鼠标移动到ul图片会摆动_我们可以从摆动时序分析中学到的三件事

鼠标移动到ul图片会摆动

An opportunity for a new kind of analysis of Major League Baseball data may be upon us soon. Here’s how we can prepare.

不久之后,我们将有机会对美国职棒大联盟数据进行新的分析。 这是我们准备的方法。

It is tempting to think that we are currently tracking everything we possibly can in a baseball game, but this is untrue. Although we have a sea of data relating to the position of the baseball at all times, there are still plenty of aspects of the game that are completely ignored in our data collection (as far as I know). The foremost aspect that is ripe for analysis once we begin tracking it, in my opinion, is data relating to whether each swing was early, on time, or late which I will refer to as swing timing data.

很容易以为我们目前正在跟踪棒球比赛中可能会发生的一切,但这是不正确的。 尽管我们一直都有与棒球位置相关的大量数据,但据我们所知,在我们的数据收集中,仍有很多游戏方面被完全忽略。 我认为,一旦我们开始对其进行跟踪分析,最适合进行分析的方面是有关每个挥杆是早,准时还是晚的数据,我将其称为挥杆计时数据。

Although we have understood for decades that the timing of a swing can influence whether the pitch is hit or not, there is no publicly available data about this piece of information! Throughout the data revolution in baseball, the timing of each swing has simply not been recorded. One likely reason for this is that it is difficult to figure out whether a swing was too early, too late, or on time, but that didn’t stop me from trying!

尽管几十年来我们已经了解到,挥杆的时间会影响是否击中音高,但没有关于此信息的公开数据! 在棒球的整个数据革命中,每次挥杆的时间都没有被记录下来。 造成这种情况的一个可能原因是,很难确定挥杆是否为时过早,太晚或准时,但这并没有阻止我尝试!

Armed with a bit of sample data and some imagination, I set out to unearth a whole new area of baseball analysis that may be an important part of the impending arms race of MLB teams competing to find the best new uses for their Hawkeye data.

有了一些样本数据和一些想象力,我开始发掘棒球分析的一个全新领域,这可能是美国职业棒球大联盟(MLB)团队为寻找其Hawkeye数据的最佳新用途而即将进行的军备竞赛中的重要组成部分。

样本数据 (Sample Data)

For this project, I watched all 160 total whiffs from 5 different hitters in 2020 and did my best to label each swing as being Early, On Time, Late, or a Checked Swing. This data was collected to give us an idea of what kinds of questions we could answer with comprehensive swing timing data and NOT to actually answer any of those questions with certainty.

在这个项目中,我观看了2020年来自5个不同击球手的全部160种鞭打动作,并尽力将每个挥杆标记为“早起,准时,迟发或格纹”。 收集这些数据是为了让我们了解我们可以使用全面的挥杆计时数据回答哪些问题,而不是确定地实际回答任何这些问题。

I selected five sample players who had different offensive profiles and watched each of their 2020 whiffs (as of August 13th), grouping them by their timing, early, on time, late, or checked. These hitters were:

我选择了五名样本球员,他们的进攻方式各不相同,并观看了他们2020年的每种嗅觉(截至8月13日),并按时间,早,时,晚或检查的时间分组。 这些打击者是:

DJ LeMahieu: High Production, Low Whiff (88th percentile xwOBA, 99th percentile Whiff Rate)

DJ LeMahieu:高产量,低通气度(xwOBA为88%,通气率为99%)

Eddie Rosario: Low Production, Low Whiff (22nd, 87th)

埃迪·罗萨里奥 ( Eddie Rosario):低产量,低嗅觉(第22名,第87名)

Fernando Tatis, Jr.: High Production, Low Whiff (87th, 21st)

小费尔南多·塔蒂斯( Fernando Tatis ):高产量,低嗅觉(第87、21名)

Mike Zunino: Low Production, High Whiff (0th, 6th)

迈克·祖尼诺 ( Mike Zunino):低产量,高嗅觉(0、6)

Max Muncy: Average Production, Average Whiff (52nd, 52nd)

最大黏度平均产量,平均嗅觉(第52、52)

By choosing a diverse group of hitters, I was able to expand the types of questions I could ask from this kind of analysis! Without further adieu, here are the most interesting questions that I think could be answered with comprehensive swing timing data.

