diff --git a/src/me/zombie_striker/neuralnetwork/NNAI.java b/src/me/zombie_striker/neuralnetwork/NNAI.java
index a5fc8b2..91541b2 100644
--- a/src/me/zombie_striker/neuralnetwork/NNAI.java
+++ b/src/me/zombie_striker/neuralnetwork/NNAI.java
@@ -116,6 +116,11 @@ public static NNAI generateAI(NNBaseEntity nnEntityBase,
 
 	public boolean[] think() {
 		this.tick();
+		for(Layer l : this.layers){
+			for(Neuron n: l.neuronsInLayer){
+				n.forceTriggerStengthUpdate();
+			}
+		}
 		boolean[] points = new boolean[getOutputNeurons().size()];
 		for (Neuron n : getOutputNeurons()) {
 			if (n.isTriggered()) {
@@ -189,6 +194,7 @@ public NNAI(Map<String, Object> map) {
 				if (n != null) {
 					n.setAI(this);
 					addNeuron(n);
+					//getNeuronsInLayer(n.layer).add(n);
 				}
 			}
 		}
diff --git a/src/me/zombie_striker/neuralnetwork/NNBaseEntity.java b/src/me/zombie_striker/neuralnetwork/NNBaseEntity.java
index 07725a0..ffafa4a 100644
--- a/src/me/zombie_striker/neuralnetwork/NNBaseEntity.java
+++ b/src/me/zombie_striker/neuralnetwork/NNBaseEntity.java
@@ -83,7 +83,6 @@ public void randomizeNeurons(){
 	}
 	
 	public boolean[] tickAndThink(){
-		ai.tick();
 		return ai.think();
 	}
 
diff --git a/src/me/zombie_striker/neuralnetwork/neurons/Neuron.java b/src/me/zombie_striker/neuralnetwork/neurons/Neuron.java
index bbbb045..43dc488 100644
--- a/src/me/zombie_striker/neuralnetwork/neurons/Neuron.java
+++ b/src/me/zombie_striker/neuralnetwork/neurons/Neuron.java
@@ -62,7 +62,7 @@ public Neuron(NNAI ai, int layer) {
 	}
 
 	public boolean isTriggered() {
-		return getTriggeredStength() > threshold;
+		return getThreshold() < getTriggeredStength();
 	}
 
 	/**
diff --git a/src/me/zombie_striker/neuralnetwork/neurons/input/InputBlockNeuron.java b/src/me/zombie_striker/neuralnetwork/neurons/input/InputBlockNeuron.java
index 0b0b1de..30e1e75 100644
--- a/src/me/zombie_striker/neuralnetwork/neurons/input/InputBlockNeuron.java
+++ b/src/me/zombie_striker/neuralnetwork/neurons/input/InputBlockNeuron.java
@@ -21,6 +21,11 @@
 import me.zombie_striker.neuralnetwork.senses.Senses2D;
 import me.zombie_striker.neuralnetwork.senses.Sensory2D_Numbers;
 
+/**
+ * DO NOT USE. Still updating!
+ * @author ZombieStriker
+ *
+ */
 
 public class InputBlockNeuron extends InputNeuron {
 	
diff --git a/src/me/zombie_striker/neuralnetwork/neurons/input/InputLetterNeuron.java b/src/me/zombie_striker/neuralnetwork/neurons/input/InputLetterNeuron.java
index 4692a65..bd9ef65 100644
--- a/src/me/zombie_striker/neuralnetwork/neurons/input/InputLetterNeuron.java
+++ b/src/me/zombie_striker/neuralnetwork/neurons/input/InputLetterNeuron.java
@@ -56,19 +56,16 @@ public static InputNeuron generateNeuronStatically(NNAI ai, int index,
 			if(letters[index2]==letter)
 				break;	
 		InputNeuron link = new InputLetterNeuron(ai, index, index2, sl);
-		// link.addNeuronData(ai);
 		return link;
 	}
 	public static InputNeuron generateNeuronStatically(NNAI ai, int index,
 			int letter, Sensory2D_Letters sl) {
 		InputNeuron link = new InputLetterNeuron(ai, index, letter, sl);
-		// link.addNeuronData(ai);
 		return link;
 	}
 
