diff --git a/src/uci.cpp b/src/uci.cpp
index 8dd485b0..e28dba32 100644
--- a/src/uci.cpp
+++ b/src/uci.cpp
@@ -222,6 +222,28 @@ namespace {
          << "\nNodes/second    : " << 1000 * nodes / elapsed << endl;
   }
 
+  // The win rate model returns the probability (per mille) of winning given an eval
+  // and a game-ply. The model fits rather accurately the LTC fishtest statistics.
+  int win_rate_model(Value v, int ply) {
+
+     // The model captures only up to 240 plies, so limit input (and rescale)
+     double m = std::min(240, ply) / 64.0;
+
+     // Coefficients of a 3rd order polynomial fit based on fishtest data
+     // for two parameters needed to transform eval to the argument of a
+     // logistic function.
+     double as[] = {-8.24404295, 64.23892342, -95.73056462, 153.86478679};
+     double bs[] = {-3.37154371, 28.44489198, -56.67657741,  72.05858751};
+     double a = (((as[0] * m + as[1]) * m + as[2]) * m) + as[3];
+     double b = (((bs[0] * m + bs[1]) * m + bs[2]) * m) + bs[3];
+
+     // Transform eval to centipawns with limited range
+     double x = Utility::clamp(double(100 * v) / PawnValueEg, -1000.0, 1000.0);
+
+     // Return win rate in per mille (rounded to nearest)
+     return int(0.5 + 1000 / (1 + std::exp((a - x) / b)));
+  }
+
 // When you calculate check sum, save it and check the consistency later.
   uint64_t eval_sum;
 } // namespace