Outright Model

Default data tool: BirdieAPI (birdieapi-tool). get_ratings with as_of (2 credits) is the input. get_course_history (5) adds fit. get_winner_odds (5) gives the market to compare against. get_leaderboard (2) gives past fields and cut sizes.

You are an expert in simulation models for golf winners. Your goal is to produce honest win probabilities with uncertainty, show which assumptions move them most, and compare them with the market without implying a bet follows.

When to Use

• "What is the chance Scheffler wins the PGA Championship?"
• "Simulate the Open with these ratings."
• "Why does my model give the favourite 7% when the books imply 15%?"
• Building the probability input for edge-detection

When NOT to Use

• Staking or EV -- see edge-detection
• Testing the model over history -- see walk-forward-validation and backtesting
• Calibrating the output -- see probability-calibration

Commands Available

Commands That Do NOT Exist

Model

For golfer i in a field of n, in round r:

strokes_vs_field[i, r] = skill[i] + fit[i] - field_mean + eps[i, r]
skill[i]  ~ Normal(rating[i], tau)        # rating uncertainty, drawn once per simulated tournament
eps[i, r] ~ Normal(0, sigma)               # round-to-round noise

Higher is better. Winner = highest total over four rounds (after the cut: 36 holes, then the cut line, then the weekend). Ties are rare in a continuous simulation; real ties go to a playoff, which is close to a coin toss among the tied.

Inputs:

• rating[i] from get_ratings with as_of = the day before the first round.
• fit[i] from course-fit (optional, shrunk).
• sigma: round-to-round standard deviation, estimated from get_player_results (adj_vs_field per round).
• tau: how uncertain the rating is. Larger for few weighted_rounds.
• The field: n golfers and their ratings. The field mean cancels in the winner comparison but matters for the cut.

How It Works

Step 1: Ratings before the event.

get_ratings {"as_of": "2024-04-04", "limit": 500, "min_rounds": 20}

Real response: 313 rated players, best 2.88 (Scottie Scheffler), lowest -1.68. Ratings are within one tour, so for LIV golfers in a major, widen tau and say why; their rating is built against LIV fields.

Step 2: The field. The upcoming field is not in the data. Take the priced golfers as a proxy:

get_winner_odds {"tournament_id": "401580344", "limit": 200, "bookmaker": "draftkings"}

115 golfers were priced for the 2024 Masters; 97 of them had a rating. The unrated ones (old champions, amateurs, qualifiers) are mostly weak. Dropping them makes the field artificially strong and pushes every probability down. Give them a rating below the lowest rated player, or flag it in the output.

Step 3: Estimate sigma.

get_player_results {"player_id": "9478", "season": 2023, "limit": 50}

Real response: 96 rounds with adj_vs_field, mean 3.35, standard deviation 2.53. Pool several players rather than using one. Note this standard deviation also includes slow drift in form, so it slightly overstates pure round noise; that is a modelling choice to state, not hide.

Step 4: Simulate.

import numpy as np

def simulate(rating, sigma, tau, cut_n, n_sims=20000, seed=7):
    rng = np.random.default_rng(seed)
    wins = np.zeros(len(rating))
    for _ in range(n_sims // 1000):
        skill = rating[None, :] + rng.normal(0, tau, (1000, len(rating)))
        tot = np.zeros((1000, len(rating)))
        for rd in range(4):
            tot += skill + rng.normal(0, sigma, (1000, len(rating)))
            if rd == 1:                                   # cut after round 2
                kth = np.sort(tot, axis=1)[:, -cut_n][:, None]
                tot = np.where(tot < kth, -1e9, tot)
        wins += np.bincount(np.argmax(tot, axis=1), minlength=len(rating))
    return wins / n_sims

cut_n comes from the tournament row (cut_count was 50 for the 2024 Masters). Ties at the cut line would let a few more through; ignore them for winner probabilities and say so.

Step 5: Run the sensitivity, not one number. First-pass illustration on the 97 rated, priced golfers of the 2024 Masters (ratings as of 2024-04-04, 20,000 simulations, seed 7, no course fit):

For comparison, the DraftKings latest price devigged by the power or Shin method put Scheffler at 15% to 16% (see odds-analysis). The favourite's probability swings from 6.7% to 14.2% on two assumptions. That is the main lesson: the output of a rating-based simulation is governed by sigma and tau, which must be estimated and validated, not picked to match the market. Matching the market by tuning the inputs defeats the point of the comparison.

Step 6: Add fit and the market. Add fit[i] from course-fit to skill. Then compare to the devigged market: where the model disagrees by more than its own sensitivity range, ask why before concluding anything. Common reasons: ratings lag a form change; injury or withdrawal knowledge the data lacks; a field mismatch; a wrong sigma.

Step 7: Blend, don't replace. The market contains information a rating does not (injury news, equipment, preparation). A defensible final probability is a weighted average of model and devigged market in log-odds, with the weight set by out-of-sample log loss in walk-forward-validation.

Limits to state every time

• Only about two dozen majors have winner prices in the data, so only about two dozen winners to validate against. See backtesting.
• Score-based ratings, no shot data, no weather, no tee times, no injury information.
• Four-round stroke play only. Match play and team formats are not modelled.
• The 2024 numbers above are a worked illustration of method, not a forecast or a recommendation.

Credit Usage

Anti-patterns

Output Format

2024 Masters win probability, rating-based simulation (97 golfers, 20,000 runs)
Scottie Scheffler: 7% to 14% depending on sigma and tau (see table). Market, devigged: 15% to 16%.
Inputs: ratings as of 2024-04-04; sigma from data; no course fit; no weather or injury information.

What to Do Next