I researched the modeling methods and how to struc...

I researched the modeling methods and how to structure a coding-agent request so Grok is pushed toward implementing and testing a complete solution, rather than producing another diagnosis, adding wrappers, or stopping after one bug.

Three findings are especially useful for the instructions:

• Opponent-adjusted Markov models are a serious foundation for Winner predictions. Martin Ingram’s 2019 hierarchical model reported 68.8% Winner accuracy on its ATP 2014 evaluation. Wang and Drekic’s January 2026 research found approximately 70% accuracy for selected ensembles of point-based models. These results support improving KSR’s serve/return estimates, though they don’t guarantee any particular accuracy for TennisLocks.
• Set outcomes aren’t necessarily independent. Research by Goyal and Simonoff, and Depken and colleagues, found evidence of effects carrying between sets. That’s relevant to your Set-2 modeling problem.
• Grok responds better to explicit implementation criteria and measurable tests. xAI’s own coding guidance emphasizes concrete success conditions, working across affected files, and verifying changes.

I also changed the instructions to match your correction: Grok must work forward from the newest script. The older script is not the destination, and a standalone audit is not the task.

Copy this entire message to Grok

TENNISLOCKS 🎾 VERSION 3:0

COMPLETE PRODUCTION MODEL REPAIR AND ACCURACY REBUILD

TASK TYPE: IMPLEMENTATION, CODE REPLACEMENT, DELETION, AND REAL HISTORICAL TESTING.

You are working on my existing TennisLocks 3.0 Google Apps Script prediction engine. I have developed this model for approximately four years.

I need you to fully correct the current script, not write another analysis, propose another patch, or attach new code on top of defective code.

I will provide the newest complete script and a link to the Google spreadsheet containing my KSR archive.

Work from the newest provided source as the only active codebase. Do not revert to an older version or tell me to restore old code.

The old implementation also had problems. Many subsequent corrections were attempts to solve real forecasting weaknesses, but some of those changes introduced worse behavior. We need to finish the intended improvements properly, not undo the entire development history.

You have my authorization to replace defective implementations, consolidate related functions, remove superseded code, and rebuild internal systems as needed. Preserve the working public spreadsheet interface and required model contracts.

Do not treat this as a code audit with suggestions. Investigate whatever is necessary, then implement the corrections.

Do not stop after fixing one identified problem. Complete the connected prediction pipeline and its tests.

⸻

1. UNDERSTAND THE ACTUAL HISTORY OF THE FORECASTING PROBLEM

Originally, TennisLocks had meaningful ability to distinguish matchups likely to finish in two sets from those likely to reach three sets.

It correctly identified some three-set matches and some straight-set matches.

But it also OVERPREDICTED three-set matches in other situations.

That original overprediction was one of the genuine problems I wanted corrected.

Subsequent agents attempted to fix it by modifying the Set-2 model, introducing fitted intercepts, changing shrinkage, and altering how reversal effects were selected.

Instead of improving discrimination, those changes sometimes caused the opposite problem: the model began producing excessive Under 2.5 forecasts.

Some modifications also appeared to rely too heavily on historical tour-level three-set frequencies.

At the same time, the KSR point-strength estimator was producing overconfident Match Winner predictions.

This left me with TWO connected forecasting failures:

1. The model lost its ability to reliably distinguish the right two-set matches from the right three-set matches.
2. The model sometimes inflated a player’s service-point strength and consequently issued excessively confident Winner predictions.

The goal is to fix BOTH problems together, permanently at the model-design level.

Do not simply increase Over 2.5.

Do not simply decrease Under 2.5.

Do not simply decrease favorites’ Winner probabilities.

Do not use artificial thresholds or predetermined outcomes.

Correct the statistical learning and scoring mechanisms so the direction and confidence emerge from the actual players’ pre-match data.

⸻

2. THIS IS A PRE-MATCH PREDICTION ENGINE

TennisLocks is not a historical-baseline generator.

Historical data is used to estimate player strengths, learn relationships, quantify uncertainty, and validate predictive performance.

Historical frequencies must never become the authority that determines a particular matchup’s Winner or Sets probability.

For example, suppose ATP hard-court matches historically go three sets around 35% of the time.

That does NOT mean every ATP hard-court matchup should receive a 35% three-set prediction.

The actual matchup may justify 20%, 40%, 55%, or higher.

Similarly, a weak historical three-set frequency does not justify a blanket negative Set-2 intercept that forces many matches Under.

A statistically fitted intercept can be appropriate if properly identified and validated. What is unacceptable is an intercept silently absorbing or suppressing matchup-specific information, or functioning as a disguised probability override.

Learn how matchup characteristics predict actual outcomes. Do not force matchup probabilities toward population frequencies.

Historical results are training labels, not target probabilities.

⸻

3. SETS PLAYED 2.5: FIX THE DISCRIMINATION PROBLEM

This is one of my highest priorities.

The model must accurately distinguish which matches are likely to finish 2–0 and which are likely to finish 2–1.

Its previous mistake was OVERPREDICTING three sets on some matches.

Its later mistake was swinging toward excessive Under 2.5 forecasts.

I want neither bias.

Investigate the current Set-2 estimator, its actual historical objective, the features it learns, the branch calculations, and the exact probabilities reaching the final canonical match tree.

