The game of basketball continues to evolve as time passes. It is hard to imagine, but the game began without dribbling and the three point shot is a ‘modern’ invention many people alive today can remember the introduction of. In addition to formal rule changes, informal trends influence the composition of teams, what skills are valued, and how players play the game. In the ‘sprawlball’ era of today, it is clear that the traditional positions of Center, Forward, and Guard are archaic labels which do little to describe the function of players within the game. Today, a Center might bring the ball up the court (where is the point guard?) and a Guard might rebound better than a Forward. While it might sound like positions should just be thrown out and any player should be expected to perform any action at an elite level, it doesn’t seem we’ve entered into a ‘positionless’ era. If anything players have become more specialized (beyond the five classic positions). As such, articles commonly put forth fresh attempts to define new positions which more accurately describe the various ways players specialize within the game.

随着时间的流逝,篮球比赛不断发展。 很难想象,但是比赛是在没有盘带的情况下开始的,三分球是当今许多活着的人都能记得的“现代”发明。 除了正式的规则更改外,非正式的趋势还会影响团队的组成,重视的技能以及玩家的游戏方式。 在当今的“ 蔓延球 ”时代,很明显,中锋,前锋和后卫的传统位置都是过时的标签,几乎无法描述游戏中玩家的功能。 今天,中锋可能会把球带到球场上(控球后卫在哪里?),而后卫的反弹可能比前锋更好。 虽然听起来好像应该丢掉位置,应该让任何球员都在精英级别上执行任何动作,但似乎我们没有进入“无位置 ”时代。 如果有的话,球员会变得更加专业(超过五个经典位置)。 这样,文章通常提出新的尝试来定义新的位置,以更准确地描述玩家在游戏中的各种专长方式。

Like any game of skill, players play the game differently. In a sense, the loose style of play we recognize players as having (a ‘driving scorer’ or a ‘three point specialist’) are like personalities of play. Each player is more likely to do some things, and less likely to do others than the majority of players. These tendencies are like traits, defining how a player plays the game. We inherently notice some of these easily when watching (e.g., from behind the arc James Harden is likely to try a stepback, while Danny Green is more likely to catch and shoot), but there are likely patterns of play which are not readily visible. For these hidden patterns of play machine learning can be used to mathematically derive these patterns from measured data, grouping players into categories of similar play.

像任何技巧游戏一样,玩家玩游戏的方式也有所不同。 从某种意义上说,我们认为球员具有“打球得分手”或“三分专家”的宽松风格就像打法。 与大多数玩家相比,每个玩家更可能做某事,而不太可能做其他事情。 这些趋势就像特质一样,定义了玩家玩游戏的方式。 我们本来就很容易在观看时注意到其中一些(例如,詹姆斯·哈登(James Harden)很可能从弧线后方尝试后退,而丹尼·格林(Danny Green)则更有可能接球和投篮),但是有些比赛方式可能并不明显。 对于这些隐藏的游戏模式, 机器学习可用于从测量数据中数学得出这些模式 ,将玩家分为相似游戏的类别。

先前的研究和拟议的模型 (Prior Research and Proposed Model)

This idea is not a new one. Many articles have appeared in the last twenty years with the intention of clustering players according to an algorithm and giving the resulting set of new positions descriptive labels. Articles published from Medium to academic journals have advocated for anywhere from six to thirteen new positions using a variety of features (player statistics) and clustering algorithms. This paper is different.

这个想法不是一个新想法。 在过去的20年中,出现了许多文章,目的是根据一种算法对玩家进行聚类,并为结果提供一组新的位置描述性标签。 从中型刊物到学术期刊的文章都提倡使用各种功能(玩家统计信息)和聚类算法在六个到十三个新职位上任职。 本文是不同的。

First, most previous research has utilized PCA (principal components analysis) followed by k-means clustering, a machine learning algorithm which forces each observation into a single class. However, this method is not probabilisitic, so one cannot easily use the results to predict the position of new observations. It is a one-shot description of the past. Alternatively, this paper utilizes the power of probabilistic mixture models (a Gaussian Mixture Model, to be precise). A GMM can model non-linear relationships well and provides a class estimate (probability of class membership) for each class for each player. Thus, it outputs the likelihood that a player fits each new position (e.g., how similar is LeBron to other players in this group?). A GMM also gives estimated parameters for each class (so the model can be used to give position estimates for future data). Thus, when looking at draft prospects (or a potential trade/free agent signing) a player’s stats can be fed into the model and estimated class probabilities would be given. This could dramatically change how teams are composed, helping general managers better understand how a new player will ‘fit’ with the others.

