Crowd Wisdom In NFL Point Spread and Over/Under Betting Group 4: Jonathan Bell, Tyler Ventura, Sam Cantor, Alan Gadjev, Nate Mays Background Sports Betting is a growing and already substantial business in the United States. Until recently, sports related betting was only legal in specific areas of the United States such as Las Vegas, Delaware, Montana, and Oregon.1 Under the Professional and Amateur Sports Protection Act of 1992 (PASPA), sports betting was banned for all sports excluding parimutuel horse and dog racing and jai alai.2 While sports betting is limited in availability, as of November 2017 $4.9 billion per month was legally bet on sports in Nevada alone. Despite the prohibition, illegal sports betting is very popular in the United States. As of the Supreme Court case Murphy v. ​ National Collegiate Athletic Association (2018), the PASPA was found unconstitutional and the ​ decision to sponsor sports related gambling was relegated to the state governments.3 Since this ruling, a number of states have brought forward bills related to legalization of sports betting. Ohio State economist Jay L. Zagorsky conservatively estimates that the total value of the sports betting market will be $70 billion dollars, but many argue that the value will be approximately $150 billion, with largest estimates at close to $380 billion.4 This project will focus on sports betting on the NFL, specifically point spread betting and over/under betting. Point Spread betting is a form of sports gambling that is particularly popular in Las Vegas sportsbooks. Spread betting involves the setting of a “favorite” and an “underdog” by oddsmakers, and the assigning of additional points to be used when calculating the score. A bettor picks the favorite (F), minus the number of points (B) that they have been docked (F-B=A), or picks the underdog (U), plus the number of points (B) that they have been spotted, 1 https://en.wikipedia.org/wiki/Sports_betting#1970s-2018:_Prohibition_on_sports_betting ​ 2 https://www.govinfo.gov/content/pkg/STATUTE-106/pdf/STATUTE-106-Pg4227.pdf ​ 3 https://caselaw.findlaw.com/us-supreme-court/16-476.html ​ 4https://theconversation.com/market-for-illegal-sports-betting-in-us-is-not-really-a-150-billion-business-966 18 1 (U+B=C), and the winner is determined by if the modified score totals A>C or A<C. Over/Under betting is a form of sports gambling that is not based on the winner or loser of a particular game, but rather on the combined total score of the two opponents. Oddsmakers set a number for the combined total score of the two teams, and bettors have to predict whether or not they think the combined total score of the game will be higher (over) or lower (under) than the number given by the oddsmakers. For this project, crowd wisdom will be represented by Wunderdog Sports compilation of “Public Consensus.” These projections, scraped from other public betting sources, offer the percentage of the public that selected the “favorite” or “underdog” and the “over” or “under” for NFL matchups in the 2017-2018 and 2018-2019 seasons. The “expert” in this project will be represented by the Wunderdog Sports Computer algorithm predictions. Wunderdog, a subscription-based handicapping service, does not publicly offer their final bet predictions, but does offer a free record of their computer algorithm projections. Their actual algorithm is not publicly available, but considers “statistics, power ratings, and hundreds of very high-percentage proprietary historical situational systems. The systems purposefully avoid hunches, ‘soft’ data or personal gut opinions. I look for agreement between all of my sources which results in a few games selected, but they are the cream of the crop.”5 The public therefore benefits from consideration of injuries, trends, situations that are not factored in to Wunderdog’s Computer Models (these are factored in to Wunderdog’s Predictions after the computer calculations and are not publicly available). 5 https://www.wunderdog.com ​ 2 Literature Review & Similar Studies Prediction markets attracted the attention of the public after James Surowiecki published the The Wisdom of Crowds in 2004. It sparked the popularity of prediction markets. Since 2004, ​ ​ a number of studies have been published that examined how to harness crowd wisdom. We have looked at other studies that explore the ways crowds can enhance our predictive abilities for sporting events. The first study we analyzed is titled “Are Crowds Wise When Predicting Against Point Spreads? It Depends on How You Ask” and was published by a group of research professors ​ ​ from various universities. The study tests Surowiecki’s hypothesis that “the judgements of a crowd (as measured by any form of central tendency) will be relatively accurate, even when most of the individuals