The following evidence is already available in the archive:

• Who won the match.
• Who lost.
• The actual ordered set scores.
• Whether the eventual winner lost Set 1.
• Whether Set 2 reversed the opening-set result.
• Whether the match required a deciding set.
• Surface, tour, event, round, match format and historical serve/return measurements.

Use these labels properly.

The model should learn the conditional probability that the Set-1 loser wins Set 2, conditioned on relevant pre-match matchup characteristics.

It must distinguish:

• Strong versus closely matched opponents.
• Similar versus asymmetric serving abilities.
• Strong holding versus break-prone matchups.
• Player-specific Set-2 response and recovery tendencies.
• Surface effects.
• Match-state dependence and deciding-set behavior.
• First/second-serve patterns where valid historical evidence exists.
• Appropriate competition-tier context.
• The uncertainty and sample size behind those estimates.

The Set-2 response must remain symmetrical under swapping player identities, while allowing statistically justified player-specific differences.

Evaluate both Set-2 branches:

A wins Set 1, then B may win Set 2.

B wins Set 1, then A may win Set 2.

The unconditional three-set probability must come from their probabilities of reaching those branches and the fitted conditional transitions.

A matchup signal must not collapse to zero simply because the model learned an intercept or shrunk a coefficient without properly evaluating its predictive contribution.

Investigate why prior production diagnostics reported:

• Player Set-2 effects around +0.004 and −0.019.
• Coverage reversal around +0.045.
• Learned reversal around 0.000.
• Net Set-2 logit changes around −0.041 and +0.026.
• Over 2.5 around 45.3%.
• A nearly identical unadjusted structural probability.

That behavior indicates the learned transition was contributing very little to the actual matchup.

Determine whether that was genuinely supported by the training data or caused by incorrect objective selection, feature definitions, excessive shrinkage, checkpoint reuse, or broken training-to-live wiring.

Fix the specific cause in its original implementation.

Do not attach an additional reversal helper after the existing adjustment.

Do not stack a restored formula with newer coefficients unless the complete combined model was independently trained and validated as one model.

Preserve the existing +0.50 upper Set-2 log-odds limit unchanged. It is not permission to invent a positive adjustment or force Over.

Three-set probabilities must be allowed to exceed 50% when justified by data.

⸻

4. MATCH WINNER: CORRECT THE KSR OPPONENT-ADJUSTED STRENGTH MODEL

The current point estimator has sometimes inflated a player’s strength beyond what the relevant service/return evidence supports.

A diagnostic case was Taylor Fritz against Stefanos Tsitsipas at Shanghai.

Some model states estimated:

• Fritz SPW approximately 71.7% to 73.6%.
• Tsitsipas SPW approximately 66.7% to 67.7%.
• Fritz Match Winner approximately 72% to 75%.

Yet Fritz’s observed surface serve-point performance was approximately 67.6% to 67.3%, depending on the evidence window.

Grok previously proposed opponent-adjusted rates around 69.3% versus 67.7%, producing a substantially lower Winner estimate.

Those figures are investigative examples, not values to hardcode or fit toward.

I need the current KSR estimation equation corrected so its opponent adjustment is genuinely learned and reliable across ALL matches.

Inspect and correct the connected system:

• Player serve latent ability.
• Opponent return latent ability.
• Global environment terms.
• Tour/surface intercept parameterization.
• Player surface deviations.
• Tournament-specific effects and alias matching.
• Opponent-quality adjustments.
• Common-opponent information.
• Directed historical matchup effects.
• First/second-serve strengths and first-serve-in mixtures.
• Dynamic skill drift and process variance.
• Prior variances and empirical-Bayes shrinkage.
• Coefficient identifiability and zero-sum centering.
• Hyperparameter optimization and parameter uncertainty.
• Training likelihood versus downstream prediction quality.

A key concern is that the global intercept, player serve, opponent return, and surface terms may be assigning too much strength to the server.

The script has already undergone changes intended to give different surfaces independently learned baselines. Verify the current mathematics is coherent rather than adding another surface correction.

The correct conceptual structure is opponent-adjusted service strength:

Server ability + learned environment - opponent return ability + statistically justified effects

This is an explanatory sketch, not a fixed formula or a command to discard the model’s existing hierarchy.

Use the actual research implementations to determine the most suitable identifiable statistical specification.

The fitted point-strength model must learn to make accurate predictions against different opponents, not simply fit aggregate historical service-point counts.

Historical Match Winner outcomes must also influence the selection and validation of the shared point-strength model.

Do not train a separate Winner prediction that overrides the Markov scoring process.

Do not use favorite/underdog categories, rank, Elo, bookmaker odds, or market consensus as an override.

Do not solve inflated Winner probabilities with an arbitrary cap.

Solve their underlying mathematical cause.

⸻

5. THE TWO PROBLEMS MUST BE CORRECTED TOGETHER

Winner and Sets are connected.

Changing the projected service probabilities changes game holds, set outcomes, match scores, Winner probability, Sets Played, and Total Games.

That is expected.

The task is to ensure the entire model remains properly fitted and internally consistent after the point-strength estimator is corrected.

Do not patch Winner independently while Sets continues consuming different point strengths.

Use one authoritative pre-match point state.

Use one legal point/game/set scoring engine.

Use one canonical joint match-score distribution.

