simplify risk tolerance

Passed Non-regression STC:
LLR: 2.98 (-2.94,2.94) <-1.75,0.25>
Total: 73408 W: 19028 L: 18844 D: 35536
Ptnml(0-2): 201, 8709, 18743, 8807, 244
https://tests.stockfishchess.org/tests/view/68051f3698cd372e3ae9f63a

Passed Non-regression LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 91236 W: 23193 L: 23045 D: 44998
Ptnml(0-2): 34, 9908, 25599, 10030, 47
https://tests.stockfishchess.org/tests/view/6805239498cd372e3ae9fa41

closes https://github.com/official-stockfish/Stockfish/pull/6000

bench 1864632

src/search.cpp

@@ -97,31 +97,6 @@ int correction_value(const Worker& w, const Position& pos, const Stack* const ss
     return 7685 * pcv + 7495 * micv + 9144 * (wnpcv + bnpcv) + 6469 * cntcv;
 }
 
-int risk_tolerance(const Position& pos, Value v) {
-    // Returns (some constant of) second derivative of sigmoid.
-    static constexpr auto sigmoid_d2 = [](int x, int y) {
-        return 644800 * x / ((x * x + 3 * y * y) * y);
-    };
-
-    int m = pos.count<PAWN>() + pos.non_pawn_material() / 300;
-
-    // a and b are the crude approximation of the wdl model.
-    // The win rate is: 1/(1+exp((a-v)/b))
-    // The loss rate is 1/(1+exp((v+a)/b))
-    int a = 356;
-    int b = ((65 * m - 3172) * m + 240578) / 2048;
-
-    // guard against overflow
-    assert(abs(v) + a <= std::numeric_limits<int>::max() / 644800);
-
-    // The risk utility is therefore d/dv^2 (1/(1+exp(-(v-a)/b)) -1/(1+exp(-(-v-a)/b)))
-    // -115200x/(x^2+3) = -345600(ab) / (a^2+3b^2) (multiplied by some constant) (second degree pade approximant)
-    int winning_risk = sigmoid_d2(v - a, b);
-    int losing_risk  = sigmoid_d2(v + a, b);
-
-    return -(winning_risk + losing_risk) * 32;
-}
-
 // Add correctionHistory value to raw staticEval and guarantee evaluation
 // does not hit the tablebase range.
 Value to_corrected_static_eval(const Value v, const int cv) {
@@ -150,6 +125,29 @@ void update_correction_history(const Position& pos,
           << bonus * 143 / 128;
 }
 
+int risk_tolerance(Value v) {
+    // Returns (some constant of) second derivative of sigmoid.
+    static constexpr auto sigmoid_d2 = [](int x, int y) {
+        return 644800 * x / ((x * x + 3 * y * y) * y);
+    };
+
+    // a and b are the crude approximation of the wdl model.
+    // The win rate is: 1/(1+exp((a-v)/b))
+    // The loss rate is 1/(1+exp((v+a)/b))
+    int a = 356;
+    int b = 123;
+
+    // guard against overflow
+    assert(abs(v) + a <= std::numeric_limits<int>::max() / 644800);
+
+    // The risk utility is therefore d/dv^2 (1/(1+exp(-(v-a)/b)) -1/(1+exp(-(-v-a)/b)))
+    // -115200x/(x^2+3) = -345600(ab) / (a^2+3b^2) (multiplied by some constant) (second degree pade approximant)
+    int winning_risk = sigmoid_d2(v - a, b);
+    int losing_risk  = sigmoid_d2(v + a, b);
+
+    return -(winning_risk + losing_risk) * 32;
+}
+
 // Add a small random component to draw evaluations to avoid 3-fold blindness
 Value value_draw(size_t nodes) { return VALUE_DRAW - 1 + Value(nodes & 0x2); }
 Value value_to_tt(Value v, int ply);
@@ -1218,7 +1216,7 @@ moves_loop:  // When in check, search starts here
         r -= std::abs(correctionValue) / 29696;
 
         if (PvNode && std::abs(bestValue) <= 2000)
-            r -= risk_tolerance(pos, bestValue);
+            r -= risk_tolerance(bestValue);
 
         // Increase reduction for cut nodes
         if (cutNode)