首先,大多数先前的研究都利用PCA (主要成分分析),然后利用k均值聚类 ,这是一种机器学习算法,可将每个观察结果强制为一个类。 但是,这种方法并非不可能,因此无法轻易使用结果来预测新观测值的位置。 这是对过去的一击式描述。 另外,本文利用概率混合模型(精确地说是高斯混合模型)的强大功能。 GMM可以很好地对非线性关系进行建模,并为每个玩家提供每个班级的班级估计(班级成员的概率)。 因此,它输出玩家适合每个新位置的可能性(例如,勒布朗与该组中的其他玩家有多相似?)。 GMM还为每个类别提供估计的参数(因此该模型可用于为将来的数据提供位置估计)。 因此,当查看潜在选秀权(或潜在的贸易/自由球员签约)时,可以将球员的统计信息输入模型中,并给出估计的类别概率。 这可能会极大地改变团队的组成方式,从而帮助总经理更好地了解新员工将如何“适应”其他员工。

Second, previous research utilizes a limited number of features (statistics) and is overly dependent upon traditional box score metrics. With advanced player tracking software keeping tabs on player and ball movements we now have available new metrics which measure how players interact and play the game, why not use them? As long as features provide additional information which can explain variation, and are not overly correlated with one another, they can help us find differences in style of play not otherwise noticeable to the human eye (or too mundane for us to actively track, like screens leading to made field goals). This paper incorporates a much larger feature set than previous research, while maintaining the integrity of each feature (metrics are not combined) to aid in understanding the patterns the model produces.

其次,先前的研究利用了有限的功能(统计数据),并且过度依赖传统的盒式评分指标。 借助先进的球员跟踪软件,我们可以随时掌握球员和球的运动情况,从而提供了衡量球员互动和玩游戏方式的新指标,为什么不使用它们呢? 只要功能提供了可以解释变化并且彼此之间没有过多关联的其他信息,它们就可以帮助我们找到人眼无法察觉的游戏风格差异(或者太平凡,无法像屏幕一样主动跟踪)导致制定出实地目标)。 本文包含了比以前的研究大得多的功能集,同时保持每个功能的完整性(未合并指标)以帮助理解模型产生的模式。

So, instead of another simple descriptive attempt to redefine player positions from past seasons this paper provides a coherent set of new positions which can be easily understood in terms of their differences (what players in the class do more/less often than others) and can be used to predict class membership with new data.


方法 (Methodology)

Data were compiled from NBA.com regular-season totals for the most recent five seasons (2015–16 through 2019–20) on 150 player-level variables. Statistics were converted to comparable metrics (per 36 minutes) to allow for even comparisons across different usage levels. To prevent those with limited minutes from muddling the data, only players appearing in at least 10 games and averaging a minimum of 5 minutes per game were kept. These preprocessing steps led to a final dataset used for analysis containing 2,234 observations.

数据来自NBA.com最近五个赛季(2015-16至2019-20)的常规赛季总数,涉及150个球员水平变量。 统计信息被转换为可比较的指标(每36分钟),以允许在不同使用级别之间进行均匀比较。 为了防止分钟数有限的人混淆数据,仅保留至少参加10场比赛且平均每场比赛至少5分钟的球员。 这些预处理步骤导致用于分析的最终数据集包含2,234个观测值。

Unique to this study is the sheer breadth of this feature set. Variables are included which measure both ends of the floor (offense, defense), movement (distance, speed), location (where shots take place, where defense takes place), passing (passes sent, passes received), possession (time with the ball), hustle (deflections, loose ball recoveries), and more. The inclusion of nearly 50 features more than many previous analyses allows for a finer-grained system of defining how players actually play the game. By including location and movement data, the true style of play can be envisioned in more detail than traditional box score metrics (rebounds, assists, etc.) can provide.

这项研究的独特之处在于该功能集的绝对广度。 包含变量,这些变量测量楼层的两端(进攻,防守),移动(距离,速度),位置(进行投篮,进行防守的位置),通过(发送的传球,接收的传球),控球权(发球时间)球),奔忙(偏斜,球恢复不佳)等等。 与以前的许多分析相比,包含了近50种功能,可以使用更细粒度的系统来定义玩家实际玩游戏的方式。 通过包括位置和移动数据,可以比传统的箱式得分指标(篮板,助攻等)提供更详细的真实比赛方式。

To reduce the feature set (many of the 150 features are essentially redundant), a correlation matrix was constructed and variables with correlations greater than |.8| were examined in greater detail. Variables which were essentially duplicate features were dropped (e.g. player height and player weight are strongly correlated; keeping just player height keeps this predictive information in the model and reduces multicollinearity amongst features). Trimming features through this step, and the application of human intuition (knowledge of how the game is played and whether a variable might be useful), left a final set of 80 features.