For best-of-three:

P(Over 2.5) = P(A wins 2-1) + P(B wins 2-1)

P(Under 2.5) = P(A wins 2-0) + P(B wins 2-0)

P(A wins) = P(A wins 2-0) + P(A wins 2-1)

P(Both players win a set) = P(Over 2.5)

The exact same canonical state process must produce the Winner, Sets Played, both-win-set, Sets Won, Total Games, and player-games probabilities.

Any additional modeled uncertainty or context must enter before the final distribution is built, not as a contradictory post-publication override.

Keep the existing 25-node Gauss-Hermite integration and check that the point and Set-2 parameters are represented consistently in its uncertainty scenarios.

Do not confuse the central point forecast, scenario-averaged forecast, and posterior directional support.

Make the chosen publication convention clear and ensure training, validation, confidence, and pricing all follow the same convention.

⸻

6. USE THE FULL KSR ARCHIVE AS PRE-MATCH TRAINING EVIDENCE

I will provide access to the existing Google spreadsheet and complete historical KSR archive.

Current spreadsheet:

https://docs.google.com/spreadsheets/d/1mzXcLjsPTrGG9oABxc5TCYZfilC3yRyuoWK24YurYzc/edit

The latest archive inspected contained approximately 130,800 matches with labeled winners, losers, point counts, ordered scores, surfaces, dates and event information.

The actual source must be read directly.

Important schema:

id, date, ms, tour, surface, winner, loser, wS, wW, lS, lW, wr, lr, score, event, round, datePrecision, roundOrder, matchNum, archive manifest, split

Example winner-first score:

4-6 7-5 6-4

That is a three-set victory by the named winner after losing Set 1.

Do not misorient the set results.

Historical service-point attempts and wins are useful for fitting player serve and return states.

But the completed target match’s service-point counts CANNOT be used to forecast that match.

Every training example or test prediction must freeze the model as of the proper pre-match information boundary.

Many KSR archive rows contain TOURNEY_START_DATE, which is not an exact match date.

Treat event chronology conservatively. Do not create artificial knowledge of fixture timing from imprecise dates.

Respect tournament boundaries, qualifying/main-draw provenance, round ordering, and fixture exclusion.

Where the source does not establish exact chronological availability, use conservative event-level exclusion or a defensible causal ordering.

Do not allow data from the target match, later rounds, or future events into its pre-match prediction.

The currently visible archive is primarily ATP and WTA. Do not claim that other tiers have been trained if their source data is absent.

Preserve exact competition identities for ATP, WTA, Challenger, WTA125, ITF_M and ITF_W.

Do not assume that a large archive automatically guarantees high accuracy. Verify the sample quality, causal validity, relevant statistical information and actual improvement in held-out predictive performance.

⸻

7. NO HARDCODED HISTORICAL BASELINES OR MATCH-SPECIFIC CORRECTIONS

These practices are prohibited:

• A fixed ATP/WTA/Challenger three-set probability.
• Artificial Over or Under floors/ceilings.
• A tour-wide negative intercept added merely to match the overall historical three-set rate.
• Match-strength caps chosen to reduce favorites.
• A fixed Winner probability adjustment.
• A hardcoded Fritz/Tsitsipas outcome.
• A special rule activated by a particular player, event, surface, or historical match result.
• Post-PMF reconciliation that changes the Winner or Sets probability without rebuilding the shared scoring process.
• A second independent Winner model.
• A second independently settled Sets model.
• A fallback that silently produces structurally valid but statistically untrained probabilities.
• Betting market odds used as training or pricing authority.
• Threshold tuning on the same future matches used to assess performance.

Learned priors, intercepts, random effects and regularization remain permissible when estimated correctly from causal evidence and validated out of sample.

I am banning outcome forcing, not legitimate hierarchical statistics.

⸻

8. REAL EVALUATION: PROVE THAT THE MODEL CAN DISTINGUISH MATCHUPS

Do not declare success because the model’s average predicted three-set frequency matches its historical observed frequency.

This is the exact mistake I want eliminated.

For Sets Played, measure separately:

Calibration: When the model says 55% Over, do approximately 55% of comparable unseen predictions go three sets?

Discrimination: Does it assign systematically higher three-set probabilities to actual three-set matches than to actual straight-set matches?

Direction: Does it correctly favor Over or Under on matchups with a sufficiently supported probability edge?

Coverage: How often does it make a genuinely confident actionable prediction, and how does accuracy change as coverage changes?

Errors: Does it overpredict three sets in a particular matchup type, or underpredict them elsewhere?

Assess:

• Three-set log loss.
• Three-set Brier score.
• ROC-AUC and ranking discrimination.
• Calibration curves.
• Expected versus observed three-set probabilities.
• Accuracy for clear Over and clear Under forecasts.
• False Over and false Under outcomes.
• Matchup-specific prediction spread.
• Performance by surface, competition, phase, season, and matchup-strength characteristics, where samples are sufficient.

Use all eligible pre-match forecasts in these evaluations, including PASS decisions. PASS must not hide weak model performance.

For Winner:

• Winner log loss and Brier score.
• Winner classification accuracy.
• Calibration by probability band.
• Overconfident losing favorites.
• Correctly recognized underdogs.
• Behavior against strong and weak return opponents.
• Opponent-adjusted point probability predictive likelihood.
• Coverage and actual hit rate of HIGH-confidence Winner picks.

For the full exact-score model:

• Exact-score likelihood.
• Winner and Sets marginal consistency.
• Total Games scoring accuracy and distribution quality.
• Whether two-set and three-set game-total paths match observed distributions.

