The Hot (Invisible?) Hand: Can Time Sequence Patterns of Success/Failure in Sports Be Modeled as Repeated Random Independent Trials?

The long lasting debate initiated by Gilovich, Vallone and Tversky in is revisited: does a “hot hand” phenomenon exist in sports? Hereby we come back to one of the cases analyzed by the original study, but with a much larger data set: all free throws taken during five regular seasons () of the National Basketball Association (NBA). Evidence supporting the existence of the “hot hand” phenomenon is provided. However, while statistical traces of this phenomenon are observed in the data, an open question still remains: are these non random patterns a result of “success breeds success” and “failure breeds failure” mechanisms or simply “better” and “worse” periods? Although free throws data is not adequate to answer this question in a definite way, we speculate based on it, that the latter is the dominant cause behind the appearance of the “hot hand” phenomenon in the data

“Hot Hand” on Strike: Bowling Data Indicates Correlation to Recent Past Results, Not Causality

Recently, the “hot hand” phenomenon regained interest due to the availability and accessibility of large scale data sets from the world of sports. In support of common wisdom and in contrast to the original conclusions of the seminal paper about this phenomenon by Gilovich, Vallone and Tversky in 1985, solid evidences were supplied in favor of the existence of this phenomenon in different kinds of data. This came after almost three decades of ongoing debates whether the “hot hand” phenomenon in sport is real or just a mis-perception of human subjects of completely random patterns present in reality. However, although this phenomenon was shown to exist in different sports data including basketball free throws and bowling strike rates, a somehow deeper question remained unanswered: are these non random patterns results of causal, short term, feedback mechanisms or simply time fluctuations of athletes performance. In this paper, we analyze large amounts of data from the Professional Bowling Association(PBA). We studied the results of the top 100 players in terms of the number of available records (summed into more than 450,000 frames). By using permutation approach and dividing the analysis into different aggregation levels we were able to supply evidence for the existence of the “hot hand” phenomenon in the data, in agreement with previous studies. Moreover, by using this approach, we were able to demonstrate that there are, indeed, significant fluctuations from game to game for the same player but there is no clustering of successes (strikes) and failures (non strikes) within each game. Thus we were lead to the conclusion that bowling results show correlation to recent past results but they are not influenced by them in a causal manner

Best-of-Three Contests: Experimental Evidence

We conduct an experimental analysis of a best-of-three Tullock contest. Intermediate prizes lead to higher efforts, while increasing the role of luck (as opposed to effort) leads to lower efforts. Both intermediate prizes and luck reduce the probability of contest ending in two rounds. The patterns of players‟ efforts and the probability that a contest ends in two rounds is consistent with „strategic momentum‟, i.e. momentum generated due to strategic incentives inherent in the contest. We do not find evidence for „psychological momentum‟, i.e. momentum which emerges when winning affects players‟ confidence. Similar to previous studies of contests, we find significantly higher efforts than predicted and strong heterogeneity in effort between subjects

March Madness? Underreaction to Hot and Cold Hands in NCAA Basketball

The article of record as published may be found at http://dx.doi.org/10.1111/ecin.12558The hot hand bias is the widely documented bias toward overestimation of positive serial correlation in sequential events. We test for the hot hand bias in a novel real-world context, NCAA basketball tournament seeds. That is, we examine whether teams that perform relatively well heading into ``March Madness'' are seeded too high, and/or teams with poor recent performance are seeded too low. The seeds are determined by a 10-member committee that only has implicit incentives, but these incentives are still substantial as the committee's decisions are highly scrutinized by the media, fans, and other stakeholders. We find that, contra the hot hand bias, the committee \emph{underreacts} to signals of momentum heading into the NCAA tournament. Various results indicate this behavior can be attributed both to inattention to relatively detailed information indicating momentum, and under-appreciation of the predictive value of this information. Betting markets incorporate this information efficiently, but neglect some additional information that is predictive of winning NCAA tournament games but not of beating the spread. We note that the NCAA tournament has been highly popular and lucrative partly due to the ``madness’'' (high frequency of wins by lower-seeded teams), which the bias we document contributes to, making the persistence of bias less surprising