Footballonomics: The Anatomy of American Football; Evidence from 7 years of NFL game data

Do NFL teams make rational decisions? What factors potentially affect the probability of wining a game in NFL? How can a team come back from a demoralizing interception? In this study we begin by examining the hypothesis of rational coaching, that is, coaching decisions are always rational with respect to the maximization of the expected points scored. We reject this hypothesis by analyzing the decisions made in the past 7 NFL seasons for two particular plays; (i) the Point(s) After Touchdown (PAT) and (ii) the fourth down decisions. Having rejected the rational coaching hypothesis we move on to examine how the detailed game data collected can potentially inform game-day decisions. While NFL teams personnel definitely have an intuition on which factors are crucial for winning a game, in this work we take a data-driven approach and provide quantifiable evidence using a large dataset of NFL games for the 7-year period between 2009 and 2015. In particular, we use a logistic regression model to identify the impact and the corresponding statistical significance of factors such as possession time, number of penalty yards, balance between passing and rushing offense etc. Our results clearly imply that avoiding turnovers is the best strategy for winning a game but turnovers can be overcome with letting the offense on the field for more time. Finally we combine our descriptive model with statistical bootstrap in order to provide a prediction engine for upcoming NFL games. Our evaluations indicate that even by only considering a small number of (straightforward) factors, we can achieve a very good prediction accuracy. In particular, the average accuracy during seasons 2014 and 2015 is approximately 63%. This performance is comparable to the more complicated state-of-the-art prediction systems, while it outperforms expert analysts 60% of the time.Comment: Working study - Papers has been presented at the Machine Learning and Data Mining for Sports Analytics 2016 workshop and accepted at PLOS ON

Analyzing and Modeling Special Offer Campaigns in Location-based Social Networks

The proliferation of mobile handheld devices in combination with the technological advancements in mobile computing has led to a number of innovative services that make use of the location information available on such devices. Traditional yellow pages websites have now moved to mobile platforms, giving the opportunity to local businesses and potential, near-by, customers to connect. These platforms can offer an affordable advertisement channel to local businesses. One of the mechanisms offered by location-based social networks (LBSNs) allows businesses to provide special offers to their customers that connect through the platform. We collect a large time-series dataset from approximately 14 million venues on Foursquare and analyze the performance of such campaigns using randomization techniques and (non-parametric) hypothesis testing with statistical bootstrapping. Our main finding indicates that this type of promotions are not as effective as anecdote success stories might suggest. Finally, we design classifiers by extracting three different types of features that are able to provide an educated decision on whether a special offer campaign for a local business will succeed or not both in short and long term.Comment: in The 9th International AAAI Conference on Web and Social Media (ICWSM 2015