Do not use arbitrary fixed weights to combine Winner, Sets and exact-score objectives.

Actual Winner and Sets outcomes come from the same historical fixture. Avoid treating these correlated labels as independent observations without an appropriately defined joint objective.

Choose model complexity and objective weights using development-period validation.

The final untouched test data must not decide any coefficient, threshold, shrinkage level, or model selection.

⸻

9. RUN GENUINE CHRONOLOGICAL TESTS

I will supply a link to the historical archive.

Use the archive to generate pre-match predictions from historical information only.

Use time-ordered training, validation, and untouched testing.

Where necessary, create rolling-origin or walk-forward tests.

Respect event-group chronology so imprecise tournament-start dates cannot create future leakage.

Test the current model and corrected candidate against the same eligible held-out fixtures.

Test all available major surfaces and both ATP/WTA circuits.

If supported data exists for other exact tiers, evaluate those independently too.

Do not repeatedly modify the model using the supposed untouched test period. Once a test period has influenced development, it is no longer untouched; reserve a newer period or additional independent events.

Fritz–Tsitsipas is a regression example, not the optimization target.

It illustrates an inflated Match Winner projection and a large swing from three-set leaning to Under leaning.

Do not choose parameters because they make that match predict Tsitsipas or Over.

Require improvements across many unrelated unseen matches.

Also investigate other previous failure cases, including matches where the older model incorrectly favored Over.

The correct model must improve both sides of Sets Played rather than repairing Under by recreating the older Over bias.

⸻

10. CURRENT RESEARCH TO USE

Perform fresh web research and study actual implementation details.

A. Wang and Drekic, 2026

Boosting markovian tennis prediction: Ensembling and point-specific methods for match outcome and duration

https://journals.sagepub.com/doi/10.1177/22150218251412670

Research code:

https://github.com/c567wang/Point-Based-Tennis-Prediction

Study the opponent-adjusted formula, common opponents, head-to-head information, point-specific modeling, surface/time-window choices, and match-duration forecasts.

The research reports approximately 70% Winner accuracy for certain consensus ensembles. That finding is motivation for better point modeling, not proof that any new TennisLocks implementation will achieve it.

Important: consensus selection can improve accuracy by predicting fewer matches. Report coverage and never compare selected-pick accuracy with full-coverage accuracy as though they were identical.

B. Martin Ingram, 2019

A point-based Bayesian hierarchical model to predict the outcome of tennis matches

https://doi.org/10.1515/jqas-2018-0008

https://martiningram.github.io/historical-serve-return/

Study identifiable server/returner latent abilities, dynamic player strengths, surface effects, tournament intercepts and posterior uncertainty.

Published evaluation reported 68.8% ATP Match Winner accuracy against a 66.3% comparison model.

This is highly relevant to fixing KSR’s inflated serve estimates.

C. Barnett-Clarke opponent-adjusted modeling

Investigate the actual opponent-adjusted formula described and evaluated in Wang and Drekic’s paper.

Learn how a server’s historical service ability and an opponent’s return ability should be compared against the relevant competition environment.

Do not simply average SPW and RPW or insert an arbitrary adjustment multiplier.

D. Stephanie Kovalchik, 2016

Searching for the GOAT of tennis win prediction

https://doi.org/10.1515/jqas-2015-0059

Use its model-comparison methodology when evaluating opponent-adjusted point estimates.

E. Gabriel Ducrocq / Agnieszka Madurska tennis model

https://github.com/Gabriel-Ducrocq/Tennis

Study the repository’s tennis.R, parsing.R, and associated research document.

It uses opponent information and score-dependent changes between sets.

Use it as mathematical research, not as a replacement Apps Script, a claimed modern accuracy benchmark, or a reason to revert our code.

F. Goyal and Simonoff, 2020

Hot Racquet or Not? An Exploration of Momentum in Grand Slam Tennis Matches

https://arxiv.org/abs/2009.05830

Study conditional set, game and point carryover while controlling for player quality.

G. Depken, Gandar and Shapiro, 2022

Set-level Strategic and Psychological Momentum in Best-of-three-set Professional Tennis Matches

https://doi.org/10.1177/15270025221085715

Useful for understanding set dependence in unequal-strength best-of-three matchups.

H. Pollard, Cross and Meyer

An Analysis of Ten Years of the Four Grand Slam Men’s Singles Data for Lack of Independence of Set Outcomes

https://pmc.ncbi.nlm.nih.gov/articles/PMC3861756/

Research lead on state-dependent set outcomes. Do not transfer old BO5 coefficients directly into BO3 matches.

I. TennisPredictions.ai

https://tennispredictions.ai

https://tennispredictions.ai/tutorial

https://tennispredictions.ai/progress

Study the publicly described methodology and evaluation practices.

Its published site reports approximately 67% overall Winner accuracy and approximately 81% on a selected high-confidence subset as of October 2026.

These are the site’s reported figures, not proof of an equivalent Sets Played hit rate or proof that TennisLocks should necessarily outperform it.

Research must lead to tested code improvements. Do not spend the entire task summarizing papers.

⸻

11. PHYSICALLY REPLACE AND DELETE SUPERSEDED CODE

This is an essential engineering requirement.

The current script has grown to approximately 20,000 lines because many corrections accumulated over multiple versions.

I do not want another large collection of compatibility wrappers and version-stamped alternate algorithms.