 	@Override
 	public boolean isTriggered() {
-		//if (((Sensory2D_Letters) s).getWord().length() > xlink)
 			if (((Sensory2D_Letters) s).getCharacterAt(xlink) == letter)
 				return true;
 		return false;
@@ -76,7 +73,7 @@ public boolean isTriggered() {
 
 	public InputLetterNeuron(Map<String, Object> map) {
 		super(map);
-		this.letter=(char) map.get("char");
+		this.letter= (map.get("char")+"").toCharArray()[0];
 	}
 	@Override
 	public Map<String, Object> serialize() {
diff --git a/src/me/zombie_striker/neuralnetwork/neurons/input/InputMobNeuron.java b/src/me/zombie_striker/neuralnetwork/neurons/input/InputMobNeuron.java
index 165a760..4412239 100644
--- a/src/me/zombie_striker/neuralnetwork/neurons/input/InputMobNeuron.java
+++ b/src/me/zombie_striker/neuralnetwork/neurons/input/InputMobNeuron.java
@@ -20,6 +20,11 @@
 import me.zombie_striker.neuralnetwork.NNAI;
 import me.zombie_striker.neuralnetwork.senses.*;
 
+/**
+ * DO NOT USE. Still updating!
+ * @author ZombieStriker
+ *
+ */
 public class InputMobNeuron extends InputNeuron {
 	
 	public InputMobNeuron(NNAI ai,Sensory2D_Numbers sn) {
diff --git a/src/me/zombie_striker/neuralnetwork/neurons/input/InputNeuron.java b/src/me/zombie_striker/neuralnetwork/neurons/input/InputNeuron.java
index 3349852..ccd65b1 100644
--- a/src/me/zombie_striker/neuralnetwork/neurons/input/InputNeuron.java
+++ b/src/me/zombie_striker/neuralnetwork/neurons/input/InputNeuron.java
@@ -29,7 +29,7 @@ public class InputNeuron extends Neuron {
 	public int ylink;
 	public int zlink;
 	
-	protected Senses s;
+	public Senses s;
 	
 	
 	///public Sensory_Vision entitiesVision;
diff --git a/src/me/zombie_striker/neuralnetwork/neurons/input/InputNumberNeuron.java b/src/me/zombie_striker/neuralnetwork/neurons/input/InputNumberNeuron.java
index 77a1eb2..9169022 100644
--- a/src/me/zombie_striker/neuralnetwork/neurons/input/InputNumberNeuron.java
+++ b/src/me/zombie_striker/neuralnetwork/neurons/input/InputNumberNeuron.java
@@ -17,6 +17,8 @@
  along with this program.  If not, see <https://www.gnu.org/licenses/>.
  **/
 
+import java.util.Map;
+
 import me.zombie_striker.neuralnetwork.NNAI;
 import me.zombie_striker.neuralnetwork.senses.*;
 
@@ -27,6 +29,10 @@ public InputNumberNeuron(NNAI ai, Sensory2D_Numbers sl) {
 		this.s = sl;
 	}
 
+	public InputNumberNeuron(Map<String,Object> map) {
+		super(map);
+	}
+	
 	public InputNumberNeuron(NNAI ai, int row, int col,
 			Sensory2D_Numbers sl) {
 		super(ai, row, col,sl);
diff --git a/src/me/zombie_striker/neuralnetwork/neurons/input/InputTickNeuron.java b/src/me/zombie_striker/neuralnetwork/neurons/input/InputTickNeuron.java
index cdc975b..0d89d8d 100644
--- a/src/me/zombie_striker/neuralnetwork/neurons/input/InputTickNeuron.java
+++ b/src/me/zombie_striker/neuralnetwork/neurons/input/InputTickNeuron.java
@@ -20,6 +20,11 @@
 import me.zombie_striker.neuralnetwork.NNAI;
 import me.zombie_striker.neuralnetwork.senses.*;
 
