As the baseball community has recently seen with the Astros 2017 cheating scandal, knowing what pitch is being thrown gives batters a game-breaking advantage. However, unless you have an intricate system of cameras and trash cans set up, knowing what pitch is about to be thrown is incredibly difficult. Batters have mere fractions of a second to pick up on signals that might indicate the type of pitch coming their way and even fewer milliseconds to process and act on that information. Signals that could indicate what pitch is being thrown could be a subconscious tell from the pitcher or a sign from a runner looking in at second. But these are either situational or easily avoided by experienced pitchers. A much harder to avoid pitch indicator is the pitcher’s release point.

正如棒球界最近在2017年《太空人》中的作弊丑聞中所看到的那樣，知道投擲什么球距會讓擊球手具有突破性的優勢。 但是，除非您設置了復雜的照相機和垃圾桶系統，否則要知道將要拋出的間距是非常困難的。 擊球手只有幾分之一秒的時間就能接收到可能表明音高類型的信號，甚至更少的毫秒來處理和處理該信息。 可能表明投擲了什么音調的信號可能是投手下意識地發出的信號，也可能是跑步者看了一眼的信號。 但是這些都是有條件的，或是經驗豐富的投手很容易避免。 投手的釋放點是一個更難避免的投球指示器。

The figure above shows Oakland A’s pitcher Sean Manaea’s release point for all of his pitches thrown since 2018. As we can see from the plot, there are relatively distinct differences in release point depending on the type of pitch. For Manaea, he tends to release his fastball lower than any other pitch and his changeup is, on average, released at the highest point. An observant batter could pick up on these signals and use them to their advantage. No trash cans needed, just skill. However, not all pitchers have such distinct differences in their release point. Take Justin Verlander for example:

上圖顯示了Oakland A的投手Sean Manaea自2018年以來投出的所有投球的釋放點。從圖中可以看出，根據投球類型的不同，釋放點存在相對不同的差異。 對于Manaea而言，他傾向于將自己的快球釋放得比其他任何俯仰都低，并且他的換乘平均是在最高點釋放的。 細心的擊球手可能會撿起這些信號，并利用它們來發揮優勢。 無需垃圾桶，只需技巧即可。 但是，并非所有的投手在釋放點上都有如此明顯的不同。 以Justin Verlander為例：

Looking at the graph above, we can see that Verlander’s release points are much more uniform. He doesn’t seem to have a very distinct difference in release point depending on his pitch type. This makes it much harder for a batter to predict what pitch Verlander is throwing based on differences in arm slot.

查看上圖，我們可以看到Verlander的發布點更加統一。 根據他的音高類型，他的釋放點似乎沒有非常明顯的不同。 這使擊球手很難根據臂隙的不同來預測Verlander的投球角度。

Quantifying Ability to Hide Pitches:

量化隱藏音高的能力：

Now that we have seen this difference in ability to hide pitches, a natural question would be: how can we quantify this difference? The way I decided to quantify this ability is by using a classification model. If you don’t know what a classification model is, here is a quick summary. Classification is a machine learning model that attempts to classify data based on certain features. In this particular model the ‘classes’ are the pitch types and the features are the coordinates for the release point and the count the pitch was thrown in. So the model takes the the release point coordinates and the count and does its best to determine what type of pitch was thrown based on that information.

既然我們已經看到了隱藏音高的能力上的差異，那么一個自然的問題將是：如何量化這種差異？ 我決定量化此功能的方法是使用分類模型。 如果您不知道分類模型是什么，請快速總結一下。 分類是一種機器學習模型，試圖基于某些功能對數據進行分類。 在這個特定的模型中，“類”是音高類型，特征是釋放點的坐標以及被拋出音高的計數。因此，該模型將獲取釋放點的坐標和計數，并盡最大努力確定根據該信息拋出的音高類型。

Feature Selection:

功能選擇：

Of course, if I simply wanted to accurately classify what pitch was thrown I could include spin rate and the movement metrics of the pitch as features to make a much more accurate model. But I want to quantify how well pitchers hide their pitches from batters so I only want information that is available to the batter up until the ball is released from the pitcher’s hand. As a consequence, in this model we only have the release coordinates of the pitch and the count it was thrown in as features.

當然，如果我只是想準確地對拋出的音高進行分類，則可以將旋轉速度和音高的運動指標包括在內，以形成更準確的模型。 但是我想量化投手對擊球手的掩飾效果，所以我只希望擊球手能得到的信息直到球從投手手中釋放出來為止。 因此，在此模型中，我們僅具有音高的釋放坐標和作為特征拋出的音高。

Evaluating the Model:

評估模型：

Once the model has attempted to classify the data it is given, we need a way to evaluate how well it classified the data. This is how we will measure a pitcher’s ability to hide pitches from batters. The metric used to evaluate the model is called the precision score. This essentially returns the proportion of pitches that were classified correctly so it will range from 0 to 1. If the model is able to classify a large proportion of pitches correctly (a precision score value closer to 1) that tells us that the pitcher has more distinct differences in release points for his pitches and/or he is very predictable in the pitches he throws in certain counts. A precision score closer to 0 indicates that the model could not effectively classify the pitch type based on release point and count which tells us that the pitcher is much better at releasing pitches from the same point and mixes them up well depending on the count. One thing that needs to be kept in mind is that pitchers with a larger pitch repertoire will have a lower score simply because there are more pitches to classify. To counteract this I will be measuring how well the model does based on how much better it performs compared to simply randomly classifying the pitches. For example, if a pitcher has 4 pitches and you randomly guessed the pitch type you would expect to get 25% of them correct. So if the precision score for a pitcher with 4 pitches is 0.5, its adjusted score would be 2 because it is twice as effective compared to randomly guessing.