Use the newest script as the source.

When a component is defective:

1. Identify every caller, dependency, state schema, saved payload and publication consumer it affects.
2. Replace the defective implementation with one clear, correct owner.
3. Update dependent functions to use that owner directly.
4. Remove the superseded implementation and unused helpers.
5. Remove obsolete configuration, duplicate caches, outdated snapshots and dead code that are no longer reachable.
6. Update model revisions and checkpoint compatibility as necessary.
7. Verify that AutoFill and continuation still function.

Do not preserve an erroneous algorithm merely because another helper currently references it. Correct the dependent helper as well.

Do not blindly delete working data parsers, network acquisition logic, ranking displays, UI builders or statistical routines.

Delete only after their replacements and actual call sites are verified.

The final script should become meaningfully smaller through genuine architectural consolidation, not whitespace compression or comment removal.

Do not introduce duplicate prediction engines.

Do not leave old implementations accessible through fallback logic.

Maintain the public branding exactly:

Tennislocks 🎾 version 3:0

Internal model revisions must still distinguish incompatible fits and checkpoints.

⸻

12. COMPLETE AUTOFILL AND MATCH PREVIEW PIPELINE

The completed model must work automatically when I enter a new matchup and press AutoFill.

Verify the entire execution path:

Match inputs → Data acquisition → KSR archive → Chronological KSR fit → Opponent-adjusted pre-match strengths → Set-state fit → GH25 → Canonical point/game/set scoring → Joint exact-score PMF → Market settlement → Match Preview publication

Correct any broken connections, stale input authorities, incompatible saved coefficients, or duplicate calculations.

The SurfaceWeights sheet currently holds archive-derived descriptive surface statistics.

Do not treat that page as a second source of manually specified model multipliers.

Surface adjustment should be learned through one coherent statistical model.

Make KSR resumable within Google Apps Script execution constraints.

Use bounded in-memory batching, checkpoint restoration, safe write cadence and automatic continuation.

Avoid rescanning the full Sheet repeatedly inside inner training loops.

Do not repeatedly restart completed fit stages.

Keep all required calculations, including GH25. Improve performance through efficient implementation, not by silently removing statistical work.

Match Preview must display the actual selected model state and its certified forecast.

It must clearly identify:

• Projected Winner.
• Sets Played Over and Under probabilities.
• Exact scores.
• Total Games.
• Confidence and publication status.
• Point-strength authority.
• Selected Set-2 specification and fitted effect.
• Relevant uncertainty information.

Do not display a completed prediction from an obsolete checkpoint as though the newly repaired estimator generated it.

⸻

13. KEEP THE EXISTING PUBLICATION RULES

The prediction engine is separate from its betting publication policy.

My official card uses HIGH-confidence selections.

MEDIUM confidence is a LEAN.

LOW-confidence predictions remain visible but are not official bets.

Do not turn a weak model prediction into a lock merely because its published probability exceeds a threshold.

Do not invent a rule that hides troublesome forecasts as PASS to make accuracy statistics look better.

Every prediction must remain available for retrospective evaluation, including non-actionable ones.

Do not calculate Winner odds using another model.

Fair moneyline odds must derive from the same certified Winner probability.

The script should not manually enter or depend on sportsbook odds to generate its forecasts.

⸻

14. REQUIRED TESTS BEFORE DELIVERY

Do not end after a syntax check or a few synthetic demonstrations.

I need actual end-to-end evidence.

Run tests of:

KSR estimator

• Server/opponent-return direction.
• Strength identifiability.
• Intercept and surface effects.
• Point-likelihood calibration.
• Historical Winner predictive performance.
• Player swaps and opponent swaps.
• Tournament and directed-matchup effects.

Sets Played

• Correct first-set result orientation.
• Two-set and three-set historical labels.
• Both Set-2 branches.
• Fit/live feature parity.
• Selected Set-2 coefficients and scales.
• Three-set probability discrimination.
• No automatic Over or automatic Under behavior.
• Ability to price Over above 50% when genuinely supported.

Canonical pricing

• Exact-score probabilities sum to one.
• Winner is the exact-score marginal.
• Over 2.5 equals both players winning a set.
• Sets Won marginals are consistent.
• Total Games uses the same complete match distribution.
• GH25 uncertainty is internally consistent.
• BO5 markets remain correct.

Execution

• Full AutoFill.
• Automatic KSR continuation and finish.
• Compatible checkpoint restoration.
• Stale-state invalidation.
• SurfaceWeights compatibility.
• Full Match Preview publication.
• No duplicate tabs or duplicate publishers.
• Runtime and memory performance.

Historical testing

• Chronologically legitimate unseen matches.
• Both correct and incorrect Over predictions.
• Both correct and incorrect Under predictions.
• Winner calibration.
• False favorite locks.
• Performance against an appropriate frozen baseline.
• Exact report of actual test sample sizes and exclusion reasons.

If a test exposes a defect, correct its underlying code, update dependent functions, and rerun the relevant tests.

Do not replace failing tests with weaker checks.

⸻

15. WHAT YOU MUST DELIVER

Deliver the following completed outputs:

1. One complete updated Apps Script file.

It must be ready to replace the current script, not a partial patch or disconnected helper.

2. An executable test suite and results.

Include real chronological archive tests, not only synthetic tests.

