diff --git a/pinnacle_experiments.py b/pinnacle_experiments.py
new file mode 100644
index 0000000..db9d7e1
--- /dev/null
+++ b/pinnacle_experiments.py
@@ -0,0 +1,108 @@
+import numpy as np
+import pandas as pd
+from scipy.special import logit as sp_logit
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import log_loss
+
+
+def compute_metrics(df: pd.DataFrame,
+                    n_bins: int = 10,
+                    bin_strategy: str = 'uniform',  # 'uniform' or 'quantile'
+                    include_draws: bool = True,
+                    eps: float = 1e-6) -> dict:
+    """
+    计算预测评估指标并拟合校准关系。
+
+        参数:
+            - df: 包含至少两列: 'win_prob' (预测主胜概率), 'res' (取 'won','refunded','lost')
+      - n_bins: ECE 分箱数
+      - bin_strategy: 'uniform' (等宽) 或 'quantile' (等频)
+      - include_draws: 若 True, 将 'draw' 视为非胜 (y=0)。若 False, 丢弃 'draw' 行。
+      - eps: 概率裁剪下限，用于数值稳定
+
+    返回:
+      dict 包含 logloss, brier, ece, accuracy, reg_alpha, reg_beta, ece_bins, n_samples
+    """
+    # 处理 refunded
+    if include_draws:
+        mask = df['res'].isin(['won', 'refunded', 'lost'])
+    else:
+        mask = df['res'].isin(['won', 'lost'])
+    df = df[mask].copy()
+
+    # 标签: won=1, others=0 (包括 refunded)
+    y = df['res'].map({'won': 1, 'refunded': 0, 'lost': 0}).astype(int).values
+    p = df['win_prob'].astype(float).values
+
+    # 裁剪概率以保证数值稳定
+    p_clip = np.clip(p, eps, 1 - eps)
+
+    # logloss: 使用 sklearn 实现以获得更稳健的数值行为
+    try:
+        logloss = float(log_loss(y, p_clip, labels=[0, 1]))
+    except Exception:
+        # 备用实现
+        logloss = float(-np.mean(y * np.log(p_clip) + (1 - y) * np.log(1 - p_clip)))
+
+    # brier score
+    brier = float(np.mean((p_clip - y) ** 2))
+
+    # ECE 计算（支持 uniform 或 quantile）
+    if bin_strategy == 'quantile':
+        # quantile bin edges
+        try:
+            edges = np.unique(np.percentile(p_clip, np.linspace(0, 100, n_bins + 1)))
+            if len(edges) - 1 <= 0:
+                # fallback to uniform
+                bin_idxs = np.minimum((p_clip * n_bins).astype(int), n_bins - 1)
+            else:
+                # searchsorted to assign bins
+                bin_idxs = np.clip(np.searchsorted(edges, p_clip, side='right') - 1, 0, len(edges) - 2)
+        except Exception:
+            bin_idxs = np.minimum((p_clip * n_bins).astype(int), n_bins - 1)
+    else:
+        bin_idxs = np.minimum((p_clip * n_bins).astype(int), n_bins - 1)
+
+    ece = 0.0
+    total = len(y)
+    bin_stats = []
+    for b in range(n_bins):
+        idx = bin_idxs == b
+        count = int(idx.sum())
+        if count == 0:
+            bin_stats.append({'count': 0, 'mean_pred': float('nan'), 'emp_freq': float('nan')})
+            continue
+        mean_pred = float(p_clip[idx].mean())
+        emp_freq = float(y[idx].mean())
+        ece += abs(mean_pred - emp_freq) * count
+        bin_stats.append({'count': count, 'mean_pred': mean_pred, 'emp_freq': emp_freq})
+    ece = float(ece / total) if total > 0 else float('nan')
+
+    # accuracy
+    acc = float(np.mean((p_clip >= 0.5) == (y == 1)))
+
+    # 校准拟合: 使用 LogisticRegression 拟合 logit(E[y]) = alpha + beta * logit(p)
+    X = sp_logit(p_clip).reshape(-1, 1)
+    clf = LogisticRegression(C=1e6, solver='lbfgs', max_iter=200)
+    clf.fit(X, y)
+    alpha = float(clf.intercept_[0])
+    beta = float(clf.coef_[0][0])
+
+    return {
+        'logloss': logloss,
+        'brier': brier,
+        'ece': ece,
+        'accuracy': acc,
+        'reg_alpha': alpha,
+        'reg_beta': beta,
+        # 'ece_bins': bin_stats,
+        'n_samples': int(total)
+    }
+
+
+
+if __name__ == '__main__':
+    df = pd.read_feather("data/p_res.feather")
+    df['win_prob'] = df['power_p']
+    res = compute_metrics(df)
+    print(res)