通过选择多样化的击球手,我可以扩展从此类分析中可以提出的问题类型! 没有更多的理由,我认为可以通过综合的挥杆计时数据来回答最有趣的问题。

#1。 为什么击球手会摇摆而错过? (#1. Why Do Hitters Swing And Miss?)

Hitting a baseball is hard. But why? In my view, hitting has 3 main components: timing, power, and barrel placement. You need all 3 to make hard contact. If your swing only has two of the main components, you can expect to whiff or get weak contact.

打棒球很难。 但为什么? 在我看来,击球有3个主要组成部分:计时,力量和发条盒位置。 您需要三者才能保持密切联系。 如果您的挥杆动作只有两个主要成分,则您可能会闻起来或接触不良。

Image for post
Image By Author
图片作者

As you can see, I believe not all whiffs are created equally and that there are two main types of whiffs which I will name and describe here.

如您所见,我相信并不是所有的鞭子都是一样的,我将在这里命名和描述两种主要的鞭子。

A Type 1 Whiff is a swing with power and good timing, but the bat is in the wrong place. The whiff is a result of the hitter’s miscalculation of where the pitch will be in the strike zone when it crosses the plate. Against Jakob Junis on August 7th, Rosario swung under a high Fastball. His swing was both powerful and on time, but was too far under the ball, resulting in a Type 1 Whiff. (Video)

1号鞭子是一种挥杆有力且时机不错的挥杆,但蝙蝠的位置不正确。 挥动是由于击球手错误估计了击球越过板时击球区域中的音高位置。 8月7日,对阵雅各布·朱尼斯(Jakob Junis),罗萨里奥(Rosario)在高水平的快球(Fastball)下摆动。 他的挥杆既有力又准时,但在球下距离太远,造成了1型鞭打。 ( 视频 )

In contrast, a Type 2 Whiff is a swing with power and good barrel placement but poor timing. This type of whiff is the result of the hitter’s miscalculation of when the pitch will cross the strike zone. These occur when the hitter swings too late or too early. On July 24th against Hyun-Jin Ryu, Mike Zunino was too early on a Changeup. (Video)

相比之下, 2型小号挥杆具有力量和良好的发条盒位置,但计时性差。 这种类型的鞭打是击球手错误估算出俯仰何时会越过打击区的结果。 当击球手摆动得太迟或太早时,就会发生这种情况。 在7月24日对阵Hyun-Jin Ryu的情况下,Mike Zunino参加Changeup还为时尚早。 ( 视频 )

Zunino had good barrel placement, as the path of his bat intersected the path of the ball (because the two are overlapping), but he swung too early, so his bat missed the ball.

Zunino的枪管位置很好,因为球拍的路径与球的路径相交(因为两者重叠),但他摆动得太早,因此球拍错过了球。

My theory here is that there are two distinct types of whiffs. Type 1 Whiffs occur when the pitch moves differently than the hitter expects it to. Type 2 Whiffs occur when the pitch is slower or faster than the hitter expects. A third type of whiff would be a whiff that follows the definition of both previous types, and these are typically pretty damn ugly.

我的理论是,有两种截然不同的类型。 当音高的变化与击球手所预期的不同时,将发生1类啸叫。 当音调比击球手预期的速度慢或快时,将发生2类啸叫。 第三种类型的鞭子是遵循两种先前类型的定义的鞭子,它们通常非常难看 。

Having comprehensive swing timing data could essentially prove or disprove this theory and help us better understand why hitters whiff.

具有全面的挥杆计时数据可以从本质上证明或反驳这一理论,并帮助我们更好地理解击球手为何挥之不去。

#2。 不同的击球手有不同的挥杆姿势吗? (#2. Do Different Hitters Have Different Whiff Profiles?)