+/**
+ * DO NOT USE. Still updating!
+ * @author ZombieStriker
+ *
+ */
 public class InputTickNeuron extends InputNeuron {
 
 	int maxTick=0;
diff --git a/src/me/zombie_striker/neuralnetwork/senses/Sensory2D_Letters.java b/src/me/zombie_striker/neuralnetwork/senses/Sensory2D_Letters.java
index f4d6e56..8f5198f 100644
--- a/src/me/zombie_striker/neuralnetwork/senses/Sensory2D_Letters.java
+++ b/src/me/zombie_striker/neuralnetwork/senses/Sensory2D_Letters.java
@@ -26,8 +26,8 @@ public class Sensory2D_Letters implements Senses2D{
 
 
 	private char[] letters= InputLetterNeuron.letters;
-	private String word;
-	private String fullWord;
+	private String word="null";
+	private String fullWord="null";
 	public static final int MAX_LETTERS = 19;
 	
 	@Override
@@ -49,7 +49,15 @@ public Sensory2D_Letters(String word) {
 	}
 
 	public char getCharacterAt(int index) {
+		if(fullWord==null)return ' ';
+		try{
 		return fullWord.charAt(index);
+		}catch(Exception e){
+			/**
+			 * Seriously, I have tried doing a length check, a null check. Do I seriously need to add a catch statement?>
+			 */
+			return ' ';
+		}
 	}
 	public String getWord(){
 		return word;
@@ -64,8 +72,6 @@ public void changeWord(String word) {
 		fullWord = sb.toString();
 	}
 	public Sensory2D_Letters(Map<String, Object> map) {
-		word = null;
-		fullWord=null;
 	}
 
 	@Override
diff --git a/src/me/zombie_striker/neuralnetwork/util/DeepReinforcementUtil.java b/src/me/zombie_striker/neuralnetwork/util/DeepReinforcementUtil.java
index 42f8a66..3e3d319 100644
--- a/src/me/zombie_striker/neuralnetwork/util/DeepReinforcementUtil.java
+++ b/src/me/zombie_striker/neuralnetwork/util/DeepReinforcementUtil.java
@@ -40,6 +40,28 @@ public class DeepReinforcementUtil {
 	 */
 	public static void instantaneousReinforce(NNBaseEntity base,
 			Neuron[] neuronsThatShouldBeTrue, int repetitions) {
+		instantaneousReinforce(base, neuronsThatShouldBeTrue, repetitions, 0);
+	}
+
+	/**
+	 * Teaches the NN for an instantaneous, constant answer regarding a single
+	 * response. Useful for true-false situations
+	 * 
+	 * @param Base
+	 *            The NeuralEntity
+	 * @param neuronsThatShouldBeTrue
+	 *            Array of all neuons that should be equal to 1. The rest will
+	 *            try to be decreased to -1;
+	 * @param repetitions
+	 *            the amount of repetitions for the training.
+	 * @param chanceForSkip
+	 *            The chance that a neuron will be skipped. This is done to make
+	 *            sure neurons are not over specialized for each time the NN is
+	 *            reinforced.
+	 */
+	public static void instantaneousReinforce(NNBaseEntity base,
+			Neuron[] neuronsThatShouldBeTrue, int repetitions,
+			double chanceForSkip) {
 		HashMap<Neuron, Double> h = new HashMap<>();
 		for (Neuron n : base.ai.getOutputNeurons()) {
 			boolean isRightNeuron = false;
@@ -51,7 +73,7 @@ public static void instantaneousReinforce(NNBaseEntity base,
 			}
 			h.put(n, isRightNeuron ? 1.0 : -1.0);
 		}
-		instantaneousReinforce(base, h, repetitions);
+		instantaneousReinforce(base, h, repetitions, chanceForSkip);
 	}
 
