What stats do player prop tools actually use, and why those ones?
Ours weights recent form at 0.6 and season form at 0.4, and refuses to show a hit rate at all below 20 games of history. The split exists because either number alone is wrong in a predictable direction: season form is too slow to notice a change and a 10 game window is mostly noise. The minimum matters far more than it sounds: the median batter on our board has 109 games of history and the median pitcher has 27, so a rule that excludes 5% of batters excludes 30% of pitchers.
Most pages on this question list stats. This one is about the weighting, because the weighting is where the choices are and the choices are where these tools differ.
Here is what ours actually does.
The form score: 0.6 recent, 0.4 season
Our form number is:
0.6 x last 10 games + 0.4 x season
That is it. No hidden term, no model output. The interesting part is why it is a blend rather than either half.
Season form alone is too slow. A player who changed something in July is still being described by April. By the time a season average moves enough to notice, the thing that moved it is months old.
A 10 game window alone is mostly noise. Ten games of a yes-or-no outcome is a very small number. “Six of his last ten” reads as 60% and is compatible with a true rate almost anywhere in the middle of the range. Treated as a fact it is one of the most misleading numbers in prop betting, and it is also the number most tools put in the largest font.
The split is a working compromise between those two failures, weighted toward the recent because that is the half carrying new information. We are not claiming 60/40 is optimal. We have not run an outcome study that would let us claim it, and a number arrived at by tuning against results we already saw would be worth less than the honest version.
The 20 game minimum, and why it bites unevenly
Below 20 games of history, we do not show a hit rate at all. Thin samples are dropped rather than displayed in small print, because a number on a screen gets read as a fact no matter what is underneath it.
That rule sounds mild. On one side of the board it is, and on the other it is severe:
| Players | Median games of history | Share under the 20 game minimum | |
|---|---|---|---|
| Batters | 297 | 109 | 5% |
| Pitchers | 27 | 27 | 30% |
Batters play nearly every day. Starters pitch every fifth. So the same rule excludes one batter in twenty and three pitchers in ten, and a pitcher who clears it clears it with a quarter of the history behind a typical batter’s number.
This is the most practical thing on this page. When you see a strikeout prop with a confident looking hit rate beside it, the sample behind that rate is usually a fraction of the sample behind a hits prop, and almost no tool tells you which you are looking at.
The trap that makes short-window stats look predictive
This one is worth more than the weighting, and we found it the expensive way.
Filtering a game log moves the window, not just the population. Take a player’s last 10 games and filter them for some condition, and you no longer have his last 10 anything. You have a stretch of games reaching further back in time, and everything that changed since then is now mixed into your result.
We tested this on one of our own ideas: a filter on the opposing starter, applied to unders. The filtered sample looked meaningfully different from the unfiltered one. Splitting it apart, 78% of the apparent effect was the window reaching further back, and 22% was the hypothesis we were actually testing.
Nearly four fifths of the finding was the calendar.
The fix is to hold the recent window fixed and vary only the thing you are testing. It sounds obvious written down. It is not what most “he hits 70% in this split” numbers have done, and it is the reason a split can look strong, be reproducible, and still be mostly an artefact.
What is deliberately not in the score
No rank of inputs by predictive power. We could tell you form matters more than matchup, or the reverse, and it would sound authoritative. We have not measured it, so we do not say it. This page describes how the number is built, not what predicts an outcome.
No claim about our results. Our pick record is being restarted as it goes public. Nothing here cites a record.
No single metric explained twice. What a given stat means for a given market is a different page: home runs, total bases, strikeouts and RBIs each cover their own inputs. This page is the layer above them.
The short version
If you are evaluating any prop tool, ours included, three questions separate the useful ones from the rest:
- What is the window, and is it weighted? A pure L10 number is noise with a decimal point on it.
- What is the minimum sample, and what happens below it? Dropping thin samples is a harder choice than showing them, and it is the right one.
- Does any split hold the time window fixed? If not, you are being shown a calendar effect with a story attached.
The Lab board shows the form number, the window behind it and the sample size on every prop, so you can apply those three questions to our own output rather than taking this page’s word for it.
If you want the narrower version of the first question, do L5 and L10 prop trends matter is about short windows specifically.
Form weighting and the 20 game minimum are read from our own board code. Sample depth figures measured 12 September 2026 across the players priced on that slate.
Quick answers
How does a player prop form score work?
Ours is 0.6 times the last 10 games plus 0.4 times the season, with a 20 game minimum before any hit rate is shown. The weighting exists because a short window is mostly noise and a season number is too slow to notice a real change.
Why not just use last 10 games?
Because ten games of a binary outcome is a very small number. A player who has hit a prop in 6 of his last 10 reads as 60%, and the honest interval around that figure covers most of the range you would care about.
Why do prop tools need a minimum number of games?
To stop a rate that is really one good week from being displayed as a tendency. Ours is 20 games, and thin samples are dropped rather than shown small, because a number on a screen gets read as a fact regardless of the sample under it.
Do these stats predict whether a prop hits?
We do not claim that. This page describes how our form score is built and why. We have run no outcome study tying these inputs to results, and if we ever do it will get its own page with the sample attached.
What is the L10 window trap?
Filtering a game log moves the time window as well as the population. Take a player's last 10 games and filter them, and you are now looking at a stretch reaching further back in time, so any difference mixes your hypothesis with a recency effect. In one of our own tests, 78% of an apparent effect turned out to be the window moving.
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The Lab prices every player prop on the slate, shows the form and the matchup behind each line, and posts a short card before first pitch. Every pick is graded in public afterwards, win or lose. It is free and there is nothing to sign up for.