With current public data we can quantify which pitch types give each hitter the most trouble at the plate, but looking at timing can give us another layer of understanding as to why a hitter struggles with certain pitches.

利用最新的公开数据,我们可以量化哪些击球类型会给每个击球手带来最大的麻烦,但是看一下计时可以使我们对击球手为什么要在特定的击球中挣扎感到另一层理解。

Between 2009 and 2010, Jose Bautista transformed himself from a fourth outfielder into the league leader in Home Runs. In this ESPN feature (which I highly recommend), Bautista mentions that he added a leg kick between 2009 and 2010 which helped improve his timing at the plate.

在2009年至2010年之间,何塞·包蒂斯塔(Jose Bautista)从第四名外野手转变为全垒打的联盟领袖。 在这个ESPN功能中 (我强烈推荐),包蒂斯塔提到他在2009年至2010年之间增加了踢腿动作,这有助于改善他在板块比赛中的时机。

Essentially, Bautista attributed one of the biggest player development success stories of the decade to fixing his swing timing, something we do not currently measure. How many other players are currently struggling as a result of fixable timing issues that are going unrecognized?

本质上,包蒂斯塔将十年来最大的球员发展成功案例归因于固定他的挥杆时间,这是我们目前无法衡量的。 由于无法解决的固定时间问题,目前还有多少其他参与者在苦苦挣扎?

My theory is that every hitter has certain tendencies with his timing. Here is an example of the kind of quantification of timing profiles this data should be able to provide for us to help diagnose and understand our hitters:

我的理论是,每个击球手的时机都有一定的倾向。 这是一个时序配置文件量化示例,该数据应该能够为我们提供帮助,帮助他们诊断和理解击球手:

Image for post
Image By Author
图片作者

On this plot, the height of the bars represents total whiffs on Fastballs. Tatis has the most, LeMahieu has the least. That is important info. On Fastball whiffs, Muncy is the best at being on time and Rosario is usually late. Also potnetially important!

在此图上,条形图的高度代表“快球”上的总鞭打声。 Tatis最多,LeMahieu最少。 那是重要的信息。 在Fastball嗅觉上,Munncy是最准时的,而Rosario通常会迟到。 也是重要的!

What about whiff profiles for Curveballs, for example?

例如,Curveballs的嗅探轮廓如何?

Image for post
Image By Author
图片作者

We can see that Tatis has whiffed the most from Curveballs (although a larger scale analysis should probably be looking at whiff rates, not raw whiffs). When Rosario has whiffed on Curveballs, he has always been early. When Zunino has whiffed on Curveballs, he has always been on time. Interesting!

我们可以看到Tatis从Curveballs中获得的收益最大(尽管更大规模的分析可能应该关注的是成鞭率,而不是原始成脂)。 当罗萨里奥(Rosario)在Curveballs上狂奔时,他总是很早。 当Zunino挥舞着Curveballs时,他一直都很准时。 有趣!

Looking at the bigger picture, we can easily see that our hitters have whiffed more on Fastballs than Curveballs this season, that hitters are typically late on Fastballs when they whiff and early on Curveballs when they whiff but that this is not always the case. Similar charts and trends could be analyzed for all pitch types and in many different scenarios.

从更大的角度来看,我们可以很容易地看到,本赛季我们的击球手在Fastballs上的鞭打比在Curveballs上鞭打更多,当击打者在鞭打时,击打者通常在Fastballs上晚,而在Curveball击打时则更早,但是并非总是如此。 可以针对所有音高类型以及在许多不同情况下分析相似的图表和趋势。

We could also answer questions about league-wide trends like whether players who have good timing on their whiffs tend to be more productive when they aren’t whiffing:

我们还可以回答有关联盟范围内趋势的问题,例如,那些有良好时机的球员是否会在不挥杆时提高生产率:

Image for post
Image By Author
图片作者

To answer the question: Yes, there is a positive association in the sample data! (No, this trend should not be extrapolated to the league at large at this time!)