 	/**
@@ -68,72 +90,93 @@ public static void instantaneousReinforce(NNBaseEntity base,
 	 */
 	public static void instantaneousReinforce(NNBaseEntity base,
 			HashMap<Neuron, Double> correctValues, int repetitions) {
+		instantaneousReinforce(base, correctValues, repetitions, 0);
+	}
+
+	/**
+	 * Teaches the NN for an instantaneous, constant answer regarding a single
+	 * response. Useful for true-false situations
+	 * 
+	 * @param Base
+	 *            the NeuralEntity
+	 * @param correctValues
+	 *            a Hashmap of all neurons and the values they should be equal
+	 *            to
+	 * @param repetitions
+	 *            the amount of repetitions for the training.
+	 * @param chanceForSkip
+	 *            The chance that a neuron will be skipped. This is done to make
+	 *            sure neurons are not over specialized for each time the NN is
+	 *            reinforced.
+	 * 
+	 */
+	public static void instantaneousReinforce(NNBaseEntity base,
+			HashMap<Neuron, Double> correctValues, int repetitions,
+			double chanceForSkip) {
 		final double step = 0.01;
 
 		for (int loops = 0; loops < repetitions; loops++) {
 			HashMap<Neuron, Double> suggestedValueForNeuron = correctValues;
 			// Subtract -1 a second time to not get the outputs
-			//for (int layer = base.ai.MAX_LAYERS - 1 - 1; layer >= 0; layer--) {
-			for (int layer = 0;layer <base.ai.MAX_LAYERS - 1 ; layer++) {
+			// for (int layer = base.ai.MAX_LAYERS - 1 - 1; layer >= 0; layer--)
+			// {
+			for (int layer = 0; layer < base.ai.MAX_LAYERS - 1; layer++) {
+				HashMap<Neuron, Double> lastOutputs = new HashMap<Neuron, Double>();
+				// Since no new outputs are created, nor are any destroyed, we
+				// do not need to clear it: simply adding new values will
+				// override the existing vals.
 				for (Neuron n : base.ai.getNeuronsInLayer(layer)) {
-					
-					
-					//Do thresh checks before if is not triggerred
+					if (Math.random() < chanceForSkip)
+						continue;
+
+					// Do thresh checks before if is not triggered
 