为了减少特征集(150个特征中的许多本质上是冗余的),构造了一个相关矩阵,并且相关性大于| .8 |的变量。 被更详细地检查。 删除了本质上是重复特征的变量(例如,选手身高和选手体重之间有很强的相关性;仅保持选手身高将这些预测信息保留在模型中,并减少了要素之间的多重共线性)。 通过这一步骤对功能进行修剪,以及人类直觉的应用(了解游戏的玩法以及变量是否有用),最终形成了80项功能。

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The 80 features used to estimate position profiles.

These 80 features were then estimated across the 2,234 observations with model-based clustering. Specifically, an expectation-maximization algorithm was used to fit Gaussian finite mixture models to the 80-dimensional dataset. No specific number of clusters were predicted to exist ahead of time, so models were fitted to estimate class sizes from three to eleven.

然后使用基于模型的聚类在2234个观测值中估算了这80个特征。 具体来说,使用期望最大化算法将高斯有限混合模型拟合到80维数据集。 预计没有特定数量的聚类会提前存在,因此对模型进行了拟合以估计三到十一的班级规模。

结果 (Results)

Evaluating model fit indices (which can be done with GMM but not k-means) revealed a ten cluster model fit the data best. But how confident is the model of its predictions of class membership? Very confident. Nearly all players were predicted to belong to their class with greater than 90% probability. When given the statistics of a player for the 80 variables describing how they play, this model can confidently place them into a single position out of the 10 identified.

评估模型拟合指数(可以使用GMM进行,但不能使用k-means进行评估)表明,十个聚类模型最适合数据。 但是,其对班级成员身份的预测模型的信心如何? 非常有信心。 几乎所有的玩家都被认为以90%以上的概率属于自己的职业。 当获得球员的80个变量的统计数据以描述他们的比赛方式时,该模型可以放心地将其置于确定的10个变量中的单个位置。

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So what do these positions look like? Looking at the thee traditional positions for these players (as classified on NBA.com), now split into the ten new positions, we can see how the model appears to both follow the traditional idea (groupings tend to have more of one traditional position than another) but also finds more specific ways to differentiate players (members of each traditional position can be found in nearly each of the ten new positions).

那么这些职位是什么样的呢? 看一下这些球员的传统位置(在NBA.com上已分类),现在分为十个新位置,我们可以看到模型看起来既遵循了传统观念(群体倾向于拥有一个以上的传统立场,而不是另一种),但也找到了更具体的方法来区分参与者(每个传统职位的成员几乎都可以在十个新职位中找到)。

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10个新职位 (10 New Positions)

To get a grasp of what patterns the model discovered in the math we need to look at what each position does significantly better, and significantly worse, than the league average. For the charts that follow (one for each new position), of the 80 features included in the model, each position’s top 10 and bottom 10 features are plotted, with the values representing standard deviations above and below the mean (a league average player). Like taking a personality inventory, this helps us to visualize what traits make up each of our ten new positions. For a quick reference point, the table below includes a new name and selection of players currently classified under each position.

为了掌握模型在数学中发现的模式,我们需要查看每个位置比联盟平均水平有明显好转和明显差的情况。 对于随后的图表(每个新位置一个),模型中包含80 个功能,绘制了每个位置的前10个功能和后10个功能 ,其值表示平均值(均值)上下的平均值(联盟平均玩家) 。 就像盘点个性一样,这可以帮助我们直观地看出构成我们十个新职位的每个特征。 为了快速参考,下表包括新名称和当前分类在每个位置下的球员的选择。

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The ten new positions and a selection of players classified as such.

1.三双 (1. Triple-Double)

The king of the ISO play, the triple-double threat player does it all on offense. He scores significant points from ISO plays, but also uses this attention to spread the ball to teammates, as he is well above league average in both actual assists and passes which could have become an assist (had the teammate made the shot). This scoring threat also runs the floor on the fast break, earns a large number of free throws and is likely to shoot threes when holding the ball, as opposed to directly from a pass.

