diff --git a/DESCRIPTION b/DESCRIPTION
index ac7741ecd..35a58af93 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.10.8.6
+Version: 0.10.8.7
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",
diff --git a/NEWS.md b/NEWS.md
index 5984b7b9d..f3bb5fdd3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -10,6 +10,9 @@
 * Fixed issue in `binned_residuals()` for models with binary outcome, where
   in rare occasions empty bins could occur.
 
+* `performance_score()` should no longer fail for models where scoring rules
+  can't be calculated. Instead, an informative message is returned.
+
 # performance 0.10.8
 
 ## Changes
diff --git a/R/performance_score.R b/R/performance_score.R
index eb5ee9b31..cd5b7efd6 100644
--- a/R/performance_score.R
+++ b/R/performance_score.R
@@ -65,7 +65,7 @@ performance_score <- function(model, verbose = TRUE, ...) {
 
   if (minfo$is_ordinal || minfo$is_multinomial) {
     if (verbose) {
-      insight::print_color("Can't calculate proper scoring rules for ordinal, multinomial or cumulative link models.\n", "red")
+      insight::format_alert("Can't calculate proper scoring rules for ordinal, multinomial or cumulative link models.")
     }
     return(list(logarithmic = NA, quadratic = NA, spherical = NA))
   }
@@ -74,10 +74,7 @@ performance_score <- function(model, verbose = TRUE, ...) {
 
   if (!is.null(ncol(resp)) && ncol(resp) > 1) {
     if (verbose) {
-      insight::print_color(
-        "Can't calculate proper scoring rules for models without integer response values.\n",
-        "red"
-      )
+      insight::format_alert("Can't calculate proper scoring rules for models without integer response values.")
     }
     return(list(logarithmic = NA, quadratic = NA, spherical = NA))
   }
@@ -127,7 +124,14 @@ performance_score <- function(model, verbose = TRUE, ...) {
   } else {
     datawizard::to_numeric(resp, dummy_factors = FALSE, preserve_levels = TRUE)
   }
-  p_y <- prob_fun(resp, mean = pr$pred, pis = pr$pred_zi, sum(resp))
+  p_y <- .safe(prob_fun(resp, mean = pr$pred, pis = pr$pred_zi, sum(resp)))
+
+  if (is.null(p_y)) {
+    if (verbose) {
+      insight::format_alert("Can't calculate proper scoring rules for this model.")
+    }
+    return(list(logarithmic = NA, quadratic = NA, spherical = NA))
+  }
 
   quadrat_p <- sum(p_y^2)
 
diff --git a/tests/testthat/test-glmmPQL.R b/tests/testthat/test-glmmPQL.R
new file mode 100644
index 000000000..64caaccea
--- /dev/null
+++ b/tests/testthat/test-glmmPQL.R
@@ -0,0 +1,15 @@
+skip_if_not_installed("MASS")
+test_that("r2", {
+  example_dat <- data.frame(
+    prop = c(0.2, 0.2, 0.5, 0.7, 0.1, 1, 1, 1, 0.1),
+    size = c("small", "small", "small", "large", "large", "large", "large", "small", "small"),
+    x = c(0.1, 0.1, 0.8, 0.7, 0.6, 0.5, 0.5, 0.1, 0.1),
+    species = c("sp1", "sp1", "sp2", "sp2", "sp3", "sp3", "sp4", "sp4", "sp4"),
+    stringsAsFactors = FALSE
+  )
+  mn <- MASS::glmmPQL(prop ~ x + size,
+    random = ~ 1 | species,
+    family = "quasibinomial", data = example_dat
+  )
+  expect_message(performance_score(mn), regex = "Cant calculate")
+})