in the crowd are ignorant and error-prone.”6 Points spread in the NFL and other sports are argued to be an accurate representation of the crowds’ opinion, however, there has also been evidence that crowds are bad at betting against the point spreads and bet too much on the favorite.7 This study tests Surowiecki’s hypothesis by analyzing the way the crowd performed in betting against point spreads that were adjusted to favor the underdog ( i.e. points spread was increased). The study was conducted over 17 weeks of the NFL season in which more than $20,000 (money was put up by conductors of the study) was wagered. The betting was open to anyone in the United States, so the overall population of the participants was geographically diverse. The aggregation mechanism allowed the conductors of the study to 6 http://www.asecib.ase.ro/mps/TheWisdomOfCrowds-JamesSurowiecki.pdf ​ 7 3 perform statistical analyses. As we can see above, all conditions of Surowiecki’s “wise crowd” were satisfied in this study. The study investigated three hypotheses, the first of which was that “crowds will wisely choose against biased point spreads even when they are not told that the spreads are biased.”8 Hypothesis 1 was rejected and it was found that the crowd unwisely bet on the favorite in 89.4% of the cases (p < .001), the crowd lost 56.8% of the wagers made and overall performed worse than 93% of participants. Hypothesis 2 predicts that “crowds will wisely choose against biased point spreads when they are told that the spreads have been increased”. The participants who were told that the point spread was tampered with and to ensure they were informed they had to read the following statement before making any predictions: “Although official point spreads are designed to give each team an equal chance to win the bet, the point spreads inserted below are not necessarily the official point spreads. In fact, some of the point spreads have been increased, though none of them have been decreased. If you have read these instructions, please click the box below.”9 Hypothesis 2 was rejected and it was found that the crowd unwisely bet on the favorite in 82.7% of the cases (p < .001), the crowd lost 57.9% of the wagers made and overall performed worse than 97.4% of participants. The third hypothesis tested whether the crowd had the ability to learn and correct itself along the study: “even if crowds are unwise at the start of the study, they should improve over 8http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C00B25FFFC2AD49E56B03502ED761A67?do i=10.1.1.153.8517&rep=rep1&type=pdf 9 Ibid. 4 time, as the crowd’s members accumulate evidence of the inferiority of favorites.”10 Hypothesis 3 was rejected and there was no statistical evidence to support that the crowd’s performance got better or worse over time. Throughout the entire study participants bet heavily on the favorite and failed to learn from their unsuccessful predictions.11 Lastly, instead of betting against the spread and simply trying to predict a team that beats the spread, participants were simply asked to predict a winner and a point differential between the two teams. Participants in this bucket performed much better than what we have seen above: overall the crowd predicted the underdog in 82.7% (p < .001), correctly predicted the team that beats the spread 55.4% of the time and performed better than 95.6% of the crowd.12 The study above showed us that the crowd is not wise when dealing with the conventional ways the betting world operates in (betting against a points spread). However, a prediction market that would ask the participants to predict a point differential would act as a successful prediction mechanism the majority of the time. The second study that we chose to take a closer look at is titled “Testing the Effectiveness of Semi-Predictive Markets: Are Fight Fans Smarter Than Expert Bookies,” conducted by Sean Wise, Milan Miric and Dr. Dave Valliere from Ryerson University. To test for “wisdom of crowds” this study looks at UFC (Ultimate Fighting Championship) main-event cards for a three way period. One important aspect of this study is that UFC main-events are one-off events and differ from football in the sense that fighters rarely fight each other more than once, thus 10 Ibid. 11 Ibid. 12 Ibid. 5 mitigating any of the biases. Even if they do it usually takes at least 6 months to schedule a rematch. The authors of the study state that “one-off events serve as a better proxy for assessing the predictive capacity of a crowd vs. experts as they remove many of the biases which arise when new information emerges” The study compares the predictive abilities of crowds vs. expert bookies in the binary outcome, one-off scenario of UFC events, thus eliminating potential biases towards an irrational outcome.