Hit rate is not value

Hit rate is one of the most useful numbers FootyMetrics shows on a player trend card or in the Props Finder, and one of the easiest to misread. Here is what it actually measures, why a high one alone proves nothing about value, a worked example with the arithmetic checked, and how to read it alongside the price rather than instead of it.

What FootyMetrics hit rate measures

FootyMetrics hit rate is a recent-form number, not a projection. On a player trend card, HIT is a count, for example 6/8, showing how many of the most recent qualifying matches cleared the line, and RATE is that same fraction shown as a rounded percentage. The Player Props Finder works the same way: pick a stat, a line and a match window, and the tool answers one question, in the player's last set number of matches, how many times did they clear that line, shown as a live summary like "1+ shots on target in 8 of last 10 matches (80%)."

Every match in the window counts equally, so a hit rate does not get skewed by a single outlier game the way a plain average can. But it is still only a description of what already happened over a chosen window, not a stated probability for what happens next. Both pages linked above make the same point in their own way: an 80% hit rate over 10 matches means 8 of those 10 games cleared the line, nothing more.

Why a high hit rate alone is misleading

A hit rate is a frequency. A price is a cost. Value only exists when the price on offer is better than what that frequency justifies, and a hit rate by itself tells you nothing about the price.

Any hit rate implies its own fair, break-even price, the same way any probability implies fair odds. See what are fair odds for the full maths on turning a probability into a price. Applied to a hit rate, the formula is the same one run the other way: fair odds equal 1 divided by the hit rate expressed as a decimal. A 90% hit rate implies fair odds of 1 divided by 0.90, which is about 1.11. That is the price at which backing that hit rate would break even over the long run, not a price you would expect a bookmaker to actually offer.

Once that fair price exists, the trap is easy to state: it is entirely possible for a stat to clear its line 90% of the time and still lose money betting on it, if the price on offer does not compensate for the 10% of the time it misses. This is the same trap set out for strike rate in general in yield vs ROI vs strike rate, just applied here to a hit rate on a specific stat line rather than a whole betting record. The same idea, comparing your own probability estimate against the market's, is what expected value and a value bet are built on. A hit rate is simply one way of arriving at that probability estimate.

Worked example: the same hit rate, two prices

Two cases, both built on the exact numbers already worked through on yield vs ROI vs strike rate, so the arithmetic below is consistent with that page rather than a fresh, unverified example.

Case A: a 90% hit rate at odds of 1.10 loses money

A 90% hit rate implies fair odds of 1 divided by 0.90, about 1.11. Suppose the price on offer is 1.10, just under that fair price. 100 bets, level stakes of 1 unit, 90 hits, consistent with the hit rate.

• Total staked: 100 x 1 = 100 units.
• Profit per winning bet: 1 x (1.10 - 1) = 0.10 units.
• Total profit from the 90 hits: 90 x 0.10 = 9 units.
• Total loss from the 10 misses: 10 x 1 = 10 units.
• Net result: 9 - 10 = -1 unit.

A 90% hit rate, and the result is a loss of 1 unit on 100 staked, a yield of -1%. Nothing about the run is unlucky. The price simply didn't cover the 10% of the time the stat missed.

Case B: a 40% hit rate at odds of 3.00 profits

A 40% hit rate implies fair odds of 1 divided by 0.40, which is 2.50. Suppose the price on offer is 3.00, well above that fair price. 100 bets, level stakes of 1 unit, 40 hits.

• Total staked: 100 x 1 = 100 units.
• Amount returned per winning bet: 1 x 3.00 = 3 units.
• Total returned from the 40 hits: 40 x 3 = 120 units.
• Total profit: 120 - 100 = 20 units.
• Yield: (20 / 100) x 100 = 20%.

A hit rate less than half the size of Case A, and the result is a 20 unit profit on 100 staked, a 20% yield. The hit rate was lower. The price was the one doing the work.

The higher hit rate lost. The lower hit rate won. Only the price decided it.

Check the hit rate and the price together

The practical rule from the worked example above is straightforward to apply. Take the hit rate, convert it to an implied fair price with 1 divided by the hit rate, and compare that against the actual odds on offer for the same line. If the odds on offer are longer than the hit-rate-implied fair price, that gap is where a possible edge lives, the same logic what is a value bet sets out for a model's probability estimate, just applied here to a hit rate instead. If the odds on offer are shorter than or equal to that fair price, the price isn't compensating for the times the stat misses, however high the percentage looks.

This isn't a claim that any particular price is fair or unfair. It is a general caution about reading a hit rate, wherever it comes from, without checking it against the price attached to it. On FootyMetrics, hit rate shows up on player trend cards and in Props Finder results, both of which can be read alongside a live bookmaker price where one is available.