Yep, I understand the issue. Send the agent this: ...

Yep, I understand the issue. Send the agent this:

The problem is not that TennisLocks cannot predict OVER 2.5 sets. It can. The problem is that its BO3 Set-2 reversal mechanism does not create enough matchup-specific dispersion in P(3 sets).

Right now, a lot of matches that should separate clearly from one another get compressed around roughly 49–52% P3. So the model may technically choose OVER 2.5, but it often does so at only ~51%, instead of recognizing when the matchup evidence should produce something materially stronger, such as 55%, 60%, 65%+, or conversely materially lower for strong straight-set/persistence matches.

This is primarily a Set-1 → Set-2 conditional transition problem, not a publication threshold problem and not something that should be fixed by boosting P3.

In a BO3 match, the correct deciding-set probability should come from the two actual reversal branches:

**P3 = P(A wins Set 1) × P(B wins Set 2 | A won Set 1)

• P(B wins Set 1) × P(A wins Set 2 | B won Set 1)**

The current structural point/game/set tree tends toward a near-stationary set-strength assumption. Under stationary set probabilities, P3 naturally lives near the familiar 2p(1-p) geometry, which maxes around 50%. So unless the Set-2 conditional branches genuinely move away from the Set-1 probabilities, P3 stays compressed around 50%.

The fix is not to increase P3 directly. The fix is to make the two Set-2 reversal probabilities more accurately conditional on the actual matchup and Set-1 state.

The agent should audit and rebuild the branch-specific Set-2 state estimator so it can naturally create larger or smaller changes when justified. It should use:

• the current canonical serve/return point-strength root as the structural baseline;
• separate branches for B reversing after A wins Set 1 and A reversing after B wins Set 1;
• player-specific, pre-match Set-1→Set-2 behavior;
• same-surface / appropriate competition-class evidence;
• strict chronology, with no future rows;
• first-set margin / tiebreak state when useful;
• strength-centering so a historically stronger player is not falsely labeled as having “momentum”;
• measured uncertainty so sparse histories shrink appropriately;
• the resulting branch state converted back into point-strength space, followed by rebuilding the canonical Set-2 game/set distribution.

Do not use corpus P3 rates, tour-average three-set rates, population targets, fixed P3 lifts, floors, ceilings, hardcoded 55–65% bands, or a rule that favors OVER.

The desired behavior is dispersion, not higher probabilities by default. For example:

• neutral/mixed evidence might legitimately remain around 48–52%;
• genuine reversal-heavy matches should be capable of naturally reaching the mid-50s, 60s, or beyond;
• genuine persistence/straight-set matches should be capable of falling well below 50%.

There should be no artificial range. A 63% P3 should occur because both conditional reversal branches support it, not because the model was told that strong Overs should equal 63%.

Very important: do not alter P3 after the exact-score root is built. The branch-specific Set-2 probabilities must change upstream inside the point → game → set recursion. Then the final exact-score PMF should automatically produce:

P3 = P(2-1) + P(1-2)

and that same PMF must remain the sole owner of Sets Played, Both Win a Set, Player Sets Won, Total Games, and Player Games.

So if the corrected model says P3 = 61%, Total Games must inherit the additional three-set paths from that same tree. There cannot be a separate Sets model saying 61% while Total Games is still using the old ~51% match-length geometry.

The core debugging question is:
“Why are the two Set-2 reversal branches not separating enough from their stationary structural probabilities when matchup-specific evidence supports a state change?”

Audit every source of compression:

1. overly strong prior/shrinkage precision;
2. treating point observations as more independent information than they really are;
3. pooling the two reversal directions together;
4. averaging player histories before branch conditioning;
5. state evidence getting centered away twice;
6. nuisance controls absorbing the actual reversal signal;
7. weak evidence being used as if it were full observations;
8. Set-1 margin information being computed but not actually affecting the branch;
9. converting the Set-2 target to point strength too weakly;
10. rebuilding the branch correctly but later overwriting it with the stationary root;
11. exact-score reconciliation accidentally restoring the old P2/P3 margin;
12. posterior averaging collapsing branch differences back toward the central tree.

Success criterion: TennisLocks should distinguish matches, not manufacture more Overs. A large collection of different matchups should show materially broader P3 dispersion when their actual player/state evidence differs, while neutral matches stay near structural values. A/B label symmetry, exact-score mass, Winner ownership, Total Games ownership, and all one-root identities must remain intact.

And this part is worth emphasizing to them because it captures the issue in one sentence:

“The model already knows how to select OVER 2.5. What it lacks is enough conditional Set-2 reversal separation to know when an Over deserves 51% versus 58% versus 65%, so too many fundamentally different matches collapse into the same ~50–52% P3 neighborhood.”

That is the problem you’ve been describing.

Do live web research to correct this and fully replace and wire correctly no hardcoding

After send me the updated script