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We also exclude foul in ball sports. In order to get well-defined action boundaries, we only count the common part of human motions in ball sports, e.g., passing a basketball starts from the player straightening his arms but does not include holding the ball and doing fake actions. For example, when two teams of equal talent play a game without a home advantage, outcome uncertainty is maximized; e.g., the outcome of the game is equivalent to a coin flip. Quality Control. For the first stage of annotation, at least one annotator Email List with domain knowledge per sport are responsible to double-check the annotations by correcting wrong or inaccurate ones and also adding missing annotations for a higher recall, e.g., adding missed defence action in football and modifying inconsistent action boundaries. Predictions from such general-purpose models tend to be inexact, which we correct or reject based on the knowledge constraints to gather high quality in-domain training data without manual labelling. The professional annotators and careful quality control together provide consistent and clean annotations.

They both use the interface shown in Figure 2(b), where annotators in the first stage add records of the starting and ending frame and the action label to the file and annotators in the second stage adjust bounding boxes for each record to finish the annotations of actions. For similar reasons, our method does not apply as well to pitcher metrics because of a compounding decreasing trend in the average number of innings pitched per game due to the increased role of relief pitchers in MLB over time; accounting for such strategic shifts in the role and use of individual player types could also be included within our framework, but is outside the scope of the present discourse and so we leave it for future work. However, if operational constraints make it more convenient to use a lightweight laptop for the front-end, the back-end might not be able to run on it (it requires a powerful GPU card). However, this problem has been less intensively studied in the realm of general discourse where ground truth descriptions of states may be loosely defined and state changes are less densely distributed over utterances.

Finally, the history-related descriptions of the game are identified and removed. In practice, human operators capture the game from various types of views such as top views and side views. We remove any events that occur during regulation overtime (0.88% of all events), because these events follow different scoring processes than events in regular game time merritt2014scoring . Instead, professional sports appear to exhibit little strategic entailment, and events are driven instead by short-term optimization for scoring as quickly as possible. All videos in our dataset are high-resolution records of professional competitions covering a diversity of countries and performance levels. Such historical comparison is more than fodder for casual discussion among sports fans, as it is also an issue of critical importance to the multi-billion dollar professional sport industry and the institutions (e.g. Hall of Fame) charged with preserving sports history and the legacy of outstanding players and achievements. If OKS is larger than one threshold value (e.g. 0.5), the corresponding groundtruth and prediction are considered as a matching pair and the correctness of predicted keypoint types is further analysed.

One bot is responsible for 20 channels. With this method, only one GIF was generated for each corresponding video using the default parameters. To address this cultural heritage management issue, we report an objective statistical method for renormalizing career achievement metrics, one that is particularly tailored for common seasonal performance metrics, which are often aggregated into summary career metrics – despite the fact that many player careers span different eras. Thus, here the average prowess serves as a baseline ‘deflator index’ for comparing accomplishments achieved in different years and thus distinct historical eras. We have followed here Lenten and Winchester (2015), Butler et al. POSTSUBSCRIPT (Errors of repeated detections): a detection result that has tubelet IoU larger than a threshold and the right action class with some ground-truth tubelets, but the ground-truths have been matched by other detection results before with a confidence score larger than it. POSTSUBSCRIPT : a detection result that has no spatial-temporal intersection with any ground-truth tubes and appears out of thin air. POSTSUBSCRIPT are obtained by applying the FFDD descriptors along the tract, over these points.