ISO比赛的王者,三重双重威胁球员在进攻端做到了一切。 他在ISO比赛中得分高,但同时也利用这种注意力将球传给队友,因为他的实际助攻和传球远高于联盟平均水平,而传球本可以成为助攻(如果队友出手了)。 这种得分威胁还可以在快攻中发挥作用,获得大量罚球,并且在持球时可能直接射出三分,而不是直接传球。

However, players in this position don’t move much, nor are they particularly quick on defense. In fact, nearly all of their bottom traits revolve around not doing much on defense (below average rim defense, speed on defense, distance covered on defense, personal fouls). Don’t tell LeBron, but apparently he’s a similar defender to James Harden…

但是,处于此位置的玩家移动不多,防守也不是特别快。 实际上,他们几乎所有的基本特质都是围绕防守做得不好(低于篮筐平均防守,防守速度,防守距离,个人犯规)。 不要告诉勒布朗,但显然他是詹姆斯·哈登的类似后卫……

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Representative Players


LeBron James, James Harden, Luka Doncic, Russell Westbrook


2.任何地方 (2. Anywhere)

Another ISO scorer, this player manufactures points through a variety of moves, though they are most likely to start from the perimeter. This scorer dominates offensive possessions, receiving significantly more passes from teammates and holds the ball for much of the shot clock. However, they are hard to defend, as they score in a variety of ways, from coming off a screen, to pull up jumpers, to driving to the basket. With all of this attention, they also include their teammates in productive ways more than most other players through assists and potential assists, though the passes to teammates from this scorer are rarely to bigs (most of their assists are to three point shooters, so don’t fall for that fake alley-oop, this scorer isn’t going to give it up down low).

另一个ISO得分手,这位球员通过各种动作制造得分,尽管它们很可能从外围开始。 这位得分手占据了进攻性所有物,从队友处获得了更多的传球机会,并且在大部分投篮时间里都将球保持在球上。 但是,他们很难防守,因为他们有多种得分方式,包括从掩护下得分,跳投跳投到上篮得分。 有了所有这些注意力,他们也通过助攻和潜在助攻以比其他大多数球员更多的方式让队友包括进了队友,尽管这位得分手向队友的传球很少给大个子(他们的大部分助攻都是三分射手,所以不会因为那个假小巷而倒下,这个得分手不会低得离谱。

However, this scoring-focused player does not add to their team’s possessions, rarely obtaining offensive rebounds or even being in a position to get an easy rebound (which keeps them from being a triple-double threat). This appears to be a key difference between these first two positions. Both are kings of ISO plays and clearly dominate their teams’ offenses while ‘phoning it in’ on defense. However, this anywhere scorer isn’t quite as valuable to his team as he hasn’t yet realized the importance of grabbing a few extra possessions for his team (in the form of rebounds). Tell Kawhi to grab a few more boards and he can move up to Triple-Double status.

但是,这位注重得分的球员并没有增加球队的资产,很少获得进攻篮板,甚至无法轻松篮板(这使他们免受三双威胁)。 这似乎是前两个位置之间的关键区别。 两位都是ISO比赛的王者,显然在防守时“打电话给”他们在球队进攻中占主导地位。 但是,这位得分手对他的球队而言并不那么有价值,因为他还没有意识到为球队赢得一些额外财富的重要性(以篮板的形式)。 告诉Kawhi再夺几局,他就可以升到三双。

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Representative Players


Kawhi Leonard, Jimmy Butler, Damian Lillard, Kemba Walker, Chris Paul, Stephen Curry, Ben Simmons, Kyrie Irving, Jamal Murray, Trae Young

Kawhi Leonard,Jimmy Butler,Damian Lillard,Kemba Walker,Chris Paul,Stephen Curry,Ben Simmons,Kyrie Irving,Jamal Murray,Trae Young

3.跳线 (3. Jumper)

Instead of driving to the basket, or getting their teammates involved through assists, this player uses their above average ISO plays to shoot jumpers. Whether a pull up or a spot up, from behind the arc or inside, you better get your hand up, then box out, because this guy is likely to try to shoot over you. This heavy minutes player also runs the floor well (scoring significant points from fast breaks and off of turnovers), so you also better get back on defense when they’re on the floor or you’re likely to pay.