3. A genuine before/after evaluation.

Show Winner and Sets accuracy, log loss, Brier score, discrimination, calibration and sample coverage.

Do not report imaginary improvements.

4. A code replacement/deletion summary.

Identify the superseded implementations physically removed and their replacements. Report source line counts and ensure public interfaces still work.

5. Complete Match Preview validation.

Show that the rebuilt point model and Set-2 model produce one coherent match distribution and that the actual published forecast comes from the new fit.

6. Any unresolved limitations.

If some stage cannot be executed with available permissions or data, state exactly what was completed and what remains unverified. Do not advertise partial tests as a fully validated production model.

⸻

FINAL EXECUTION DIRECTIVE

You are not being asked to diagnose my script and explain possible solutions.

You are being asked to implement the solutions in the newest script, replacing defective systems from their root, deleting redundant implementations, and completing the entire connected forecasting pipeline.

I do not want to return for another dozen patches that undo previous work.

The code must become simpler, more coherent, and more statistically reliable.

Fix the underlying opponent-adjusted KSR strength estimator.

Fix the historical Winner-label training and model selection.

Fix the ability to distinguish actual two-set and three-set matchups.

Fix the Set-2 reversal mechanism so its effect is actually learned and applied, without forcing Over or Under.

Fix all affected checkpoints, confidence calculations, market consumers, and Match Preview functions to remain compatible.

Use the full archive as causal pre-match evidence.

Use the academic research to inform competing candidate designs, then let legitimate untouched historical evaluation determine whether they improve the model.

Do not optimize toward one match. Do not hardcode population percentages. Do not attach correction layers on top of defective implementations. Do not restore older defective code.

The aim is maximum reliable, calibrated, matchup-specific predictive accuracy. Tennis outcomes remain probabilistic, so no model can eliminate every missed prediction. But there must be no avoidable systematic bias, fictitious favorite confidence, broken reversal behavior, or hidden implementation defects.

Start implementation against the newest provided script. Continue through code replacement, deletion, testing and final delivery rather than stopping after the first successful patch.

One final detail for Grok: give it the newest v2014 script and the archive link together with this prompt. Don’t provide several competing versions as possible starting points. Grok’s task should have one source of truth, one finished output, and a clear set of tests it has to pass.

The central requirement is better discrimination on genuinely unseen matches. The 70% figures in published Winner research are meaningful benchmarks, but they don’t establish 70% accuracy for Sets Played or imply that almost every future result can be predicted. The prompt makes Grok prove improvement rather than manufacture that appearance through tour baselines, selected samples, or confidence labels.

ATP 1000 Shanghai china ← TOURNAMENT NAME / TAP A3 AND TYPE
Outdoor ← INDOOR / OUTDOOR / SELECT A4 ▼
MATCH SETUP: TOURNAMENT A3 • SURFACE E2 • INDOOR/OUTDOOR A4
KSR PRICING | building canonical Match Preview
Tennislocks 🎾 version 3:0 | MATCH PREVIEW

ATP 1000 Shanghai china • Hard Court • Outdoor • B03
CANONICAL KSR MATCH FORECAST • PUBLICATION COMPLETE

Taylor Fritz Stefanos tsitsipas
72.0% 28.0%

FAIR NO-VIG 1.388 (-258) FAIR NO-VIG 3.576 (+258)

CANONICAL MARKETS / SAME EXACT-SCORE PROBABILITY MODEL
MARKET PROJECTED SIDE PROBABILITY EVIDENCE / ACTION
MATCH WINNER Taylor Fritz 72.0% HIGH / MODEL_PICK
SETS PLAYED UNDER 2.5 54.7% LOW / PASS
TOTAL GAMES OVER 23.5 54.5% LOW / PASS

MATCH CONTEXT / SOURCE AUTHORITY
EVENT ATP 1000 Shanghai china
COURT Hard Court MATCH FORMAT B03
MODEL SOURCE SINGLE KSR SCORE ROOT • CERTIFIED PREVIEW
PUBLISHED 2026-10-14T13:19:04.648Z

