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標(biāo)題: Titlebook: Machine Learning and Data Mining for Sports Analytics; 5th International Wo Ulf Brefeld,Jesse Davis,Albrecht Zimmermann Conference proceedi [打印本頁]

作者: Covenant 時間: 2025-3-21 17:24
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書目名稱Machine Learning and Data Mining for Sports Analytics網(wǎng)絡(luò)公開度學(xué)科排名

書目名稱Machine Learning and Data Mining for Sports Analytics被引頻次

書目名稱Machine Learning and Data Mining for Sports Analytics被引頻次學(xué)科排名

書目名稱Machine Learning and Data Mining for Sports Analytics年度引用

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書目名稱Machine Learning and Data Mining for Sports Analytics讀者反饋

書目名稱Machine Learning and Data Mining for Sports Analytics讀者反饋學(xué)科排名

作者: MELON 時間: 2025-3-22 00:18

作者: Saline 時間: 2025-3-22 03:43
Distinguishing Between Roles of Football Players in Play-by-Play Match Event Datatal importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and advanced statistics. However, the question whether a given player would fit the team’s playing style often still remains unanswered.

作者: ABASH 時間: 2025-3-22 07:12
Player Valuation in European Football In this paper, we compare and contrast which attributes and skills best predict the success of individual players in their positions in five European top football leagues. Further, we evaluate different machine learning algorithms regarding prediction performance. Our results highlight features dis

作者: 隼鷹 時間: 2025-3-22 09:00

作者: SOBER 時間: 2025-3-22 15:14
Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHLccess, given the current match state. However, the black-box opacity of neural networks prohibits understanding why and when some actions are more valuable than others. This paper applies interpretable Mimic Learning to distill knowledge from the opaque neural net model to a transparent regression t

作者: 紀(jì)念 時間: 2025-3-22 18:28
Player Pairs Valuation in Ice Hockeycount the context of the players’ actions and perform look-ahead. However, as ice hockey is a team sport, knowing about individual ratings is not enough and coaches want to identify players that play particularly well together. In this paper we therefore extend earlier work for evaluating the perfor

作者: Arrhythmia 時間: 2025-3-22 23:18

作者: inspiration 時間: 2025-3-23 03:38
Evaluating NFL Plays: Expected Points Adjusted for Schedulek gains 4 yards, even though it will not necessarily show up in the yardage statistics. While this problem has been addressed to some extent with the introduction of expected point models, there is still another inequality omission in the creation of yards and this is the opposing defense. Gaining 6

作者: 勾引 時間: 2025-3-23 06:11

作者: Obliterate 時間: 2025-3-23 10:52

作者: Receive 時間: 2025-3-23 16:29

作者: 植物群 時間: 2025-3-23 19:10
Football Pass Prediction Using Player Locationsance. In this paper, we describe a novel model for football pass prediction, developed to participate in the Prediction Challenge of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics, collocated with ECML PAKDD 2018. The model called Football Pass Predictor (FPP) considers va

作者: construct 時間: 2025-3-24 01:44
Deep Learning from Spatial Relations for Soccer Pass Prediction passes between players, as a submission to the prediction challenge organized for the 5th Workshop on Machine Learning and Data Mining for Sports Analytics. The goal of the challenge was to predict the receiver of a pass given location of the sender and all other players. From each soccer situation

作者: Aids209 時間: 2025-3-24 05:34
Predicting the Receivers of Football Passes. Anticipating the receiver of a pass can help football players build better collaborations and help coaches make informed tactical decisions. In this work, we analyze a public dataset that contains 12,124 passes performed by professional football players. We extract five dimensions of features from

作者: Noctambulant 時間: 2025-3-24 07:07
Philippe Fournier-Viger,Tianbiao Liu,Jerry Chun-Wei Line haben sich dabei ergeben, die mehr den Eindruck des Zufalls als das Streben nach Eindeutigkeit und die Suche nach absoluten Begriffen erkennen lassen. Zu einem gro?en Teil hat dabei die jeweilige technische L?sung des Bewegungsvorganges Pate gestanden. Es ist nur zu verst?ndlich, da? einer solchen

作者: 正式演說 時間: 2025-3-24 10:52

作者: Thyroiditis 時間: 2025-3-24 15:49
Player Valuation in European Footballtinguishing top-tier players and show that prediction performance is higher for forwards than for other positions, suggesting that equally good prediction of defensive players may require more advanced metrics.

作者: Rct393 時間: 2025-3-24 19:12
Ranking the Teams in European Football Leagues with Agonyur goal is to solve this issue for European football leagues. Our approach is based on a generalized version of agony which was introduced by Gupte et al.?(WWW’11). Our experiments yield natural rankings of the teams in four major European football leagues into a small number of interpretable quality levels.

作者: 類似思想 時間: 2025-3-25 01:46
Player Pairs Valuation in Ice Hockeymance of players to the related problem of evaluating the performance of player pairs. We experiment with data from seven NHL seasons, discuss the top pairs, and present analyses and insights based on both the absolute and relative ice time together.

作者: chance 時間: 2025-3-25 05:17

作者: Intend 時間: 2025-3-25 10:12

作者: 統(tǒng)治人類 時間: 2025-3-25 12:25

作者: 要控制 時間: 2025-3-25 17:19

作者: Immunoglobulin 時間: 2025-3-25 21:09
Real-Time Power Performance Prediction in Tour de Franceth the latency constraints. As a result, our proposed method reduced prediction error by 56.79% compared to the conventional physics model and satisfied the latency requirement. Our module was used during the . to indicate . that was shared with fans via media.

作者: 錫箔紙 時間: 2025-3-26 02:55
Measuring Football Players’ On-the-Ball Contributions from Passes During Games only. To help bridge this gap, we propose a novel approach to measure players’ on-the-ball contributions from passes during games. Our proposed approach measures the expected impact of each pass on the scoreline.

作者: Ventricle 時間: 2025-3-26 06:55

作者: musicologist 時間: 2025-3-26 10:37

作者: 寬敞 時間: 2025-3-26 13:23

作者: IST 時間: 2025-3-26 18:08

作者: assail 時間: 2025-3-27 00:27

作者: Obsequious 時間: 2025-3-27 04:16

作者: Precursor 時間: 2025-3-27 05:57
Machine Learning and Data Mining for Sports Analytics978-3-030-17274-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

作者: 污穢 時間: 2025-3-27 11:12
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620452.jpg

作者: Catheter 時間: 2025-3-27 17:07
https://doi.org/10.1007/978-3-030-17274-9artificial intelligence; classification accuracy; classification algorithm; data mining; learning algori

作者: vocation 時間: 2025-3-27 18:58
Predicting Pass Receiver in Football Using Distance Based FeaturesL/PKDD 2018. Our solution uses distance based features to predict the receiver of a pass. We show that our model is able to improve prediction results obtained on a similar dataset. One particularity of our approach is the use of failed passes to improve the predictions.

作者: 貞潔 時間: 2025-3-28 01:30
Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHLansparent tree structure facilitates understanding the general action values by feature influence and partial dependence plots, and player’s exceptional characteristics by identifying player-specific relevant state regions.

作者: Femish 時間: 2025-3-28 02:56
Evaluating NFL Plays: Expected Points Adjusted for Scheduledifferent point values to the offensive and defensive units of the same play, which is the rational thing to do especially in a league with an uneven schedule such as the NFL. The average absolute difference between the raw and adjusted points is 0.07 points/play (p-value? 作者: conjunctivitis 時間: 2025-3-28 10:15