 					// Do thresholds after everything else
 					if (!n.isTriggered()) {
 						double orgThr = n.getThreshold();
-						HashMap<Neuron, Double> currentValsThre = new HashMap<>();
-						for (Neuron outputs : suggestedValueForNeuron.keySet()) {
-							currentValsThre.put(outputs,
+						for (Neuron outputs : suggestedValueForNeuron.keySet())
+							lastOutputs.put(outputs,
 									outputs.getTriggeredStength());
-						}
-						
-						if (!(n instanceof InputNeuron))
-							if (n.getTriggeredStength() <= n.getThreshold()) {
-								n.setThreshold(-2);
-								n.forceTriggerUpdateTree();
-								double shouldLowerVal = 0;
-								for (Entry<Neuron, Double> c : currentValsThre
-										.entrySet()) {
-									double sug = suggestedValueForNeuron.get(c
-											.getKey());
-									shouldLowerVal += (Math.abs(c.getValue()
-											- sug) - Math.abs(c.getKey()
-											.getTriggeredStength() - sug));
-								}
-								n.setThreshold(orgThr);
-								if (shouldLowerVal > 0) {
-									n.setThreshold(removeExtremes(n.getThreshold() - step));
-								}
+
+						if (!(n instanceof InputNeuron)
+								&& n.getTriggeredStength() <= n.getThreshold()) {
+							n.setThreshold(-2);
+							n.forceTriggerUpdateTree();
+							double shouldLowerVal = 0;
+							for (Entry<Neuron, Double> c : lastOutputs
+									.entrySet()) {
+								double sug = suggestedValueForNeuron.get(c
+										.getKey());
+								shouldLowerVal += (Math.abs(c.getValue() - sug) - Math
+										.abs(c.getKey().getTriggeredStength()
+												- sug));
+							}
+							n.setThreshold(orgThr);
+							if (shouldLowerVal > 0) {
+								n.setThreshold(removeExtremes(n.getThreshold()
+										- step));
 							}
+						}
 						continue;
 					}
-					
-					
-					
-					
-					
-					HashMap<Neuron, Double> currentVals2 = new HashMap<>();
-					for (Neuron outputs : suggestedValueForNeuron.keySet()) {
-						currentVals2
-								.put(outputs, outputs.getTriggeredStength());
-					}
-					n.setWeight(n.getWeight() - (step));
+
+					for (Neuron outputs : suggestedValueForNeuron.keySet())
+						lastOutputs.put(outputs, outputs.getTriggeredStength());
+
+					n.setWeight(n.getWeight() - step);
 					n.forceTriggerUpdateTree();
 					double change2 = 0.0;
-					for (Entry<Neuron, Double> c : currentVals2.entrySet()) {
+					for (Entry<Neuron, Double> c : lastOutputs.entrySet()) {
 						// TODO: Multipled by 100 so the change is not a small
 						// number.
-						int wasRightDirection = (Math
-								.abs((suggestedValueForNeuron.get(c.getKey()) - c
-										.getKey().getTriggeredStength())) <= Math
-								.abs(suggestedValueForNeuron.get(c.getKey())
-										- c.getValue()) ? 1 : -1);
+						double sug = suggestedValueForNeuron.get(c.getKey());
+						int wasRightDirection = Math.abs(sug
+								- c.getKey().getTriggeredStength()) <= Math
+								.abs(sug - c.getValue()) ? 1 : -1;
 
 						change2 += wasRightDirection
-								* (Math.abs((c.getKey().getTriggeredStength() * 100)
-										- (c.getValue() * 100)));
+								* Math.abs(c.getKey().getTriggeredStength()
+										- c.getValue());
 						/*
 						 * change2 += ((Math
 						 * .abs((c.getKey().getTriggeredStength() * 100) -
@@ -147,7 +190,7 @@ public static void instantaneousReinforce(NNBaseEntity base,
 					}
 
 					if (change2 == 0.0) {
-						n.setWeight(n.getWeight() + (step));
+						n.setWeight(n.getWeight() + step);
 					} else if (change2 < 0.0) {
 						n.setWeight(n.getWeight() + (step * 2));
 					}
@@ -158,29 +201,26 @@ public static void instantaneousReinforce(NNBaseEntity base,
 						Neuron outputNeuronInstance = n.getAI()
 								.getNeuronFromId(outputNeuronId);
 
-						HashMap<Neuron, Double> currentVals = new HashMap<>();
-						for (Neuron outputs : suggestedValueForNeuron.keySet()) {
-							currentVals.put(outputs,
+						for (Neuron outputs : suggestedValueForNeuron.keySet())
+							lastOutputs.put(outputs,
 									outputs.getTriggeredStength());
-						}
+
 						n.setStrengthForNeuron(outputNeuronInstance,
 								n.getOutputForNeuron(outputNeuronInstance)
-										- (step));
+										- step);
 						n.forceTriggerStengthUpdate();
 						outputNeuronInstance.forceTriggerUpdateTree();
 						double change = 0.0;
-						for (Entry<Neuron, Double> c : currentVals.entrySet()) {
-							int wasRightDirection = (Math
-									.abs((suggestedValueForNeuron.get(c
-											.getKey()) - c.getKey()
-											.getTriggeredStength())) <= Math
-									.abs(suggestedValueForNeuron.get(c.getKey())
-											- c.getValue()) ? 1 : -1);
+						for (Entry<Neuron, Double> c : lastOutputs.entrySet()) {
+							double sug = suggestedValueForNeuron
+									.get(c.getKey());
+							int wasRightDirection = (Math.abs(sug
+									- c.getKey().getTriggeredStength()) <= Math
+									.abs(sug - c.getValue()) ? 1 : -1);
 