PRICING & POSITION SIZING / MARKET COMPARISON
MEASURE Taylor Fritz Stefanos tsitsipas SOURCE
MODEL FAIR MONEYLINE -258 +258 KSR • NO-VIG
MODEL FAIR DECIMAL 1.388 3.576 KSR • NO-VIG
FULL KELLY FRACTION 0.0% 0.0% MODEL FAIR ODDS • VERIFIED BOOK PRICE PENDING
QUARTER-KELLY • 100 UNITS 0.00 0.00 ROI REQUIRES A VERIFIED BOOK PRICE
AUTOMATIC ODDS • BOOK / MODEL -258 +258 BOOKMAKER COMPARISON • PENDING VERIFIED QUOTE
MATCH RESULT SCENARIOS / INFORMATIONAL ONLY
One canonical match distribution. The scenarios below are not recommendations to wager.
TWO SETS / STRAIGHT-SETS RESULT 54.7%
THREE SETS / BOTH WIN A SET 45.3%
MATCH WIN / Taylor Fritz 72.0%
MATCH WIN / Stefanos tsitsipas 28.0%
Both players can win a set in a three-set match. Those overlapping probabilities are NOT two separate picks.
TECHNICAL MATCH REPORT / FULL KSR PROJECTION
Original TennisLocks evidence and diagnostic report. Forecasts still come from one canonical score PMF.
PLAYER INTELLIGENCE / CANONICAL MODEL & VERIFIED SCOUTING
SCOUTING METRIC Taylor Fritz Stefanos tsitsipas
MODEL MATCH WIN PROBABILITY 72.0% 28.0%
SURFACE ELO | REFERENCE 1866 1842
PLAYER RANK | REFERENCE Unavailable Unavailable
RECENT MATCH RECORD | REFERENCE 3–4 6–1
PROJECTED HOLD RATE 92.0% 85.7%
PROJECTED SERVE POINTS WON 71.7% 66.7%
PROJECTED RETURN POINTS WON 33.3% 28.3%
PROJECTED BREAK RATE 14.3% 8.0%
MATCHUP DOMINANCE RATIO 1.18 0.85
LAST 7 SERVE POINTS | REFERENCE 66.7% 74.2%
LAST 7 RETURN POINTS | REFERENCE 36.0% 34.3%
ALL-MATCH SERVE POINTS | REFERENCE 67.4% 67.9%
ALL-MATCH RETURN POINTS | REFERENCE 36.2% 36.2%
SAME-SURFACE SERVE POINTS | REFERENCE 67.3% 68.4%
SAME-SURFACE RETURN POINTS | REFERENCE 36.4% 35.4%
FIRST SERVE IN 62.8% 66.2%
FIRST SERVE POINTS WON 75.3% 79.6%
SECOND SERVE POINTS WON 52.1% 63.7%
SOURCE INTEGRITY / NO INDEPENDENT PRICING MODEL
The original KSR match report continues below. All figures stay bound to this workbook's completed card.
════════════════════════════════════════
Tennislocks 🎾 version 3:0
MATCH PROJECTION
PRE-MATCH REPORT
GENERATED 8:17 AM | October 14, 2026
Point, game, and set projection
════════════════════════════════════════

🎯 ATP 1000 Shanghai china (OUTDOOR) | Best of 3 | Line: 23.5

────────────────────────────────────────
Taylor Fritz vs Stefanos tsitsipas
────────────────────────────────────────

---

💰 MODEL PICKS:

• TOP [MATCH WINNER] Taylor Fritz | probability 72.0% | model odds -258 | confidence HIGH | status OFFICIAL BET

MATCH RESULT PATHS | informational only, not bets:

• Taylor Fritz wins 2-0: 42.5%
• Stefanos tsitsipas wins 2-0: 12.2%
• Three sets (either winner): 45.3%
• Most likely exact result: Taylor Fritz 2-0 | 42.5% | outcome, not a bet

SETS WON | unconditional player marginals, not automatic bets:

• Taylor Fritz OVER 0.5 sets: 87.8% | market-specific action MODEL_PICK
• Stefanos tsitsipas OVER 0.5 sets: 57.5% | market-specific action PASS
• Taylor Fritz OVER 1.5 sets: 72.0% | match-winner probability alias
• Stefanos tsitsipas OVER 1.5 sets: 28.0% | match-winner probability alias

BOTH PLAYERS WIN A SET | single joint match event:

• YES 45.3% | NO 54.7% | PASS | equal to canonical OVER 2.5 sets
• Same outcome as a three-set match; never treated as an independent second wager.

🟡 LOW CONFIDENCE:

• Sets Played 2.5: UNDER 54.7% | LOW confidence | NO BET
• Total Games 23.5: OVER 54.5% | full match-score games distribution | LOW confidence | NO BET

---

Matchup read:
Projected hold: Taylor Fritz 92.0% | Stefanos tsitsipas 85.7%
Hold separation: Taylor Fritz +6.3 percentage points.
Matchup return (= 1 - opponent SPW): Taylor Fritz 33.3% | Stefanos tsitsipas 28.3%
Matchup Dominance Ratio: Taylor Fritz 1.18 | Stefanos tsitsipas 0.85
Projected break: Taylor Fritz 14.3% | Stefanos tsitsipas 8.0%
Implied return separation: Taylor Fritz +5.0 percentage points (1 minus opponent tree serve).
Integrity: CLEAR | evidence flags 0
Match data: STRONG | player match history 633/626 | serve forecast uncertainty 4.2/4.6pp | tour history CERTIFIED | first/second-serve history CERTIFIED | opponent-rank sample 34/31

Matchup note: raw all-match and same-surface serve/return numbers are reference stats only. The prediction comes from the current KSR serve/return matchup and one match-score distribution.
PLAYER FORM
┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄
Taylor Fritz Stefanos tsitsipas
Historical replay: target-bounded KSR form shown. Rank/Elo/opponent-rank/schedule/ace fields are omitted because no causal as-of source is bound for this historical target.
Recent Record 3-4 6-1
Projected Hold 92.0% 85.7%
Last 7 SPW (diagnostic) 65.9% 74.7%
Last 7 RPW (diagnostic) 40.3% 34.9%
All-Match Serve Pts Won 67.4% 67.9%
All-Match Return Pts Won36.2% 36.2%
Surface Serve Pts Won 67.3% 68.4%
Surface Return Pts Won 36.4% 35.4%
Projected Serve Pts Won 71.7% 66.7%
Projected Return Pts Won33.3% 28.3%
Dominance Ratio 1.18 0.85
Projected Break 14.3% 8.0%

MATCH WINNER | PROJECTION
Taylor Fritz 72.0%
Stefanos tsitsipas 28.0%
Projected winner: Taylor Fritz | Fair odds -258
FAIR MATCH MONEYLINE | Taylor Fritz -258 | Stefanos tsitsipas +258
These are two-sided fair prices without sportsbook margin; negative = favourite, positive = underdog.