要回答这个问题:是的,样本数据中存在正相关! (不,目前不应该将这种趋势推断给整个联盟!)

In my mind, there could be lots of predictive power in a swing timing variable that could help us better predict how a hitter is likely to do going forward. But whether this is the case or not, being able to visualize a hitter’s timing profile has a chance to be a valuable player evaluation tool going forward.

在我看来,挥杆计时变量中可能有很多预测力,可以帮助我们更好地预测击球手的前途。 但是无论是否如此,能够可视化击球手的时间配置文件都有机会成为有价值的球员评估工具。

#3。 其他因素如何影响摆正时? (#3. How Do Other Factors Influence Swing Timing?)

There are so many variables to what happens in a plate appearance, so which of those variables impact swing timing? Previous pitch? Pitch number of the plate appearance?

印版外观中发生的变数太多,那么哪些变数会影响摆动时间呢? 以前的音高? 板数的节距多少?

I’ve heard some anecdotal speculation that a hitter is more likely to have good timing on a pitch if they see it two times in a row. Swing timing analysis could test that! But more broadly, we would be able to better understand the interplay between subsequent pitches. Are batters more likely to be late on a high fastball when the previous pitch was a slow curve? Which pitch should the pitcher throw next after a hitter is late on a fastball?

我听到一些传闻说,如果击球手连续两次见识击球手,他们更有可能在场上有良好的时机。 摆动时序分析可以证明这一点! 但更广泛地说,我们将能够更好地理解后续音高之间的相互作用。 当先前的球速是缓慢的曲线时,击球手在高球速击中更可能迟到吗? 击球手迟到快球后,投手接下来应该投哪个距离?

Currently, we could speculate all day about the answers to these questions and try to estimate how pitches interact. With comprehensive swing timing data, we could essentially know for sure.

目前,我们可以整天猜测这些问题的答案,并尝试估算音高如何相互作用。 有了全面的挥杆计时数据,我们基本上可以肯定地知道。

In my sample data, I was able to make a few fun graphs:

在示例数据中,我可以制作一些有趣的图形:

Image for post
Image By Author
图片作者

I found that our sample hitters were most commonly late on fastballs that came one pitch after another fastball, but were on time a fair bit of the time as well. Keep in mind, each observation you see above was a whiff. Offspeed pitches after fastballs in this data most commonly elicited a late swing. This all lines up with our existing understanding, which is a good sign!

我发现我们的样本击球手通常是在快球上迟到的,快球是接一个球接一个快球的,但是在准时时间上也相当。 请记住,您在上面看到的每个观察结果都是一阵嗅觉。 在此数据中,快球之后的超速俯仰最常引起后期挥杆。 所有这些都与我们现有的理解一致,这是一个好兆头!

Additionally, we could look at whether hitters are more likely to be on time the more pitches they see in a plate appearance:

另外,我们可以看看击球手在板状外观中看到的音高越多,击球手是否更可能准时到达:

Image for post
Image By Author
图片作者

By tracking the heights of the purple bars, we can see that hitters in this sample were not more likely to be on time as the plate appearance went on. But is this the case for the rest of the league? We don’t know, but we would with swing timing data!

通过跟踪紫色条的高度,我们可以看到,随着板外观的进行,该示例中的击打者不太可能准时到达。 但这对联盟其他成员来说是这样吗? 我们不知道,但是我们会提供挥杆计时数据!

结论 (Conclusion)

Hopefully this article has sparked your imagination and convinced you that there is plenty to learn about the game of baseball (especially the batter-pitcher interaction) from swing timing data. This article only focused on swings and misses from 5 MLB hitters in about 3 weeks of MLB play. Imagine how much we could learn from swing timing data on all swings by all hitters in all games!

希望本文能够激发您的想象力,并说服您从挥杆计时数据中可以学到很多有关棒球比赛(尤其是击球手与投手之间的互动)的知识。 本文仅关注大约3周的美国职业棒球大联盟比赛中5名美国职业棒球大联盟击球手的摇摆和失误。 想象一下,我们可以从所有游戏中所有击球手的所有挥杆动作的挥杆计时数据中学到多少!