 							change += wasRightDirection
-									* (Math.abs((c.getKey()
-											.getTriggeredStength() * 100)
-											- (c.getValue() * 100)));
+									* Math.abs(c.getKey().getTriggeredStength()
+											- c.getValue());
 							/*
 							 * change += ((Math.abs((c.getKey()
 							 * .getTriggeredStength() * 100) - (c.getValue() *
@@ -208,70 +248,59 @@ public static void instantaneousReinforce(NNBaseEntity base,
 						n.forceTriggerStengthUpdate();
 						outputNeuronInstance.forceTriggerUpdateTree();
 					}
-					
-					
-					//Do check for inherit bias of neuron
-					HashMap<Neuron, Double> currentVals3 = new HashMap<>();
-					for (Neuron outputs : suggestedValueForNeuron.keySet()) {
-						currentVals3
-								.put(outputs, outputs.getTriggeredStength());
-					}
-					n.setBias(n.getBias()-step);
+
+					// Do check for inherit bias of neuron
+					for (Neuron outputs : suggestedValueForNeuron.keySet())
+						lastOutputs.put(outputs, outputs.getTriggeredStength());
+					n.setBias(n.getBias() - step);
 					n.forceTriggerUpdateTree();
 					double change3 = 0.0;
-					for (Entry<Neuron, Double> c : currentVals3.entrySet()) {
+					for (Entry<Neuron, Double> c : lastOutputs.entrySet()) {
 						// TODO: Multipled by 100 so the change is not a small
 						// number.
-						int wasRightDirection = (Math
-								.abs((suggestedValueForNeuron.get(c.getKey()) - c
-										.getKey().getTriggeredStength())) <= Math
-								.abs(suggestedValueForNeuron.get(c.getKey())
-										- c.getValue()) ? 1 : -1);
+						double sug = suggestedValueForNeuron.get(c.getKey());
+						int wasRightDirection = Math.abs(sug
+								- c.getKey().getTriggeredStength()) <= Math
+								.abs(sug - c.getValue()) ? 1 : -1;
 
 						change2 += wasRightDirection
-								* (Math.abs((c.getKey().getTriggeredStength() * 100)
-										- (c.getValue() * 100)));
+								* Math.abs(c.getKey().getTriggeredStength()
+										- c.getValue());
 