这位球员不用开车上篮,也没有通过助攻让他们的队友参与进来,而是使用高于平均水平的ISO射门得分。 无论是从弧线后面还是内部,是向上拉还是向上拉,您最好先把手举起来,然后再拳击,因为这个家伙可能会尝试向您射击。 这位上场时间很长的球员也能很好地控制场上位置(快速突破和失误可以得分),所以当他们在场上或有可能支付时,您​​也最好重新防守。

Noticeably, this scorer doesn’t like screens/pick-and-rolls, so if you are guarding them you should focus on keeping them in front of you. They also aren’t likely to score on you in the paint or at the elbow (they’ll likely move you to one of their favorite places to shoot a J instead). This scorer doesn’t get too many rebounds on offense, nor are they likely to defend the rim. Lastly, don’t read as much into the patterns of this player, their characteristics range below one standard deviation both above and below average. Tell KD he better mix it up some more because he’s getting predictable.

值得注意的是,这个得分手并不喜欢屏幕/挡拆,因此,如果您在保护他们,则应着重于将它们摆在您的面前。 他们也不太可能在油漆或肘部上得分(他们可能会将您带到他们最喜欢的地方射击J)。 这位得分手在进攻端不会得到太多篮板,也不太可能防守篮筐。 最后,不要过多地了解该播放器的模式,它们的特征范围要低于平均值和平均值以下的一个标准偏差。 告诉KD他最好将其混合使用,因为他变得可预测。

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Representative Players


Kevin Durant, Buddy Hield, Pascal Siakam, Marcus Morris Sr., Marcus Smart, Andrew Wiggins, Paul George, Klay Thompson, Khris Middleton, Kyle Kuzma

凯文·杜randint(Kevin Durant),巴迪·希尔德(Buddy Hield),帕斯卡·西卡姆(Pascal Siakam),马库斯·莫里斯(Marcus Morris Sr.),马库斯·斯玛特(Andrew Wiggins),保罗·乔治(Paul George),克莱·汤普森(Klay Thompson),凯里·米德尔顿(Khris Middleton),凯尔·库兹马(Kyle Kuzma)

4.钱球 (4. Moneyball)

Living at the perimeter, this three-point maestro isn’t going to drive on you. Instead, he’s going to either catch a pass and send it towards the rim or spot up on you, almost certainly from behind the arc. This makes him an efficient scorer (in terms of points per touch). But he also plays defense, covering a lot of ground (likely good at getting back at changes of possession) and nabbing long rebounds (perhaps his own?).

生活在外围,这个三点大师不会驱使您前进。 取而代之的是,他要么抓住传球,然后将球传给篮筐,要么将球传到你身上,几乎可以肯定是从弧线后面。 这使他成为高效的得分手(就单次得分而言)。 但是他也能防守,可以打很多场球(可能擅长改变控球权),还能抢到长个篮板(也许是他的篮板?)。

Likely because of his penchant for the three-ball, this arc-assassin doesn’t draw many fouls, nor does he score in the paint (not likely to drive on you). He might be active on defense, but he’s not likely to block your shot. Further, on offense, he doesn’t get the ball much (low touches), so when he does, he’s likely to shoot (low passes made). Danny Green shoots threes? What?

可能是因为他对三分球的爱好,这名弧光刺客没有太多犯规,也没有在得分上得分(不太可能对你不利)。 他可能会积极防守,但不太可能挡住您的射门。 此外,在进攻端,他没有得到很多球(低触),因此当他得到球时,他很可能投篮(低传)。 丹尼·格林(Danny Green)射门得分? 什么?

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Representative Players


Danny Green, J.J. Reddick, P.J. Tucker, Jae Crowder, Royce O’Neale, Landry Shamet, Kyle Korver

Danny Green,JJ Reddick,PJ Tucker,Jae Crowder,Royce O’Neale,Landry Shamet,Kyle Korver

5.先通过 (5. Pass First)

A primary ball handler, this player is similar to the traditional point-guard of the past. He leads the offense (high time of possession, passes received, time with the ball) but is a pass-first scorer. He is far more likely to drive on you than spot up and shoot over you and he generates offense for his teammates through his passes.