Swing timing data is not currently available publicly (as far as I know), but it could be available in the not-so-distant future if Hawkeye is able to function as advertised. If it is, teams will have a great opportunity to use that data better than their competitors to gain a competitive advantage and as with any large new data source, it can be hard to even know where to start your analysis. But after reading this, I hope teams will begin by examining this topic as the potentially game-changing topic that it is.

(据我所知)当前尚未公开摆动时间数据,但如果Hawkeye能够像宣传的那样工作,那么摆动时间数据将在不久的将来提供。 如果是这样,团队将有很大的机会比竞争对手更好地使用该数据,以获取竞争优势,并且与任何大型新数据源一样,甚至很难知道从哪里开始分析。 但是,在阅读本文之后,我希望团队首先从研究这个话题开始,这个话题可能会改变游戏规则。

Thank you for reading! If you have any comments or questions, let me know on Twitter: @Moore_Stats

感谢您的阅读! 如果您有任何意见或疑问,请在Twitter上告诉我: @Moore_Stats

Data and video from baseballsavant.com

来自balloonsavant.com的数据和视频

翻译自: https://towardsdatascience.com/3-things-we-could-learn-from-swing-timing-analysis-84486fdab209

鼠标移动到ul图片会摆动

本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处:http://www.mzph.cn/news/391733.shtml

如若内容造成侵权/违法违规/事实不符,请联系多彩编程网进行投诉反馈email:809451989@qq.com,一经查实,立即删除!

相关文章

回到网易后开源APM技术选型与实战

篇幅一:APM基础篇\\1、什么是APM?\\APM,全称:Application Performance Management ,目前市面的系统基本都是参考Google的Dapper(大规模分布式系统的跟踪系统)来做的,翻译传送门《google的Dappe…

如何选择优化算法遗传算法_用遗传算法优化垃圾收集策略

如何选择优化算法遗传算法Genetic Algorithms are a family of optimisation techniques that loosely resemble evolutionary processes in nature. It may be a crude analogy, but if you squint your eyes, Darwin’s Natural Selection does roughly resemble an optimisa…

PullToRefreshListView中嵌套ViewPager滑动冲突的解决

PullToRefreshListView中嵌套ViewPager滑动冲突的解决 最近恰好遇到PullToRefreshListView中需要嵌套ViewPager的情况,ViewPager 作为头部添加到ListView中,发先ViewPager在滑动过程中流畅性太差几乎很难左右滑动。在网上也看了很多大神的介绍,看了ViewP…

神经网络 卷积神经网络_如何愚弄神经网络?

神经网络 卷积神经网络Imagine you’re in the year 2050 and you’re on your way to work in a self-driving car (probably). Suddenly, you realize your car is cruising at 100KMPH on a busy road after passing through a cross lane and you don’t know why.想象一下…

数据特征分析-分布分析

分布分析用于研究数据的分布特征,常用分析方法: 1、极差 2、频率分布 3、分组组距及组数 df pd.DataFrame({编码:[001,002,003,004,005,006,007,008,009,010,011,012,013,014,015],\小区:[A村,B村,C村,D村,E村,A村,B村,C村,D村,E村,A村,B村,C村,D村,E村…

如何在Pandas中使用Excel文件

From what I have seen so far, CSV seems to be the most popular format to store data among data scientists. And that’s understandable, it gets the job done and it’s a quite simple format; in Python, even without any library, one can build a simple CSV par…

数据特征分析-对比分析

对比分析是对两个互相联系的指标进行比较。 绝对数比较(相减):指标在量级上不能差别过大,常用折线图、柱状图 相对数比较(相除):结构分析、比例分析、空间比较分析、动态对比分析 df pd.DataFrame(np.random.rand(30,2)*1000,columns[A_sale…