一旦模型嘗試對給定的數據進行分類，我們需要一種方法來評估其對數據的分類程度。 這就是我們測量投手隱藏擊球手投球能力的方式。 用于評估模型的度量標準稱為精度得分。 這實際上會返回正確分類的音調的比例，因此范圍為0到1。如果模型能夠正確分類很大比例的音調(精度得分值接近1)，則告訴我們該音調器具有更多他的投擲點在釋放點上的明顯差異和/或他在某些計數下投出的投擲點非常可預測。 精度得分接近0表示該模型無法根據釋放點和計數有效地對音高類型進行分類，這告訴我們該投手在釋放同一點的音高方面要好得多，并且根據計數將它們很好地混合在一起。 需要牢記的一件事是，具有更大音調庫的投手將具有較低的分數，這僅僅是因為要分類的音調更多。 為了解決這個問題，我將根據模型的性能好于簡單隨機分類的音調來衡量模型的性能。 例如，如果一個投手有4個音高，而您隨機猜測了音高類型，那么您會期望其中的25％正確。 因此，如果一個具有4個音高的投手的精確度得分為0.5，則其調整后的得分將為2，因為它的效率是隨機猜測的兩倍。

Results:

結果：

Now that we have defined our model and evaluation metrics, let’s see the results. Here I picked 16 random pitchers from 2019 and ran their pitch data through the model.

現在我們已經定義了模型和評估指標，讓我們看看結果。 在這里，我從2019年挑選了16個隨機水罐，并通過模型運行了它們的水罐數據。

Our ‘winner’ is Blake Snell who has both the highest precision score and highest adjusted score. Snell’s high score suggests that he has distinct release points for his different, let’s see a plot of his release points to verify this.

我們的“勝利者”是布雷克·斯內爾(Blake Snell)，他同時擁有最高的準確度得分和最高的調整后得分。 斯內爾(Snell)的高分表明他有不同的釋放點，讓我們看一下他的釋放點圖以驗證這一點。

The graph above seems to fall in line with Snell’s high precision score. Snell appears to have very distinct areas where he releases his pitches with his changeup being released lower and to the right and his curveball and fastball being released higher and to the left.

上圖似乎與Snell的高精度得分相符。 斯內爾(Snell)似乎有非常不同的區域，他的球高和球高分別向左下方和右下方釋放，而他的曲線球和快球則在上方和左側釋放。

This difference is made even clearer when compared to Gerrit Cole who had the lowest precision score out of the players I tested.

與Gerrit Cole相比，這種差異更加明顯，后者在我測試的球員中得分最低。

Gerrit Cole’s release points are much more muddled and there aren’t clear patterns for where he releases certain pitches. This makes it much more difficult for batters to pick up what pitch is being thrown out of his hand.

杰里特·科爾(Gerrit Cole)的釋放點更加混亂，他釋放特定音高的位置也沒有明確的模式。 這使得擊球手要撿起他手中的球變得更加困難。

Applications:

應用范圍：

While this little experiment was more for my sake, I believe there are a couple ways teams could utilize this data and model.

就我而言，雖然這個小實驗更多，但我相信團隊可以采用幾種方法來利用此數據和模型。

The first application would be for batters to find pitchers with distinct differences in release point. Teams and hitters can then look at the release point plots of those pitchers to find the patterns for the different pitches and use that information to help figure out what pitch is being thrown based on the pitcher’s arm slot. Granted, that is much easier said than done but skilled players could use that information to great effect.

擊球手的第一個應用程序是查找釋放點明顯不同的投手。 然后，團隊和擊球手可以查看那些投手的釋放點圖，以找到不同投手的模式，并使用該信息來幫助根據投手的臂隙來找出要投擲的投手。 當然，這說起來容易做起來難，但是熟練的玩家可以利用這些信息產生巨大的效果。

The second application would be for pitchers. Pitchers that have high precision scores may want to try and lower their precision score by consistently releasing from the same point regardless of the pitch to make it harder for batters to pick up on what is being thrown.

第二個應用是投手。 具有高精確度得分的投手可能想要通過不斷地從同一點釋放而不管其間距如何來降低其精確度得分，從而使擊球手更難撿起所投擲的東西。

If you have any more ideas for how this data can be applied feel free to let me know!

如果您對如何應用這些數據還有其他想法，請隨時告訴我！

The code for the model and data can be found here on my GitHub. This is my first time using classification so any tips or criticisms are greatly appreciated.

該模型和數據的代碼可以在我的GitHub上找到 。 這是我第一次使用分類，因此非常感謝任何提示或批評。