作为一名主要的控球手,该球员与过去的传统控球后卫相似。 他带领进攻(高占有率,传球,发球时间),但他是传球第一得分手。 他比直射并向您开枪的可能性更大,可以驱使您前进,并且他会通过传球为队友发动进攻。

Not likely to post you up or to receive a pass in the paint (does he ever cut to the basket?), his tendency to avoid the paint also cuts down on his ability to secure rebounds on both offense or defense (limited rebounds). Tell Patty Mills he needs to shoot more because the model says he looks an awful lot like Lonzo Ball…

不太可能使您发球或获得传球机会(他有没有切入篮筐?),他回避涂料的倾向也降低了他在进攻或防守上都能获得篮板的能力(有限的篮板)。 告诉Patty Mills他需要射击更多,因为模特说他看起来像Lonzo Ball一样可怕……

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Representative Players


Ricky Rubio, Patty Mills, Kyle Lowry, Lonzo Ball, Rajon Rondo, Patrick Beverley, Cory Joseph, Shai Gilgeous-Alexander

瑞奇·卢比奥(Ricky Rubio),帕蒂·米尔斯(Patty Mills),凯尔·洛瑞(Kyle Lowry),朗佐·鲍尔(Lonzo Ball),拉贡·朗多(Rajon Rondo),帕特里克·贝弗利(Patrick Beverley),科里·约瑟夫(Cory Joseph),柴吉·亚历山大(Shai Gilgeous-Alexander)

6.后卫 (6. Defender)

What sets this player apart is clearly their defense. They cover enormous ground, and do so with high average speed, leading to steals and points off of turnovers.

这位球员与众不同的明显是他们的防守。 他们覆盖了巨大的土地,并且以很高的平均速度这样做,导致抢断和失误。

However, when on offense they are more likely than others to shoot contested threes (maybe not a good idea), and relatedly, rarely pass the ball to their teammates (well below average on passes made, assists, and potential assists). They score often in the paint, but shoot well below average from the field (again, maybe they should shoot less threes). Perhaps because of their poor shooting and lack of assists, they also don’t get many touches/passes on offense. Tell Harrison Barnes if he makes a few more passes when someone closes out on him at the arc he might make a few more friends (and get a few more passes/open shots in the future).

但是,在进攻端,他们比其他人更有可能投三分(可能不是一个好主意),并且相关地,很少将球传给队友(远传,助攻和潜在助攻均低于平均水平)。 他们通常会在油漆区得分,但远低于投篮命中率(再次,也许他们应该少投三分)。 也许由于他们的投篮不佳和缺乏助攻,他们在进攻上也没有得到太多的帮助。 告诉哈里森·巴恩斯(Harrison Barnes),如果有人在三分线外将他挡住,他还会再传球,他可能会再交一些朋友(并在将来获得更多传球/空投)。

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Representative Players


Harrison Barnes, Kyle Anderson, Gary Payton II, Thaddeous Young, Thabo Sefolosha, Mikal Bridges, Thanasis Antetokounmpo, Cam Reddish

哈里森·巴恩斯(Harrison Barnes),凯尔·安德森(Kyle Anderson),加里·佩顿二世(Gary Payton II),Thaddeous Young,塔博·塞弗洛莎(Thabo Sefolosha),米哈尔·布里奇(Mikal Bridges),塔那西斯·安特托昆姆波(Thanasis Antetokounmpo),卡姆·雷迪什(Cam Reddish)

7.大跳 (7. Big Jump)

Blending roles of a traditional center and forward, this player scores his points from pick-and-roll and catch-and-shoot opportunities (as opposed to post-ups or elbow moves). Using his height, he helps his team immensely on defense, both defending the rim, contesting two point shots, and boxing out to get defensive boards.

这位球员融合了传统的中锋和前锋的角色,从挡拆和接球机会中得分(与俯卧撑或肘部移动相反)。 利用他的身高,他可以极大地帮助他的球队防守,既可以防守篮筐,也可以两分球,还可以拳击出防守篮板。

However, you won’t see him driving to the basket from the perimeter, nor does he hold the ball for long when he gets it. While he contributes on defense through contesting shots and gathering rebounds, he doesn’t run the floor well on fast breaks, nor is he likely to get a hand on passes to deflect or steal a pass. I’ve seen Kevin Love’s outlet passes, they do the fast breaking for him…

但是,您不会看到他从外围射向篮筐,也不会在他拿到球时将球握很长时间。 尽管他通过比赛投篮和抢下篮板为防守做出贡献,但他在快攻中表现不佳,也不太可能传中传球以偏向或抢断传球。 我看过凯文·洛夫(Kevin Love)的出场证,他们为他做着快攻…

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Representative Players


Al Horford, Brook Lopez, Kristaps Porzingis, Kevin Love, LaMarcus Aldridge, Paul Millsap, Draymond Green, Markieff Morris, Marc Gasol

Al Horford,Brook Lopez,Kristaps Porzingis,Kevin Love,LaMarcus Aldridge,Paul Millsap,Draymond Green,Markieff Morris,Marc Gasol

8.双双 (8. Double-Double)

One of the most efficient scorers (in terms of made shots), this is another pick-and-roller but instead of shooting a jumper, this player is more likely to score from the elbow or in the paint. While this player doesn’t get many ISO plays, he earns a high number of touches (he’s an offensive threat) all over the frontcourt. His efficiency likely comes from a number of dunks, layups, and cleaning up missed shots (which help him lead the way in double-doubles).