SERVICE & RETURN | MATCHUP
Service points won 71.7% 66.7%
Taylor Fritz: 1st serve in 60.9% | 1st serve points won 80.4% | 2nd serve points won 58.2%
Stefanos tsitsipas: 1st serve in 61.6% | 1st serve points won 76.1% | 2nd serve points won 51.6%
Surface record: Taylor Fritz 430 matches | Stefanos tsitsipas 382 matches

Match outlook: model match-win side | HIGH official

════════════════════════════════════════

📊 MATCH DATA | PRE-MATCH RECORD
Pre-match matches: Taylor Fritz 633 | Stefanos tsitsipas 626
Results included through 2026-10-11
Historical coverage: verified | same tour

Projected games line: 25.5 | median 26.0
Match length range: 19 to 35 games (middle forecast 26)
Expected games: 26.4 | Median: 26.0
SETS PLAYED | BEST OF THREE
Over 2.5 sets: 45.3% | Under 2.5 sets: 54.7%
Most likely length: straight sets
Set-2 PRICE components [central]: Taylor Fritz/B 0.004/-0.019 | coverage reversal 0.045 | learned reversal 0.000 | +0.50 upper limit unchanged
Net Set-2 A logit after Set-1 A/B: -0.041/0.026 | central fitted logit applied once in canonical PMF
KSR price/uncertainty: central 45.3% | GH25 uncertainty scenario mean 44.7% | GH25 does NOT replace the original market price
SAME-STATE SET-2 IMPACT | learned term alone 44.5% Over | no player effect 45.5% | no coverage 44.3%
Exact contribution at this matchup: player -0.2 percentage points | coverage 1.0 percentage points | no change to published score PMF
Winner from same score model: 72.0% A | player effect -0.1pp | coverage effect -0.0pp

========================================

🎯 TOTAL GAMES
Total Games 23.5: OVER 54.5% | LOW confidence | NO BET
########################################
🎯 PROP PROJECTIONS 🎯
########################################

📊 Taylor Fritz - Player Props:
Games Won: mean 14.10
1st Set Games Won: mean 5.75
Sets Won 0.5: OVER 87.8%
Serve Games: mean 12.8 | median 12.0 | most likely total 11.0
Serve Points Played: mean 78.2 | median 76.0 | most likely total 63.0
Serve Points Won: mean 56.0 | median 54.0 | most likely total 50.0
Aces: mean 11.8 | median 11.0 | most likely total 10.0
Double Faults: mean 1.9 | median 1.0 | most likely total 0.0
Breaks Won: mean 1.8 | median 2.0 | most likely total 2.0
Break Points Created: mean 5.5 | median 5.0 | most likely total 4.0
BP Conversion: 33% projected (1.8 / 5.5)
Opp BP Save: 67% projected (3.6 saved / 5.5 faced)
Opponent Matchup: opp return 26% | context only; official pricing uses the final match tree
Historical sample: 12.4 service games | projected Games Won CV: 21% | projected full exposure 12.8

📊 Stefanos tsitsipas - Player Props:
Games Won: mean 12.26
1st Set Games Won: mean 4.99
Sets Won 0.5: OVER 57.5% | LOW confidence | NO BET
Serve Games: mean 12.7 | median 12.0 | most likely total 10.0
Serve Points Played: mean 82.1 | median 80.0 | most likely total 66.0
Serve Points Won: mean 54.8 | median 54.0 | most likely total 45.0
Aces: mean 11.8 | median 11.0 | most likely total 9.0
Double Faults: mean 1.4 | median 1.0 | most likely total 0.0
Breaks Won: mean 1.0 | median 1.0 | most likely total 0.0
Break Points Created: mean 3.6 | median 3.0 | most likely total 2.0
BP Conversion: 28% projected (1.0 / 3.6)
Opp BP Save: 72% projected (2.6 saved / 3.6 faced)
Opponent Matchup: opp return 23% | context only; official pricing uses the final match tree
Historical sample: 12.4 service games | projected Games Won CV: 32% | projected full exposure 12.7

🎲 Match-Level Context:
Both win a set: NO 54.7% | YES 45.3% | LOW confidence | NO BET
Exact match-score paths: Taylor Fritz 2-0 42.5% | Taylor Fritz 2-1 29.5% | Stefanos tsitsipas 2-0 12.2% | Stefanos tsitsipas 2-1 15.8%
Games/aces/breaks use the same score tree as the match, not a single 2-0 or 2-1 scoreline.
Surface: HARD | service-rate priors: 2024 hierarchical training, player exposure required
Service Outlook: projected aces 23.6 total | double faults 3.3 total

Underdog Games Won: Stefanos tsitsipas (match dog) | Stefanos tsitsipas mean 12.26
Tiebreak action: PICK YES TB | P(any TB) 62.4% | P(no TB) 37.6% | PICK | MEDIUM
Count outlook: expected tiebreaks 0.83 | exactly one 44.0% | chance of 2+ 18.4%
P(any) uncertainty: 35.8% to 88.7% | crosses/touches the 50% decision boundary
Betting status: PASS | the forecast range or betting-confidence rule is not fully satisfied

Update my script then send me it so I can download it