 					}
-					//Step > 0 is already accounted for, since we already deceased the step
+					// Step > 0 is already accounted for, since we already
+					// deceased the step
 					if (change3 == 0.0) {
-						n.setBias(n.getBias()+step);
+						n.setBias(n.getBias() + step);
 					} else if (change3 < 0.0) {
-						n.setBias(n.getBias()+(step*2));
+						n.setBias(n.getBias() + (step * 2));
 					}
-					n.setBias(removeExtremes(n.getBias(),50));
+					n.setBias(removeExtremes(n.getBias(), 50));
 					n.forceTriggerUpdateTree();
-					
-					
-					
-					//Do Thresh checks after everything else if is triggered
-						if (!(n instanceof InputNeuron))
-							if (n.getTriggeredStength() > n.getThreshold()) {
-								double orgThr = n.getThreshold();
-								HashMap<Neuron, Double> currentValsThre = new HashMap<>();
-								for (Neuron outputs : suggestedValueForNeuron.keySet()) {
-									currentValsThre.put(outputs,
-											outputs.getTriggeredStength());
-								}
-								
-								n.setThreshold(2);
-								n.forceTriggerUpdateTree();
-								double shouldIncreaseVal = 0;
-								for (Entry<Neuron, Double> c : currentValsThre
-										.entrySet()) {
-									double sug = suggestedValueForNeuron.get(c
-											.getKey());
-									shouldIncreaseVal += (Math.abs(c.getValue()
-											- sug) - Math.abs(c.getKey()
-											.getTriggeredStength() - sug));
-								}
-								n.setThreshold(orgThr);
-								if (shouldIncreaseVal > 0) {
-									n.setThreshold(removeExtremes(n.getThreshold() + step));
-								}
-							}
+
+					// Do Thresh checks after everything else if is triggered
+					if (!(n instanceof InputNeuron)
+							&& n.getThreshold() < n.getTriggeredStength()) {
+						double orgThr = n.getThreshold();
+						for (Neuron outputs : suggestedValueForNeuron.keySet())
+							lastOutputs.put(outputs,
+									outputs.getTriggeredStength());
+
+						n.setThreshold(2);
+						n.forceTriggerUpdateTree();
+						double shouldIncreaseVal = 0;
+						for (Entry<Neuron, Double> c : lastOutputs.entrySet()) {
+							double sug = suggestedValueForNeuron
+									.get(c.getKey());
+							shouldIncreaseVal += (Math.abs(c.getValue() - sug) - Math
+									.abs(c.getKey().getTriggeredStength() - sug));
+						}
+						n.setThreshold(orgThr);
+						if (shouldIncreaseVal > 0)
+							n.setThreshold(removeExtremes(n.getThreshold()
+									+ step));
 					}
-				
+				}
 			}
 		}
 	}
@@ -283,6 +312,7 @@ private static double removeExtremes(double d) {
 			return -1;
 		return d;
 	}
+
 	private static double removeExtremes(double d, double max) {
 		if (d > max)
 			return max;
diff --git a/src/me/zombie_striker/nnmain/Main.java b/src/me/zombie_striker/nnmain/Main.java
index ba03939..bc61620 100644
--- a/src/me/zombie_striker/nnmain/Main.java
+++ b/src/me/zombie_striker/nnmain/Main.java
@@ -100,6 +100,8 @@ public void onLoad() {
 
 	protected static Main plugin;
 
+	protected boolean enableMetrics = true;
+
 	@Override
 	public void onDisable() {
 		plugin = null;
@@ -133,6 +135,12 @@ public void onEnable() {
 		Bukkit.getPluginManager().registerEvents(
 				new ExampleSwearListener(getNn()), this);
 
+		if (!getConfig().contains("enableStats")) {
+			getConfig().set("enableStats", true);
+			saveConfig();
+		}
+		enableMetrics = getConfig().getBoolean("enableStats");
+
 		new Updater(this, 280241);
 		/**
 		 * Everyone should want the most up to date version of the plugin, so
@@ -141,14 +149,22 @@ public void onEnable() {
 		 * anything, and even if I did, I would deprecate the methods for a long
 		 * period of time before I do, nothing should really break.
 		 */
-
-		Metrics metrics = new Metrics(this);
-		/**
-		 * I use bStats metrics to monitor how many servers are using my API.
-		 * This does not send any personal/private information (this only sends
-		 * the server version, Java version, the plugin's version, and system
-		 * architecture)
-		 */
+		if (enableMetrics) {
+			/**
+			 * I use bStats metrics to monitor how many servers are using my
+			 * API. This does not send any personal/private information. This
+			 * only sends:
+			 * 
+			 * the server version, Java version, 
+			 * the plugin's version, 
+			 * system architecture,
+			 * Core count,
+			 * 
+			 * You can view the stats being collected at:
+			 * https://bstats.org/plugin/bukkit/NeuralNetworkAPI
+			 */
+			Metrics metrics = new Metrics(this);
+		}
 	}
 
 	public static Main getMainClass() {