作为得分手最高效的得分手之一,这是另一次挡拆球,但是比起跳投,他更容易从肘部或油漆中得分。 尽管该球员没有获得多少ISO比赛机会,但他在前场获得了很多的进攻机会(他是进攻性威胁)。 他的效率很可能来自于扣篮,上篮和清除失误(这帮助他以双打领先)。

Like the other specialist in rolling from pick-and-rolls, he isn’t likely to take too many threes, nor does he hold the ball for very long when he does get it. Interestingly, he doesn’t move much on offense or defense, nor does he move quickly at either end, staying mostly near the rim unless setting a screen. This doesn’t mean he isn’t a defensive threat, he just likely sticks near the basket on D. Don’t tell Joel Embiid I said he doesn’t move much on D.

就像其他在挡拆中滚动的专家一样,他不可能拿太多的三分球,也不会在拿到球时很长时间握住球。 有趣的是,他在进攻或防守上动作不大,也没有在两端快速移动,除非设置掩护,否则他通常会保持在篮筐附近。 这并不意味着他不是防守威胁,他很可能会在D的篮筐附近坚持。不要告诉Joel Embiid我说他在D上不会有太大动作。

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Representative Players


Giannis Antetokounmpo, Zion Williamson, Karl-Anthony Towns, Anthony Davis, Joel Embiid, Nikola Jokic, Serge Ibaka, Hassan Whiteside


9.画家 (9. Painter)

When this player sets screens, he means business. His screens actually lead to made field goals, though he is also an offensive threat himself (just not off of the screen). He scores his points in the paint, often working from the elbow, and dominates offensive and easy rebounds. You aren’t likely to get any easy shots off of him, either, as he contests twos and defends the rim well.

当该玩家设置屏幕时,他表示是生意。 尽管他本身也是一个进攻性威胁(并非不在屏幕外),但他的屏幕实际上导致了射门得分。 他经常在肘部上得分,在油漆区得分,并且在进攻篮板和轻松篮板上占主导地位。 您也不太可能从他身上得到任何轻松的投篮机会,因为他在二人制比赛中很好地防守篮筐。

If you do spot him outside the arc, though, you shouldn’t expect him to take, or make, many shots. He also won’t drive on you or shoot a pull up J when he catches a pass. This man lives in the paint (unless he’s about to blindside you with a solid screen). These are like the offensive linemen of basketball, if you score from one of their screens you owe them a Rolex or something nice. I better see Steven Adams glistening with some new jewelry next year.

但是,如果您确实发现他在三分线外,就不应该指望他会出手或出手很多次。 当他接过球时,他也不会继续对你开球或射门。 这个人生活在油漆中(除非他要用坚固的屏幕遮盖你)。 这些就像篮球的进攻边锋一样,如果您从他们的一个屏幕上得分,您将获得劳力士之类的东西。 我最好看到史蒂文·亚当斯(Steven Adams)明年会闪耀一些新珠宝。

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Representative Players


Rudy Gobert, Jarrett Allen, Steven Adams, Ivica Zubac, DeAndre Jordan, JaVale McGee, Clint Capela, Enes Kanter, Mason Plumlee

鲁迪·戈伯特(Rudy Gobert),贾里特·艾伦(Jarrett Allen),史蒂文·亚当斯(Steven Adams),艾维卡·祖巴克(Ivica Zubac),德安德烈·乔丹(DeAndre Jordan),贾维尔·麦基(JaVale McGee),克林特·卡佩拉(Clint Capela),埃内斯·坎特(Enes Kanter),梅森·普卢利(Mason Plumlee)

10.中心 (10. Center)

Nearly identical to the Painter, this position differs by being far more likely to record a block or shoot an uncontested two (aka dunk). This player also either doesn’t set many screens, or at least is less effective when doing so (low screen assists). This position appears dead (no one registered as such in the 2019–20 season), and the players previously featured are commonly classified as Painters or Tall Jumpers today. Perhaps the rising importance of setting screens in offensive sets led to the reclassification? Either way, as of now, this position seems moot.