Linux基线合规检查中各文件的作用及配置脚本

1./etc/motd 操作:echo " Authorized users only. All activity may be monitored and reported " > /etc/motd 效果:telnet和ssh登录后的输出信息 2. /etc/issue和/etc/issue.net 操作:echo " Authorized users only. All…

tableau使用_使用Tableau升级Kaplan-Meier曲线

tableau使用In a previous article, I showed how we can create the Kaplan-Meier curves using Python. As much as I love Python and writing code, there might be some alternative approaches with their unique set of benefits. Enter Tableau!在上一篇文章中 &#x…

Nexus3.x.x上传第三方jar

exus3.x.x上传第三方jar: 1. create repository 选择maven2(hosted),说明: proxy:即你可以设置代理,设置了代理之后,在你的nexus中找不到的依赖就会去配置的代理的地址中找hosted:你可以上传你自…

责备的近义词_考试结果危机:我们应该责备算法吗?

责备的近义词I’ve been considering writing on the topic of algorithms for a little while, but with the Exam Results Fiasco dominating the headline news in the UK during the past week, I felt that now is the time to look more closely into the subject.我一直…

c/c++编译器的安装

MinGW(Minimalist GNU For Windows)是个精简的Windows平台C/C、ADA及Fortran编译器,相比Cygwin而言,体积要小很多,使用较为方便。 MinGW最大的特点就是编译出来的可执行文件能够独立在Windows上运行。 MinGW的组成: 编译器(支持C、…

numpy 线性代数_数据科学家的线性代数—用NumPy解释

numpy 线性代数Machine learning and deep learning models are data-hungry. The performance of them is highly dependent on the amount of data. Thus, we tend to collect as much data as possible in order to build a robust and accurate model. Data is collected i…

spring 注解方式配置Bean

概要: 再classpath中扫描组件 组件扫描(component scanning):Spring可以从classpath下自己主动扫描。侦測和实例化具有特定注解的组件特定组件包含: Component:基本注解。标示了一个受Spring管理的组件&…

零元学Expression Blend 4 - Chapter 25 以Text相关功能就能简单做出具有设计感的登入画面...

原文:零元学Expression Blend 4 - Chapter 25 以Text相关功能就能简单做出具有设计感的登入画面本章将交大家如何运用Blend 4 内的Text相关功能做出有设计感的登入画面 让你五分钟就能快速做出一个登入画面 ? 本章将教大家如何运用Blend 4 内的Text相关功能做出有设计感的登入…

冠状病毒时代的负责任数据可视化

First, a little bit about me: I’m a data science grad student. I have been writing for Medium for a little while now. I’m a scorpio. I like long walks on beaches. And writing for Medium made me realize the importance of taking personal responsibility ove…

集合_java集合框架

转载自http://blog.csdn.net/zsw101259/article/details/7570033 Java集合框架图 简化图: Java平台提供了一个全新的集合框架。“集合框架”主要由一组用来操作对象的接口组成。不同接口描述一组不同数据类型。 1、Java 2集合框架图 ①集合接口:6个…

显示随机键盘

显示随机键盘 1 <!DOCTYPE html>2 <html lang"zh-cn">3 <head>4 <meta charset"utf-8">5 <title>7-77 课堂演示</title>6 <link rel"stylesheet" type"text/css" href"style…

数据特征分析-统计分析

一、统计分析 统计分析是对定量数据进行统计描述&#xff0c;常从集中趋势和离中趋势两个方面分析。 集中趋势&#xff1a;指一组数据向某一中心靠拢的倾向&#xff0c;核心在于寻找数据的代表值或中心值-统计平均数&#xff08;算数平均数和位置平均数&#xff09; 算术平均数…

数据eda_银行数据EDA:逐步

数据edaThis banking data was retrieved from Kaggle and there will be a breakdown on how the dataset will be handled from EDA (Exploratory Data Analysis) to Machine Learning algorithms.该银行数据是从Kaggle检索的&#xff0c;将详细介绍如何将数据集从EDA(探索性…