value score in plain English.

Step 1

Estimate each player's scoring probability

Step 2

Compare it with the odds-implied chance

Step 3

Use match-field ranking as supporting context

1. Player scoring probability

Each ATS player gets an independent scoring probability from try-scoring rate, recent form, expected role, team attack, opponent weakness, venue and availability context.

Footylab shrinks smaller samples toward a baseline so a short run of tries does not get treated as certainty.

This keeps the main ATS value score on the same footing as H2H: a stats probability compared with a market probability.

2. Odds-implied chance

The listed decimal price is converted into a raw implied scoring chance by dividing 100 by the odds.

ATS prices are not mutually exclusive outcomes, so this is not de-vigged like H2H. Multiple players can score in the same match.

The raw odds-implied chance is still the cleanest way to ask whether the price looks generous relative to Footylab's player probability.

3. Match-field ranking

Footylab also ranks eligible ATS players inside the same match by stats profile and by market position.

Those ranks are converted into percentiles so the page can explain whether the market is rating a player higher or lower than the stats do.

This rank gap is secondary context. It helps explain the angle, but the canonical ATS value score now comes from the probability gap.

4. Overall value score

The ATS overall value score is the gap between Footylab's player scoring probability and the odds-implied scoring probability.

If Footylab's probability is higher than the price implies, the score moves positive and the offer looks more like value.

If the price implies more chance than Footylab's stats support, the score moves negative and the offer looks more like a ripoff.

Why ATS still shows ranking context

ATS markets usually involve a larger player field than H2H markets, and the outcomes are not mutually exclusive. Ranking players inside the same match keeps the explanation understandable and shows whether the market is underrating or overrating the player relative to the field, without replacing the probability-edge score.