该位置几乎与画家相同,不同之处在于更可能记录一个方块或射击一个毫无争议的两个(也称为扣篮)。 该播放器也没有设置很多屏幕,或者这样做的效果至少很差(低屏幕辅助功能)。 这个职位似乎已经死了(没有人在2019-20赛季注册过),而以前的球员如今通常被划分为Painters或Tall Jumpers。 也许在进攻场景中设置屏幕越来越重要,导致了重新分类? 无论哪种方式,到目前为止,这一立场似乎都没有定论。

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Representative Players


None in 2019–20. In the past: Michael Beasley, LaMarcus Aldridge, Boban Marjonovic, Zach Randolph, Andre Drummond

在2019-20年没有。 过去:Michael Beasley,LaMarcus Aldridge,Boban Marjonovic,Zach Randolph,Andre Drummond

结论 (Conclusion)

Using a player’s style of play to define their position, instead of classic positions, reveals new information useful to both teams and fans.


With twice the number of positions to define players, teams can develop finer grained analyses of rosters. Additional positions make it easier to separate style of play, which makes it easier to evaluate how different positions play together, allowing teams to answer questions like ‘which combinations of positions are more successful than others?’ and ‘which positions contribute the most to success of a team?’

拥有两倍的职位定义球员数量, 团队可以开发更细粒度的球员名单分析 。 额外的职位可以更轻松地区分游戏风格,这可以更轻松地评估不同职位如何共同发挥作用,使团队可以回答诸如“哪些职位组合比其他职位更成功?”这样的问题。 和“哪些职位对团队的成功贡献最大?”

Additional ‘style of play’ positions also allow for fine-tuning defensive strategy. Shot charts are publicly available (similar to a spray chart of hits in baseball), but with these new positions we now also have information about passing, rebounding, movement, etc. This means player tendencies can more easily be expanded beyond just shooting and which direction a player favors when dribbling. For example, defensive strategies can now easily incorporate additional tendencies, such as passing on a drive versus shooting, lobbing an alley-oop versus passing to the arc, or swinging the ball versus shooting a contested shot. While these can certainly be developed at the individual level with available statistics, remembering the tendencies of a position is far easier than of all possible players one might guard.

额外的“打法”位置也可以调整防守策略 。 投篮表是公开可用的(类似于棒球命中的喷射图),但是现在有了这些新位置,我们还可以获得有关传球,篮板,运动等方面的信息。这意味着球员的倾向可以更轻松地扩展到不仅仅是投篮以及运球时球员喜欢的方向。 例如,防御策略现在可以轻松地融入其他趋势,例如传球与投篮,传递小路球与传给弧线,或者挥舞球与投篮。 虽然可以肯定地使用可用的统计数据在各个级别上开发这些功能,但记住位置的趋势要比所有可能维护的参与者容易得多。

Finally, with more emphasis on style of play we can evaluate whether/how players change their style over time. Did Kawhi play differently with the Spurs versus the Clippers? Did Kyrie Irving change across his time with teams? Is there a pipeline to Triple-Double status? Understanding how a player may adapt to either a new lineup/system or advancing through their career can be important for determining whether to roster a potential free agent or draft pick.

最后,在更加强调比赛风格的同时,我们可以评估球员是否/如何随着时间改变他们的风格 。 Kawhi在对阵快船时与马刺的表现有所不同吗? 凯里·欧文(Kyrie Irving)在整个团队期间都改变了吗? 是否有通往“三双”状态的管道? 了解球员如何适应新的阵容/系统或在职业生涯中前进对于确定是否排定潜在的自由球员或选秀权很重要。

下一步 (Next Steps)

In the future look for posts where I analyze these positions by lineup (what combinations add the most value/least value?) and answer the question of whether/how player styles change over time (can you ‘grow into’ some of these positions?). In the meantime, you can play with the output from the model and use it to predict player positions yourself! Find the relevant data over on my GitHub here.

在未来,我会寻找我按阵容分析这些位置(哪些组合会增加最大的价值/最小的价值?)并回答玩家风格是否/如何随时间变化的问题(您能否“成长”其中一些位置)的帖子? )。 同时,您可以使用模型的输出,并用它自己预测玩家的位置! 找到了我的GitHub上的相关数据在这里 。

翻译自: https://medium.com/@derekjhanson/whats-your-basketball-personality-499fd943b34d



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