Hedge Fund Moneyball: Big Data, Sports and Finance

In the 2011 Hollywood filmMoneyball, based on the Michael Lewis book by the same name, Oakland A’s assistant general manager Peter Brand (played by Jonah Hill) tells his boss Billy Beane (played by Brad Pitt), “It’s about getting things down to one number. Using the stats the way we read them, we’ll find value in players that no one else can see. People are overlooked for a variety of biased reasons and perceived flaws. Age, appearance, personality. ... Mathematics cut[s] straight through that. Billy, of the 20,000 notable players for us to consider, I believe that there is a championship team of 25 people that we can afford, because everyone else in baseball undervalues them.”

Beane later declares, “We’re shopping in a new store — full of complicated statistical analysis and equations.”

Sabermetrics famously brought the quantification of play-by-play performance to professional sports. A slew of new technologies currently being tested by Major League Baseball and the National Basketball Association promises to take quantified athletic performance to the next level, allowing the consumers and clients of sporting activities — that is, the fans — to participate in measuring and metering every assist, basket, blocked shot, catch, pitch, rebound, run, swing and throw, with a view to how these swirling gusts of data might possibly predict the one simple ordinal outcome that everyone most cares about: the final score.

在体育技术是什么nology today has obvious, and important, implications for institutional fund management. By employing high-speed analytics software to capture every movement and ounce of data on a player in real time, professional sports is getting into the big-data game with a view to boosting fan engagement via so-called second screen experiences (whereby you watch a game on one screen and use a second to track the statistical representation of what you are viewing). In the future institutional investors will be able to track fund managers using similarly sophisticated methods.

大数据的出现以来,专业运动leagues have explored ways to use the massive amounts of information they collect to maximize the consumer experience. Baseball recently introduced a new tool,Fieldf/x, developed by Sportvision, a company best known for its 1998 introduction of yellow first-down markers superimposed on broadcast images of football fields during games. Fieldf/x tracks and displays every movement of every player on the field via cameras installed throughout a baseball stadium.

Sportvision products are not new to baseball. Its K-Zone service is used by TV networks to display the location of pitches thrown during a broadcast. Its Pitchf/x product broadcasts pitch locations and speeds in real time through MLB Advanced Media’sAt Bat app。But Fieldf/x can show far more advanced data than any of its predecessors, such as the elevation angle of a batted ball and the highest point in its trajectory, or the distance covered and top speed reached by a player fielding a ball. In order to track these figures, Fieldf/x uses algorithms developed by Sportvision’s in-house data scientists to analyze thousands of terabytes of data, correlating events related to all movements captured by the cameras on the field.

The figures collected don’t simply help settle fan disputes over which player covers more ground as a shortstop; reviewing raw data — as opposed to just video of the plays — can assist baseball clubs in more accurately determining the relative value of different players who play the same position (which is the essence of sabermetrics as displayed inMoneyball). When used by clubs, these data can have an enormous effect on player evaluation.

For example, by comparing the number of strikes called for a catcher in relation to every other catcher in the league, the data can illustrate how good any one catcher is at getting umpires to call strikes. Additionally, general managers and scouts can use the data to show how close pitchers get to their targets. This type of information would be impossible to ascertain by simply watching videos of a game.

Whereas performance data are useful for a sports club — or for an institutional investor — in optimizing performance internally, enabling an organization to place the best players or fund managers in the roles and situations in which they statistically excel, the value of all this analytic power was historically invisible to the outside world. The information Billy Beane collected on players was by definition private. It was his edge, and he would hardly ever dream of sharing it with other teams — let alone making it completely public by sharing it with the fans. But that is precisely what the latest sports technology purports to do: share the most detailed play-by-play analytics with any member of the public who visits NBA.com or MLB.com.

In February 2013 the NBA launched a new stats service on its web site, powered by SAP Hana technology developed by the German enterprise software makerSAP。Hana代表了与实时播放器分析和统计预测合并的真正大数据。在部署它时，NBA的步骤比任何其他联盟更远，并通过迄今为止，利用游戏期间收集的数据的最重要的方法创建，并以其基于网络的平台调用亚慱体育app视频盒得分。Using the Hana database system, which hosts large amounts of video content and processes it in real time, a fan can manipulate the data to display an index of player statistics. After the user has selected which statistics or combination of statistics he wants to view, the index then links to individual videos of each play associated with those statistics. Because Hana stores its massive amounts of aggregate data in live memory, rather than in traditional databases, it’s able to rapidly process more than 4.5 quadrillion combinations of NBA stats and handle up to 20,000 concurrent users.

想象一下，投资者能够精确地选择他们希望考虑到一套市场状况的绩效指标和风险调整的返回行为 - 因为这些条件实时展开（或者在预期） - 并拥有一个系统从对冲基金管理人员的整个宇宙和投资组合管理人员的排名究竟是哪些在这些条件下最符合这些性能特征的管理员。这种技术将使当前的系统“一切归功于一年中的一个号码”似乎是古老的，原始，古怪，迷人，看着从一个透视摄像头登上高度的单一透视相机观看棒球的黑白视频亚慱体育app漂白剂。

要确定，该技术并不琐碎。Hana存储在游戏期间收集的数据，粉丝可以通过网站访问，但收集，匹配和处理视频数据的过程发生在幕后，并且是令人生畏的。亚慱体育app在每个游戏技术人员期间，在新泽西州Secaucus的NBA设施中，输入和漏斗统计数据库。从那里，视频记录系统将统计数亚慱体育app据与播放发生的相应部分匹配，在此过程中创建索引。然后在HANA数据库中复制此索引，将每个播放作为单独的视频存储在HANA数据库中。亚慱体育app然后，粉丝可以在几秒钟内通过特定播放器查询和查看每个播放或戏剧集合。由于HANA实际上与游戏的统计数据匹配了视频日志的查询，因此它可以快速生亚慱体育app成符合用户请求的一系列视频，并且可以在Web浏览器中播放。粉丝可以filter the statisticsby shot type (dunk, three-point field goal, buzzer beater), assists, rebounds and steals, in addition to where on the court the player was located when the play was made.

The NBA’s library of videos in the Hana databases is astoundingly expansive, as any similar system for capital markets would have to be. A New York Knicks fan, for example, can search for and watch a video of every single one of forward Carmelo Anthony’s 669 completed field goals in the 2012–’13 season. Fans can search any statistic, play or player and view hundreds of videos related to their query — oftentimes only an hour after a game has finished. Although the NBA generates revenue from the video box score feature by running ads at the beginning of the videos, the long-term reward lies in maintaining fan interest and engagement with the game.

Does this development suggest the possibility that the kind of internal metrics and data that the best and most quantitatively advanced funds use to track the performance of portfolio managers might some day be made public for institutional investors, and even individuals, to watch and track?

Data such as the historical probability that a given manager will be down in a fourth month if he was down the prior three months in a row (which, like the number of errors thrown by a pitcher when his team is down by a certain margin, might suggest traits related to psychological performance under pressure or response behaviors triggered by spurts of failure) might one day be listed on a transparent central registry or web site for all to see, as surely as one today can look up the percentage of three-pointers that Miami Heat star LeBron James successfully throws in the second half when playing against the Celtics and his team is down by more than ten points.

Few funds today are likely even to track internally such high-resolution information as the examples suggested — not to mention the myriad others made possible by modeling the intersection of the month-by-month returns of every individual portfolio manager and multifactor market conditions. Nevertheless, the data are all there, and they could quickly become public, just as surely as Billy Beane’s private, edge-conferring “new store” of “complicated statistical analysis and equations” is now a few clicks away on the web.