Ph.D in Biotechnology who have joined a eCommerce startup in Hong Kong
When you track the NFL using chips in footballs and shoulder pads, analyse player
When Scalable Machine Learning Meets The NFL The NFL vernacular entered my world in June 1984 when Machintosh was featured in the Super bowl half-time ad. Tiltilating video of busty tank-top woman hammer thrower and a new computer didn't make sense, neither did the NFL game. Coming from Asia, footballs were round and didn't have pointy ends. The NFL didn't show up on my radar again until Jerry Maguire and again when I wept over Forrest Gump. But every other American male I met gushes over the NFL, convincing me that I've missed out on a beautiful game. A former boss berrated that the alltime NFL best picks were Tom Brady and Ray Lewis in 2000 and 1996 respectively (read more).
Too Much Of A Good Thing? The NFL have been late to using statistical analysis to improve game performance, playing catch-up with Major League Baseball and NBA. But when is a good thing too much? Besides live-streaming the entire game on Twitter, The NFL football and shoulder pads have embedded chips to digitally track a player's on-court coordinates in relation to how the ball moves on the field. Motion-tracking cameras in every arena were installed to further track player and ball positions 25 times a second(read more). Post-game real-time telemetry event data points allow both fans and the media to pour over their favourite sport team. Once worshipped and awe inspiring sporting heroes can now be statistically examined for every shots created, defensive play and ball touches.
Abstract: Predicting the outcomes of future events is a challenging problem for which a variety of solution methods have been explored and attempted. We present an empirical comparison of a variety of online and offline adaptive algorithms for aggregating experts' predictions of the outcomes of five years of US National Football League games (1319 games) using expert probability elicitations obtained from an Internet contest called ProbabilitySports. We find that it is difficult to improve over simple averaging of the predictions in terms of prediction accuracy, but that there is room for improvement in quadratic loss. Somewhat surprisingly, a Bayesian estimation algorithm which estimates the variance of each expert's prediction exhibits the most consistent superior performance over simple averaging among our collection of algorithms.
Pub.: 27 Jun '12, Pinned: 16 Apr '17
Abstract: How does one objectively measure the performance of an individual offensive lineman in the NFL? The existing literature proposes various measures that rely on subjective assessments of game film, but has yet to develop an objective methodology to evaluate performance. Using a variety of statistics related to an offensive lineman's performance, we develop a framework to objectively analyze the overall performance of an individual offensive lineman and determine specific linemen who are overvalued or undervalued relative to their salary. We identify eight players across the 2013-2014 and 2014-2015 NFL seasons that are considered to be overvalued or undervalued and corroborate the results with existing metrics that are based on subjective evaluation. To the best of our knowledge, the techniques set forth in this work have not been utilized in previous works to evaluate the performance of NFL players at any position, including offensive linemen.
Pub.: 10 Apr '16, Pinned: 16 Apr '17
Abstract: Based on NFL game data we try to predict the outcome of a play in multiple different ways. An application of this is the following: by plugging in various play options one could determine the best play for a given situation in real time. While the outcome of a play can be described in many ways we had the most promising results with a newly defined measure that we call "progress". We see this work as a first step to include predictive analysis into NFL playcalling.
Pub.: 04 Jan '16, Pinned: 16 Apr '17
Abstract: Evaluating the accuracies of models for match outcome predictions is nice and well but in the end the real proof is in the money to be made by betting. To evaluate the question whether the models developed by us could be used easily to make money via sports betting, we evaluate three cases: NCAAB post-season, NBA season, and NFL season, and find that it is possible yet not without its pitfalls. In particular, we illustrate that high accuracy does not automatically equal high pay-out, by looking at the type of match-ups that are predicted correctly by different models.
Pub.: 17 Feb '17, Pinned: 16 Apr '17
Abstract: The ubiquity of professional sports and specifically the NFL have lead to an increase in popularity for Fantasy Football. Users have many tools at their disposal: statistics, predictions, rankings of experts and even recommendations of peers. There are issues with all of these, though. Especially since many people pay money to play, the prediction tools should be enhanced as they provide unbiased and easy-to-use assistance for users. This paper provides and discusses approaches to predict Fantasy Football scores of Quarterbacks with relatively limited data. In addition to that, it includes several suggestions on how the data could be enhanced to achieve better results. The dataset consists only of game data from the last six NFL seasons. I used two different methods to predict the Fantasy Football scores of NFL players: Support Vector Regression (SVR) and Neural Networks. The results of both are promising given the limited data that was used.
Pub.: 26 May '15, Pinned: 16 Apr '17
Abstract: Big data analytics is one of the emerging technologies as it promises to provide better insights from huge and heterogeneous data. Big data analytics involves selecting the suitable big data storage and computational framework augmented by scalable machine-learning algorithms. Despite the tremendous buzz around big data analytics and its advantages, an extensive literature survey focused on parallel data-intensive machine-learning algorithms for big data has not been conducted so far. The present paper provides a comprehensive overview of various machine-learning algorithms used in big data analytics. The present work is an attempt to identify the gaps in the work already performed by researchers, thus paving the way for further quality research in parallel scalable algorithms for big data.For further resources related to this article, please visit the WIREs website.
Pub.: 15 Sep '16, Pinned: 16 Apr '17
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